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Carbohydrate Quality, Glycemic Index, Glycemic Load and Cardiometabolic Risks in the US, Europe and Asia: A Dose-Response Meta-Analysis
Journal Pre-proof Carbohydrate Quality, Glycemic Index, Glycemic Load and Cardiometabolic Risks in the US, Europe and Asia: A Dose-Response Meta-Analysis D.S. Hardy, J.T. Garvin, H. Xu PII:
S0939-4753(20)30001-6
DOI:
https://doi.org/10.1016/j.numecd.2019.12.050
Reference:
NUMECD 2205
To appear in:
Nutrition, Metabolism and Cardiovascular Diseases
Received Date: 17 May 2019 Revised Date:
26 December 2019
Accepted Date: 29 December 2019
Please cite this article as: Hardy D, Garvin J, Xu H, Carbohydrate Quality, Glycemic Index, Glycemic Load and Cardiometabolic Risks in the US, Europe and Asia: A Dose-Response Meta-Analysis, Nutrition, Metabolism and Cardiovascular Diseases, https://doi.org/10.1016/j.numecd.2019.12.050. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition, and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.
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Carbohydrate Quality, Glycemic Index, Glycemic Load and Cardiometabolic Risks in the US, Europe and Asia: A Dose-Response Meta-Analysis DS. Hardy a*, JT. Garvin b, H. Xu c a
Department of Medicine, Morehouse School of Medicine, Atlanta GA 30331
b
School of Nursing, University of Saint Augustine for Health Sciences, Saint Augustine, FL 32086
c
Department of Population Health Sciences, Augusta University, Augusta GA 30912
Send correspondence and reprint requests to: *Dale Hardy, PhD, RD, LD, CDE CHES Department of Medicine Morehouse School of Medicine 720 Westview Drive, SW Atlanta, GA 30331 Phone: (404) 756-1346 Email: [email protected] Article Type: Meta-analysis
Word count: 5996 words from Introduction through Acknowledgement sections. Abstract: 250 words Number of figures: 1 Number of tables: 5 Supplemental data: S1 Tables: 3, S1 Figures: 32, S2 Figure: 24, S3 Figures: 31, S4 Figures: 2; S5 Figures: 5
1
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Abstract
34
Background and Aims: Despite the proven evidence of high glycemic index (GI) and glycemic
35
load (GL) diets to increase cardiometabolic risks, knowledge about the meta-evidence for
36
carbohydrate quality within world geographic regions is limited. We conducted a meta-analysis
37
to synthesize the evidence of GI/GL studies and carbohydrate quality, gathering additional
38
exposures for carbohydrate, high glycemic carbohydrate, total dietary fiber, and cereal fiber and
39
risks for type 2 diabetes (T2DM), coronary heart disease (CHD), stroke, and mortality, grouped
40
into the US, Europe, and Asia. Secondary aims examined cardiometabolic risks in
41
overweight/obese individuals, by sex, and dose-response dietary variable trends.
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Methods and Results: 40-prospective observational studies from 4-Medline bibliographical
43
databases (Ovid, PubMed, EBSCOhost, CINAHL) were search up to November 2019. Random-
44
effects hazard ratios (HR) and 95% confidence intervals (CI) for highest vs. lowest categories
45
and continuous form combined were reported. Heterogeneity (I2>50%) was frequent in US
46
GI/GL studies due to differing study characteristics. Increased risks
47
((HRGI,T2DM,US=1.14;CI:1.06,1.21), HRGL,T2DM,US=1.09;1.06,1.11), HRGI,T2DM,Asia=1.25;1.02,1.53),
48
and HRGL,T2DM,Asia=1.25;1.03,1.52)) were associated with cardiometabolic diseases. GI/GL in
49
overweight/obese females had the strongest magnitude of risks in US-and Asian studies. Total
50
dietary fiber (HRT2DM,US=0.92;0.88,0.96) and cereal fiber(HRT2DM,US=0.83;0.77,0.90) decreased
51
risk of developing T2DM. Among females, we found protective dose-response risks for total
52
dietary fiber (HR5g-total-dietary-fiber,T2DM,US=0.94;0.92,0.97), but cereal fiber showed better ability to
53
lower T2DM risk (HR5g-cereal-fiber,T2DM,US=0.67;0.60,0.74). Total dietary-and cereal fibers’ dose-
54
response effects were nullified by GL, but not cereal fiber with GI.
2
55
Conclusions: Overweight/obese females can shift their carbohydrate intake for higher cereal
56
fiber to decrease T2DM risk, but GL may cancel-out this effect.
57 58 59
KEYWORDS: Glycemic index and glycemic load, total dietary fiber, cereal fiber, type 2
60
diabetes, cardiometabolic risks, dose-response, geographic regions
61 62
List of Acronyms
63
GI: Glycemic index
64
GL: Glycemic load
65
T2DM: Type 2 diabetes
66
CHD; Coronary heart disease
67
HR: Hazard ratio
68
CI: Confidence interval
69
NHS: Nurses’ Health Study
70
NHS II: Nurses’ Health Study II
71
HPFS: Health Professionals Follow-up Study
72
BMI: Body mass index
73
3
74 75
Introduction High glycemic index (GI) and glycemic load (GL) diets are associated with increased risk
76
of cardiometabolic diseases [1-4]. Cardiometabolic diseases vary across geographic regions. US
77
reports show that coronary heart disease (CHD) and stroke rates have declined from 2006 to
78
2019[5]. However, disparities in rates of cardiometabolic diseases remain, with Whites having a
79
lower age adjusted prevalence compared to minority populations [5]. In addition, other
80
geographic regions such as China have an increased prevalence of type 2 diabetes (T2DM) and
81
CHD [6]. This has been attributed to the recent Westernization of the diet and its changing
82
nutrient composition [6].
83
GI, a carbohydrate quality classification, ranks the impact of foods based on their
84
carbohydrate absorption and glycemic response. While GL combines GI and the quantity of
85
carbohydrate [7,8], both GI and GL estimate the carbohydrate glycemic burden on blood
86
glucose, insulin resistance, and other abnormal metabolic parameters. It is widely known that the
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continual ingestion of high GI and GL diets that contain large amounts of high glycemic and
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refined carbohydrate and low fiber foods, over time are associated with increased risk of
89
cardiometabolic diseases and mortality [1-4]. Other studies report that total dietary fiber [9,10],
90
and cereal fiber can decrease the risk of developing T2DM [3,11,11-14], CHD and stroke
91
[15,16], and the risk of death from diabetes [17]. Women [12,18], and those who are obese
92
[12,18,19] are reported to be disproportionately affected by the unfavorable effects of high GI
93
and GL diets.
94
Recently, three meta-analyses published on GI and GL found dose-response associations
95
for cardiometabolic disease risks and mortality. Reynolds et al. [20] reported that total dietary
96
fiber and whole grains were better dose-response markers of good health than GI and GL for
4
97
cardiometabolic risk factors and associated disease outcomes. Conversely, two other studies
98
showed that dose-response intakes of GI and GL can predict the development of type 2 diabetes
99
[21] and CHD [22] among healthy European and East Asian ancestry populations. Additionally,
100
Shahdadian et al. [23] reported that GI and GL were associated with mortality in women, but not
101
among men [23].
102
Currently there is a lack of information on carbohydrate quality in relation to GI and GL
103
in overweight/obese individuals, and among males and females within the US, Europe, and Asia.
104
Additionally, the comparison of carbohydrate quality-GI/GL combination effects needs to be
105
assessed. Furthermore, the investigation of dose-response trends on disease risk and the ability of
106
total dietary fiber and cereal fiber to curb the deleterious effect of high GI and high GL on
107
disease risk should be evaluated in different disease outcomes across geographic regions.
108
Moreover, many published meta-analyses included related studies that used the same
109
datasets, and studies that used odds ratios, both of which can bias the pooled estimates.
110
Furthermore, other meta-analyses combined published study estimates from different scales,
111
such as beta coefficients from linear regression with hazard ratios, introducing additional bias in
112
the true estimate results.
113
The primary aim of the current study was to investigate the carbohydrate quality of diets
114
high in GI and GL that included additional exposures for carbohydrate, high glycemic and
115
refined carbohydrate (as a measure of rapidly absorbed carbohydrate), total dietary fiber, and
116
cereal fiber, for risk of developing T2DM, CHD, stroke, and mortality in the US, Europe, and
117
Asia. Secondary aims investigated whether there were increased risks in overweight/ obese
118
individuals, by sex, and dose-response trends for dietary variables-cardiometabolic disease risks
119
within these world geographic regions.
5
120
Methods and Results
121
Search strategy
122
We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses
123
(PRISMA) method of reporting in this meta-analysis [24]. No protocol is available in
124
PROSPERO currently. To identify initial studies on adults, four Medline databases (Ovid,
125
PubMed, EBSCOhost, and CINAHL) were searched through November 2019 as per our
126
inclusion criteria. Additional studies were identified from references in identified meta-analysis.
127
Search-terms used to identify articles, included GI or GL with their outcomes (T2DM, CHD,
128
stroke, and mortality). For example, search terms were: “glyc(a)emic index” or “glyc(a)emic
129
load” with “type 2 diabetes” or “type 2 diabetes mellitus” or “diabetes type 2”, “coronary heart
130
disease/s” or “coronary disease/s” or “disease/s coronary”, or “stroke/s” or “cerebrovascular
131
accident/s” or “CVA (Cerebrovascular Accident)” or “mortality” or “death”. When we
132
identified the GI and GL publications, other exposures (carbohydrate, high glycemic and refined
133
carbohydrate, total dietary fiber, and cereal fiber) were recorded [3,11-13]. We performed
134
additional searches for “fiber” and the outcomes listed above and kept publications that
135
contained information for GI and GL for our meta-analysis.
136 137 138
Study selection Studies were eligible for inclusion in the analysis: if they were written in English; the
139
exposures were dietary GI and/or GL that may have contained additional exposures for
140
carbohydrate, high glycemic and refined carbohydrate, total dietary fiber, and cereal fiber; and
141
their disease outcomes were incident T2DM, CHD, stroke, and mortality. All studies required an
142
observational prospective design with Cox proportional regression and hazard ratios and 95% 6
143
confidence intervals reported. When stratified analyses, e.g. male or female, or male and female
144
combined was reported, male and female results were extracted only. If the combined male-
145
female results contained additional reported statistics for dietary variables-disease outcomes that
146
were not in the stratified male-female results, then the combined male-female results were
147
extracted. We included all data on mortality, unless all-cause mortality was reported. In that case,
148
we excluded specific types of mortality results and retained the all-cause mortality results. Most
149
importantly, studies that used odds ratios as the measure of association were excluded because
150
the odds ratio overestimates the hazard ratio by at least two times, when the disease is common
151
[25]. Because our disease outcomes (T2DM, CHD, stroke, and mortality) were common, this
152
criterion was applied to avoid overestimating their risks.
153 154 155
Data extraction All data were extracted using an electronic spreadsheet. All articles were abstracted for
156
information by two or three reviewers to determine eligibility. When the high glycemic and
157
refined carbohydrate variable was not available, refined carbohydrate intake, sucrose,
158
sugar/sugar products, white rice and refined wheat products were used as proxy variables [26-
159
31]. Other information was collected as indicated in Table 1. Study information was collected in
160
continuous form, or tertiles, quartiles, or quintiles for hazard ratios and 95% confidence intervals
161
of highest category compared to lowest category of their disease outcomes. When reported
162
results showed the highest protective category as the reference, such as for cereal fiber, then that
163
result was inverted to reflect the same methodology as in the other publications [15]. Exposures
164
were mean or median values of the dietary variables.
7
165
Because some studies used either bread or glucose as the referent, we converted all GI
166
and GL values that used bread as the referent to the glucose referent [12,16,32]. We used the
167
conversion formula of GI or GL value x 0.71=glucose scale value [7,33]. When we encountered
168
a study that was missing the GI or GL measurements, but had information on GI or GL,
169
carbohydrate, and/or calories, we derived the missing GI or GL using these information present
170
in the manuscript [11,12,27]. We used the following formula to derive the missing information
171
on GL as GI=GL/ grams of carbohydrate or on GL as: GL= (GI x grams of carbohydrate)/100
172
[8]. Other derivations were calculated if the GL or GL were not present in the manuscripts, but
173
the needed variables were present to be make calculations. For example, if GL was not
174
presented, but the GI, calories, protein, and fat intake were recorded in the manuscript, then
175
carbohydrate intake was calculated as: carbohydrate (grams) = Total calories – (calories from
176
protein + calories from fat)/4. Subsequently, we used this result and the formula above to
177
calculate the GL.
178
The studies reported energy-adjustment of GI and GL using the residual method as
179
described by Willett [34]. This process of energy-adjustment holds total caloric intake constant
180
in the study population, while the varied quantity of GI or GL is compared between groups.
181
Because multiple studies used the same datasets with more recent datasets having a longer
182
duration [4,11-14,35], we used the most recent study results published [3,11-13]. An example is
183
the Nurses’ Health Study (NHS), the Nurses’ Health Study II (NHS II), and the Health
184
Professionals Follow-up Study (HPFS) that published multiple papers on GI and GL.
185 186 187
8
188 189
Evaluation of quality of the meta-evidence Possible publication bias was explored in GRADE for pooled results of dietary variables
190
and disease outcome by geographic region having ≥ 10 study estimates (See the Cochrane
191
handbook at https://training.cochrane.org/handbook/current/chapter-13). Multiple funnel plots
192
and Begg and Egger’s tests were used to explore publication bias [36]. This was performed by
193
visually checking for asymmetry that could have been related to selection bias, publication bias,
194
or other influential factors. In Begg’s tests the estimated log hazard ratios were plotted against
195
their standard errors (SEs). This specified a correlation (using Kendall rank correlation) between
196
the adjusted effect size and the meta-analysis weight. In Egger’s test, the (log hazard ratios/SE) x
197
1/SE were computed. This test evaluated whether the intercept deviates significantly from zero in
198
a regression of standardized hazard ratios against their precision. The presence of publication
199
bias would induce a skewness in the plots. This was assessed by p <0.10 if publication bias was
200
present. We further assessed suspected publication bias after analyzing the results from the above
201
tests. If there was suspected publication bias, we did further testing using the ‘trim and fill’
202
method. The ‘trim and fill’ method is a rank data imputation technique that estimates the number
203
and outcomes of missing studies. This method adjusts the meta-analysis to incorporate the
204
imputed data to re-estimate the overall meta-analytic effect using the random-effects method.
205
Quality of the evidence for this meta-analysis was evaluated using the Risk of Bias
206
ROBIN-I tool for non-randomized studies at https://training.cochrane.org/handbook/current/
207
chapter-25. The ROBIN-I tool includes domains for biases in relation to confounding, selection
208
into the study, information and measurement, and reporting. We assessed these risk of bias
209
domains scores into a total score computed as an overall judgement for risk of bias.
9
210
We used the GRADE criteria [37] that score all the meta-evidence to evaluate its overall
211
quality of the risk of bias, inconsistency, indirectness, imprecision, effect size, dose-response
212
gradient, and publication bias in the confidence-rating of each dietary variable-disease outcome
213
as either high, moderate, low, or very low GRADE quality. We evaluated each pooled estimate
214
in GRADE to rate the confidence in the ratings of the studies for each dietary variable-disease
215
outcome. We downgraded the GRADE quality for substantial heterogeneity, imprecision,
216
inconsistency, e.g. variation in the effect estimate, lack of randomization, blinding, and self-
217
report. We upgraded the GRADE quality studies if there was a dose-response relationship. In
218
case there was a dose-response relationship in the presence of publication bias, the dose-response
219
override the downgrading due to publication bias.
220 221
Statistical analysis
222
Meta-analysis by dietary variable, sex and disease outcome within geographic region
223
We performed our analysis by geographic region. We grouped the countries as follows:
224
US (all United states), Europe (Australia, Denmark, Finland, France, Greece, Italy, Netherlands,
225
Spain, Sweden, United Kingdom), and Asia (China, Japan). Australia’s diet has been influenced
226
by Europe, and because they had few studies, it was grouped with Europe.
227
Our meta-analysis was performed using random-effects models. We modelled the binary
228
outcome of the study effects by dietary variable-disease outcome within geographic region to
229
produce pooled estimates. In subsequent models, we modelled the pooled effect by sex. In doing
230
so, studies that reported estimates for male-female combined could not be included in these
231
pooled estimates because there was no way to tease the male-female effects apart.
232
10
233 234
Heterogeneity assessment We calculated Cochrane Q and I2 statistics to quantify statistical heterogeneity in-and-
235
between study variation by dietary variable and disease outcome. An I2 > 50% was considered
236
potentially significant statistical heterogeneity. Homogeneity of multiple studies results was
237
evaluated by considering a suitable weighted sum of differences between the number of
238
individual study results and the pooled hazard ratios [36]. When fixed and random-effects
239
models that computed pooled hazard ratios were compared, we encountered substantial
240
heterogeneity in many pooled estimates. Consequently, we use the random-effects method in
241
model building to account for heterogeneity of the effects across studies by dietary variable-
242
disease outcome. This method incorporates the between study variability into the study weights
243
and the SEs of the pooled hazard ratios [36]. We tested these differences using the random-
244
effects method by meta-regression using the restricted maximum likelihood method. This
245
method evaluates to the extent additional statistical residual heterogeneity between the results of
246
multiple studies that could be related to one or more characteristics of the studies [36].
247 248 249
Dose-response analysis by dietary variable and disease outcome We examined the dose-response trends using the drmeta Stata package for dietary-
250
variable disease outcomes that contained ≥ 10 hazard ratios. When a statistically significant
251
effect was present in main model, we proceeded further to stratify by sex. We investigated dose-
252
response adjustment for GI or GL x other dietary variable interactions on disease outcomes and
253
assessed these relationships by meta-regression using the maximum likelihood method. In dose-
254
response meta-analysis, we applied within-study covariance using the Hamling method [38]. We
255
computed risk ratios using the number of cases, dietary variable measure, sample size (instead of
11
256
person time), and their log relative risks and SEs. Person time was not available in some studies
257
to compute the hazard ratio; therefore, we used the risk ratio instead. The hazard ratio
258
approximates the risk ratio when the disease is common [39] as in this current study. We used
259
goodness-of-fit tests to choose models with the best fit and maximum R2. These relationships for
260
the full sample, males, and females were depicted on graphs in drmeta. Statistical analyses were
261
performed using Stata 16.0 (Stata Corp., College Station, TX). We used a 2-sided p value <0.05
262
in all tests to determine statistical significance of results.
263 264
Results
265
Descriptive characteristics of studies
266
We identified 7,018 citations for our meta-analysis. Through screening and application of
267
inclusion and exclusion criteria, 40 full-text publications from cohort studies that analyzed data
268
using incident Cox proportional regression met inclusion criteria (see Figure 1). Among the 40
269
studies, 34 studies presented results on T2DM (16 US, 15 Europe, 3 Asia), 18 studies presented
270
results on CHD (5 US, 11 Europe, 2 Asia), 15 studies presented results on stroke (1 US, 6
271
Europe, 1 Asia), and 10 studies presented results on mortality (1 US, 5 Europe, 3 Asia). Studies
272
reported diseases as T2DM, CHD, and stroke that were ascertained from medical records, self-
273
report, or physician reports. The studies ascertained mortality from medical records, autopsy
274
reports, death certificates, and death databases.
275
Table 1 describes the characteristics of our meta-analysis. According to our inclusion
276
criteria, studies were conducted in the US, Europe, and Asia from 1997 to 2018. The length of
277
study time ranged from four years [40] to 28 years [41] with a total mean or median duration of
278
475.64 years for all studies combined. This meta-analysis included 2,207,241 participants with
12
279
190,987 cases of disease. This included participants/cases of 903,205/160,064 for T2DM;
280
578,658/20,487 for CHD; 561,522/5,624 for stroke; and 163,856/4,822 for mortality. The sample
281
size of studies ranged from 646 [32] to 91,249 [11] participants. Most studies used validated
282
food frequency questionnaires that gathered information about food intake over the past year
283
[12,18,31,42-46]. However, a few studies utilized diet records that collected intake over a few
284
days or 24-hour recall/diet history [19,31,32]. Items on food frequency questionnaires or dietary
285
records ranged from 61 to 260 items.
286
Table 2 shows confounding factors used in adjustment in the studies. All adjusted
287
appropriately for confounders. Studies adjusted for age, BMI, energy intake, smoking status,
288
alcohol intake, physical activity, and energy intake. Type 2 diabetes studies adjusted for family
289
history of diabetes. Other covariates used in adjustment by some studies were total dietary fiber,
290
cereal fiber, and other micronutrients as well as hormone replacement therapy, menopausal
291
status, and cardiometabolic disease precursors such as lipid levels.
292 293 294
Pooled hazard ratios of dietary variables within geographic regions We first assessed the pooled hazard ratios of the studies by geographic region (US,
295
Europe, Asia) for dietary variables (GI, GL, carbohydrate, high glycemic and refined
296
carbohydrate, total dietary fiber, and cereal fiber) on disease outcomes (T2DM, CHD, stroke and
297
mortality). For overweight/obese individuals, we presented only results for GI and GL and not
298
for other dietary variables, due to lack or scarcity of information reported in the published
299
manuscripts.
300
Table 3 shows the pooled hazard ratios by dietary variable-disease outcome which varied
301
by world geographic regions. For risk for developing T2DM, in US studies, we observed a 1.14-
13
302
fold increase in risk with higher GI (Hazard Ratio (HR)=1.14; 95% Confidence Interval:
303
1.06,1.21; I2=94.4%, Pheterogeneity=0.000); and 1.02-fold increase in risk with higher GL
304
(HR=1.09; 1.06,1.11; I2=98.8%, Pheterogeneity=0.000). The magnitude of risk for GL for developing
305
T2DM was substantially higher for Asian studies, which had a 1.25-fold increased pooled risk
306
for GI (HR=1.25; 1.02, 1.53; I2=12.8%, Pheterogeneity=0.318) and GL (HR=1.25;1.03, 1.52);
307
I2=9.1%, Pheterogeneity=0.333). There were no significant associations for GI or GL for
308
development of CHD across geographic regions or for stroke; except among Asians studies that
309
showed a 1.19 increased risk for stroke (GI: HR=1.19; 1.04,1.36; I2=0%, Pheterogeneity= 0.693) and
310
(GL: (HR=1.19; 1.07, 1.33). (See S1 Figures 1-3, 5-8).
311
We observed higher overall magnitude of associations for developing T2DM, CHD, and
312
stroke for GI and GL among individuals who were overweight/obese within all three geographic
313
regions, but risks were not statistically significant for mortality. Furthermore, Asian studies
314
compared to US and European studies, had the highest magnitude of associations for total
315
carbohydrate and CHD (HR=2.88;1.44, 5.78; I2=0%, Pheterogeneity=0.700), and high glycemic
316
carbohydrate and T2DM (HR=1.78;1.48, 2.15; I2=.%, Pheterogeneity=.) and CHD (HR=1.79 (1.02,
317
3.16; I2=0%, Pheterogeneity=0.641). Except for GL and CHD among overweight/obese individuals
318
(HR=1.97;1.31, 2.96; I2=.%, Pheterogeneity=.). However, Asian effect estimates were generally
319
based on only one or two studies. See S1 figures 19-16.
320
Total dietary fiber and cereal fiber were protective against developing cardiometabolic
321
diseases (see S1 Figures 24 to 31). More specifically in US studies, total dietary fiber (HR=0.92;
322
0.88,0.96; I2=78.3%, Pheterogeneity=0.000) and cereal fiber (HR=0.83; 0.77, 0.90; I2=83.2%,
323
Pheterogeneity=0.000) showed consistent evidence of protection against developing T2DM. Cereal
324
fiber compared to total dietary fiber was more protective in reducing risk of developing T2DM.
14
325
There appeared to be no clear picture on the associations for dietary variables on stroke risk in
326
European studies or mortality risk in Asian studies. Results were not statistically significant for
327
these associations. In addition, results were sparse for mortality in US studies as only one study
328
was published that had a non-significant pooled association for cereal fiber. Among European
329
studies on mortality, only one study on total dietary fiber (HR=0.83; 0.75,0.91; I2=.,
330
Pheterogeneity=.) was statistically significant.
331 332 333
Pooled hazard ratios of dietary variables by sex within geographic regions Tables 4 and 5 show the pooled results for males and females by dietary variable-disease
334
outcome within geographic regions. In US studies, GI in males (HR=1.30 (1.15,1.47; I2=.,
335
Pheterogeneity=.) and females (HR=1.20 (1.01,1.41 I2=85.4%, Pheterogeneity=0.000.), and GL in males
336
(HR=1.30 (1.09,1.55; I2=., Pheterogeneity=.) increased the risk for developing T2DM. See S2 Figure
337
1 and 5 and S3 Figure 1.
338
In US studies, total dietary fiber showed a protective association for developing T2DM in
339
males (HR=0.79 (0.67,0.92; I2=47.3%, Pheterogeneity=0.127) and females (HR=0.87 (0.78,0.98;
340
I2=53.5%, Pheterogeneity=0.057 ). Additionally, cereal fiber in males (HR=0.86 (0.74,0.99;
341
I2=48.1%, Pheterogeneity=0.123) and females (HR=0.79 (0.71,0.88; I2=58.1%, Pheterogeneity=0.036)
342
show a protective association from developing T2DM, but the confidence interval boundary was
343
close to 1 for males. However, in US studies, in males, cereal fiber not total dietary fiber showed
344
protective associations for developing CHD (HR=0.84 (0.75,0.94; I2=., Pheterogeneity=.). However,
345
these associations in males and females were based on one study each. See S2 Figures 22 and 23
346
and S3 Figures 24 and 25.
15
347
In European studies, in females but not males, GI (HR=1.17 (1.03,1.31; I2=21.1%,
348
Pheterogeneity=0.284.) and GL (HR=1.11 (1.00,1.23; I2=0%, Pheterogeneity=0.520) increased risk for
349
developing CHD (See S3 Figures 2 and 6). In European studies, in males, GI (HR=1.19
350
(1.02,1.39; I2=0%, Pheterogeneity=0.664) increased the risk of developing stroke (See S2 Figure 3).
351
In Asian studies, among males, GI (HR=1.96 (1.04,3.68; I2=., Pheterogeneity=.) increased risk of
352
T2DM, but this was each based on one study (See S2 Figure 1). Moreover, among Asian female
353
studies, GI (HR=1.19 (1.04,1.36); I2=0%, Pheterogeneity=0.693), GL (HR=1.26 (1.04,1.53; I2=0%,
354
Pheterogeneity=0.707), and high glycemic CHO (HR=1.19 (1.01,1.42; I2=0%, Pheterogeneity=0.649)
355
increased the risk of stroke (See S3 Figures 3,7, and 23). However, these pooled hazard ratios
356
were based on one study each that had more than one stratification.
357
Females who were overweight or obese in US studies were more affected by GI and GL
358
diets for risk of developing T2DM (GI: 1.28 (1.04,1.59; I2=40%, Pheterogeneity=0.197), CHD (GL:
359
1.97 (1.31,2.96; I2=., Pheterogeneity=.), and stroke (GI: 1.39 (1.25,1.54; I2=0%, Pheterogeneity=0.807),
360
(GL: 1.60 (1.06,2.40; I2=0%, Pheterogeneity=0.859.) (See S3 Figures 9-11, and 13-15). These
361
statistically significant associations were not present among males in European studies except for
362
the GL-CHD association where males had a high magnitude of risk (GL: 2.05 (1.30, 3.23; I2=.,
363
Pheterogeneity=.) (See S2 Figure 12). There were no studies on Asians for overweight/obese males
364
or females on CHD or stroke risk.
365 366 367
Heterogeneity for pooled estimates Because there was substantial heterogeneity (I2 > 50%) for GI and GL in US studies, we
368
performed a sensitivity analysis using meta-regression to examine the impact variables that
369
contribute to excess heterogeneity had on their effect size. As indicated in S1 Table 1, within
16
370
pooled estimates, heterogeneity was attributed to differing study characteristics for sex, study
371
time, cases, and sample size in US studies.
372 373
Dose-response trends for total dietary fiber and cereal fiber on type 2 diabetes risk
374
We investigated dose-response relationships for ≥ 10 estimates by dietary variable-
375
disease outcome within geographic regions using random-effects analysis. We investigated
376
relationships for GI-T2DM, GI-CHD, and GL-T2DM among European studies, and total dietary
377
fiber-T2DM and cereal fiber-T2DM among US studies. We observed a dose-response
378
relationship with total dietary fiber and cereal fiber on risk of developing T2DM only in US
379
studies. Total dietary fiber had cumulative protective associations as noted for every 1g increase
380
in total dietary fiber (HR= 0.99; 0.98, 0.99), 3g (HR=0.96; 0.94, 0.98), 5g (HR=0.94, 0.92; 0.96).
381
Cereal fiber dose-response effects were more profound than total dietary fiber. For every 1g
382
increase in cereal fiber, there was 0.07-fold decreased (HR=0.93; 0.90, 0.95) in risk for
383
developing T2DM. This dose-response protective trend effect was more evident with every 3g
384
(HR=0.80; 0.73, 0.87) and 5g (HR=0.68; 0.59, 0.79) of daily cereal fiber intake.
385
After adjustment for GI in the total dietary fiber model (n=6), the total dietary fiber effect
386
estimate became non-significant (HR=0.98; 0.96, 1.01). Moreover, after adjustment for GI in the
387
cereal fiber model (n=7), cereal fiber effect estimate remained statistically significant (HR=0.88;
388
0.84, 0.93), and GI was non-significant in the model (HR=0.94; 0.87, 1.01). Furthermore, after
389
adjustment for GL, both the total dietary fiber (HR=1.00; 0.98, 1.02) and the cereal fiber
390
(HR=1.00; 0.95, 1.04) dose-response pooled effects became non-significant in the models. GL
391
remained significant in the models for total dietary fiber (HR=1.005; 1.001, 1.001) and cereal
392
fiber (HR=1.003; 1.002, 1.004), but the magnitude of effect was small for each unit of GL. After
17
393
adjustment for sex, both total dietary fiber (HR=0.97; 0.96, 0.99) and cereal fiber (HR=0.93;
394
0.91, 0.94) remained statistically significant in their models. We did not find a cereal fiber x sex
395
interaction in the dose-response random-effects models for development of T2DM in US studies.
396
We observed statistically significant dose-response effects for females and not males.
397
Females benefited from higher intakes of total dietary fiber and cereal fiber protective effects for
398
risk of developing T2DM in US studies. The dose-response protective associations were similar
399
for females as in the full sample. Total dietary fiber showed protective association for 1g
400
(HR=0.99; 0.98, 0.99), 3g (HR=0.97; 0.95, 0.98), 5g (HR=0.94; 0.92, 0.97), and 10g (HR=0.89;
401
0.85, 0.94) against development of T2DM. The dose-response protective association for cereal
402
fiber for developing T2DM was evident for every 1g increase in cereal fiber (HR=0.92; 0.90,
403
0.94), every 3g increase (HR=0.78; 0.74, 0.83), and every 5g increase (HR=0.67; 0.60, 0.74) in
404
daily cereal fiber intake. The addition of calories to the models did not appreciably changed the
405
effect estimates. Similarly, as in the full models, after adjustment for GL in US female studies
406
(n=7 study estimates) and the GL adjustment obliterated total dietary fiber and cereal fiber risks
407
and made them non-significant. GL remained statistically significant in the cereal fiber model
408
(HR=1.003; 1.002, 1.004) but became non-significant in the total dietary fiber model. S4 Figures
409
1 and 2 depict the decreasing dose-response linear trends for total dietary-and cereal fiber-T2DM
410
relationship.
411 412 413
Sensitivity analysis for study influence We investigated the influence of each study on the pooled hazard ratios in the meta-
414
analysis by omitting one study in each turn by dietary variable and disease outcome. The most
415
profound results were with the T2DM pooled hazard ratio for GI in US studies. After deleting of
18
416
the three Bhupathiraju et al. studies [3] on NHS, NHS II, and HPFS, the T2DM pooled hazard
417
ratio was significantly decreased and was no longer statistically significant for GI (HR=1.00;
418
0.98,1.03) and GL (HR=1.00; 1.00, 1.01).
419 420
Evaluation of the quality of the meta-evidence
421
Publication bias
422
We explored the possible impact of publication bias on pooled results of dietary variables
423
and disease outcome by geographic region (S5 Figures 1 to 5). We examined publication bias in
424
pooled estimates that contained ≥ 10 study estimates. Pooled analyses with fewer studies may be
425
more subject to bias due to small study effects. Furthermore, we explored the possible impact of
426
publication bias on the interpretation of the data. After we explored publication bias using the
427
‘trim and fill’ method and by inspecting Begg’s and Egger’s test results for small study effects (p
428
<0.10), total dietary fiber-T2DM and cereal fiber-T2DM in US studies revealed publication bias
429
(S5 Figures 4 and 5).
430 431
Risk of bias and GRADE quality rating of studies
432
We evaluated risk of bias using the ROBIN-I Tool at https://training.cochrane.org
433
/handbook/current/chapter-25 (see S1 Table 1). Most risk of bias domains scored as moderate to
434
serious risk of bias because of the non-randomized observational nature of the studies. In
435
addition, several studies used mailed questionnaires (self-reported information) instead of annual
436
exams to collect information and included substantial lower rates of return of questionnaires
437
(selection bias). We evaluated each pooled estimate in GRADE. The confidence in the ratings for
438
most studies for the dietary variable-disease outcome pooled estimates had low to very low
19
439
GRADE quality (See S1 Table 2). Several pooled estimates were downgraded due to substantial
440
heterogeneity, imprecision, inconsistency, e.g. variation in the effect estimate, lack of
441
randomization, blinding, and self-report. We upgraded the GRADE quality for dietary fiber and
442
cereal fiber in US studies because of their dose-response relationships with T2DM despite the
443
presence of publication bias.
444 445
Discussion
446
This meta-analysis investigated the meta-evidence for carbohydrate quality in relation to
447
GI and GL in association with T2DM, CHD, stroke, and mortality in the US, Europe, and Asia.
448
We observed statistically significant pooled hazard ratios for risk of developing T2DM in US
449
studies that were not observe in European studies. Among males and females, GI and GL
450
increased risk of T2DM in US studies. There was an increased GI/GL-stroke risk in males and
451
GI/GL-CHD risk in females in European studies. There was an increased GI/GL-T2DM risk in
452
males and GI/GL-stroke risk in females in Asian studies. Furthermore, we observed higher
453
magnitude of risks among those who were overweight or obese in the whole sample and in Asian
454
females for risk of developing T2DM, CHD and stroke. However, some findings were sparse,
455
and many were based on one study each. At even lower mean BMIs in Asian studies, greater
456
intakes of GI, GL, carbohydrate and high glycemic and refined carbohydrate were associated
457
with higher risk of developing T2DM, CHD, and stroke compared to risks in US and European
458
studies.
459
Total dietary fiber and more so cereal fiber showed evidence to decrease the risk of
460
developing T2DM in US studies, and CHD risk in European studies. We found protective dose-
461
response risks for total dietary fiber and cereal fiber against development of T2DM in US studies
20
462
among females. Our meta-analysis findings suggest that cereal fiber’s protective effect in
463
females can be potentially sustained on a high GI diet. However, the cumulative effect from
464
large quantities of high GI foods will increase the GL and this can nullify cereal fiber’s
465
protective effect. Noticeably, total dietary fiber’s protective effect was nullified by both GI and
466
GL after adjustment in the dose-response models. Cereal fiber appears to have a stronger
467
protective effect against development of T2DM in females compared to its larger component,
468
total dietary fiber.
469
The main carbohydrate sources differ in the US (white bread), Europe (potatoes), and
470
Asia (white rice) in their proportions of amylose, amylopectin, water content, and degree of
471
refining and processing. Because these starches are rapidly digested, they are absorbed quickly
472
and have a higher glycemic response. Alternately, high fiber cereal foods such as oatmeal and
473
barley have lesser amounts of amylopectin and greater amounts of soluble fiber and amylose.
474
These features of cereal fiber assists in decreasing the digestibility of starchy foods and
475
consequently can delay carbohydrate absorption producing a lower glycemic response [47].
476
Studies report that cereal fiber in addition to whole grains, bran, and germ fiber can protect
477
against developing cardiovascular disease [48], all-cause mortality and increased risk from
478
mortality from cardiovascular disease among individuals with T2DM, [17,49,50]. In some
479
studies, cereal fiber was shown to blunt the effects of GI or GL from developing risk of T2DM
480
[11,13,14] which was only observed in our study for cereal fiber’s dose-response with GI.
481
Over time, the adoption of refined processed foods high in fat and sugar from more
482
industrialized countries such as the US and Europe, have replaced traditional meals characterized
483
by whole grain foods and vegetables, thus elevating the GI and GL of the diet. The
484
Westernization of diets from the US to Asia[51-53], coupled with the predisposition to store
21
485
more abdominal fat, is a strong risk factor for cardiometabolic diseases[54]. This has similarly
486
contributed to metabolic derangements in blood glucose, lipids levels, weight gain, insulin
487
resistance [55] and increasing rates of T2DM [54] and cardiovascular diseases among Asians
488
[51-53]. However, in the US, a low carbohydrate diet [4], and a diet high in cereal fiber and
489
beans [56], and higher levels of physical activity [57] could reduce the effects of weight gain,
490
hyperglycemia and other abnormal cardiometabolic parameters from development of
491
cardiometabolic diseases.
492
Other studies that were not included in this meta-analysis (because our study was only in
493
relation to GI and GL), report total dietary fiber’s protective effects on CHD risk [58,59]. Studies
494
report dose-response effects for GI, GL, and carbohydrate for risk for developing T2DM [60]
495
and CHD [22]which we did not observed in our study. However, we only found dose-response
496
risks for total dietary fiber and cereal fiber for developing T2DM as in other studies [20,61,62].
497
We did not find any dose-response risks for CHD and stroke reported in other meta-analyses
498
[20,20,21], or for GI/GL-mortality pooled estimates as in a recent study by Shahdadian et al.
499
[23]. This may be due to our meta-analysis being reported by geographic region and the
500
exclusion of odds ratios and beta coefficients from linear regression in our analysis. In addition,
501
we did not observe interactions for the total dietary fiber or cereal fiber’s dose-response with GI
502
or GL as reported in other nondose-response single studies [3,11,13,14].
503
A major strength of our study is that we updated our meta-analysis to include the most
504
recent studies that reported hazard ratios when studies used the same datasets during different
505
years to investigate similar research questions [3,4,11-14,35]. Other meta-analyses included
506
these same studies with repeated datasets, or used other measures of risk, such as odds ratios to
507
approximate the dietary-disease relationship that may have augmented their pooled estimates
22
508
and caused our results to differ [25]. As a result, we did not include studies that reported odds
509
ratios as this would have over-estimated the effect estimates and drawn exaggerated conclusions.
510
Another major strength is the prospective nature of all the studies that reduced the
511
chances of recall bias due to under-reporting of the dietary variables. Information bias was also
512
lessened because information on the disease outcomes was ascertained independently of the
513
collection of dietary variables. Another strength is the large sample size and large number of
514
studies for some dietary variables and disease outcomes such as total dietary fiber and cereal
515
fiber and T2DM. In addition, the large number of cases used to compute pooled hazard ratios by
516
dietary variables and disease outcomes within geographic regions was a major strength to this
517
study. To our knowledge, this is the first meta-analysis to evaluate carbohydrate quality from a
518
set of important dietary markers in relation to GI and GL by disease outcomes within world
519
geographic regions of the US, Europe, and Asia.
520
We identified several limitations in this meta-analysis. Due to the language barrier, non-
521
English publications were not utilized. We included hazard ratios in continuous form, tertiles,
522
quartiles, and quintiles in selected publications to have equal weight in the pooled analyses. As a
523
result, this may have some inherent bias in terms of the discrimination of results in finer
524
categories. Another limitation in terms of measurement bias is in the use of GI and GL values
525
tested on healthy individuals to infer risk for individuals with diabetes [8,63]. The studies
526
included in this meta-analysis contained a wide range of food items (61-260) used to calculate
527
the mean/median GI, GL, carbohydrate, and total dietary fiber content of the diet. Biases may
528
include differences in GI, GL, and carbohydrate values, amylose, starch gelatinization, water
529
content, degree of refining and processing, different cooking methods used in preparation, non-
530
standardized measurement protocols or differences in technology [8,63,64]. Because of these
23
531
differences within geographic regions, the carbohydrate contribution and quality and total dietary
532
fiber content may differ which can affect the GI and GL values of the diet. This could be more
533
evident in food frequency questionnaires limited to a smaller amount of carbohydrate containing
534
foods as they may not be able to capture the contribution of higher GI and GL containing foods,
535
and therefore could include measurement bias in reporting.
536
Because our meta-analysis included observational studies, there may be risk of bias from
537
residual confounding due to selection bias and other biases. However, for the most part, we have
538
decreased the bias in our methodology, e.g. exclusion criteria to include studies based on the risk
539
for development of cardiometabolic diseases and risk for mortality. However, the agreement
540
between the dose-response relationship from cereal fiber and T2DM in US studies together with
541
published randomized trial results[65] showed that this finding is most likely causal.
542 543 544
Conclusions In summary, our meta-analysis shows that over time from 1997 to 2018, diets high in GI
545
and GL increased the risk of developing of T2DM, CHD, and stroke in the US, Europe, and
546
Asia. Higher risks for cardiometabolic diseases across geographic regions were mainly observed
547
in overweight or obese individuals in the US and Asian studies, particularly females with greater
548
intakes of GI and GL. In US studies, among females, total dietary fiber and especially cereal
549
fiber was protective in decreasing the risk of T2DM in a dose-response fashion. However, the
550
protective dose-response effects were nullified by a diet high in GL, but not for cereal fiber with
551
GI. These findings suggest that overweight/obese females across geographic regions could shift
552
their carbohydrate intake to higher cereal fiber foods to lower risk of developing T2DM.
24
553
However, a high GL diet can cancel out this protective association from cereal fiber for risk of
554
developing T2DM.
555 556
All authors declare no competing conflicts of interests.
557 558
Funding
559
This work was supported by a K01 grant by the National Heart, Lung, and Blood Institute, grant
560
no. K01 HL127278.
561 562
Acknowledgements
563
All authors contributed to designing the research project and served as a reviewer for inclusion
564
of studies. DSH and JTG provided articles. DSH wrote paper and performed statistical analysis.
565
JTG co-wrote paper and provided feedback on statistical analysis. HXU helped with setting up
566
electronic spreadsheet for data analysis. DSH and JTG had primary responsibility for final
567
content.
25
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[50] Steffen LM, Jacobs DR,Jr, Stevens J, Shahar E, Carithers T, Folsom AR. Associations of whole-grain, refined-grain, and fruit and vegetable consumption with risks of all-cause mortality and incident coronary artery disease and ischemic stroke: the Atherosclerosis Risk in Communities (ARIC) Study. Am J Clin Nutr 2003;78(3):383-390. [51] Baker P, Friel S. Food systems transformations, ultra-processed food markets and the nutrition transition in Asia. Global Health 2016;12(1):80-016-0223-3. [52] Baker P, Friel S. Processed foods and the nutrition transition: evidence from Asia. Obes Rev 2014;15(7):564-577. [53] Ding EL, Malik VS. Convergence of obesity and high glycemic diet on compounding diabetes and cardiovascular risks in modernizing China: An emerging public health dilemma. Global Health 2008;4:4. [54] World Health Organization. Waist Circumference and Waist-to Hip Ratio: Report of a WHO Expert Consultation, Geneva, 2008;8-11 [55] Pereira MA, Kartashov AI, Ebbeling CB, Van Horn L, Slattery ML, Jacobs DR,Jr, et al. Fast-food habits, weight gain, and insulin resistance (the CARDIA study): 15-year prospective analysis. Lancet 2005;365(9453):36-42. [56] Lillioja S, Neal AL, Tapsell L, Jacobs DR,Jr. Whole grains, type 2 diabetes, coronary heart disease, and hypertension: links to the aleurone preferred over indigestible fiber. Biofactors 2013;39(3):242-258. [57] Marwick TH, Hordern MD, Miller T, Chyun DA, Bertoni AG, Blumenthal RS, et al. Exercise training for type 2 diabetes mellitus: impact on cardiovascular risk: a scientific statement from the American Heart Association. Circulation 2009;119(25):3244-3262. [58] Pereira MA, O'Reilly E, Augustsson K, Fraser GE, Goldbourt U, Heitmann BL, et al. Dietary fiber and risk of coronary heart disease: a pooled analysis of cohort studies. Arch Intern Med 2004;164(4):370-376. [59] Liu S, Buring JE, Sesso HD, Rimm EB, Willett WC, Manson JE. A prospective study of dietary fiber intake and risk of cardiovascular disease among women. J Am Coll Cardiol 2002;39(1):49-56. [60] Greenwood DC, Threapleton DE, Evans CE, Cleghorn CL, Nykjaer C, Woodhead C, et al. Glycemic index, glycemic load, carbohydrates, and type 2 diabetes: systematic review and dose-response meta-analysis of prospective studies. Diabetes Care 2013;36(12):4166-4171. [61] Livesey G, Taylor R, Livesey HF, Buyken AE, Jenkins DJA, Augustin LSA, et al. Dietary Glycemic Index and Load and the Risk of Type 2 Diabetes: A Systematic Review and Updated Meta-Analyses of Prospective Cohort Studies. Nutrients 2019;11(6):10.3390/nu11061280. 30
[62] Yao B, Fang H, Xu W, Yan Y, Xu H, Liu Y, et al. Dietary fiber intake and risk of type 2 diabetes: a dose-response analysis of prospective studies. Eur J Epidemiol 2014;29(2):79-88. [63] Atkinson FS, Foster-Powell K, Brand-Miller JC. International tables of glycemic index and glycemic load values: 2008. Diabetes Care 2008;31(12):2281-2283. [64] Wolever TM, Brand-Miller JC, Abernethy J, Astrup A, Atkinson F, Axelsen M, et al. Measuring the glycemic index of foods: interlaboratory study. Am J Clin Nutr 2008;87(1):247S-257S. [65] Davison KM, Temple NJ. Cereal fiber, fruit fiber, and type 2 diabetes: Explaining the paradox. J Diabetes Complications 2018;32(2):240-245. [66] Meyer KA, Kushi LH, Jacobs DR,Jr, Slavin J, Sellers TA, Folsom AR. Carbohydrates, dietary fiber, and incident type 2 diabetes in older women. Am J Clin Nutr 2000;71(4):921930. [67] Stevens J, Ahn K, Juhaeri, Houston D, Steffan L, Couper D. Dietary fiber intake and glycemic index and incidence of diabetes in African-American and white adults: the ARIC study. Diabetes Care 2002;25(10):1715-1721. [68] Krishnan S, Rosenberg L, Singer M, Hu FB, Djousse L, Cupples LA, et al. Glycemic index, glycemic load, and cereal fiber intake and risk of type 2 diabetes in US black women. Arch Intern Med 2007;167(21):2304-2309. [69] van Woudenbergh GJ, Kuijsten A, Sijbrands EJ, Hofman A, Witteman JC, Feskens EJ. Glycemic index and glycemic load and their association with C-reactive protein and incident type 2 diabetes. J Nutr Metab 2011;2011:623076. [70] Simila ME, Valsta LM, Kontto JP, Albanes D, Virtamo J. Low-, medium- and highglycaemic index carbohydrates and risk of type 2 diabetes in men. Br J Nutr 2011;105(8):1258-1264. [71] Simila ME, Kontto JP, Valsta LM, Mannisto S, Albanes D, Virtamo J. Carbohydrate substitution for fat or protein and risk of type 2 diabetes in male smokers. Eur J Clin Nutr 2012;66(6):716-721. [72] Rossi M, Turati F, Lagiou P, Trichopoulos D, Augustin LS, La Vecchia C, et al. Mediterranean diet and glycaemic load in relation to incidence of type 2 diabetes: results from the Greek cohort of the population-based European Prospective Investigation into Cancer and Nutrition (EPIC). Diabetologia 2013;56(11):2405-2413. [73] Sluijs I, Beulens JW, van der Schouw YT, van der ADL, Buckland G, Kuijsten A, et al. Dietary glycemic index, glycemic load, and digestible carbohydrate intake are not associated with risk of type 2 diabetes in eight European countries. J Nutr 2013;143(1):93-99.
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32
Table 1. Description of published studies. Study
T2DM Salmeron et al. 1997(men) [13]
Meyer et al. 2000 [66] Stevens et al. (Whites) 2001 [67] Stevens et al. (African Am) 2001 [67]
Schultz et al. 2004 [11]
Krishnan et al. 2007 [68] Hopping et al. 2010 (Whites) [10] Hopping et al. 2010 (Japanese) [10] Hopping et al. 2010 (Hawaiian) [10] Hopping et al. 2010 (Whites) [10] Hopping et al. 2010 (Japanese) [10] Hopping et al. 2010 (Hawaiian) [10] Bhupathiraju et al. 2014 [3] Bhupathiraju et al. 2014 [3] AlEssa et al. 2015
[9]
Country
Dataset
US
HPFS
US
IWHS
US
ARIC
US
ARIC
US
Hawaii
NHS II Black Women ’s Health study Hawaii MEC Hawaii MEC Hawaii MEC Hawaii MEC Hawaii MEC Hawaii MEC
US
NHS11
US Hawaii Hawaii Hawaii Hawaii Hawaii
US US
HPFS NHS
Data Form
Sample Size/Cases
Study Time (Y)
Age (Y)
Sex
BMI
quintil es
42759/915
6
58
M
26
1995
T2DM
35988 /228
6
62
F
27
1803
T2DM
12251/148
9
54
MF
27
1625
T2DM
12251/253
9
53
MF
29
1601
T2DM
quintil es contin uous contin uous
quintil es
quintil es quintil es quintil es quintil es quintil es quintil es quintil es quintil es quintil es quintil es
91249/741
59000/193 8 15116/107 3 16572/268 5 4568/799 14643/717 5 18672/237 1 5941/945 90411/451 5 40498/311 2 70025/693 4
Kcal
Disease
8
36
F
25
1812
T2DM
8
F
30
1759
T2DM
14
37 4575 4575 4575 4575 4575 4575
18
36.1
14 14 14 14 14
M
2162
T2DM
M
2164
T2DM
M
2540
T2DM
F
1707
T2DM
F
1709
T2DM
F
2061
T2DM
1789
T2DM
F
25
Dietary Exposure Variables
CHO, DFIB, CFIB GI, GL, CHO, high glycemic CHO, DFIB, CFIB GI, GL, DFIB, CFIB GI, GL, DFIB, CFIB CHO, DFIB, CFIB, BMI GI, BMI GL GI, GL, CFIB, BMI GL, BMI GL
GL, DFIB, CFIB GL, DFIB, CFIB GL, DFIB, CFIB GL, DFIB, CFIB GL, DFIB, CFIB GL, DFIB, CFIB GI, GL
Dietary Data/ # Items
FFQ/131
FFQ//127 FFQ/66 FFQ/66
FFQ/133
FFQ/68 QFFQ/ 8 categories QFFQ/ 8 categories QFFQ/ 8 categories QFFQ/ 8 categories QFFQ/8 food categories QFFQ/ 8 categories FFQ/126-133
GI, GL 21 24
52.9 50.2
M F
25 25
1995 1727
T2DM T2DM
FFQ/126-133 CHO, DFIB, CFIB
FFQ/116-133
33
Barclay et al. 2007 Van Woudenbergh et al. 2011[69] Similä et al. 2011[70]
Europe Netherla nds Findland
Similä et al. 2012[71]
Findland
Rossi et al. 2013 [72]
Greece
Slujis et al. 2013 (Denmark) [73]
Denmark
Slujis et al. 2013 (France) [73]
France
Slujis et al. 2013 (Germany) [73]
Germany
Slujis et al. 2013 (Italy) [73]
Italy
Slujis et al. 2013 (Netherlands) [73]
Netherla nds
Slujis et al. 2013 (Spain) [73]
Spain
Slujis et al. 2013 (Sweden) [73]
Sweden
Slujis et al. 2013 (UK) [73] The Interact Consortium-EPIC 2015 [74]
Villegas et al. 2007 [75]
United Kingdom United Kingdom
China
Australi an cohort Rotterd am study ATBC ATBC EPICGreek cohort EPICInterAc t Study EPICInterAc t Study EPICInterAc t Study EPICInterAc t Study EPICInterAc t Study EPICInterAc t Study EPICInterAc t Study EPICInterAc t Study EPICInterAc t Study
SWHS
contin uous
GI, CHO, DFIB, CFIB 1833/138
10
70+
MF
T2DM
FFQ/145 GI, GL
tertiles quintil es quintil es
4366/456 25943/109 8 25943/109 8
quartil es
22295/233 0
15
66
MF
26
1981
T2DM
12
56
M
26
2594
T2DM
12
56
M
26
2594
T2DM
11.3
50.4
MF
28
2051
T2DM
T2DM
GI, GL, CHO, high glycemic CHO, BMI GI, BMI GL
12
52.5
MF
26
quartil es
16835/124 03
12
52.5
MF
26
T2DM
quartil es
16835/124 03
12
52.5
MF
26
T2DM
quartil es
16835/124 03
12
52.5
MF
26
T2DM
quartil es
16835/124 03
12
52.5
MF
26
T2DM
quartil es
16835/124 03
12
52.5
MF
26
T2DM
quartil es
16835/124 03
12
52.5
MF
26
T2DM
quartil es quartil es
16835/124 03 15258/115 59
12 12
52.5 52.4
MF MF
26 26
64227/160 8
4.6
51
MF
2140
1683
T2DM
FFQ/145
FFQ/~150
T2DM T2DM
16835/124 03
2140
FFQ/145 high glycemic CHO BMI GI, BMI GL GI, GL, CHO, high glycemic CHO GI, GL, CHO, high glycemic CHO GI, GL, CHO, high glycemic CHO GI, GL, CHO, high glycemic CHO GI, GL, CHO, high glycemic CHO GI, GL, CHO, high glycemic CHO GI, GL, CHO, high glycemic CHO GI, GL, CHO, high glycemic CHO DFIB, CFIB
quartil es
quintil es
FFQ/170 GI, GL
FFQ/up to 260
FFQ/ up to 260
FFQ/ up to 260
FFQ/ up to 260
FFQ/ up to 260
FFQ/ up to 260
FFQ/ up to 260
FFQ/ up to 260 FFQ/ up to 260
FFQ/77
34
Sakurai et al. 2011 [76] He et al. 2018 He et al. [77]
Japan
Japanes e Factory study
quintil es
China
Guangz hou study
contin uous
GI, GL, DFIB, BMI GI 1995/133
6
46
M
23
2198
T2DM
FFQ/147 GI, GL
640/127
MF
5
24
1879
T2DM
3-day food record
Coronary Heart Disease
Liu et al. 2001 [12]
Hardy et al. (Whites) 2010 [42] Hardy et al. (African Am) 2010 [42]
US
NHS
US
ARIC
US
quintil es
contin uous contin uous
75521/761
10
51
F
25
1743
CHD
11673/130 3
17
55
MF
27
1625
CHD
Mursu et al. 2009 [19]
Finland
Levitan et al. 2010 [43]
Sweden
Sieri et al. 2010 [31]
Italy
EPICO R
quartil es
47749/305
11
50
M
27
2126
CHD
Seri et.al 2010 [31]
Italy
EPICO
quartil
44132/158
11
50
F
26
2509
CHD
Netherla nds
Beulens et al. 2007 [18]
Netherla nds
Levitan et al. 2007 [44]
Sweden
FFQ/126
FFQ/66 GI, GL
ARIC Zutphe n Elderly study Prospec t-EPICBreastC cancer Cohort of Swedis h men Kuopio KIHD Swedis h MC
Van Dam et al 2000 [32]
GL, CHO, high glycemic CHO, BMI GL GI, GL
11673/380
17
54
MF
30
1606
CHD
FFQ/66 GI
tertiles
646/94
10
71
M
25
2257
CHD
quartil es
15714/556
9
57
F
26
1458
CHD
36246/132 4
5
59
M
26
2712
CHD
quartil es quartil es quartil es
1981/376 36234/113 8
16.1
52
M
27
2400
CHD
9
62
F
26
1739
CHD
Diet history
GI, GL GI, GL
FFQ/77
FFQ/96 GI, GL, BMI GI, BMI GL GI, GL, BMI GI BMI GL GI, GL, CHO high glycemic CHO GI, GL, CHO
4-day food record FFQ
24-hr recall/159 24-hr recall/159
35
R
contin uous
8855/581
11.9
43
M
25
2603
CHD
contin uous
10753/300
11.9
42
F
25
1984
CHD
ATBC
quintil es
21955/437 9
19
57
M
26
2604
CHD
Greece
EPIC Greek cohort
tertiles
20275/417
10.4
2088
MF
China
SMHS
quartil es
52512/189
5.4
54.1
M
24
1930
CHD
China
SWHS
quartil es
64854/120
51.5
F
24
1684
CHD
high glycemic CHO GI, GL, CHO high glycemic CHO GI, GL, CHO high glycemic CHO GI, GL, CHO high glycemic CHO GL,CHO high glycemic CHO, BMI GL GI, GL, CHO high glycemic CHO GI, GL, CHO high glycemic CHO
US
HPFS
quintil es
42865/405 3
53.3
M
25.5
1983
CHD
CHO, DFIB, CFIB
FFQ/126-133
US
NHS
quintil es
50.3
F
24.9
1726
CHD
CHO, DFIB, CFIB
FFQ/116-133
Burger et al. 2011 [29]
Netherla nds
Burger et al.2011 [29]
Netherla nds
EPICMORG EN EPICMORG EN
Similä et al. 2013[78]
Findland
Turati et al. 2015 [79]
Yu et al. 2013 [27]
Yu et al. 2013 [27] AlEssa et al. 2018[41] AlEssa et al. 2018[41]
es
75020/405 3
9.8 26 28
CHD
FFQ/79
FFQ/79
FFQ/145
FFQ/150
FFQ/77
FFQ/77
Stroke
Oh et al. 2005 [16]
US
Oh et al. 2005 [16]
US
Beulens et al. 2007 [18]
Netherla nds
Levitan et al.2007 [44]
Sweden
NHS
NHS prospec t-EPICbreast ca Cohort of Swedis
quintil es
quintil es
78779/515
18
46
F
24
1541
Ischemi c stroke
78779/279
18
46
F
24
1541
Hemorr hagic stroke
15714/243
9
57
F
26
14581
Total stroke
GI, GL, CHO, DFIB, CFIB, BMI GI, BMI GL GI, GL, CHO, DFIB, CFIB, BMI GI, BMI GL GI, GL
FFQ/61
FFQ/61
FFQ/77 GI, GL
quartil es
36246/692
5
59
M
26
2712
Ischemi c stroke
FFQ/96
36
h men
Burger et al. 2011 [29]
Netherla nds
Burger et al. 2011 [29]
Netherla nds
Cohort of Swedis h men EPICMORG EN EPICMORG EN
Seri et al. 2013 [80]
Italy
Seri et al. 2013 [80]
Italy
Rossi et al. 2015 [81]
Greece
Levitan et al. 2007[44]
Rossi et al. 2015 [81]
Rossi et al. 2015 [81]
Rossi et al. 2015 [81]
Yu et al. 2016 [40]
Yu et al. 2016 [40]
Sweden
Greece
Greece
Greece
GI, GL quartil es
36246/165
5
59
M
26
2712
Hemorr hagic stroke
contin uous
8855/120
11.9
43
M
25
2603
Total stroke
contin uous
10753/109
11.9
42
F
25
1984
Total stroke
EPICO R
quintil es
44099/195
11
50
MF
26
1804
EPICO R EPICGreek cohort EPICGreek cohort EPICGreek cohort EPICGreek cohort
quintil es
44099/83
11
50
MF
26
1804
tertiles
19824/67
15
2086
M
Ischemi c stroke Hemorr hagic stroke
FFQ/96 GI, GL, CHO high glycemic CHO GI, GL, CHO high glycemic CHO GI, GL, CHO high glycemic CHO, DFIB GI, GL, CHO high glycemic CHO, DFIB GL
Ischemi c stroke
FFQ/79
FFQ/79
FFQ/154
FFQ/154
FFQ/150 GL
tertiles
tertiles
tertiles
19824/67
19824/49
19824/49
China
SWHS
quintil es
64328/299 1
China
SWHS
quintil es
64328/299 1
15
15
15
4
4
2087 2086 2087
Ischemi c stroke Hemorr hagic stroke Hemorr hagic stroke
F
M
F
52
F
24
1684
52
F
24
1684
Ischemi c stroke Hemorr hagic stroke
2712
Allcaus e mortalu ty Allcaus e mortalit
FFQ/150 GL FFQ/150 GL FFQ/150 GI, GL, CHO high glycemic CHO GI, GL, CHO high glycemic CHO
FFQ/77
FFQ/77
Mortality
He et al 2010 [82]
Levitan et al. 2007 [44]
US
Sweden
NHS Cohort of Swedis
quintil es quartil es
7822/852 36246/295 9
26
5
F
59
M
30
26
CFIB
FFQ/126 GI, GL FFQ/96
37
h men
Kaushik et al. 2009 [15]
Kaushik et al. 2009 [15]
Australia
Australia
Blue Mounta ins Eye Study Blue Mounta ins Eye Study
y GI, CFIB
tertiles
2897/1158
13
65.4
MF
26
FFQ/145 GI, CFIB
tertiles
2897/95
13
65.4
MF
Netherla nds
EPICMORG EN
Turati et al. 2014 [79]
Greece
EPIC Greek cohort
tertiles
12029/162
10.4
20-9
MF
Castro-Quezada et al. 2014 [83]
Spain
PREDI MED
quartil es
3583/123
4.7
66.7
MF
Burger et al. 2012 [28]
CHD mortalit y
quartil es
6192/791
9.2
57.4
MF
Stroke mortalit y
26
29
30
2047
T2DM mortalit y
2274
CHD mortalit y Total mortalit y
FFQ/145 GI, GL, CHO high glycemic CHO, DFIB, BMI GI, BMI GL GL, CHO high glycemic CHO, BMI GL GI, GL
FFQ/79
FFQ/150
FFQ/137 GI, GL, CHO Takaya Stroke high glycemic ma mortalit CHO, BMI quartil Oba et al. 2010 [30] Japan study es 12561/120 7 53.7 M 23 2617 y GI, BMI GL FFQ/169 GI, GL, CHO Takaya Stroke high glycemic ma quartil mortalit CHO, BMI Oba et al. 2010 [30] Japan study es 15301/127 7 54.9 F 22 2131 y GI, BMI GL FFQ/169 Stroke GI, GL, CHO quintil 64328/299 mortalit high glycemic Yu et al. 2016 [26] China SWHS es 1 4 52 F 24 1684 y CHO FFQ/77 Abbreviations: M, males; F, females; MF, males plus females; T2DM, T2DM; CHD, coronary heart disease; FFQ, food frequency questionnaire, QFFQ, quasi-food frequency questionnaire; HPFS, Health Professionals Follow-Up Study; IWHS, Iowa Women’s Health Study; NHS, Nurses’ Health Study; NHSII, Nurses’ Health Study; ARIC, Atherosclerosis Risk in Communities Study; Hawaii MEC, Hawaii Multiethnic Cohort; EPIC, European Prospective Investigation into Cancer and Nutrition; EPICOR, European Prospective Investigation into Cancer and Nutrition in Italian cohorts; ATBC, The Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study; KIHD, Kuopio Ischaemic Heart Disease Risk Factor; SWHS, Shanghai Women’s Health Study; SMHS, Shanghai Men’s Health Study; EPIC-MORGEN, European Prospective Investigation into Cancer and
38
Nutrition; EPICOR, European Prospective Investigation into Cancer and Nutrition in three Dutch populations (Amsterdam, Doetinchem, and Maastricht) in the Netherlands; PREDIMED, PREvencion con DIeta MEDiterranea; GI, glycemic index; GL, glycemic load; CHO, carbohydrate; DFIB, dietary fiber; CFIB, cereal fiber. Study Time (Y) and Age (Y) are mean or median. Blank entries indicate data were not present in published studies.
39
40 Table 2. List of confounders adjusted for in published studies in this meta-analysis. Study Country Datasets Confounders Controlled Type 2 Diabetes Salmeron et al. 1997(men) [13]
US
HPFS
Age, BMI, smoking, alcohol intake, physical activity, family history of diabetes, other fiber sources (dietary fiber, fruit fiber, vegetable fiber) Age, energy, physical activity, BMI, WHR, smoking, alcohol intake, education, family history of diabetes
Meyer et al. 2000 [66]
US
IWHS
Stevens et al. 2001 [67]
US
ARIC
Age, BMI, education, smoking status, physical activity, sex, field center
Schultz et al. 2004 [11]
US
NHS II
Age, BMI, smoking, alcohol intake, physical activity, family history of diabetes, history of high blood pressure, history of high blood cholesterol, postmenopausal hormone use, oral contraceptives, energy intake, cereal fiber, magnesium, caffeine, saturated fat, monounsaturated fat, polyunsaturated fat, trans fatty acids,
Krishnan et al. 2007 [68]
US
Black Women’ s Health study
Age, BMI, energy intake, family history of diabetes, smoking, physical activity, cereal fiber, total fat intake, protein intake
Hopping et al. 2010 [10]
Hawaii
Hawaii MEC
Ethnicity, BMI, physical activity, education, energy intake, carbohydrate, (stratified by sex and ethnicity)
Bhupathiraju et al. 2014 [3]
US
NHS11, HPFS, NHS
Race, several lifestyle factors, family history of diabetes, menopausal status, and postmenopausal hormone use, premenopausal, postmenopausal, oral contraceptive use, physical activity, BMI , smoking status, energy intake, coffee intake, cereal fiber, trans fatty acids, polyunsaturated fats, saturated fats, monounsaturated fats, protein intake
AlEssa et al. 2015[9]
US
NHS
Age, BMI, race energy intake, smoking status, alcohol consumption, physical activity, postmenopausal hormone use, family history of diabetes, and multivitamin use, red meat, coffee, ratio of polyunsaturated fat to saturated fat, trans fat (percentage of total energy), sugarsweetened beverage intake, fruits and vegetables, magnesium
Barclay et al. 2007[84]
Australia
Australia n cohort
Age, sex, family history of diabetes, smoking, triglycerides, HDL cholesterol, and METs, as well as vegetable fiber for the GI analyses
Van Woudenbergh et al. 2011[69]
Netherla nds
Rotterda m study
Age, sex, BMI, smoking, and family history of diabetes, intakes of energy, protein, saturated fat, alcohol, and fiber, C-reactive protein
Similä et al. 2011[70]
Findland
ATBC
Age, intervention group, BMI, smoking (years, number of cigarettes per day), physical activity, total energy and coffee consumption
Similä et al. 2012[71]
Findland
ATBC
Rossi et al. 2013 [72]
Greece
EPICGreek cohort
Age, intervention group, BMI, smoking (years, number of cigarettes per day), physical activity, total energy and coffee consumption Age, sex, education, BMI, physical activity, waist to hip ratio, energy intake, non-carbohydrate energy intake
40
41 Slujis et al. 2013 (Denmark) [73]
Europe
EPICInterAct Study
Age, sex, education, physical activity, BMI, menopausal status, smoking status, and alcohol consumption, energy intake, dietary protein, polyunsaturated:saturated fat ratio, and dietary fiber
The Interact Consortium-EPIC 2015 [74]
Europe
Villegas et al. 2007 [75]
China
SWHS
Age, education, family income, occupation, smoking status, alcohol consumption, physical activity, and hypertension
Sakurai et al. 2011 [76]
Japan
Japanese Factory study
Age, BMI, family history of diabetes, alcohol intake, smoking, physical activity, high blood pressure, dyslipidemia, energy intake, fiber intake
He et al. 2018 He et al. [77]
China
Guangzh ou study
Age, sex, body mass index, moderate/vigorous physical activity, education, monthly income, current smoking status, current drinking status and familial history of diabetes, intake of fat, protein and fiber.
Liu et al. 2001 [12]
US
NHS
Age, BMI, physical activity, smoking, alcohol intake, parental family history of MI before age 60, HTN, history of high cholesterol, menopausal status, aspirin use, vitamin E supplements, dietary vitamin E, protein intake, dietary fiber, folate, total energy from food, saturated fat, monounsaturated fat, polyunsaturated fat, trans fat. Confounders in varying in 4 models
Hardy et al. 2010 [42]
US
ARIC
Van Dam et al 2000 [32]
Netherla nds
Zutphen Elderly study
Propensity score (age, sex, BMI, physical activity, smoking status, systolic blood pressure, total cholesterol, HDL cholesterol, fasting blood glucose, Keys score, antihypertensive medications, total calories, race were regressed against energy-adjusted glycemic index or glycemic load) Age, BMI, physical activity, smoking status, prescribed diet, energy intake, saturated fat, polyunsaturated fat, carbohydrate, alcohol
Beulens et al. 2007 [18]
Netherla nds
ProspectEPICBreast Cancer
Age, BMI, physical activity, smoking status, pack years of smoking, mean systolic blood pressure, HTN, high cholesterol, waist to hip ratio, menopausal status, hormone replacement therapy, oral conceptive use, total energy, Vitamin E, multivitamins, alcohol, protein, fiber, folate, saturated fat, polyunsaturated fat
Levitan et al. 2007 [44]
Sweden
Mursu et al. 2009 [19]
Finland
Cohort of Swedish men Kuopio KIHD
Age, BMI, smoking status, alcohol intake, history HTN, family history of MI, aspirin use, marital status, education, total energy, physical activity, carbohydrate intake, saturated fat, polyunsaturated fat, protein, cereal fiber Age, BMI, smoking, exam years, systolic blood pressure, hypertension medication, serum HDL and LDL cholesterol, triglycerides, LTPA, education, family history of cardiovascular disease, diabetes, alcohol intake, dietary intake of energy, energy-adjusted folate, fiber, vitamin C, polyunsaturated fat, saturated fat
Age, sex, smoking, physical activity, education, alcohol, energy and energy-adjusted carbohydrates, magnesium, vitamin B1, saturated fatty acids, BMI All models for types of fibre were mutually adjusted
41
42 Levitan et al. 2010 [43]
Sweden
EPICOR
Age, BMI, smoking, physical activity, living alone, postmenopausal hormone use, aspirin use, education, family history of MI before 60 years, HTN, high cholesterol, total energy intake, alcohol intake, fiber, polyunsaturated fat, protein intake, carbohydrate intake
Sieri et al. 2010 [31]
Italy
EPICOR
Burger et al. 2011 [29]
Netherla nds
EPICMORGE N
Age, BMI, physical activity, smoking, non-alcohol energy intake, fiber intake, hypertension, education, alcohol intake, saturated fat intake Age, smoking, pack years, education, BMI, physical activity, hypertension, and oral contraceptives use, energy intake, alcohol, vitamin C, fiber, saturated, monounsaturated, polyunsaturated fat, carbohydrates and protein
Turati et al. 2015 [79]
Greece
Yu et al. 2013 [27]
China
EPIC Greek cohort SWHS
Age, sex, education, physical activity, smoking status, arterial hypertension, energy intake without carbohydrates, and Mediterranean diet score Educational, income, smoking status, alcohol consumption, physical activity level, waist-to-hip ratio, history of hypertension, and dietary intakes of total energy, saturated fat, and protein
AlEssa et al. 2018[41]
US
HPFS, NHS
Age, BMI, race, family history of myocardial Infarction, menopausal status and postmenopausal hormone use, smoking, alcohol, physical activity, multivitamin use, aspirin use, vitamin E use, energy intake, polyunsaturated fat:saturated fat ratio and trans fat % energy
Oh et al. 2005 [16]
US
NHS
Age, BMI, smoking, alcohol intake, physical activity, parental history of myocardial infarction, history of hypertension, hypercholesterolemia, and diabetes, menopausal status and postmenopausal hormone use, aspirin use, multivitamin, vitamin E supplement use, energy intake, cereal fiber, saturated fat, monounsaturated fat, polyunsaturated fat, trans-fat, and omega-3 fatty acids
Seri et al. 2013 [80]
Italy
EPICOR
Rossi et al. 2015 [81]
Greece
EPICGreek cohort
Age, sex, smoking, years of education, BMI, alcohol intake, nonalcohol energy intake, physical activity, cereal fiber intake, saturated fat, monounsaturated fat, polyunsaturated fat. Age, sex, education, smoking, body mass index (BMI), physical activity, presence of hypertension, Mediterranean diet score, and energy intake without carbohydrates
Yu et al. 2016 [40]
China
SWHS
Education, cigarette smoking, BMI, family history of stroke, history of hypertension, history of dyslipidemia, total energy intake, saturated fat intake, and a partial diet quality score
42
43 He et al. 2010 [82]
US
NHS
Age, smoking status, BMI, alcohol intake, physical activity, parental history of MI, menopausal status and use of hormone therapy, and duration of diabetes, energy, polyunsaturated fat, saturated fat, trans fat, magnesium, folate
Levitan et al. 2007 [44]
Sweden
Cohort of Swedish men
Age, BMI, smoking status, alcohol intake, history HTN, family history of MI, aspirin use, marital status, education, total energy, physical activity, carbohydrate intake, saturated fat, polyunsaturated fat, protein, cereal fiber
Kaushik et al. 2009 [15]
Australia
Blue Mountai ns Eye Study
Age, sex, systolic blood pressure, diastolic blood pressure, antihypertensive medication use, BMI, smoking status, educational qualifications, fair or poor self-rated health, history of myocardial infarction and stroke, and presence of diabetes
Burger et al. 2012 [28]
Netherla nds
EPICMORGE N
Smoking, smoking duration, education, BMI, WHR, physical activity, alcohol intake, menopausal status, and hormone replacement therapy use, diabetes duration, insulin use, glycated hemoglobin level, total energy, vitamin C, saturated, monounsaturated, and polyunsaturated fat, dietary fiber, carbohydrates
Similä et al. 2013[78]
Findland
ATBC
Age, intervention group, smoking, BMI, physical activity, serum total and HDL-cholesterol, blood pressure and intakes of energy, alcohol, total fat, protein, Magnesium and potassium
Castro-Quezada et al. 2014 [83]
Spain
PREDIM ED
Age, sex, recruitment center and intervention group (Med Diet + EVOO, Med Diet + Nuts and control diet), smoking, education, marital status, physical activity, BMI, history of cancer, history of arterial hypertension, dyslipidemia, history of cardiovascular disease, energy intake, alcohol intake dietary fiber intake, saturated fatty acids and monounsaturated fatty acids
Oba et al. 2010 [30]
Japan
Takayam a study
Yu et al. 2016 [26]
China
SWHS
Age, BMI, smoking status, Physical activity, history of hypertension, education, energy intake, alcohol, dietary fiber, salt, total fat Education, cigarette smoking, BMI, family history of stroke, history of hypertension, history of dyslipidemia, total energy intake, saturated fat intake, and a partial diet quality score
Abbreviations: HPFS, Health Professionals Follow-Up Study; IWHS, Iowa Women’s Health Study; NHS, Nurses’ Health Study; NHSII, Nurses’ Health Study; ARIC, Atherosclerosis Risk in Communities Study; Hawaii MEC, Hawaii Multiethnic Cohort;
43
44 EPIC, European Prospective Investigation into Cancer and Nutrition; EPICOR, European Prospective Investigation into Cancer and Nutrition in Italian cohorts; ATBC, The Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study; KIHD, Kuopio Ischaemic Heart Disease Risk Factor; SWHS, Shanghai Women’s Health Study; SMHS, Shanghai Men’s Health Study; EPICMORGEN, European Prospective Investigation into Cancer and Nutrition; EPICOR, European Prospective Investigation into Cancer and Nutrition in three Dutch populations (Amsterdam, Doetinchem, and Maastricht) in the Netherlands; PREDIMED, PREvencion con DIeta MEDiterranea.
44
45 Table 3. Pooled hazard ratios of dietary variables by disease outcomes in the whole sample in the US, Europe, and Asia Pooled Hazard Ratio (95% Confidence Interval) T2DM Glycemic index Glycemic load GI and high BMI GL and high BMI CHO High glycemic CHO Dietary fiber Cereal fiber
1.14 (1.06, 1.21) 1.09 (1.06, 1.11) 1.28 (1.04, 1.59) 1.21 (1.00, 1.48) 0.96 (0.88, 1.04) 0.87 (0.70, 1.08)b 0.92 (0.88, 0.96) 0.83 (0.77, 0.90)
Glycemic index Glycemic load GI and high BMI GL and high BMI CHO High glycemic CHO Dietary fiber Cereal fiber
1.03 (0.94, 1.13) 1.02 (0.92, 1.13) 1.26 (1.08, 1.47)b 0.98 (0.86, 1.10) 1.00 (0.91, 1.10) 0.91 (0.83, 0.99) 0.95 (0.85, 1.07)
CHD US 1.05 (0.95, 1.16)a 1.04 (0.93, 1.16)a 1.97 (1.31, 2.96)b 1.05 (0.98, 1.13)a 1.22 (0.94, 1.59)b 0.96 (0.89, 1.04)a 0.81 (0.76, 0.87)a Europe 1.06 (0.97, 1.16) 1.04 (0.96, 1.12) 1.31 (1.02, 1.68) 1.63 (1.28, 2.07) 1.08 (0.98, 1.19) 1.10 (0.99, 1.21)
Stroke
Mortality
1.05 (0.84, 1.33)a 1.16 (0.90, 1.50)a 1.39 (1.25, 1.54)a 1.60 (1.06, 2.40)a 1.29 (0.55, 3.02)a 0.80 (0.61, 1.04)a 0.65 (0.42, 1.01)a
1.12 (1.00, 1.27) 1.18 (0.98, 1.44)
1.02 (0.79, 1.32) 1.30 (0.86, 1.99) 0.99 (0.33, 3.01)a
0.86 (0.66, 1.12)b
1.09 (0.93, 1.29) 1.02 (0.89, 1.16) 1.02 (0.90, 1.15)b 1.34 (0.70, 2.55) 1.04 (0.90, 1.19) 1.07 (0.92, 1.25) 0.83 (0.75, 0.91)b 0.74 (.033, 1.63)a
Asia 1.19 (0.77, 1.84) Glycemic index 1.17 (0.81, 1.69)a 1.25 (1.02, 1.53) 1.19 (1.04, 1.36)a Glycemic load 1.17 (0.62, 2.21)a 1.23 (0.87, 1.73) 1.25 (1.03, 1.52) 1.26 (1.04, 1.53)a GI and high BMI 0.88 (0.41, 1.91)a 1.28 (1.05, 1.56)b GL and high BMI 0.98 (0.48, 1.98)a 1.52 (1.22, 1.89)b a b a CHO 1.16 (0.92, 1.47) 1.07 (0.72, 1.58) 1.28 (1.09, 1.50) 2.88 (1.44, 5.78) High glycemic CHO 1.19 (0.88, 1.62) 1.78 (1.48, 2.15)b 1.79 (1.02, 3.16)a 1.19 (1.01, 1.42)a Dietary fiber 0.99 (0.59, 1.66)b Cereal fiber Abbreviations: GI, glycemic index; GL, glycemic load; CHO, carbohydrate; BMI, body mass index; CHD, coronary heart disease; BMI, body mass index. Hazard Ratio used continuous form of dietary variable or highest vs lowest tertile/ quartile/quintile categories. a
Findings based on one study with more than one stratification.
b
Findings based on one study with a single entry.
Blank entries indicate data were not present in published studies. Bold: p value < 0.05 was used to judge statistical significance.
45
46 Table 4. Pooled hazard ratios of dietary variables by disease outcomes among males in the US, Europe, and Asia
Pooled Hazard Ratio (95% Confidence Interval) T2DM Glycemic index Glycemic load
CHD US
Stroke
Mortality
1.06 (0.95, 1.19)b
1.30 (1.15, 1.47)b 1.30 (1.09, 1.55)b
GI and high BMI GL and high BMI CHO High glycemic CHO
0.85 (0.62, 1.16)b
1.07 (0.96, 1.20)b 2.01 (0.96, 4.22)b
Dietary fiber Cereal fiber
0.79 (0.67, 0.92) 0.86 (0.74, 0.99)
1.00 (0.88, 1.13)b 0.84 (0.75, 0.94)b Europe
Glycemic index
0.87 (0.71, 1.07)
b
0.98 (0.90, 1.07)
Glycemic load
0.87 (0.65, 1.17)b
0.98 (0.89, 1.08)
1.19 (1.02, 1.39) 1.16 (0.88, 1.53)
GI and high BMI GL and high BMI
1.58 (1.03, 2.43)b 2.05 (1.30, 3.23)b 1.08 (0.83, 1.41) 1.04 (0.95, 1.14)
1.02 (0.70, 1.48)b 1.01 (0.70, 1.46)b
CHO High glycemic CHO
1.07 (0.89, 1.29)b
0.94 (0.79, 1.11)b
Dietary fiber Cereal fiber Asia Glycemic index Glycemic load GI and high BMI GL and high BMI
b
1.96 (1.04, 3.68) 1.96 (1.03, 3.72)b 1.11 (0.58, 2.12)b
CHO High glycemic CHO
1.13 (0.71, 1.80)b 1.13 (0.50, 2.55)b
0.78 (0.41, 1.48)b 1.00 (0.47, 2.14)b 0.82 (0.27, 2.50)b 0.85 (0.33, 2.19)b
3.20 (1.33, 7.69)b 2.01 (0.96, 4.22)b
1.17 (0.52, 2.63)b 0.84 (0.43, 1.63)b
Dietary fiber 0.99 (0.59, 1.66)b Cereal fiber Abbreviations: GI, glycemic index; GL, glycemic load; CHO, carbohydrate; BMI, body mass index; CHD, coronary heart disease; BMI, body mass index. Hazard Ratio used continuous form of dietary variable or highest vs lowest tertile/ quartile/quintile categories. a
Findings based on one study with more than one stratification.
b
Findings based on one study with a single entry.
Blank entries indicate data were not present in published studies. Bold: p value < 0.05 was used to judge statistical significance.
46
47
Table 5. Pooled hazard ratios of dietary variables by disease outcomes among females in the US, Europe, and Asia Pooled Hazard Ratio (95% Confidence Interval) T2DM
CHD
Stroke
Mortality
US Glycemic index
1.05 (0.84, 1.33)a
1.20 (1.01, 1.41)
Glycemic load
1.17 (1.05, 1.31)
1.98 (1.41, 2.78)
1.16 (0.90, 1.50)a
GI and high BMI
1.28 (1.04, 1.59)
1.20 (0.91, 1.58)
1.39 (1.25, 1.54)a
GL and high BMI
1.21 (1.00, 1.48)
1.97 (1.31, 2.96)b
1.60 (1.06, 2.40)a
CHO
0.97 (0.89, 1.05)
1.04 (0.95, 1.13)b
1.29 (0.55, 3.02)a
High glycemic CHO
0.87 (0.70, 1.08)
1.22 (0.94, 1.59)b
Dietary fiber
0.87 (0.78, 0.98)
0.94 (0.85, 1.03)b
0.80 (0.61, 1.04)a
Cereal fiber
0.77 (0.69, 0.85)
0.80 (0.74, 0.87)b
0.65 (0.42, 1.01)a
Glycemic index
Europe 1.17 (1.03, 1.31)
0.99 (0.81, 1.22)a
Glycemic load
1.31 (1.01, 1.70)
1.03 (0.78, 1.37)
GI and high BMI
1.20 (0.91, 1.58)b
GL and high BMI CHO
1.45 (0.93, 2.26)b 1.00 (0.81, 1.24)
0.90 (0.60, 1.34)b
High glycemic CHO Dietary fiber
1.26 (0.81, 1.98)
0.95 (0.63, 1.43)b
b
0.86(0.66, 1.12)b
Cereal fiber Asia Glycemic index Glycemic load
1.24 (0.68, 2.27)b
1.19 (1.04, 1.36)a
1.41 (0.81, 2.46)
b
a
1.29 (0.88, 1.91)
1.25 (0.46, 3.41)
1.26 (1.04, 1.53)
1.16 (0.40, 3.36)b 0.94 (0.32, 2.76)b
GL and high BMI GI and high BMI CHO
2.41 (0.77, 7.56)b
1.16 (0.92, 1.47)a
1.04 (0.67, 1.63)
High glycemic CHO Dietary fiber
1.53 (0.64, 3.67)b
1.19 (1.01, 1.42)a
1.31 (0.93, 1.86)
Cereal fiber Abbreviations: GI, glycemic index; GL, glycemic load; CHO, carbohydrate; BMI, body mass index; CHD, coronary heart disease; BMI, body mass index. Hazard Ratio used continuous form of dietary variable or highest vs lowest tertile/ quartile/quintile categories. a
Findings based on one study with more than one stratification.
b
Findings based on one study with a single entry.
Blank entries indicate data were not present in published studies. Bold: p value < 0.05 was used to judge statistical significance.
47
48 S1 Table 1. Heterogeneity of glycemic index and glycemic load pooled estimates in US studies Characteristic Coefficient Confidence Interval P Value Sex Study time Cases Sample size
-0.132 0.017285 0.00005 2.92e-06
Glycemic Index -0.253, -0.010 0.005, 0.030 0.00003, 0.00007 3.09e-07, 5.53e-06
.036 .012 <.001 .032
Glycemic Load Sex -0.048 -0.183, 0.086 .456 Study time 0.011 0.007, 0.015 < .001 Cases 0.00002 0.00002, 0.00003 < .001 Sample size 2.54e-06 1.64e-06, 3.44e-06 < .001 S1 Table 1. Meta regression analysis was used to determine study characteristics that caused heterogeneity. Bold: p value < 0.05 was used to judge statistical significance.
48
49 S1 Table 2. Assessment of risk of bias in the individual studies using the ROBIN-I tool. ROBIN-I Tool Study
Countr y US
Region
Dataset
US
Meyer et al. 2000 [66]
US
Stevens et al. (Whites) 2001 [67] Schultz et al. 2004 [11] Krishnan et al. 2007 [68] Hopping et al. 2010 (Whites) [10] Bhupathiraju et al. 2014 [3] AlEssa et al. 2015 [9] Barclay et al. 2007[84] Van Woudenbergh et al. 2011[69] Similä et al. 2011[70] Similä et al. 2012[71] Rossi et al. 2013 [72] Slujis et al. 2013 [73] Villegas et al. 2007 [75] Sakurai et al. 2011 [76] He et al. [77]
Salmeron et al. 1997(men) [13]
Liu et al. 2001 [12] Hardy et al. 2010 [42] Van Dam et al 2000 [32] Levitan et al. 2007 [44] Levitan et al. 2010 [43] Beulens et al. 2007 [18] Mursu et al. 2009 [19] Sieri et al. 2010 [31]
HPFS
Confoundi ng moderate
US
IWHS
US
US
US
Selection moderate
Informatio n serious
Reportin g serious
Total serious
moderate
high
serious
serious
serious
ARIC
moderate
serious
moderate
moderate
modera te
US
NHS II
moderate
moderate
serious
serious
serious
US
US
moderate
moderate
serious
serious
serious
US
US
Black Women’s Health study Hawaii MEC
moderate
serious
serious
serious
serious
US
US
US
moderate
moderate
serious
serious
serious
US
NHS, NHS11, HFPS NHS
moderate
moderate
serious
serious
serious
Australi a Netherl ands
Europe
Australian cohort
serious
moderate
moderate
moderate
Europe
Rotterdam study
moderate
moderate
moderate
moderate
modera te modera te
Findlan d Findlan d Greece
Europe
ATBC
low
low
moderate
moderate
Europe
ATBC
low
low
moderate
moderate
Europe
moderate
moderate
moderate
moderate
Europe
Europe
moderate
moderate
moderate
moderate
China
Asia
EPIC-Greek cohort EPIC-InterAct Study SWHS
moderate
moderate
moderate
moderate
Japan
Asia
China
Asia
US
modera te modera te modera te modera te modera te modera te modera te serious
moderate
moderate
moderate
moderate
serious
moderate
moderate
moderate
US
Japanese Factory study Guangzhou study NHS
moderate
moderate
serious
serious
US
US
ARIC
moderate
serious
moderate
moderate
Netherl ands Sweden
Europe
moderate
moderate
moderate
moderate
Europe
moderate
moderate
serious
serious
Sweden
Europe
Zutphen Elderly study Cohort of Swedish men Swedish MC
modera te modera te serious
moderate
serious
serious
serious
serious
Netherl ands Finland
Europe
Italy
modera te modera te modera te
moderate
moderate
moderate
moderate
Europe
Prospect-EPICBreastCcancer Kuopio KIHD
moderate
moderate
moderate
moderate
Europe
EPICOR
moderate
moderate
moderate
moderate
49
50 Burger et al. 2011 [29] Similä et al. 2013[78] Turati et al. 2015 [79] Yu et al. 2013 [27] AlEssa et al. 2018[41]
Netherl ands Findlan d Greece
Europe
EPIC-MORGEN
moderate
moderate
moderate
moderate
Europe
ATBC
low
low
moderate
moderate
Europe
moderate
moderate
moderate
moderate
Asia
EPIC Greek cohort SMHS
moderate
moderate
moderate
moderate
US
US
HPFS
moderate
serious
serious
moderate
modera te modera te modera te modera te serious
China
Oh et al. 2005 [16] Seri et al. 2013 [80]
US
US
NHS
moderate
moderate
serious
serious
serious
Italy
Europe
EPICOR
moderate
moderate
moderate
moderate
modera te
Rossi et al. 2015 [81]
Greece
Europe
EPIC-Greek cohort
moderate
moderate
moderate
moderate
modera te
Yu et al. 2016 [40]
China
Asia
SWHS
moderate
moderate
moderate
moderate
modera te
Kaushik et al. 2009 [15]
Australi a
Europe
Blue Mountains Eye Study
moderate
moderate
serious
serious
serious
Burger et al. 2012 [28]
Netherl ands
Europe
EPIC-MORGEN
moderate
moderate
moderate
moderate
modera te
Turati et al. 2014 [79]
Greece
Europe
EPIC Greek cohort
moderate
moderate
moderate
moderate
modera te
Castro-Quezada et al. 2014 [83]
Spain
Europe
PREDIMED
moderate
low
serious
moderate
modera te
Oba et al. 2010 [30]
Japan
Europe
Takayama study
moderate
moderate
serious
serious
serious
He et al. 2010[82]
US
US
NHS
moderate
moderate
serious
serious
serious
50
51 S1 Table 2. Assessment of risk of bias of individual studies using the ROBIN-I tool. Abbreviations: HPFS, Health Professionals Follow-Up Study; IWHS, Iowa Women’s Health Study; NHS, Nurses’ Health Study; NHSII, Nurses’ Health Study; ARIC, Atherosclerosis Risk in Communities Study; Hawaii MEC, Hawaii Multiethnic Cohort; EPIC, European Prospective Investigation into Cancer and Nutrition; EPICOR, European Prospective Investigation into Cancer and Nutrition in Italian cohorts; ATBC, The Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study; KIHD, Kuopio Ischaemic Heart Disease Risk Factor; SWHS, Shanghai Women’s Health Study; SMHS, Shanghai Men’s Health Study; EPIC-MORGEN, European Prospective Investigation into Cancer and Nutrition; EPICOR, European Prospective Investigation into Cancer and Nutrition in three Dutch populations (Amsterdam, Doetinchem, and Maastricht) in the Netherlands; PREDIMED, PREvencion con DIeta MEDiterranea. Risk of Bias was assessed using the ROBIN-I tool for non-randomized studies at https://training.cochrane.org/handbook/current/ chapter-25. The ROBIN-I tool includes domains for biases in relation to confounding, selection into the study, information and measurement, and reporting, to form an overall Risk of Bias score.
51
52 S1 Table 3. GRADE Assessment for rating of the meta-evidence. US Dietary Variable
Disease Outcome
No. of Studies
No. of Effect Estimates
Study Time (Y)
Cases
Sample Size
Pooled Hazard Ratio (Effect Size)
I2
P Value
GRADE Quality Rating
5
8
101
19422
367406
1.14 (1.06, 1.21)
94.4
0
very low
Glycemic index
Type 2 diabetes CHD
1
2
17
10894
247611
1.05 (0.95, 1.16)
71.9
0.059
very low
Glycemic index
Stroke
1
2
36
794
157558
1.05 (0.84, 1.33)
0
0.969
low
Glycemic index
Mortality
1
1
26
852
7822
1.09 (0.93, 1.29)
62
0.031
low
glycemic load
5
13
197
39348
434196
1.02 (1.01, 1.03)
92
0
very low
glycemic load
Type 2 diabetes CHD
2
3
44
2444
98867
1.28 (0.98, 1.67)
82
0.004
very low
glycemic load
Stroke
1
2
36
794
157558
1.16 (0.90, 1.50)
0
0.73
low
Glycemic index
glycemic load
Mortality
BMI high GI
4
5
16
2679
150249
1.28 (1.04, 1.59)
40
0.197
very low
BMI high GI
Type 2 diabetes CHD
BMI high GI
Stroke
1
2
36
794
157558
1.39 (1.25, 1.54)
0
0.807
moderate
BMI high GI
Mortality
1
1
9.2
791
6192
1.02 (0.90, 1.15)
.
.
very low
BMI high GL
4
5
16
2679
150249
1.21 (1.00, 1.48)
0
0.732
moderate
BMI high GL
Type 2 diabetes CHD
1
1
10
761
75521
1.97 (1.31, 2.96)
.
.
low
BMI high GL
Stroke
1
2
36
794
157558
1.60 (1.06, 2.40)
0
0.859
low
BMI high GL
Mortality
Carbohydrate
3
3
38
8590
204033
0.96 (0.88, 1.04)
0
0.8
low
Carbohydrate
Type 2 diabetes CHD
1
2
54
8106
117885
1.05 (0.98, 1.13)
0
0.692
low
Carbohydrate
Stroke
1
2
36
794
157558
1.29 (0.55, 3.02)
79
0.028
very low
1
1
6
228
35988
0.87 (0.70, 1.08)
.
.
very low
1
1
10
761
75521
1.22 (0.94, 1.59)
.
.
low
Carbohydrate
Mortality
High glycemic CHO High glycemic CHO High glycemic CHO High glycemic
Type 2 diabetes CHD Stroke Mortality
52
53 CHO Dietary fiber
5
Dietary fiber
Type 2 diabetes CHD
24039
304047
0.92 (0.88, 0.96)
78
0
moderate*
1
2
Dietary fiber
Stroke
1
2
54
8106
117885
0.96 (0.89, 1.04)
0
0.442
low
36
794
157558
0.80 (0.61, 1.04)
0
0.792
low
Dietary fiber
Mortality
Cereal fiber
6
12
148
Cereal fiber
Type 2 diabetes CHD
25977
363047
0.83 (0.77, 0.90)
83
0
moderate*
1
2
Cereal fiber
Stroke
1
2
54
8106
117885
0.81 (0.76, 0.87)
0
0.491
moderate
36
794
157558
0.65 (0.42, 1.01)
62
0.107
moderate
Cereal fiber
Mortality
1
1
26
852
7822
0.86 (0.66, 1.12)
.
.
low
No. of Studies 5
No. of Entries
Glycemic index
Disease Outcome Type 2 diabetes CHD
Cases
Sample Size
112613
183913
Pooled Hazard Ratio (Effect Size) 1.03 (0.94, 1.13)
I2 (%) 20
P Value 0.248
GRADE Quality
13
Study Time (Y) 155
Glycemic index
Stroke
7
9
163.1
11203
364977
1.06 (0.97, 1.16)
50.4
0.034
very low
4
7
64.8
1607
196012
1.12 (1.00, 1.27)
0
0.724
very low
Glycemic index
Mortality
4
5
44.9
2952
51815
1.09 (0.93, 1.29)
62
0.031
very low
glycemic load
4
11
134.4
103108
187284
1.02 (0.92, 1.13)
0
0.709
very low
glycemic load
Type 2 diabetes CHD
8
10
114.3
9534
243894
1.04 (0.96, 1.12)
9.9
0.352
very low
glycemic load
Stroke
5
11
124.8
1839
275308
1.18 (0.98, 1.44)
18
0.272
low
glycemic load
Mortality
4
4
29.3
1861
58050
1.02 (0.89, 1.16)
23
0.272
low
BMI high GI BMI high GI
Type 2 diabetes CHD
2
2
25.1
1514
38215
1.31 (1.02, 1.68)
11
0.291
low
BMI high GI
Stroke
BMI high GI
Mortality
2
2
9.2
791
6192
1.02 (0.90, 1.15)
.
.
low
BMI high GL
1
2
11.34
2330
22295
1.26 (1.08, 1.47)
.
.
low
BMI high GL
Type 2 diabetes CHD
3
3
35.5
1931
58490
1.63 (1.28, 2.07)
0
0.496
low
BMI high GL
Stroke
Europe Dietary Variable Glycemic index
1
140
very low
53
54 BMI high GL
Mortality
2
2
19.6
953
18221
1.34 (0.70, 2.55)
75
0.047
very low
Carbohydrate
1
8
96
99224
134680
0.98 (0.86, 1.10)
0
0.779
very low
Carbohydrate
Type 2 diabetes CHD
4
6
75.2
6140
153719
1.08 (0.98, 1.19)
0
0.509
very low
Carbohydrate
Stroke
2
4
45.8
507
107806
1.02 (0.79, 1.32)
0
0.599
very low
Carbohydrate
Mortality
2
2
19.6
953
18221
1.04 (0.90, 1.19)
0
0.766
very low
High glycemic CHO High glycemic CHO High glycemic CHO High glycemic CHO Dietary fiber
Type 2 diabetes CHD
2
9
108
100322
160623
1.00 (0.91, 1.10)
0
0.83
very low
4
6
75.2
6140
153719
1.10 (0.99, 1.21)
30.7
0.205
very low
Stroke
2
4
45.8
507
107806
1.30 (0.86, 1.99)
59
0.065
very low
Mortality
2
2
19.6
953
18221
1.07 (0.92, 1.25)
10
0.292
low
1
1
4
12
11559
0.91 (0.81, 1.03)
0
0.902
low
Dietary fiber
Type 2 diabetes CHD
1
1
10
94
646
0.91 (0.81, 1.03)
.
.
low
Dietary fiber
Stroke
1
2
22
278
88198
0.99 (0.33, 3.01)
69
0.074
very low
Dietary fiber
Mortality
1
1
9.2
791
6192
0.83 (0.75, 0.91)
.
.
low
Cereal fiber
1
1
4
12
11559
0.95 (0.83, 1.08)
.
.
low
Cereal fiber
Type 2 diabetes CHD
1
1
10
94
646
0.95 (0.83, 1.08)
.
.
low
Cereal fiber
Stroke
Cereal fiber
Mortality
1
2
26
1253
5794
0.74 (0.33, 1.63)
84
0.013
very low
Dietary Variable
Disease Outcome
No. of Studies
No. of Entries
Study Time (Y)
Cases
Sample Size
Pooled Hazard Ratio (Effect Size)
I2
P Value
GRADE Quality
Glycemic index
2
2
15.6
1868
66862
1.25 (1.02, 1.53)
12.8
0.318
low
Glycemic index
Type 2 diabetes CHD
1
2
15.2
309
117366
1.17 (0.81, 1.69)
0
0.811
low
Glycemic index
Stroke
2
2
8
2991
128656
1.19 (1.04, 1.36)
0
0.693
low
Glycemic index
Mortality
3
3
18
856
92190
1.19 (0.77, 1.84)
50
0.134
very low
Glycemic load
2
2
10.6
1741
66222
1.02 (1.01, 1.04)
86
0
low
Glycemic load
Type 2 diabetes CHD
1
2
15.2
309
117366
1.87 (0.98, 3.55)
3
0.31
low
glycemic load
Stroke
1
2
8
2991
128656
1.26 (1.04, 1.53)
0
0.707
moderate
Asia
54
55 glycemic load
Mortality
2
3
40
1099
35684
1.23 (0.87, 1.73)
0
0.811
moderate
BMI high GI
2
2
10.6
1741
66222
1.28 (1.05, 1.56)
0
0.648
low
BMI high GI
Type 2 diabetes CHD
BMI high GI
Stroke
BMI high GI
Mortality
1
2
14
247
27862
0.88 (0.41, 1.91)
0
0.863
low
BMI high GL
1
1
4.6
1608
64227
1.30 (1.17, 1.45)
.
.
moderate
BMI high GL
Type 2 diabetes CHD
BMI high GL
Stroke
BMI high GL
Mortality
1
2
14
247
27862
0.98 (0.48, 1.98)
0
0.669
very low
Carbohydrate
1.28 (1.09, 1.50)
.
.
low
Carbohydrate
Type 2 diabetes CHD
1
2
15.2
309
117366
2.88 (1.44, 5.78)
0
0.7
very low
Carbohydrate
Stroke
1
2
8
2991
128656
1.16 (0.92, 1.47)
0
0.692
very low
Carbohydrate
Mortality
2
3
18
856
92190
1.07 (0.91, 1.19)
0
0.863
very low
High glycemic CHO High glycemic CHO High glycemic CHO High glycemic CHO Dietary fiber
Type 2 diabetes CHD
1
1
4.6
1608
64227
1.78 (1.48, 2.15)
.
.
low
1
2
15.2
309
117366
1.79 (1.02, 3.16)
0
0.641
low
Stroke
1
2
8
2991
128656
1.19 (1.01, 1.42)
0
0.649
low
Mortality
2
3
18
856
92190
1.19 (0.96, 1.22)
0
0.568
very low
1
2
6
133
1995
0.99 (0.59, 1.66)
.
.
low
Dietary fiber
Type 2 diabetes CHD
Dietary fiber
Stroke
Dietary fiber
Mortality
Cereal fiber Cereal fiber
Type 2 diabetes CHD
Cereal fiber
Stroke
Cereal fiber
Mortality
S1 Table 3. GRADE assessment for rating of the quality of the meta-evidence.
55
56 GRADE[37] assessment included evaluation of the risk of bias, inconsistency, indirectness, imprecision, and publication bias in the confidence-rating of each dietary variabledisease outcome as either low or very low GRADE quality. GRADE quality was upgraded from very low to moderate* due to dose-response relationship of total
dietary fiber or cereal fiber and type 2 diabetes.
56
57 Figure Legends Figure 1. Selection of published studies for meta-analysis There were 40 studies. Some studies had >1 dietary variable and >1 disease outcome. Therefore, the studies may add up to more than 40 studies.
57
58 S1 Figure 1. Association of glycemic index and risk of type 2 diabetes within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; African Am, African Americans. Online Supporting Material
S1 Figure 2. Association of glycemic index and risk of coronary heart disease within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; African Am, African Americans. Online Supporting Material
S1 Figure 3. Association of glycemic index and risk of stroke within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females. Online Supporting Material
S1 Figure 4. Association of glycemic index and risk of mortality within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females. Online Supporting Material
S1 Figure 5. Association of glycemic load and risk of type 2 diabetes within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; African Am, African Americans. Online Supporting Material
58
59 S1 Figure 6. Association of glycemic load and risk of coronary heart disease within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females. Online Supporting Material
S1 Figure 7. Association of glycemic load and risk of stroke within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females. Online Supporting Material
S1 Figure 8. Association of glycemic load and risk of mortality within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females. Online Supporting Material
S1 Figure 9. Association of glycemic index with high BMI and risk of type 2 diabetes within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; GI, glycemic index; BMI, body mass index. Online Supporting Material
S1 Figure 10. Association of glycemic index with high BMI and risk of coronary heart disease within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; GI, glycemic index; BMI, body mass index. Online Supporting Material
59
60
S1 Figure 11. Association of glycemic index with high BMI and risk of stroke within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; GI, glycemic index; BMI, body mass index. Online Supporting Material
S1 Figure 12. Association of glycemic index with high BMI and mortality within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; GI, glycemic index; BMI, body mass index. Online Supporting Material
S1 Figure 13. Association of glycemic load with high BMI and risk of type 2 diabetes within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S1 Figure 14. Association of glycemic load with high BMI and risk of coronary heart disease within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
60
61
S1 Figure 15. Association of glycemic load with high BMI and risk of stroke within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S1 Figure 16. Association of glycemic load with high BMI and risk of mortality within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S1 Figure 17. Association of carbohydrate and risk of type 2 diabetes within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S1 Figure 18. Association of carbohydrate intake and risk of coronary heart disease within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects metaanalysis.
61
62
S1 Figure 19. Association of carbohydrate intake and risk of stroke within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Abbreviations: M, male; F, female; MF, males plus females; T2DM, type 2 diabetes; CHD, coronary heart disease. Online Supporting Material
S1 Figure 20. Association of carbohydrate intake and risk of mortality within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Abbreviations: M, male; F, female; MF, males plus females; T2DM, type 2 diabetes; CHD, coronary heart disease. Online Supporting Material
S1 Figure 21. Association of high glycemic carbohydrate and risk of type 2 diabetes within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S1 Figure 22. Association of high glycemic carbohydrate intake and risk of coronary heart disease within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis.
62
63 S1 Figure 23. Association of high glycemic carbohydrate intake and risk of stroke within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects metaanalysis. Abbreviations: M, male; F, female; MF, males plus females; T2DM, type 2 diabetes; CHD, coronary heart disease. Online Supporting Material
S1 Figure 24. Association of high glycemic carbohydrate intake and risk of mortality within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects metaanalysis. Abbreviations: M, male; F, female; MF, males plus females. Online Supporting Material
S1 Figure 25. Association of total dietary fiber and risk of type 2 diabetes within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females. Blank entries indicate data were not present in published studies. Online Supporting Material
S1 Figure 26. Association of total dietary fiber intake and risk of coronary heart disease within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis.
63
64 S1 Figure 27. Association of total dietary fiber intake and risk of stroke within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Abbreviations: M, male; F, female; MF, males plus females. Online Supporting Material
S1 Figure 28. Association of total dietary fiber intake and risk of mortality within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Abbreviations: M, male; F, female; MF, males plus females. Online Supporting Material
S1 Figure 28. Association of cereal fiber and risk of type 2 diabetes within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females. Blank entries indicate data were not present in published studies. Online Supporting Material
S1 Figure 30. Association of cereal fiber intake and risk of coronary heart disease within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects metaanalysis.
S1 Figure 31. Association of cereal fiber intake and risk of stroke within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the
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65 DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Abbreviations: M, male; F, female; MF, males plus females. Online Supporting Material
S1 Figure 32. Association of cereal fiber intake and risk of mortality within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Abbreviations: M, male; F, female; MF, males plus females. Online Supporting Material
65
66 S2 Figure 1. Association of glycemic index and risk of type 2 diabetes among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: African Am, African Americans. Online Supporting Material
S2 Figure 2. Association of glycemic index and risk of coronary heart disease among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate Abbreviations: African Am, African Americans. Online Supporting Material
S2 Figure 3. Association of glycemic index and risk of stroke among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Online Supporting Material
S2 Figure 4. Association of glycemic index and risk of mortality among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Online Supporting Material
S2 Figure 5. Association of glycemic load and risk of type 2 diabetes among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: African Am, African Americans.
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67 Online Supporting Material
S2 Figure 6. Association of glycemic load and risk of coronary heart disease among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Online Supporting Material
S2 Figure 7. Association of glycemic load and risk of stroke among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Online Supporting Material
S2 Figure 8. Association of glycemic load and risk of mortality among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Online Supporting Material
S2 Figure 9. Association of glycemic index with high BMI and risk of type 2 diabetes among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GI, glycemic index; BMI, body mass index. Online Supporting Material
S2 Figure 10. Association of glycemic index with high BMI and risk of coronary heart disease among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate.
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68 Abbreviations: GI, glycemic index; BMI, body mass index. Online Supporting Material
S2 Figure 11. Association of glycemic index with high BMI and mortality among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GI, glycemic index; BMI, body mass index. Online Supporting Material
S2 Figure 12. Association of glycemic load with high BMI and risk of coronary heart disease among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S2 Figure 13. Association of glycemic load with high BMI and risk of mortality among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S2 Figure 14. Association of carbohydrate and risk of type 2 diabetes among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate.
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69 Abbreviations: GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S2 Figure 15. Association of carbohydrate intake and risk of coronary heart disease among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
S2 Figure 16. Association of carbohydrate intake and risk of stroke among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
S2 Figure 17. Association of carbohydrate intake and risk of mortality among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects metaanalysis. Online Supporting Material
S2 Figure 18. Association of high glycemic carbohydrate intake and risk of coronary heart disease among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
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S2 Figure 19. Association of high glycemic carbohydrate intake and risk of stroke among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
S2 Figure 20. Association of high glycemic carbohydrate intake and risk of mortality among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
S2 Figure 21. Association of total dietary fiber and risk of type 2 diabetes among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Blank entries indicate data were not present in published studies. Online Supporting Material
S2 Figure 22. Association of total dietary fiber intake and risk of coronary heart disease among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
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71 S2 Figure 23. Association of cereal fiber and risk of type 2 diabetes among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Blank entries indicate data were not present in published studies. Online Supporting Material
S2 Figure 24. Association of cereal fiber intake and risk of coronary heart disease among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
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72 S3 Figure 1. Association of glycemic index and risk of type 2 diabetes among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate Abbreviations: African Am, African Americans. Online Supporting Material
S3 Figure 2. Association of glycemic index and risk of coronary heart disease among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate Abbreviations: African Am, African Americans. Online Supporting Material
S3 Figure 3. Association of glycemic index and risk of stroke among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Online Supporting Material
S3 Figure 4. Association of glycemic index and risk of mortality among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Online Supporting Material
S3 Figure 5. Association of glycemic load and risk of type 2 diabetes among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: African Am, African Americans.
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73 Online Supporting Material
S3 Figure 6. Association of glycemic load and risk of coronary heart disease among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Online Supporting Material
S3 Figure 7. Association of glycemic load and risk of stroke among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Online Supporting Material
S3 Figure 8. Association of glycemic load and risk of mortality among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Online Supporting Material
S3 Figure 9. Association of glycemic index with high BMI and risk of type 2 diabetes among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GI, glycemic index; BMI, body mass index. Online Supporting Material
S3 Figure 10. Association of glycemic index with high BMI and risk of coronary heart disease among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate.
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74 Abbreviations: GI, glycemic index; BMI, body mass index. Online Supporting Material
S3 Figure 11. Association of glycemic index with high BMI and risk of stroke among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GI, glycemic index; BMI, body mass index. Online Supporting Material
S3 Figure 12. Association of glycemic index with high BMI and mortality among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GI, glycemic index; BMI, body mass index. Online Supporting Material
S3 Figure 13. Association of glycemic load with high BMI and risk of type 2 diabetes among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S3 Figure 14. Association of glycemic load with high BMI and risk of coronary heart disease among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies.
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75 Online Supporting Material
S3 Figure 15. Association of glycemic load with high BMI and risk of stroke among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S3 Figure 16. Association of glycemic load with high BMI and risk of mortality among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S3 Figure 17. Association of carbohydrate and risk of type 2 diabetes among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S3 Figure 18. Association of carbohydrate intake and risk of coronary heart disease among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis.
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76 Online Supporting Material
S3 Figure 19. Association of carbohydrate intake and risk of stroke among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects metaanalysis. Online Supporting Material
S3 Figure 20. Association of carbohydrate intake and risk of mortality among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects metaanalysis. Online Supporting Material
S3 Figure 21. Association of high glycemic carbohydrate and risk of type 2 diabetes among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S3 Figure 22. Association of high glycemic carbohydrate intake and risk of coronary heart disease among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
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S3 Figure 23. Association of high glycemic carbohydrate intake and risk of stroke among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
S3 Figure 24. Association of high glycemic carbohydrate intake and risk of mortality among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
S3 Figure 25. Association of total dietary fiber and risk of type 2 diabetes among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Blank entries indicate data were not present in published studies. Online Supporting Material
S3 Figure 26. Association of total dietary fiber intake and risk of coronary heart disease among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
S3 Figure 27. Association of total dietary fiber intake and risk of stroke among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed
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78 using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects metaanalysis. Online Supporting Material
S3 Figure 28. Association of cereal fiber and risk of type 2 diabetes among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Blank entries indicate data were not present in published studies. Online Supporting Material
S3 Figure 29. Association of cereal fiber intake and risk of coronary heart disease among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
S3 Figure 30. Association of cereal fiber intake and risk of stroke among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
S3 Figure 31. Association of cereal fiber intake and risk of mortality among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects metaanalysis. Online Supporting Material
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79 S4 Figure 1. Dose-response trend for total dietary fiber. A parametric dose-response model based on summarized data for linear dose-response relationships for total dietary fiber and T2DM, performed in drmeta. The Hamling’s method of covariance structure, random-effects, and maximum likelihood features were specified. Effect measures for risk ratios were calculated. Online Supporting Material
S4 Figure 2. Dose-response trend for cereal fiber. A parametric dose-response model based on summarized data for linear dose-response relationships for cereal fiber and T2DM, performed in drmeta. The Hamling’s method of covariance structure, random-effects, and maximum likelihood features were specified. Effect measures for risk ratios were calculated. Online Supporting Material
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80 S5 Figure 1. GI T2DM Europe. Trim and filled data with 12 estimates (as indicated by the circles on the graph. The ‘trim and fill’ method is a rank data imputation technique that estimates the number and outcomes of missing studies. This method adjusts the meta-analysis to incorporate the imputed data to re-estimate the overall meta-analytic effect using the random-effects method. In this analysis, no studies were imputed. Publication bias test results from Begg (z=1.03; p=0.304) and Egger (Slope=-0.107; 95% Confidence Interval (CI): -0.374, 0.159; p=0.391) showed that publication bias was not present. As noted in the Eggers test, bias was not significant (Bias: 0.966; -0.876, 2.808). Abbreviation: GI, glycemic index; T2DM, type 2 diabetes. Online Supporting Material
S5 Figure 2. GI CHD Europe. Trim and filled data with 10 estimates (as indicated by the circles on the graph) and one imputed study (as depicted by the grey circles on the graph). The ‘trim and fill’ method is a rank data imputation technique that estimates the number and outcomes of missing studies. This method adjusts the meta-analysis to incorporate the imputed data to re-estimate the overall meta-analytic effect using the random-effects method. In this analysis, no studies were imputed. Publication bias test results from Begg (z=1.25; p=0.210) and Egger (Slope=-0116; 95% Confidence Interval (CI): -0.295, 0.064; p=0.175) showed that publication bias was not present. As noted in the Eggers test, bias was not significant (Bias: 1.75; -0.270, 3.773). Abbreviation: GI, glycemic index; CHD, coronary heart disease. Online Supporting Material
S5 Figure 3. GL T2DM Europe. Trim and filled data with 10 estimates for nine observed studies (as indicated by the circles on the graph). There were no imputed studies. The ‘trim and fill’ method is a rank data imputation technique that estimates the number and outcomes of missing studies. This method adjusts the meta-analysis to incorporate the imputed data to re-estimate the overall meta-analytic effect using the random-effects method. Publication bias test results from Begg (z=0.54 p=0.592) and Egger (Slope=-0.089; 0.468, 0.290; p=0.602). As noted in the Eggers test, bias was not significant (Bias: 0.687; -1.608, 2.983; p=0.509). Abbreviation: GL, glycemic load; T2DM, type 2 diabetes. Online Supporting Material
S5 Figure 4. Total dietary fiber T2DM US.
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81 Trim and filled data with 12 observed studies. There were six imputed studies. The ‘trim and fill’ method is a rank data imputation technique that estimates the number and outcomes of missing studies. This method adjusts the meta-analysis to incorporate the imputed data to re-estimate the overall meta-analytic effect using the random-effects method. Publication bias test results from Begg (z=0.48; p=0.631) and Egger (Slope=0.010; -0.007, 0.028; p=0.219) showed that publication bias was not present. As noted in the Eggers test, the assemetry was due to small study effects that was statistically significant (p=0.005) . Bias was significant (Bias= -1.78; -2.905, -0.660; p=0.005). Abbreviation: T2DM, type 2 diabetes. Online Supporting Material
S5 Figure 5. Cereal Fiber T2DM US. Trim and filled data with 13 estimates for 7 observed studies and nine imputed studies. The ‘trim and fill’ method is a rank data imputation technique that estimates the number and outcomes of missing studies. This method adjusts the meta-analysis to incorporate the imputed data to re-estimate the overall meta-analytic effect using the random-effects method. Publication bias test results from Begg (z=1.77; p=0.077) and Egger (Slope=-0.004; -0.078, 0.073; p=0.918 ) showed that publication bias was not present. As noted in the Eggers test, the assemetry was due to small study effects that was statistically significant at p=0.013. (Bias= -2.32; -4.056, -0.588; p=0.013). Abbreviation: T2DM, type 2 diabetes. Online Supporting Material
81
PRISMA 2009 Flow Diagram
Identification
Records identified through database searching from Medline, Ovid Medline, PubMed, EBSCOhost (n = 7,012)
Additional records identified through other sources (n = 6)
• • •
Duplicates removed (n= 418)
• •
Screening
Records screened based on title and abstract (n = 6,558)
Full-text articles assessed for eligibility (n = 75)
Eligibility
Studies included in quantitative synthesis (meta-analysis) (n = 40)
Included
US studies (n = 13)
Included dietary variables: GI (n = 11) GL (n = 18) High BMI & GI (n = 4) High BMI & GL (n = 5) CHO (n = 8) High glycemic CHO (n = 2) Dietary fiber (n = 16) Cereal fiber (n = 18)
Included disease outcomes: Type 2 diabetes (n = 16) Coronary heart disease (n = 5) Stroke (n = 2) Mortality (n = 1)
•
• • • • •
Records excluded (n = 6,483) Abstract (n = 23) Clinical trials: phase 1-VI (n = 2204) Other experimental studies, different disease outcomes, e.g. cancer (n = 2,294) Case reports (n = 66) Reviews, meta-analysis, commentaries, letters, genotype studies (n = 1,892) Books/documents (n = 4)
Full-text articles excluded, with reasons (n = 35) Cross-sectional or case control studies (n=7) Studies with estimates besides hazard ratios such as odds ratios, linear regression (n=16) Studies that used correlations and residuals (n=5) Studies that used similar outcomes such as cardiovascular disease that was difficult to tease apart for analysis (n=4) Older studies that used the same dataset on the same outcomes and same dietary exposure (n= 3)
European studies (n = 22)
Asian studies (n = 5)
Included dietary variables: GI (n = 34) GL (n = 36) High BMI & GI (n = 3) High BMI & high GL (n = 6) CHO (n = 21) High glycemic CHO (n = 22) Dietary fiber (n = 6) Cereal fiber (n = 5)
Included dietary variables: GI (n = 10) GL (n = 10) GI and high BMI (n = 4) GL and high BMI (n = 3) CHO (n = 8) High glycemic CHO (n = 8) Dietary fiber (n = 1) Cereal fiber (n = 0)
Included disease outcomes: Type 2 diabetes (n = 15) Coronary heart disease (n = 11) Stroke (n = 11) Mortality (n = 6)
Included disease outcomes: Type 2 diabetes (n = 3) Coronary heart disease (n = 2) Stroke (n = 2) Mortality (n = 3)
From: Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and MetaAnalyses: The PRISMA Statement. PLoS Med 6(7): e1000097. doi:10.1371/journal.pmed1000097
For more information, visit www.prisma-statement.org.
Highlights •
High Glycemic index & glycemic load increased risk of type 2 diabetes in US studies
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High glycemic load increased risk of coronary heart disease in European female studies
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Glycemic index and glycemic load had the highest risks in overweight/obese persons
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Dietary and cereal fibers were protective against type 2 diabetes in US studies
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Cereal fiber dose-response was protective against type 2 diabetes in US studies
S0939-4753(20)30001-6
DOI:
https://doi.org/10.1016/j.numecd.2019.12.050
Reference:
NUMECD 2205
To appear in:
Nutrition, Metabolism and Cardiovascular Diseases
Received Date: 17 May 2019 Revised Date:
26 December 2019
Accepted Date: 29 December 2019
Please cite this article as: Hardy D, Garvin J, Xu H, Carbohydrate Quality, Glycemic Index, Glycemic Load and Cardiometabolic Risks in the US, Europe and Asia: A Dose-Response Meta-Analysis, Nutrition, Metabolism and Cardiovascular Diseases, https://doi.org/10.1016/j.numecd.2019.12.050. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 The Italian Society of Diabetology, the Italian Society for the Study of Atherosclerosis, the Italian Society of Human Nutrition, and the Department of Clinical Medicine and Surgery, Federico II University. Published by Elsevier B.V. All rights reserved.
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Carbohydrate Quality, Glycemic Index, Glycemic Load and Cardiometabolic Risks in the US, Europe and Asia: A Dose-Response Meta-Analysis DS. Hardy a*, JT. Garvin b, H. Xu c a
Department of Medicine, Morehouse School of Medicine, Atlanta GA 30331
b
School of Nursing, University of Saint Augustine for Health Sciences, Saint Augustine, FL 32086
c
Department of Population Health Sciences, Augusta University, Augusta GA 30912
Send correspondence and reprint requests to: *Dale Hardy, PhD, RD, LD, CDE CHES Department of Medicine Morehouse School of Medicine 720 Westview Drive, SW Atlanta, GA 30331 Phone: (404) 756-1346 Email: [email protected] Article Type: Meta-analysis
Word count: 5996 words from Introduction through Acknowledgement sections. Abstract: 250 words Number of figures: 1 Number of tables: 5 Supplemental data: S1 Tables: 3, S1 Figures: 32, S2 Figure: 24, S3 Figures: 31, S4 Figures: 2; S5 Figures: 5
1
33
Abstract
34
Background and Aims: Despite the proven evidence of high glycemic index (GI) and glycemic
35
load (GL) diets to increase cardiometabolic risks, knowledge about the meta-evidence for
36
carbohydrate quality within world geographic regions is limited. We conducted a meta-analysis
37
to synthesize the evidence of GI/GL studies and carbohydrate quality, gathering additional
38
exposures for carbohydrate, high glycemic carbohydrate, total dietary fiber, and cereal fiber and
39
risks for type 2 diabetes (T2DM), coronary heart disease (CHD), stroke, and mortality, grouped
40
into the US, Europe, and Asia. Secondary aims examined cardiometabolic risks in
41
overweight/obese individuals, by sex, and dose-response dietary variable trends.
42
Methods and Results: 40-prospective observational studies from 4-Medline bibliographical
43
databases (Ovid, PubMed, EBSCOhost, CINAHL) were search up to November 2019. Random-
44
effects hazard ratios (HR) and 95% confidence intervals (CI) for highest vs. lowest categories
45
and continuous form combined were reported. Heterogeneity (I2>50%) was frequent in US
46
GI/GL studies due to differing study characteristics. Increased risks
47
((HRGI,T2DM,US=1.14;CI:1.06,1.21), HRGL,T2DM,US=1.09;1.06,1.11), HRGI,T2DM,Asia=1.25;1.02,1.53),
48
and HRGL,T2DM,Asia=1.25;1.03,1.52)) were associated with cardiometabolic diseases. GI/GL in
49
overweight/obese females had the strongest magnitude of risks in US-and Asian studies. Total
50
dietary fiber (HRT2DM,US=0.92;0.88,0.96) and cereal fiber(HRT2DM,US=0.83;0.77,0.90) decreased
51
risk of developing T2DM. Among females, we found protective dose-response risks for total
52
dietary fiber (HR5g-total-dietary-fiber,T2DM,US=0.94;0.92,0.97), but cereal fiber showed better ability to
53
lower T2DM risk (HR5g-cereal-fiber,T2DM,US=0.67;0.60,0.74). Total dietary-and cereal fibers’ dose-
54
response effects were nullified by GL, but not cereal fiber with GI.
2
55
Conclusions: Overweight/obese females can shift their carbohydrate intake for higher cereal
56
fiber to decrease T2DM risk, but GL may cancel-out this effect.
57 58 59
KEYWORDS: Glycemic index and glycemic load, total dietary fiber, cereal fiber, type 2
60
diabetes, cardiometabolic risks, dose-response, geographic regions
61 62
List of Acronyms
63
GI: Glycemic index
64
GL: Glycemic load
65
T2DM: Type 2 diabetes
66
CHD; Coronary heart disease
67
HR: Hazard ratio
68
CI: Confidence interval
69
NHS: Nurses’ Health Study
70
NHS II: Nurses’ Health Study II
71
HPFS: Health Professionals Follow-up Study
72
BMI: Body mass index
73
3
74 75
Introduction High glycemic index (GI) and glycemic load (GL) diets are associated with increased risk
76
of cardiometabolic diseases [1-4]. Cardiometabolic diseases vary across geographic regions. US
77
reports show that coronary heart disease (CHD) and stroke rates have declined from 2006 to
78
2019[5]. However, disparities in rates of cardiometabolic diseases remain, with Whites having a
79
lower age adjusted prevalence compared to minority populations [5]. In addition, other
80
geographic regions such as China have an increased prevalence of type 2 diabetes (T2DM) and
81
CHD [6]. This has been attributed to the recent Westernization of the diet and its changing
82
nutrient composition [6].
83
GI, a carbohydrate quality classification, ranks the impact of foods based on their
84
carbohydrate absorption and glycemic response. While GL combines GI and the quantity of
85
carbohydrate [7,8], both GI and GL estimate the carbohydrate glycemic burden on blood
86
glucose, insulin resistance, and other abnormal metabolic parameters. It is widely known that the
87
continual ingestion of high GI and GL diets that contain large amounts of high glycemic and
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refined carbohydrate and low fiber foods, over time are associated with increased risk of
89
cardiometabolic diseases and mortality [1-4]. Other studies report that total dietary fiber [9,10],
90
and cereal fiber can decrease the risk of developing T2DM [3,11,11-14], CHD and stroke
91
[15,16], and the risk of death from diabetes [17]. Women [12,18], and those who are obese
92
[12,18,19] are reported to be disproportionately affected by the unfavorable effects of high GI
93
and GL diets.
94
Recently, three meta-analyses published on GI and GL found dose-response associations
95
for cardiometabolic disease risks and mortality. Reynolds et al. [20] reported that total dietary
96
fiber and whole grains were better dose-response markers of good health than GI and GL for
4
97
cardiometabolic risk factors and associated disease outcomes. Conversely, two other studies
98
showed that dose-response intakes of GI and GL can predict the development of type 2 diabetes
99
[21] and CHD [22] among healthy European and East Asian ancestry populations. Additionally,
100
Shahdadian et al. [23] reported that GI and GL were associated with mortality in women, but not
101
among men [23].
102
Currently there is a lack of information on carbohydrate quality in relation to GI and GL
103
in overweight/obese individuals, and among males and females within the US, Europe, and Asia.
104
Additionally, the comparison of carbohydrate quality-GI/GL combination effects needs to be
105
assessed. Furthermore, the investigation of dose-response trends on disease risk and the ability of
106
total dietary fiber and cereal fiber to curb the deleterious effect of high GI and high GL on
107
disease risk should be evaluated in different disease outcomes across geographic regions.
108
Moreover, many published meta-analyses included related studies that used the same
109
datasets, and studies that used odds ratios, both of which can bias the pooled estimates.
110
Furthermore, other meta-analyses combined published study estimates from different scales,
111
such as beta coefficients from linear regression with hazard ratios, introducing additional bias in
112
the true estimate results.
113
The primary aim of the current study was to investigate the carbohydrate quality of diets
114
high in GI and GL that included additional exposures for carbohydrate, high glycemic and
115
refined carbohydrate (as a measure of rapidly absorbed carbohydrate), total dietary fiber, and
116
cereal fiber, for risk of developing T2DM, CHD, stroke, and mortality in the US, Europe, and
117
Asia. Secondary aims investigated whether there were increased risks in overweight/ obese
118
individuals, by sex, and dose-response trends for dietary variables-cardiometabolic disease risks
119
within these world geographic regions.
5
120
Methods and Results
121
Search strategy
122
We used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses
123
(PRISMA) method of reporting in this meta-analysis [24]. No protocol is available in
124
PROSPERO currently. To identify initial studies on adults, four Medline databases (Ovid,
125
PubMed, EBSCOhost, and CINAHL) were searched through November 2019 as per our
126
inclusion criteria. Additional studies were identified from references in identified meta-analysis.
127
Search-terms used to identify articles, included GI or GL with their outcomes (T2DM, CHD,
128
stroke, and mortality). For example, search terms were: “glyc(a)emic index” or “glyc(a)emic
129
load” with “type 2 diabetes” or “type 2 diabetes mellitus” or “diabetes type 2”, “coronary heart
130
disease/s” or “coronary disease/s” or “disease/s coronary”, or “stroke/s” or “cerebrovascular
131
accident/s” or “CVA (Cerebrovascular Accident)” or “mortality” or “death”. When we
132
identified the GI and GL publications, other exposures (carbohydrate, high glycemic and refined
133
carbohydrate, total dietary fiber, and cereal fiber) were recorded [3,11-13]. We performed
134
additional searches for “fiber” and the outcomes listed above and kept publications that
135
contained information for GI and GL for our meta-analysis.
136 137 138
Study selection Studies were eligible for inclusion in the analysis: if they were written in English; the
139
exposures were dietary GI and/or GL that may have contained additional exposures for
140
carbohydrate, high glycemic and refined carbohydrate, total dietary fiber, and cereal fiber; and
141
their disease outcomes were incident T2DM, CHD, stroke, and mortality. All studies required an
142
observational prospective design with Cox proportional regression and hazard ratios and 95% 6
143
confidence intervals reported. When stratified analyses, e.g. male or female, or male and female
144
combined was reported, male and female results were extracted only. If the combined male-
145
female results contained additional reported statistics for dietary variables-disease outcomes that
146
were not in the stratified male-female results, then the combined male-female results were
147
extracted. We included all data on mortality, unless all-cause mortality was reported. In that case,
148
we excluded specific types of mortality results and retained the all-cause mortality results. Most
149
importantly, studies that used odds ratios as the measure of association were excluded because
150
the odds ratio overestimates the hazard ratio by at least two times, when the disease is common
151
[25]. Because our disease outcomes (T2DM, CHD, stroke, and mortality) were common, this
152
criterion was applied to avoid overestimating their risks.
153 154 155
Data extraction All data were extracted using an electronic spreadsheet. All articles were abstracted for
156
information by two or three reviewers to determine eligibility. When the high glycemic and
157
refined carbohydrate variable was not available, refined carbohydrate intake, sucrose,
158
sugar/sugar products, white rice and refined wheat products were used as proxy variables [26-
159
31]. Other information was collected as indicated in Table 1. Study information was collected in
160
continuous form, or tertiles, quartiles, or quintiles for hazard ratios and 95% confidence intervals
161
of highest category compared to lowest category of their disease outcomes. When reported
162
results showed the highest protective category as the reference, such as for cereal fiber, then that
163
result was inverted to reflect the same methodology as in the other publications [15]. Exposures
164
were mean or median values of the dietary variables.
7
165
Because some studies used either bread or glucose as the referent, we converted all GI
166
and GL values that used bread as the referent to the glucose referent [12,16,32]. We used the
167
conversion formula of GI or GL value x 0.71=glucose scale value [7,33]. When we encountered
168
a study that was missing the GI or GL measurements, but had information on GI or GL,
169
carbohydrate, and/or calories, we derived the missing GI or GL using these information present
170
in the manuscript [11,12,27]. We used the following formula to derive the missing information
171
on GL as GI=GL/ grams of carbohydrate or on GL as: GL= (GI x grams of carbohydrate)/100
172
[8]. Other derivations were calculated if the GL or GL were not present in the manuscripts, but
173
the needed variables were present to be make calculations. For example, if GL was not
174
presented, but the GI, calories, protein, and fat intake were recorded in the manuscript, then
175
carbohydrate intake was calculated as: carbohydrate (grams) = Total calories – (calories from
176
protein + calories from fat)/4. Subsequently, we used this result and the formula above to
177
calculate the GL.
178
The studies reported energy-adjustment of GI and GL using the residual method as
179
described by Willett [34]. This process of energy-adjustment holds total caloric intake constant
180
in the study population, while the varied quantity of GI or GL is compared between groups.
181
Because multiple studies used the same datasets with more recent datasets having a longer
182
duration [4,11-14,35], we used the most recent study results published [3,11-13]. An example is
183
the Nurses’ Health Study (NHS), the Nurses’ Health Study II (NHS II), and the Health
184
Professionals Follow-up Study (HPFS) that published multiple papers on GI and GL.
185 186 187
8
188 189
Evaluation of quality of the meta-evidence Possible publication bias was explored in GRADE for pooled results of dietary variables
190
and disease outcome by geographic region having ≥ 10 study estimates (See the Cochrane
191
handbook at https://training.cochrane.org/handbook/current/chapter-13). Multiple funnel plots
192
and Begg and Egger’s tests were used to explore publication bias [36]. This was performed by
193
visually checking for asymmetry that could have been related to selection bias, publication bias,
194
or other influential factors. In Begg’s tests the estimated log hazard ratios were plotted against
195
their standard errors (SEs). This specified a correlation (using Kendall rank correlation) between
196
the adjusted effect size and the meta-analysis weight. In Egger’s test, the (log hazard ratios/SE) x
197
1/SE were computed. This test evaluated whether the intercept deviates significantly from zero in
198
a regression of standardized hazard ratios against their precision. The presence of publication
199
bias would induce a skewness in the plots. This was assessed by p <0.10 if publication bias was
200
present. We further assessed suspected publication bias after analyzing the results from the above
201
tests. If there was suspected publication bias, we did further testing using the ‘trim and fill’
202
method. The ‘trim and fill’ method is a rank data imputation technique that estimates the number
203
and outcomes of missing studies. This method adjusts the meta-analysis to incorporate the
204
imputed data to re-estimate the overall meta-analytic effect using the random-effects method.
205
Quality of the evidence for this meta-analysis was evaluated using the Risk of Bias
206
ROBIN-I tool for non-randomized studies at https://training.cochrane.org/handbook/current/
207
chapter-25. The ROBIN-I tool includes domains for biases in relation to confounding, selection
208
into the study, information and measurement, and reporting. We assessed these risk of bias
209
domains scores into a total score computed as an overall judgement for risk of bias.
9
210
We used the GRADE criteria [37] that score all the meta-evidence to evaluate its overall
211
quality of the risk of bias, inconsistency, indirectness, imprecision, effect size, dose-response
212
gradient, and publication bias in the confidence-rating of each dietary variable-disease outcome
213
as either high, moderate, low, or very low GRADE quality. We evaluated each pooled estimate
214
in GRADE to rate the confidence in the ratings of the studies for each dietary variable-disease
215
outcome. We downgraded the GRADE quality for substantial heterogeneity, imprecision,
216
inconsistency, e.g. variation in the effect estimate, lack of randomization, blinding, and self-
217
report. We upgraded the GRADE quality studies if there was a dose-response relationship. In
218
case there was a dose-response relationship in the presence of publication bias, the dose-response
219
override the downgrading due to publication bias.
220 221
Statistical analysis
222
Meta-analysis by dietary variable, sex and disease outcome within geographic region
223
We performed our analysis by geographic region. We grouped the countries as follows:
224
US (all United states), Europe (Australia, Denmark, Finland, France, Greece, Italy, Netherlands,
225
Spain, Sweden, United Kingdom), and Asia (China, Japan). Australia’s diet has been influenced
226
by Europe, and because they had few studies, it was grouped with Europe.
227
Our meta-analysis was performed using random-effects models. We modelled the binary
228
outcome of the study effects by dietary variable-disease outcome within geographic region to
229
produce pooled estimates. In subsequent models, we modelled the pooled effect by sex. In doing
230
so, studies that reported estimates for male-female combined could not be included in these
231
pooled estimates because there was no way to tease the male-female effects apart.
232
10
233 234
Heterogeneity assessment We calculated Cochrane Q and I2 statistics to quantify statistical heterogeneity in-and-
235
between study variation by dietary variable and disease outcome. An I2 > 50% was considered
236
potentially significant statistical heterogeneity. Homogeneity of multiple studies results was
237
evaluated by considering a suitable weighted sum of differences between the number of
238
individual study results and the pooled hazard ratios [36]. When fixed and random-effects
239
models that computed pooled hazard ratios were compared, we encountered substantial
240
heterogeneity in many pooled estimates. Consequently, we use the random-effects method in
241
model building to account for heterogeneity of the effects across studies by dietary variable-
242
disease outcome. This method incorporates the between study variability into the study weights
243
and the SEs of the pooled hazard ratios [36]. We tested these differences using the random-
244
effects method by meta-regression using the restricted maximum likelihood method. This
245
method evaluates to the extent additional statistical residual heterogeneity between the results of
246
multiple studies that could be related to one or more characteristics of the studies [36].
247 248 249
Dose-response analysis by dietary variable and disease outcome We examined the dose-response trends using the drmeta Stata package for dietary-
250
variable disease outcomes that contained ≥ 10 hazard ratios. When a statistically significant
251
effect was present in main model, we proceeded further to stratify by sex. We investigated dose-
252
response adjustment for GI or GL x other dietary variable interactions on disease outcomes and
253
assessed these relationships by meta-regression using the maximum likelihood method. In dose-
254
response meta-analysis, we applied within-study covariance using the Hamling method [38]. We
255
computed risk ratios using the number of cases, dietary variable measure, sample size (instead of
11
256
person time), and their log relative risks and SEs. Person time was not available in some studies
257
to compute the hazard ratio; therefore, we used the risk ratio instead. The hazard ratio
258
approximates the risk ratio when the disease is common [39] as in this current study. We used
259
goodness-of-fit tests to choose models with the best fit and maximum R2. These relationships for
260
the full sample, males, and females were depicted on graphs in drmeta. Statistical analyses were
261
performed using Stata 16.0 (Stata Corp., College Station, TX). We used a 2-sided p value <0.05
262
in all tests to determine statistical significance of results.
263 264
Results
265
Descriptive characteristics of studies
266
We identified 7,018 citations for our meta-analysis. Through screening and application of
267
inclusion and exclusion criteria, 40 full-text publications from cohort studies that analyzed data
268
using incident Cox proportional regression met inclusion criteria (see Figure 1). Among the 40
269
studies, 34 studies presented results on T2DM (16 US, 15 Europe, 3 Asia), 18 studies presented
270
results on CHD (5 US, 11 Europe, 2 Asia), 15 studies presented results on stroke (1 US, 6
271
Europe, 1 Asia), and 10 studies presented results on mortality (1 US, 5 Europe, 3 Asia). Studies
272
reported diseases as T2DM, CHD, and stroke that were ascertained from medical records, self-
273
report, or physician reports. The studies ascertained mortality from medical records, autopsy
274
reports, death certificates, and death databases.
275
Table 1 describes the characteristics of our meta-analysis. According to our inclusion
276
criteria, studies were conducted in the US, Europe, and Asia from 1997 to 2018. The length of
277
study time ranged from four years [40] to 28 years [41] with a total mean or median duration of
278
475.64 years for all studies combined. This meta-analysis included 2,207,241 participants with
12
279
190,987 cases of disease. This included participants/cases of 903,205/160,064 for T2DM;
280
578,658/20,487 for CHD; 561,522/5,624 for stroke; and 163,856/4,822 for mortality. The sample
281
size of studies ranged from 646 [32] to 91,249 [11] participants. Most studies used validated
282
food frequency questionnaires that gathered information about food intake over the past year
283
[12,18,31,42-46]. However, a few studies utilized diet records that collected intake over a few
284
days or 24-hour recall/diet history [19,31,32]. Items on food frequency questionnaires or dietary
285
records ranged from 61 to 260 items.
286
Table 2 shows confounding factors used in adjustment in the studies. All adjusted
287
appropriately for confounders. Studies adjusted for age, BMI, energy intake, smoking status,
288
alcohol intake, physical activity, and energy intake. Type 2 diabetes studies adjusted for family
289
history of diabetes. Other covariates used in adjustment by some studies were total dietary fiber,
290
cereal fiber, and other micronutrients as well as hormone replacement therapy, menopausal
291
status, and cardiometabolic disease precursors such as lipid levels.
292 293 294
Pooled hazard ratios of dietary variables within geographic regions We first assessed the pooled hazard ratios of the studies by geographic region (US,
295
Europe, Asia) for dietary variables (GI, GL, carbohydrate, high glycemic and refined
296
carbohydrate, total dietary fiber, and cereal fiber) on disease outcomes (T2DM, CHD, stroke and
297
mortality). For overweight/obese individuals, we presented only results for GI and GL and not
298
for other dietary variables, due to lack or scarcity of information reported in the published
299
manuscripts.
300
Table 3 shows the pooled hazard ratios by dietary variable-disease outcome which varied
301
by world geographic regions. For risk for developing T2DM, in US studies, we observed a 1.14-
13
302
fold increase in risk with higher GI (Hazard Ratio (HR)=1.14; 95% Confidence Interval:
303
1.06,1.21; I2=94.4%, Pheterogeneity=0.000); and 1.02-fold increase in risk with higher GL
304
(HR=1.09; 1.06,1.11; I2=98.8%, Pheterogeneity=0.000). The magnitude of risk for GL for developing
305
T2DM was substantially higher for Asian studies, which had a 1.25-fold increased pooled risk
306
for GI (HR=1.25; 1.02, 1.53; I2=12.8%, Pheterogeneity=0.318) and GL (HR=1.25;1.03, 1.52);
307
I2=9.1%, Pheterogeneity=0.333). There were no significant associations for GI or GL for
308
development of CHD across geographic regions or for stroke; except among Asians studies that
309
showed a 1.19 increased risk for stroke (GI: HR=1.19; 1.04,1.36; I2=0%, Pheterogeneity= 0.693) and
310
(GL: (HR=1.19; 1.07, 1.33). (See S1 Figures 1-3, 5-8).
311
We observed higher overall magnitude of associations for developing T2DM, CHD, and
312
stroke for GI and GL among individuals who were overweight/obese within all three geographic
313
regions, but risks were not statistically significant for mortality. Furthermore, Asian studies
314
compared to US and European studies, had the highest magnitude of associations for total
315
carbohydrate and CHD (HR=2.88;1.44, 5.78; I2=0%, Pheterogeneity=0.700), and high glycemic
316
carbohydrate and T2DM (HR=1.78;1.48, 2.15; I2=.%, Pheterogeneity=.) and CHD (HR=1.79 (1.02,
317
3.16; I2=0%, Pheterogeneity=0.641). Except for GL and CHD among overweight/obese individuals
318
(HR=1.97;1.31, 2.96; I2=.%, Pheterogeneity=.). However, Asian effect estimates were generally
319
based on only one or two studies. See S1 figures 19-16.
320
Total dietary fiber and cereal fiber were protective against developing cardiometabolic
321
diseases (see S1 Figures 24 to 31). More specifically in US studies, total dietary fiber (HR=0.92;
322
0.88,0.96; I2=78.3%, Pheterogeneity=0.000) and cereal fiber (HR=0.83; 0.77, 0.90; I2=83.2%,
323
Pheterogeneity=0.000) showed consistent evidence of protection against developing T2DM. Cereal
324
fiber compared to total dietary fiber was more protective in reducing risk of developing T2DM.
14
325
There appeared to be no clear picture on the associations for dietary variables on stroke risk in
326
European studies or mortality risk in Asian studies. Results were not statistically significant for
327
these associations. In addition, results were sparse for mortality in US studies as only one study
328
was published that had a non-significant pooled association for cereal fiber. Among European
329
studies on mortality, only one study on total dietary fiber (HR=0.83; 0.75,0.91; I2=.,
330
Pheterogeneity=.) was statistically significant.
331 332 333
Pooled hazard ratios of dietary variables by sex within geographic regions Tables 4 and 5 show the pooled results for males and females by dietary variable-disease
334
outcome within geographic regions. In US studies, GI in males (HR=1.30 (1.15,1.47; I2=.,
335
Pheterogeneity=.) and females (HR=1.20 (1.01,1.41 I2=85.4%, Pheterogeneity=0.000.), and GL in males
336
(HR=1.30 (1.09,1.55; I2=., Pheterogeneity=.) increased the risk for developing T2DM. See S2 Figure
337
1 and 5 and S3 Figure 1.
338
In US studies, total dietary fiber showed a protective association for developing T2DM in
339
males (HR=0.79 (0.67,0.92; I2=47.3%, Pheterogeneity=0.127) and females (HR=0.87 (0.78,0.98;
340
I2=53.5%, Pheterogeneity=0.057 ). Additionally, cereal fiber in males (HR=0.86 (0.74,0.99;
341
I2=48.1%, Pheterogeneity=0.123) and females (HR=0.79 (0.71,0.88; I2=58.1%, Pheterogeneity=0.036)
342
show a protective association from developing T2DM, but the confidence interval boundary was
343
close to 1 for males. However, in US studies, in males, cereal fiber not total dietary fiber showed
344
protective associations for developing CHD (HR=0.84 (0.75,0.94; I2=., Pheterogeneity=.). However,
345
these associations in males and females were based on one study each. See S2 Figures 22 and 23
346
and S3 Figures 24 and 25.
15
347
In European studies, in females but not males, GI (HR=1.17 (1.03,1.31; I2=21.1%,
348
Pheterogeneity=0.284.) and GL (HR=1.11 (1.00,1.23; I2=0%, Pheterogeneity=0.520) increased risk for
349
developing CHD (See S3 Figures 2 and 6). In European studies, in males, GI (HR=1.19
350
(1.02,1.39; I2=0%, Pheterogeneity=0.664) increased the risk of developing stroke (See S2 Figure 3).
351
In Asian studies, among males, GI (HR=1.96 (1.04,3.68; I2=., Pheterogeneity=.) increased risk of
352
T2DM, but this was each based on one study (See S2 Figure 1). Moreover, among Asian female
353
studies, GI (HR=1.19 (1.04,1.36); I2=0%, Pheterogeneity=0.693), GL (HR=1.26 (1.04,1.53; I2=0%,
354
Pheterogeneity=0.707), and high glycemic CHO (HR=1.19 (1.01,1.42; I2=0%, Pheterogeneity=0.649)
355
increased the risk of stroke (See S3 Figures 3,7, and 23). However, these pooled hazard ratios
356
were based on one study each that had more than one stratification.
357
Females who were overweight or obese in US studies were more affected by GI and GL
358
diets for risk of developing T2DM (GI: 1.28 (1.04,1.59; I2=40%, Pheterogeneity=0.197), CHD (GL:
359
1.97 (1.31,2.96; I2=., Pheterogeneity=.), and stroke (GI: 1.39 (1.25,1.54; I2=0%, Pheterogeneity=0.807),
360
(GL: 1.60 (1.06,2.40; I2=0%, Pheterogeneity=0.859.) (See S3 Figures 9-11, and 13-15). These
361
statistically significant associations were not present among males in European studies except for
362
the GL-CHD association where males had a high magnitude of risk (GL: 2.05 (1.30, 3.23; I2=.,
363
Pheterogeneity=.) (See S2 Figure 12). There were no studies on Asians for overweight/obese males
364
or females on CHD or stroke risk.
365 366 367
Heterogeneity for pooled estimates Because there was substantial heterogeneity (I2 > 50%) for GI and GL in US studies, we
368
performed a sensitivity analysis using meta-regression to examine the impact variables that
369
contribute to excess heterogeneity had on their effect size. As indicated in S1 Table 1, within
16
370
pooled estimates, heterogeneity was attributed to differing study characteristics for sex, study
371
time, cases, and sample size in US studies.
372 373
Dose-response trends for total dietary fiber and cereal fiber on type 2 diabetes risk
374
We investigated dose-response relationships for ≥ 10 estimates by dietary variable-
375
disease outcome within geographic regions using random-effects analysis. We investigated
376
relationships for GI-T2DM, GI-CHD, and GL-T2DM among European studies, and total dietary
377
fiber-T2DM and cereal fiber-T2DM among US studies. We observed a dose-response
378
relationship with total dietary fiber and cereal fiber on risk of developing T2DM only in US
379
studies. Total dietary fiber had cumulative protective associations as noted for every 1g increase
380
in total dietary fiber (HR= 0.99; 0.98, 0.99), 3g (HR=0.96; 0.94, 0.98), 5g (HR=0.94, 0.92; 0.96).
381
Cereal fiber dose-response effects were more profound than total dietary fiber. For every 1g
382
increase in cereal fiber, there was 0.07-fold decreased (HR=0.93; 0.90, 0.95) in risk for
383
developing T2DM. This dose-response protective trend effect was more evident with every 3g
384
(HR=0.80; 0.73, 0.87) and 5g (HR=0.68; 0.59, 0.79) of daily cereal fiber intake.
385
After adjustment for GI in the total dietary fiber model (n=6), the total dietary fiber effect
386
estimate became non-significant (HR=0.98; 0.96, 1.01). Moreover, after adjustment for GI in the
387
cereal fiber model (n=7), cereal fiber effect estimate remained statistically significant (HR=0.88;
388
0.84, 0.93), and GI was non-significant in the model (HR=0.94; 0.87, 1.01). Furthermore, after
389
adjustment for GL, both the total dietary fiber (HR=1.00; 0.98, 1.02) and the cereal fiber
390
(HR=1.00; 0.95, 1.04) dose-response pooled effects became non-significant in the models. GL
391
remained significant in the models for total dietary fiber (HR=1.005; 1.001, 1.001) and cereal
392
fiber (HR=1.003; 1.002, 1.004), but the magnitude of effect was small for each unit of GL. After
17
393
adjustment for sex, both total dietary fiber (HR=0.97; 0.96, 0.99) and cereal fiber (HR=0.93;
394
0.91, 0.94) remained statistically significant in their models. We did not find a cereal fiber x sex
395
interaction in the dose-response random-effects models for development of T2DM in US studies.
396
We observed statistically significant dose-response effects for females and not males.
397
Females benefited from higher intakes of total dietary fiber and cereal fiber protective effects for
398
risk of developing T2DM in US studies. The dose-response protective associations were similar
399
for females as in the full sample. Total dietary fiber showed protective association for 1g
400
(HR=0.99; 0.98, 0.99), 3g (HR=0.97; 0.95, 0.98), 5g (HR=0.94; 0.92, 0.97), and 10g (HR=0.89;
401
0.85, 0.94) against development of T2DM. The dose-response protective association for cereal
402
fiber for developing T2DM was evident for every 1g increase in cereal fiber (HR=0.92; 0.90,
403
0.94), every 3g increase (HR=0.78; 0.74, 0.83), and every 5g increase (HR=0.67; 0.60, 0.74) in
404
daily cereal fiber intake. The addition of calories to the models did not appreciably changed the
405
effect estimates. Similarly, as in the full models, after adjustment for GL in US female studies
406
(n=7 study estimates) and the GL adjustment obliterated total dietary fiber and cereal fiber risks
407
and made them non-significant. GL remained statistically significant in the cereal fiber model
408
(HR=1.003; 1.002, 1.004) but became non-significant in the total dietary fiber model. S4 Figures
409
1 and 2 depict the decreasing dose-response linear trends for total dietary-and cereal fiber-T2DM
410
relationship.
411 412 413
Sensitivity analysis for study influence We investigated the influence of each study on the pooled hazard ratios in the meta-
414
analysis by omitting one study in each turn by dietary variable and disease outcome. The most
415
profound results were with the T2DM pooled hazard ratio for GI in US studies. After deleting of
18
416
the three Bhupathiraju et al. studies [3] on NHS, NHS II, and HPFS, the T2DM pooled hazard
417
ratio was significantly decreased and was no longer statistically significant for GI (HR=1.00;
418
0.98,1.03) and GL (HR=1.00; 1.00, 1.01).
419 420
Evaluation of the quality of the meta-evidence
421
Publication bias
422
We explored the possible impact of publication bias on pooled results of dietary variables
423
and disease outcome by geographic region (S5 Figures 1 to 5). We examined publication bias in
424
pooled estimates that contained ≥ 10 study estimates. Pooled analyses with fewer studies may be
425
more subject to bias due to small study effects. Furthermore, we explored the possible impact of
426
publication bias on the interpretation of the data. After we explored publication bias using the
427
‘trim and fill’ method and by inspecting Begg’s and Egger’s test results for small study effects (p
428
<0.10), total dietary fiber-T2DM and cereal fiber-T2DM in US studies revealed publication bias
429
(S5 Figures 4 and 5).
430 431
Risk of bias and GRADE quality rating of studies
432
We evaluated risk of bias using the ROBIN-I Tool at https://training.cochrane.org
433
/handbook/current/chapter-25 (see S1 Table 1). Most risk of bias domains scored as moderate to
434
serious risk of bias because of the non-randomized observational nature of the studies. In
435
addition, several studies used mailed questionnaires (self-reported information) instead of annual
436
exams to collect information and included substantial lower rates of return of questionnaires
437
(selection bias). We evaluated each pooled estimate in GRADE. The confidence in the ratings for
438
most studies for the dietary variable-disease outcome pooled estimates had low to very low
19
439
GRADE quality (See S1 Table 2). Several pooled estimates were downgraded due to substantial
440
heterogeneity, imprecision, inconsistency, e.g. variation in the effect estimate, lack of
441
randomization, blinding, and self-report. We upgraded the GRADE quality for dietary fiber and
442
cereal fiber in US studies because of their dose-response relationships with T2DM despite the
443
presence of publication bias.
444 445
Discussion
446
This meta-analysis investigated the meta-evidence for carbohydrate quality in relation to
447
GI and GL in association with T2DM, CHD, stroke, and mortality in the US, Europe, and Asia.
448
We observed statistically significant pooled hazard ratios for risk of developing T2DM in US
449
studies that were not observe in European studies. Among males and females, GI and GL
450
increased risk of T2DM in US studies. There was an increased GI/GL-stroke risk in males and
451
GI/GL-CHD risk in females in European studies. There was an increased GI/GL-T2DM risk in
452
males and GI/GL-stroke risk in females in Asian studies. Furthermore, we observed higher
453
magnitude of risks among those who were overweight or obese in the whole sample and in Asian
454
females for risk of developing T2DM, CHD and stroke. However, some findings were sparse,
455
and many were based on one study each. At even lower mean BMIs in Asian studies, greater
456
intakes of GI, GL, carbohydrate and high glycemic and refined carbohydrate were associated
457
with higher risk of developing T2DM, CHD, and stroke compared to risks in US and European
458
studies.
459
Total dietary fiber and more so cereal fiber showed evidence to decrease the risk of
460
developing T2DM in US studies, and CHD risk in European studies. We found protective dose-
461
response risks for total dietary fiber and cereal fiber against development of T2DM in US studies
20
462
among females. Our meta-analysis findings suggest that cereal fiber’s protective effect in
463
females can be potentially sustained on a high GI diet. However, the cumulative effect from
464
large quantities of high GI foods will increase the GL and this can nullify cereal fiber’s
465
protective effect. Noticeably, total dietary fiber’s protective effect was nullified by both GI and
466
GL after adjustment in the dose-response models. Cereal fiber appears to have a stronger
467
protective effect against development of T2DM in females compared to its larger component,
468
total dietary fiber.
469
The main carbohydrate sources differ in the US (white bread), Europe (potatoes), and
470
Asia (white rice) in their proportions of amylose, amylopectin, water content, and degree of
471
refining and processing. Because these starches are rapidly digested, they are absorbed quickly
472
and have a higher glycemic response. Alternately, high fiber cereal foods such as oatmeal and
473
barley have lesser amounts of amylopectin and greater amounts of soluble fiber and amylose.
474
These features of cereal fiber assists in decreasing the digestibility of starchy foods and
475
consequently can delay carbohydrate absorption producing a lower glycemic response [47].
476
Studies report that cereal fiber in addition to whole grains, bran, and germ fiber can protect
477
against developing cardiovascular disease [48], all-cause mortality and increased risk from
478
mortality from cardiovascular disease among individuals with T2DM, [17,49,50]. In some
479
studies, cereal fiber was shown to blunt the effects of GI or GL from developing risk of T2DM
480
[11,13,14] which was only observed in our study for cereal fiber’s dose-response with GI.
481
Over time, the adoption of refined processed foods high in fat and sugar from more
482
industrialized countries such as the US and Europe, have replaced traditional meals characterized
483
by whole grain foods and vegetables, thus elevating the GI and GL of the diet. The
484
Westernization of diets from the US to Asia[51-53], coupled with the predisposition to store
21
485
more abdominal fat, is a strong risk factor for cardiometabolic diseases[54]. This has similarly
486
contributed to metabolic derangements in blood glucose, lipids levels, weight gain, insulin
487
resistance [55] and increasing rates of T2DM [54] and cardiovascular diseases among Asians
488
[51-53]. However, in the US, a low carbohydrate diet [4], and a diet high in cereal fiber and
489
beans [56], and higher levels of physical activity [57] could reduce the effects of weight gain,
490
hyperglycemia and other abnormal cardiometabolic parameters from development of
491
cardiometabolic diseases.
492
Other studies that were not included in this meta-analysis (because our study was only in
493
relation to GI and GL), report total dietary fiber’s protective effects on CHD risk [58,59]. Studies
494
report dose-response effects for GI, GL, and carbohydrate for risk for developing T2DM [60]
495
and CHD [22]which we did not observed in our study. However, we only found dose-response
496
risks for total dietary fiber and cereal fiber for developing T2DM as in other studies [20,61,62].
497
We did not find any dose-response risks for CHD and stroke reported in other meta-analyses
498
[20,20,21], or for GI/GL-mortality pooled estimates as in a recent study by Shahdadian et al.
499
[23]. This may be due to our meta-analysis being reported by geographic region and the
500
exclusion of odds ratios and beta coefficients from linear regression in our analysis. In addition,
501
we did not observe interactions for the total dietary fiber or cereal fiber’s dose-response with GI
502
or GL as reported in other nondose-response single studies [3,11,13,14].
503
A major strength of our study is that we updated our meta-analysis to include the most
504
recent studies that reported hazard ratios when studies used the same datasets during different
505
years to investigate similar research questions [3,4,11-14,35]. Other meta-analyses included
506
these same studies with repeated datasets, or used other measures of risk, such as odds ratios to
507
approximate the dietary-disease relationship that may have augmented their pooled estimates
22
508
and caused our results to differ [25]. As a result, we did not include studies that reported odds
509
ratios as this would have over-estimated the effect estimates and drawn exaggerated conclusions.
510
Another major strength is the prospective nature of all the studies that reduced the
511
chances of recall bias due to under-reporting of the dietary variables. Information bias was also
512
lessened because information on the disease outcomes was ascertained independently of the
513
collection of dietary variables. Another strength is the large sample size and large number of
514
studies for some dietary variables and disease outcomes such as total dietary fiber and cereal
515
fiber and T2DM. In addition, the large number of cases used to compute pooled hazard ratios by
516
dietary variables and disease outcomes within geographic regions was a major strength to this
517
study. To our knowledge, this is the first meta-analysis to evaluate carbohydrate quality from a
518
set of important dietary markers in relation to GI and GL by disease outcomes within world
519
geographic regions of the US, Europe, and Asia.
520
We identified several limitations in this meta-analysis. Due to the language barrier, non-
521
English publications were not utilized. We included hazard ratios in continuous form, tertiles,
522
quartiles, and quintiles in selected publications to have equal weight in the pooled analyses. As a
523
result, this may have some inherent bias in terms of the discrimination of results in finer
524
categories. Another limitation in terms of measurement bias is in the use of GI and GL values
525
tested on healthy individuals to infer risk for individuals with diabetes [8,63]. The studies
526
included in this meta-analysis contained a wide range of food items (61-260) used to calculate
527
the mean/median GI, GL, carbohydrate, and total dietary fiber content of the diet. Biases may
528
include differences in GI, GL, and carbohydrate values, amylose, starch gelatinization, water
529
content, degree of refining and processing, different cooking methods used in preparation, non-
530
standardized measurement protocols or differences in technology [8,63,64]. Because of these
23
531
differences within geographic regions, the carbohydrate contribution and quality and total dietary
532
fiber content may differ which can affect the GI and GL values of the diet. This could be more
533
evident in food frequency questionnaires limited to a smaller amount of carbohydrate containing
534
foods as they may not be able to capture the contribution of higher GI and GL containing foods,
535
and therefore could include measurement bias in reporting.
536
Because our meta-analysis included observational studies, there may be risk of bias from
537
residual confounding due to selection bias and other biases. However, for the most part, we have
538
decreased the bias in our methodology, e.g. exclusion criteria to include studies based on the risk
539
for development of cardiometabolic diseases and risk for mortality. However, the agreement
540
between the dose-response relationship from cereal fiber and T2DM in US studies together with
541
published randomized trial results[65] showed that this finding is most likely causal.
542 543 544
Conclusions In summary, our meta-analysis shows that over time from 1997 to 2018, diets high in GI
545
and GL increased the risk of developing of T2DM, CHD, and stroke in the US, Europe, and
546
Asia. Higher risks for cardiometabolic diseases across geographic regions were mainly observed
547
in overweight or obese individuals in the US and Asian studies, particularly females with greater
548
intakes of GI and GL. In US studies, among females, total dietary fiber and especially cereal
549
fiber was protective in decreasing the risk of T2DM in a dose-response fashion. However, the
550
protective dose-response effects were nullified by a diet high in GL, but not for cereal fiber with
551
GI. These findings suggest that overweight/obese females across geographic regions could shift
552
their carbohydrate intake to higher cereal fiber foods to lower risk of developing T2DM.
24
553
However, a high GL diet can cancel out this protective association from cereal fiber for risk of
554
developing T2DM.
555 556
All authors declare no competing conflicts of interests.
557 558
Funding
559
This work was supported by a K01 grant by the National Heart, Lung, and Blood Institute, grant
560
no. K01 HL127278.
561 562
Acknowledgements
563
All authors contributed to designing the research project and served as a reviewer for inclusion
564
of studies. DSH and JTG provided articles. DSH wrote paper and performed statistical analysis.
565
JTG co-wrote paper and provided feedback on statistical analysis. HXU helped with setting up
566
electronic spreadsheet for data analysis. DSH and JTG had primary responsibility for final
567
content.
25
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[50] Steffen LM, Jacobs DR,Jr, Stevens J, Shahar E, Carithers T, Folsom AR. Associations of whole-grain, refined-grain, and fruit and vegetable consumption with risks of all-cause mortality and incident coronary artery disease and ischemic stroke: the Atherosclerosis Risk in Communities (ARIC) Study. Am J Clin Nutr 2003;78(3):383-390. [51] Baker P, Friel S. Food systems transformations, ultra-processed food markets and the nutrition transition in Asia. Global Health 2016;12(1):80-016-0223-3. [52] Baker P, Friel S. Processed foods and the nutrition transition: evidence from Asia. Obes Rev 2014;15(7):564-577. [53] Ding EL, Malik VS. Convergence of obesity and high glycemic diet on compounding diabetes and cardiovascular risks in modernizing China: An emerging public health dilemma. Global Health 2008;4:4. [54] World Health Organization. Waist Circumference and Waist-to Hip Ratio: Report of a WHO Expert Consultation, Geneva, 2008;8-11 [55] Pereira MA, Kartashov AI, Ebbeling CB, Van Horn L, Slattery ML, Jacobs DR,Jr, et al. Fast-food habits, weight gain, and insulin resistance (the CARDIA study): 15-year prospective analysis. Lancet 2005;365(9453):36-42. [56] Lillioja S, Neal AL, Tapsell L, Jacobs DR,Jr. Whole grains, type 2 diabetes, coronary heart disease, and hypertension: links to the aleurone preferred over indigestible fiber. Biofactors 2013;39(3):242-258. [57] Marwick TH, Hordern MD, Miller T, Chyun DA, Bertoni AG, Blumenthal RS, et al. Exercise training for type 2 diabetes mellitus: impact on cardiovascular risk: a scientific statement from the American Heart Association. Circulation 2009;119(25):3244-3262. [58] Pereira MA, O'Reilly E, Augustsson K, Fraser GE, Goldbourt U, Heitmann BL, et al. Dietary fiber and risk of coronary heart disease: a pooled analysis of cohort studies. Arch Intern Med 2004;164(4):370-376. [59] Liu S, Buring JE, Sesso HD, Rimm EB, Willett WC, Manson JE. A prospective study of dietary fiber intake and risk of cardiovascular disease among women. J Am Coll Cardiol 2002;39(1):49-56. [60] Greenwood DC, Threapleton DE, Evans CE, Cleghorn CL, Nykjaer C, Woodhead C, et al. Glycemic index, glycemic load, carbohydrates, and type 2 diabetes: systematic review and dose-response meta-analysis of prospective studies. Diabetes Care 2013;36(12):4166-4171. [61] Livesey G, Taylor R, Livesey HF, Buyken AE, Jenkins DJA, Augustin LSA, et al. Dietary Glycemic Index and Load and the Risk of Type 2 Diabetes: A Systematic Review and Updated Meta-Analyses of Prospective Cohort Studies. Nutrients 2019;11(6):10.3390/nu11061280. 30
[62] Yao B, Fang H, Xu W, Yan Y, Xu H, Liu Y, et al. Dietary fiber intake and risk of type 2 diabetes: a dose-response analysis of prospective studies. Eur J Epidemiol 2014;29(2):79-88. [63] Atkinson FS, Foster-Powell K, Brand-Miller JC. International tables of glycemic index and glycemic load values: 2008. Diabetes Care 2008;31(12):2281-2283. [64] Wolever TM, Brand-Miller JC, Abernethy J, Astrup A, Atkinson F, Axelsen M, et al. Measuring the glycemic index of foods: interlaboratory study. Am J Clin Nutr 2008;87(1):247S-257S. [65] Davison KM, Temple NJ. Cereal fiber, fruit fiber, and type 2 diabetes: Explaining the paradox. J Diabetes Complications 2018;32(2):240-245. [66] Meyer KA, Kushi LH, Jacobs DR,Jr, Slavin J, Sellers TA, Folsom AR. Carbohydrates, dietary fiber, and incident type 2 diabetes in older women. Am J Clin Nutr 2000;71(4):921930. [67] Stevens J, Ahn K, Juhaeri, Houston D, Steffan L, Couper D. Dietary fiber intake and glycemic index and incidence of diabetes in African-American and white adults: the ARIC study. Diabetes Care 2002;25(10):1715-1721. [68] Krishnan S, Rosenberg L, Singer M, Hu FB, Djousse L, Cupples LA, et al. Glycemic index, glycemic load, and cereal fiber intake and risk of type 2 diabetes in US black women. Arch Intern Med 2007;167(21):2304-2309. [69] van Woudenbergh GJ, Kuijsten A, Sijbrands EJ, Hofman A, Witteman JC, Feskens EJ. Glycemic index and glycemic load and their association with C-reactive protein and incident type 2 diabetes. J Nutr Metab 2011;2011:623076. [70] Simila ME, Valsta LM, Kontto JP, Albanes D, Virtamo J. Low-, medium- and highglycaemic index carbohydrates and risk of type 2 diabetes in men. Br J Nutr 2011;105(8):1258-1264. [71] Simila ME, Kontto JP, Valsta LM, Mannisto S, Albanes D, Virtamo J. Carbohydrate substitution for fat or protein and risk of type 2 diabetes in male smokers. Eur J Clin Nutr 2012;66(6):716-721. [72] Rossi M, Turati F, Lagiou P, Trichopoulos D, Augustin LS, La Vecchia C, et al. Mediterranean diet and glycaemic load in relation to incidence of type 2 diabetes: results from the Greek cohort of the population-based European Prospective Investigation into Cancer and Nutrition (EPIC). Diabetologia 2013;56(11):2405-2413. [73] Sluijs I, Beulens JW, van der Schouw YT, van der ADL, Buckland G, Kuijsten A, et al. Dietary glycemic index, glycemic load, and digestible carbohydrate intake are not associated with risk of type 2 diabetes in eight European countries. J Nutr 2013;143(1):93-99.
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32
Table 1. Description of published studies. Study
T2DM Salmeron et al. 1997(men) [13]
Meyer et al. 2000 [66] Stevens et al. (Whites) 2001 [67] Stevens et al. (African Am) 2001 [67]
Schultz et al. 2004 [11]
Krishnan et al. 2007 [68] Hopping et al. 2010 (Whites) [10] Hopping et al. 2010 (Japanese) [10] Hopping et al. 2010 (Hawaiian) [10] Hopping et al. 2010 (Whites) [10] Hopping et al. 2010 (Japanese) [10] Hopping et al. 2010 (Hawaiian) [10] Bhupathiraju et al. 2014 [3] Bhupathiraju et al. 2014 [3] AlEssa et al. 2015
[9]
Country
Dataset
US
HPFS
US
IWHS
US
ARIC
US
ARIC
US
Hawaii
NHS II Black Women ’s Health study Hawaii MEC Hawaii MEC Hawaii MEC Hawaii MEC Hawaii MEC Hawaii MEC
US
NHS11
US Hawaii Hawaii Hawaii Hawaii Hawaii
US US
HPFS NHS
Data Form
Sample Size/Cases
Study Time (Y)
Age (Y)
Sex
BMI
quintil es
42759/915
6
58
M
26
1995
T2DM
35988 /228
6
62
F
27
1803
T2DM
12251/148
9
54
MF
27
1625
T2DM
12251/253
9
53
MF
29
1601
T2DM
quintil es contin uous contin uous
quintil es
quintil es quintil es quintil es quintil es quintil es quintil es quintil es quintil es quintil es quintil es
91249/741
59000/193 8 15116/107 3 16572/268 5 4568/799 14643/717 5 18672/237 1 5941/945 90411/451 5 40498/311 2 70025/693 4
Kcal
Disease
8
36
F
25
1812
T2DM
8
F
30
1759
T2DM
14
37 4575 4575 4575 4575 4575 4575
18
36.1
14 14 14 14 14
M
2162
T2DM
M
2164
T2DM
M
2540
T2DM
F
1707
T2DM
F
1709
T2DM
F
2061
T2DM
1789
T2DM
F
25
Dietary Exposure Variables
CHO, DFIB, CFIB GI, GL, CHO, high glycemic CHO, DFIB, CFIB GI, GL, DFIB, CFIB GI, GL, DFIB, CFIB CHO, DFIB, CFIB, BMI GI, BMI GL GI, GL, CFIB, BMI GL, BMI GL
GL, DFIB, CFIB GL, DFIB, CFIB GL, DFIB, CFIB GL, DFIB, CFIB GL, DFIB, CFIB GL, DFIB, CFIB GI, GL
Dietary Data/ # Items
FFQ/131
FFQ//127 FFQ/66 FFQ/66
FFQ/133
FFQ/68 QFFQ/ 8 categories QFFQ/ 8 categories QFFQ/ 8 categories QFFQ/ 8 categories QFFQ/8 food categories QFFQ/ 8 categories FFQ/126-133
GI, GL 21 24
52.9 50.2
M F
25 25
1995 1727
T2DM T2DM
FFQ/126-133 CHO, DFIB, CFIB
FFQ/116-133
33
Barclay et al. 2007 Van Woudenbergh et al. 2011[69] Similä et al. 2011[70]
Europe Netherla nds Findland
Similä et al. 2012[71]
Findland
Rossi et al. 2013 [72]
Greece
Slujis et al. 2013 (Denmark) [73]
Denmark
Slujis et al. 2013 (France) [73]
France
Slujis et al. 2013 (Germany) [73]
Germany
Slujis et al. 2013 (Italy) [73]
Italy
Slujis et al. 2013 (Netherlands) [73]
Netherla nds
Slujis et al. 2013 (Spain) [73]
Spain
Slujis et al. 2013 (Sweden) [73]
Sweden
Slujis et al. 2013 (UK) [73] The Interact Consortium-EPIC 2015 [74]
Villegas et al. 2007 [75]
United Kingdom United Kingdom
China
Australi an cohort Rotterd am study ATBC ATBC EPICGreek cohort EPICInterAc t Study EPICInterAc t Study EPICInterAc t Study EPICInterAc t Study EPICInterAc t Study EPICInterAc t Study EPICInterAc t Study EPICInterAc t Study EPICInterAc t Study
SWHS
contin uous
GI, CHO, DFIB, CFIB 1833/138
10
70+
MF
T2DM
FFQ/145 GI, GL
tertiles quintil es quintil es
4366/456 25943/109 8 25943/109 8
quartil es
22295/233 0
15
66
MF
26
1981
T2DM
12
56
M
26
2594
T2DM
12
56
M
26
2594
T2DM
11.3
50.4
MF
28
2051
T2DM
T2DM
GI, GL, CHO, high glycemic CHO, BMI GI, BMI GL
12
52.5
MF
26
quartil es
16835/124 03
12
52.5
MF
26
T2DM
quartil es
16835/124 03
12
52.5
MF
26
T2DM
quartil es
16835/124 03
12
52.5
MF
26
T2DM
quartil es
16835/124 03
12
52.5
MF
26
T2DM
quartil es
16835/124 03
12
52.5
MF
26
T2DM
quartil es
16835/124 03
12
52.5
MF
26
T2DM
quartil es quartil es
16835/124 03 15258/115 59
12 12
52.5 52.4
MF MF
26 26
64227/160 8
4.6
51
MF
2140
1683
T2DM
FFQ/145
FFQ/~150
T2DM T2DM
16835/124 03
2140
FFQ/145 high glycemic CHO BMI GI, BMI GL GI, GL, CHO, high glycemic CHO GI, GL, CHO, high glycemic CHO GI, GL, CHO, high glycemic CHO GI, GL, CHO, high glycemic CHO GI, GL, CHO, high glycemic CHO GI, GL, CHO, high glycemic CHO GI, GL, CHO, high glycemic CHO GI, GL, CHO, high glycemic CHO DFIB, CFIB
quartil es
quintil es
FFQ/170 GI, GL
FFQ/up to 260
FFQ/ up to 260
FFQ/ up to 260
FFQ/ up to 260
FFQ/ up to 260
FFQ/ up to 260
FFQ/ up to 260
FFQ/ up to 260 FFQ/ up to 260
FFQ/77
34
Sakurai et al. 2011 [76] He et al. 2018 He et al. [77]
Japan
Japanes e Factory study
quintil es
China
Guangz hou study
contin uous
GI, GL, DFIB, BMI GI 1995/133
6
46
M
23
2198
T2DM
FFQ/147 GI, GL
640/127
MF
5
24
1879
T2DM
3-day food record
Coronary Heart Disease
Liu et al. 2001 [12]
Hardy et al. (Whites) 2010 [42] Hardy et al. (African Am) 2010 [42]
US
NHS
US
ARIC
US
quintil es
contin uous contin uous
75521/761
10
51
F
25
1743
CHD
11673/130 3
17
55
MF
27
1625
CHD
Mursu et al. 2009 [19]
Finland
Levitan et al. 2010 [43]
Sweden
Sieri et al. 2010 [31]
Italy
EPICO R
quartil es
47749/305
11
50
M
27
2126
CHD
Seri et.al 2010 [31]
Italy
EPICO
quartil
44132/158
11
50
F
26
2509
CHD
Netherla nds
Beulens et al. 2007 [18]
Netherla nds
Levitan et al. 2007 [44]
Sweden
FFQ/126
FFQ/66 GI, GL
ARIC Zutphe n Elderly study Prospec t-EPICBreastC cancer Cohort of Swedis h men Kuopio KIHD Swedis h MC
Van Dam et al 2000 [32]
GL, CHO, high glycemic CHO, BMI GL GI, GL
11673/380
17
54
MF
30
1606
CHD
FFQ/66 GI
tertiles
646/94
10
71
M
25
2257
CHD
quartil es
15714/556
9
57
F
26
1458
CHD
36246/132 4
5
59
M
26
2712
CHD
quartil es quartil es quartil es
1981/376 36234/113 8
16.1
52
M
27
2400
CHD
9
62
F
26
1739
CHD
Diet history
GI, GL GI, GL
FFQ/77
FFQ/96 GI, GL, BMI GI, BMI GL GI, GL, BMI GI BMI GL GI, GL, CHO high glycemic CHO GI, GL, CHO
4-day food record FFQ
24-hr recall/159 24-hr recall/159
35
R
contin uous
8855/581
11.9
43
M
25
2603
CHD
contin uous
10753/300
11.9
42
F
25
1984
CHD
ATBC
quintil es
21955/437 9
19
57
M
26
2604
CHD
Greece
EPIC Greek cohort
tertiles
20275/417
10.4
2088
MF
China
SMHS
quartil es
52512/189
5.4
54.1
M
24
1930
CHD
China
SWHS
quartil es
64854/120
51.5
F
24
1684
CHD
high glycemic CHO GI, GL, CHO high glycemic CHO GI, GL, CHO high glycemic CHO GI, GL, CHO high glycemic CHO GL,CHO high glycemic CHO, BMI GL GI, GL, CHO high glycemic CHO GI, GL, CHO high glycemic CHO
US
HPFS
quintil es
42865/405 3
53.3
M
25.5
1983
CHD
CHO, DFIB, CFIB
FFQ/126-133
US
NHS
quintil es
50.3
F
24.9
1726
CHD
CHO, DFIB, CFIB
FFQ/116-133
Burger et al. 2011 [29]
Netherla nds
Burger et al.2011 [29]
Netherla nds
EPICMORG EN EPICMORG EN
Similä et al. 2013[78]
Findland
Turati et al. 2015 [79]
Yu et al. 2013 [27]
Yu et al. 2013 [27] AlEssa et al. 2018[41] AlEssa et al. 2018[41]
es
75020/405 3
9.8 26 28
CHD
FFQ/79
FFQ/79
FFQ/145
FFQ/150
FFQ/77
FFQ/77
Stroke
Oh et al. 2005 [16]
US
Oh et al. 2005 [16]
US
Beulens et al. 2007 [18]
Netherla nds
Levitan et al.2007 [44]
Sweden
NHS
NHS prospec t-EPICbreast ca Cohort of Swedis
quintil es
quintil es
78779/515
18
46
F
24
1541
Ischemi c stroke
78779/279
18
46
F
24
1541
Hemorr hagic stroke
15714/243
9
57
F
26
14581
Total stroke
GI, GL, CHO, DFIB, CFIB, BMI GI, BMI GL GI, GL, CHO, DFIB, CFIB, BMI GI, BMI GL GI, GL
FFQ/61
FFQ/61
FFQ/77 GI, GL
quartil es
36246/692
5
59
M
26
2712
Ischemi c stroke
FFQ/96
36
h men
Burger et al. 2011 [29]
Netherla nds
Burger et al. 2011 [29]
Netherla nds
Cohort of Swedis h men EPICMORG EN EPICMORG EN
Seri et al. 2013 [80]
Italy
Seri et al. 2013 [80]
Italy
Rossi et al. 2015 [81]
Greece
Levitan et al. 2007[44]
Rossi et al. 2015 [81]
Rossi et al. 2015 [81]
Rossi et al. 2015 [81]
Yu et al. 2016 [40]
Yu et al. 2016 [40]
Sweden
Greece
Greece
Greece
GI, GL quartil es
36246/165
5
59
M
26
2712
Hemorr hagic stroke
contin uous
8855/120
11.9
43
M
25
2603
Total stroke
contin uous
10753/109
11.9
42
F
25
1984
Total stroke
EPICO R
quintil es
44099/195
11
50
MF
26
1804
EPICO R EPICGreek cohort EPICGreek cohort EPICGreek cohort EPICGreek cohort
quintil es
44099/83
11
50
MF
26
1804
tertiles
19824/67
15
2086
M
Ischemi c stroke Hemorr hagic stroke
FFQ/96 GI, GL, CHO high glycemic CHO GI, GL, CHO high glycemic CHO GI, GL, CHO high glycemic CHO, DFIB GI, GL, CHO high glycemic CHO, DFIB GL
Ischemi c stroke
FFQ/79
FFQ/79
FFQ/154
FFQ/154
FFQ/150 GL
tertiles
tertiles
tertiles
19824/67
19824/49
19824/49
China
SWHS
quintil es
64328/299 1
China
SWHS
quintil es
64328/299 1
15
15
15
4
4
2087 2086 2087
Ischemi c stroke Hemorr hagic stroke Hemorr hagic stroke
F
M
F
52
F
24
1684
52
F
24
1684
Ischemi c stroke Hemorr hagic stroke
2712
Allcaus e mortalu ty Allcaus e mortalit
FFQ/150 GL FFQ/150 GL FFQ/150 GI, GL, CHO high glycemic CHO GI, GL, CHO high glycemic CHO
FFQ/77
FFQ/77
Mortality
He et al 2010 [82]
Levitan et al. 2007 [44]
US
Sweden
NHS Cohort of Swedis
quintil es quartil es
7822/852 36246/295 9
26
5
F
59
M
30
26
CFIB
FFQ/126 GI, GL FFQ/96
37
h men
Kaushik et al. 2009 [15]
Kaushik et al. 2009 [15]
Australia
Australia
Blue Mounta ins Eye Study Blue Mounta ins Eye Study
y GI, CFIB
tertiles
2897/1158
13
65.4
MF
26
FFQ/145 GI, CFIB
tertiles
2897/95
13
65.4
MF
Netherla nds
EPICMORG EN
Turati et al. 2014 [79]
Greece
EPIC Greek cohort
tertiles
12029/162
10.4
20-9
MF
Castro-Quezada et al. 2014 [83]
Spain
PREDI MED
quartil es
3583/123
4.7
66.7
MF
Burger et al. 2012 [28]
CHD mortalit y
quartil es
6192/791
9.2
57.4
MF
Stroke mortalit y
26
29
30
2047
T2DM mortalit y
2274
CHD mortalit y Total mortalit y
FFQ/145 GI, GL, CHO high glycemic CHO, DFIB, BMI GI, BMI GL GL, CHO high glycemic CHO, BMI GL GI, GL
FFQ/79
FFQ/150
FFQ/137 GI, GL, CHO Takaya Stroke high glycemic ma mortalit CHO, BMI quartil Oba et al. 2010 [30] Japan study es 12561/120 7 53.7 M 23 2617 y GI, BMI GL FFQ/169 GI, GL, CHO Takaya Stroke high glycemic ma quartil mortalit CHO, BMI Oba et al. 2010 [30] Japan study es 15301/127 7 54.9 F 22 2131 y GI, BMI GL FFQ/169 Stroke GI, GL, CHO quintil 64328/299 mortalit high glycemic Yu et al. 2016 [26] China SWHS es 1 4 52 F 24 1684 y CHO FFQ/77 Abbreviations: M, males; F, females; MF, males plus females; T2DM, T2DM; CHD, coronary heart disease; FFQ, food frequency questionnaire, QFFQ, quasi-food frequency questionnaire; HPFS, Health Professionals Follow-Up Study; IWHS, Iowa Women’s Health Study; NHS, Nurses’ Health Study; NHSII, Nurses’ Health Study; ARIC, Atherosclerosis Risk in Communities Study; Hawaii MEC, Hawaii Multiethnic Cohort; EPIC, European Prospective Investigation into Cancer and Nutrition; EPICOR, European Prospective Investigation into Cancer and Nutrition in Italian cohorts; ATBC, The Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study; KIHD, Kuopio Ischaemic Heart Disease Risk Factor; SWHS, Shanghai Women’s Health Study; SMHS, Shanghai Men’s Health Study; EPIC-MORGEN, European Prospective Investigation into Cancer and
38
Nutrition; EPICOR, European Prospective Investigation into Cancer and Nutrition in three Dutch populations (Amsterdam, Doetinchem, and Maastricht) in the Netherlands; PREDIMED, PREvencion con DIeta MEDiterranea; GI, glycemic index; GL, glycemic load; CHO, carbohydrate; DFIB, dietary fiber; CFIB, cereal fiber. Study Time (Y) and Age (Y) are mean or median. Blank entries indicate data were not present in published studies.
39
40 Table 2. List of confounders adjusted for in published studies in this meta-analysis. Study Country Datasets Confounders Controlled Type 2 Diabetes Salmeron et al. 1997(men) [13]
US
HPFS
Age, BMI, smoking, alcohol intake, physical activity, family history of diabetes, other fiber sources (dietary fiber, fruit fiber, vegetable fiber) Age, energy, physical activity, BMI, WHR, smoking, alcohol intake, education, family history of diabetes
Meyer et al. 2000 [66]
US
IWHS
Stevens et al. 2001 [67]
US
ARIC
Age, BMI, education, smoking status, physical activity, sex, field center
Schultz et al. 2004 [11]
US
NHS II
Age, BMI, smoking, alcohol intake, physical activity, family history of diabetes, history of high blood pressure, history of high blood cholesterol, postmenopausal hormone use, oral contraceptives, energy intake, cereal fiber, magnesium, caffeine, saturated fat, monounsaturated fat, polyunsaturated fat, trans fatty acids,
Krishnan et al. 2007 [68]
US
Black Women’ s Health study
Age, BMI, energy intake, family history of diabetes, smoking, physical activity, cereal fiber, total fat intake, protein intake
Hopping et al. 2010 [10]
Hawaii
Hawaii MEC
Ethnicity, BMI, physical activity, education, energy intake, carbohydrate, (stratified by sex and ethnicity)
Bhupathiraju et al. 2014 [3]
US
NHS11, HPFS, NHS
Race, several lifestyle factors, family history of diabetes, menopausal status, and postmenopausal hormone use, premenopausal, postmenopausal, oral contraceptive use, physical activity, BMI , smoking status, energy intake, coffee intake, cereal fiber, trans fatty acids, polyunsaturated fats, saturated fats, monounsaturated fats, protein intake
AlEssa et al. 2015[9]
US
NHS
Age, BMI, race energy intake, smoking status, alcohol consumption, physical activity, postmenopausal hormone use, family history of diabetes, and multivitamin use, red meat, coffee, ratio of polyunsaturated fat to saturated fat, trans fat (percentage of total energy), sugarsweetened beverage intake, fruits and vegetables, magnesium
Barclay et al. 2007[84]
Australia
Australia n cohort
Age, sex, family history of diabetes, smoking, triglycerides, HDL cholesterol, and METs, as well as vegetable fiber for the GI analyses
Van Woudenbergh et al. 2011[69]
Netherla nds
Rotterda m study
Age, sex, BMI, smoking, and family history of diabetes, intakes of energy, protein, saturated fat, alcohol, and fiber, C-reactive protein
Similä et al. 2011[70]
Findland
ATBC
Age, intervention group, BMI, smoking (years, number of cigarettes per day), physical activity, total energy and coffee consumption
Similä et al. 2012[71]
Findland
ATBC
Rossi et al. 2013 [72]
Greece
EPICGreek cohort
Age, intervention group, BMI, smoking (years, number of cigarettes per day), physical activity, total energy and coffee consumption Age, sex, education, BMI, physical activity, waist to hip ratio, energy intake, non-carbohydrate energy intake
40
41 Slujis et al. 2013 (Denmark) [73]
Europe
EPICInterAct Study
Age, sex, education, physical activity, BMI, menopausal status, smoking status, and alcohol consumption, energy intake, dietary protein, polyunsaturated:saturated fat ratio, and dietary fiber
The Interact Consortium-EPIC 2015 [74]
Europe
Villegas et al. 2007 [75]
China
SWHS
Age, education, family income, occupation, smoking status, alcohol consumption, physical activity, and hypertension
Sakurai et al. 2011 [76]
Japan
Japanese Factory study
Age, BMI, family history of diabetes, alcohol intake, smoking, physical activity, high blood pressure, dyslipidemia, energy intake, fiber intake
He et al. 2018 He et al. [77]
China
Guangzh ou study
Age, sex, body mass index, moderate/vigorous physical activity, education, monthly income, current smoking status, current drinking status and familial history of diabetes, intake of fat, protein and fiber.
Liu et al. 2001 [12]
US
NHS
Age, BMI, physical activity, smoking, alcohol intake, parental family history of MI before age 60, HTN, history of high cholesterol, menopausal status, aspirin use, vitamin E supplements, dietary vitamin E, protein intake, dietary fiber, folate, total energy from food, saturated fat, monounsaturated fat, polyunsaturated fat, trans fat. Confounders in varying in 4 models
Hardy et al. 2010 [42]
US
ARIC
Van Dam et al 2000 [32]
Netherla nds
Zutphen Elderly study
Propensity score (age, sex, BMI, physical activity, smoking status, systolic blood pressure, total cholesterol, HDL cholesterol, fasting blood glucose, Keys score, antihypertensive medications, total calories, race were regressed against energy-adjusted glycemic index or glycemic load) Age, BMI, physical activity, smoking status, prescribed diet, energy intake, saturated fat, polyunsaturated fat, carbohydrate, alcohol
Beulens et al. 2007 [18]
Netherla nds
ProspectEPICBreast Cancer
Age, BMI, physical activity, smoking status, pack years of smoking, mean systolic blood pressure, HTN, high cholesterol, waist to hip ratio, menopausal status, hormone replacement therapy, oral conceptive use, total energy, Vitamin E, multivitamins, alcohol, protein, fiber, folate, saturated fat, polyunsaturated fat
Levitan et al. 2007 [44]
Sweden
Mursu et al. 2009 [19]
Finland
Cohort of Swedish men Kuopio KIHD
Age, BMI, smoking status, alcohol intake, history HTN, family history of MI, aspirin use, marital status, education, total energy, physical activity, carbohydrate intake, saturated fat, polyunsaturated fat, protein, cereal fiber Age, BMI, smoking, exam years, systolic blood pressure, hypertension medication, serum HDL and LDL cholesterol, triglycerides, LTPA, education, family history of cardiovascular disease, diabetes, alcohol intake, dietary intake of energy, energy-adjusted folate, fiber, vitamin C, polyunsaturated fat, saturated fat
Age, sex, smoking, physical activity, education, alcohol, energy and energy-adjusted carbohydrates, magnesium, vitamin B1, saturated fatty acids, BMI All models for types of fibre were mutually adjusted
41
42 Levitan et al. 2010 [43]
Sweden
EPICOR
Age, BMI, smoking, physical activity, living alone, postmenopausal hormone use, aspirin use, education, family history of MI before 60 years, HTN, high cholesterol, total energy intake, alcohol intake, fiber, polyunsaturated fat, protein intake, carbohydrate intake
Sieri et al. 2010 [31]
Italy
EPICOR
Burger et al. 2011 [29]
Netherla nds
EPICMORGE N
Age, BMI, physical activity, smoking, non-alcohol energy intake, fiber intake, hypertension, education, alcohol intake, saturated fat intake Age, smoking, pack years, education, BMI, physical activity, hypertension, and oral contraceptives use, energy intake, alcohol, vitamin C, fiber, saturated, monounsaturated, polyunsaturated fat, carbohydrates and protein
Turati et al. 2015 [79]
Greece
Yu et al. 2013 [27]
China
EPIC Greek cohort SWHS
Age, sex, education, physical activity, smoking status, arterial hypertension, energy intake without carbohydrates, and Mediterranean diet score Educational, income, smoking status, alcohol consumption, physical activity level, waist-to-hip ratio, history of hypertension, and dietary intakes of total energy, saturated fat, and protein
AlEssa et al. 2018[41]
US
HPFS, NHS
Age, BMI, race, family history of myocardial Infarction, menopausal status and postmenopausal hormone use, smoking, alcohol, physical activity, multivitamin use, aspirin use, vitamin E use, energy intake, polyunsaturated fat:saturated fat ratio and trans fat % energy
Oh et al. 2005 [16]
US
NHS
Age, BMI, smoking, alcohol intake, physical activity, parental history of myocardial infarction, history of hypertension, hypercholesterolemia, and diabetes, menopausal status and postmenopausal hormone use, aspirin use, multivitamin, vitamin E supplement use, energy intake, cereal fiber, saturated fat, monounsaturated fat, polyunsaturated fat, trans-fat, and omega-3 fatty acids
Seri et al. 2013 [80]
Italy
EPICOR
Rossi et al. 2015 [81]
Greece
EPICGreek cohort
Age, sex, smoking, years of education, BMI, alcohol intake, nonalcohol energy intake, physical activity, cereal fiber intake, saturated fat, monounsaturated fat, polyunsaturated fat. Age, sex, education, smoking, body mass index (BMI), physical activity, presence of hypertension, Mediterranean diet score, and energy intake without carbohydrates
Yu et al. 2016 [40]
China
SWHS
Education, cigarette smoking, BMI, family history of stroke, history of hypertension, history of dyslipidemia, total energy intake, saturated fat intake, and a partial diet quality score
42
43 He et al. 2010 [82]
US
NHS
Age, smoking status, BMI, alcohol intake, physical activity, parental history of MI, menopausal status and use of hormone therapy, and duration of diabetes, energy, polyunsaturated fat, saturated fat, trans fat, magnesium, folate
Levitan et al. 2007 [44]
Sweden
Cohort of Swedish men
Age, BMI, smoking status, alcohol intake, history HTN, family history of MI, aspirin use, marital status, education, total energy, physical activity, carbohydrate intake, saturated fat, polyunsaturated fat, protein, cereal fiber
Kaushik et al. 2009 [15]
Australia
Blue Mountai ns Eye Study
Age, sex, systolic blood pressure, diastolic blood pressure, antihypertensive medication use, BMI, smoking status, educational qualifications, fair or poor self-rated health, history of myocardial infarction and stroke, and presence of diabetes
Burger et al. 2012 [28]
Netherla nds
EPICMORGE N
Smoking, smoking duration, education, BMI, WHR, physical activity, alcohol intake, menopausal status, and hormone replacement therapy use, diabetes duration, insulin use, glycated hemoglobin level, total energy, vitamin C, saturated, monounsaturated, and polyunsaturated fat, dietary fiber, carbohydrates
Similä et al. 2013[78]
Findland
ATBC
Age, intervention group, smoking, BMI, physical activity, serum total and HDL-cholesterol, blood pressure and intakes of energy, alcohol, total fat, protein, Magnesium and potassium
Castro-Quezada et al. 2014 [83]
Spain
PREDIM ED
Age, sex, recruitment center and intervention group (Med Diet + EVOO, Med Diet + Nuts and control diet), smoking, education, marital status, physical activity, BMI, history of cancer, history of arterial hypertension, dyslipidemia, history of cardiovascular disease, energy intake, alcohol intake dietary fiber intake, saturated fatty acids and monounsaturated fatty acids
Oba et al. 2010 [30]
Japan
Takayam a study
Yu et al. 2016 [26]
China
SWHS
Age, BMI, smoking status, Physical activity, history of hypertension, education, energy intake, alcohol, dietary fiber, salt, total fat Education, cigarette smoking, BMI, family history of stroke, history of hypertension, history of dyslipidemia, total energy intake, saturated fat intake, and a partial diet quality score
Abbreviations: HPFS, Health Professionals Follow-Up Study; IWHS, Iowa Women’s Health Study; NHS, Nurses’ Health Study; NHSII, Nurses’ Health Study; ARIC, Atherosclerosis Risk in Communities Study; Hawaii MEC, Hawaii Multiethnic Cohort;
43
44 EPIC, European Prospective Investigation into Cancer and Nutrition; EPICOR, European Prospective Investigation into Cancer and Nutrition in Italian cohorts; ATBC, The Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study; KIHD, Kuopio Ischaemic Heart Disease Risk Factor; SWHS, Shanghai Women’s Health Study; SMHS, Shanghai Men’s Health Study; EPICMORGEN, European Prospective Investigation into Cancer and Nutrition; EPICOR, European Prospective Investigation into Cancer and Nutrition in three Dutch populations (Amsterdam, Doetinchem, and Maastricht) in the Netherlands; PREDIMED, PREvencion con DIeta MEDiterranea.
44
45 Table 3. Pooled hazard ratios of dietary variables by disease outcomes in the whole sample in the US, Europe, and Asia Pooled Hazard Ratio (95% Confidence Interval) T2DM Glycemic index Glycemic load GI and high BMI GL and high BMI CHO High glycemic CHO Dietary fiber Cereal fiber
1.14 (1.06, 1.21) 1.09 (1.06, 1.11) 1.28 (1.04, 1.59) 1.21 (1.00, 1.48) 0.96 (0.88, 1.04) 0.87 (0.70, 1.08)b 0.92 (0.88, 0.96) 0.83 (0.77, 0.90)
Glycemic index Glycemic load GI and high BMI GL and high BMI CHO High glycemic CHO Dietary fiber Cereal fiber
1.03 (0.94, 1.13) 1.02 (0.92, 1.13) 1.26 (1.08, 1.47)b 0.98 (0.86, 1.10) 1.00 (0.91, 1.10) 0.91 (0.83, 0.99) 0.95 (0.85, 1.07)
CHD US 1.05 (0.95, 1.16)a 1.04 (0.93, 1.16)a 1.97 (1.31, 2.96)b 1.05 (0.98, 1.13)a 1.22 (0.94, 1.59)b 0.96 (0.89, 1.04)a 0.81 (0.76, 0.87)a Europe 1.06 (0.97, 1.16) 1.04 (0.96, 1.12) 1.31 (1.02, 1.68) 1.63 (1.28, 2.07) 1.08 (0.98, 1.19) 1.10 (0.99, 1.21)
Stroke
Mortality
1.05 (0.84, 1.33)a 1.16 (0.90, 1.50)a 1.39 (1.25, 1.54)a 1.60 (1.06, 2.40)a 1.29 (0.55, 3.02)a 0.80 (0.61, 1.04)a 0.65 (0.42, 1.01)a
1.12 (1.00, 1.27) 1.18 (0.98, 1.44)
1.02 (0.79, 1.32) 1.30 (0.86, 1.99) 0.99 (0.33, 3.01)a
0.86 (0.66, 1.12)b
1.09 (0.93, 1.29) 1.02 (0.89, 1.16) 1.02 (0.90, 1.15)b 1.34 (0.70, 2.55) 1.04 (0.90, 1.19) 1.07 (0.92, 1.25) 0.83 (0.75, 0.91)b 0.74 (.033, 1.63)a
Asia 1.19 (0.77, 1.84) Glycemic index 1.17 (0.81, 1.69)a 1.25 (1.02, 1.53) 1.19 (1.04, 1.36)a Glycemic load 1.17 (0.62, 2.21)a 1.23 (0.87, 1.73) 1.25 (1.03, 1.52) 1.26 (1.04, 1.53)a GI and high BMI 0.88 (0.41, 1.91)a 1.28 (1.05, 1.56)b GL and high BMI 0.98 (0.48, 1.98)a 1.52 (1.22, 1.89)b a b a CHO 1.16 (0.92, 1.47) 1.07 (0.72, 1.58) 1.28 (1.09, 1.50) 2.88 (1.44, 5.78) High glycemic CHO 1.19 (0.88, 1.62) 1.78 (1.48, 2.15)b 1.79 (1.02, 3.16)a 1.19 (1.01, 1.42)a Dietary fiber 0.99 (0.59, 1.66)b Cereal fiber Abbreviations: GI, glycemic index; GL, glycemic load; CHO, carbohydrate; BMI, body mass index; CHD, coronary heart disease; BMI, body mass index. Hazard Ratio used continuous form of dietary variable or highest vs lowest tertile/ quartile/quintile categories. a
Findings based on one study with more than one stratification.
b
Findings based on one study with a single entry.
Blank entries indicate data were not present in published studies. Bold: p value < 0.05 was used to judge statistical significance.
45
46 Table 4. Pooled hazard ratios of dietary variables by disease outcomes among males in the US, Europe, and Asia
Pooled Hazard Ratio (95% Confidence Interval) T2DM Glycemic index Glycemic load
CHD US
Stroke
Mortality
1.06 (0.95, 1.19)b
1.30 (1.15, 1.47)b 1.30 (1.09, 1.55)b
GI and high BMI GL and high BMI CHO High glycemic CHO
0.85 (0.62, 1.16)b
1.07 (0.96, 1.20)b 2.01 (0.96, 4.22)b
Dietary fiber Cereal fiber
0.79 (0.67, 0.92) 0.86 (0.74, 0.99)
1.00 (0.88, 1.13)b 0.84 (0.75, 0.94)b Europe
Glycemic index
0.87 (0.71, 1.07)
b
0.98 (0.90, 1.07)
Glycemic load
0.87 (0.65, 1.17)b
0.98 (0.89, 1.08)
1.19 (1.02, 1.39) 1.16 (0.88, 1.53)
GI and high BMI GL and high BMI
1.58 (1.03, 2.43)b 2.05 (1.30, 3.23)b 1.08 (0.83, 1.41) 1.04 (0.95, 1.14)
1.02 (0.70, 1.48)b 1.01 (0.70, 1.46)b
CHO High glycemic CHO
1.07 (0.89, 1.29)b
0.94 (0.79, 1.11)b
Dietary fiber Cereal fiber Asia Glycemic index Glycemic load GI and high BMI GL and high BMI
b
1.96 (1.04, 3.68) 1.96 (1.03, 3.72)b 1.11 (0.58, 2.12)b
CHO High glycemic CHO
1.13 (0.71, 1.80)b 1.13 (0.50, 2.55)b
0.78 (0.41, 1.48)b 1.00 (0.47, 2.14)b 0.82 (0.27, 2.50)b 0.85 (0.33, 2.19)b
3.20 (1.33, 7.69)b 2.01 (0.96, 4.22)b
1.17 (0.52, 2.63)b 0.84 (0.43, 1.63)b
Dietary fiber 0.99 (0.59, 1.66)b Cereal fiber Abbreviations: GI, glycemic index; GL, glycemic load; CHO, carbohydrate; BMI, body mass index; CHD, coronary heart disease; BMI, body mass index. Hazard Ratio used continuous form of dietary variable or highest vs lowest tertile/ quartile/quintile categories. a
Findings based on one study with more than one stratification.
b
Findings based on one study with a single entry.
Blank entries indicate data were not present in published studies. Bold: p value < 0.05 was used to judge statistical significance.
46
47
Table 5. Pooled hazard ratios of dietary variables by disease outcomes among females in the US, Europe, and Asia Pooled Hazard Ratio (95% Confidence Interval) T2DM
CHD
Stroke
Mortality
US Glycemic index
1.05 (0.84, 1.33)a
1.20 (1.01, 1.41)
Glycemic load
1.17 (1.05, 1.31)
1.98 (1.41, 2.78)
1.16 (0.90, 1.50)a
GI and high BMI
1.28 (1.04, 1.59)
1.20 (0.91, 1.58)
1.39 (1.25, 1.54)a
GL and high BMI
1.21 (1.00, 1.48)
1.97 (1.31, 2.96)b
1.60 (1.06, 2.40)a
CHO
0.97 (0.89, 1.05)
1.04 (0.95, 1.13)b
1.29 (0.55, 3.02)a
High glycemic CHO
0.87 (0.70, 1.08)
1.22 (0.94, 1.59)b
Dietary fiber
0.87 (0.78, 0.98)
0.94 (0.85, 1.03)b
0.80 (0.61, 1.04)a
Cereal fiber
0.77 (0.69, 0.85)
0.80 (0.74, 0.87)b
0.65 (0.42, 1.01)a
Glycemic index
Europe 1.17 (1.03, 1.31)
0.99 (0.81, 1.22)a
Glycemic load
1.31 (1.01, 1.70)
1.03 (0.78, 1.37)
GI and high BMI
1.20 (0.91, 1.58)b
GL and high BMI CHO
1.45 (0.93, 2.26)b 1.00 (0.81, 1.24)
0.90 (0.60, 1.34)b
High glycemic CHO Dietary fiber
1.26 (0.81, 1.98)
0.95 (0.63, 1.43)b
b
0.86(0.66, 1.12)b
Cereal fiber Asia Glycemic index Glycemic load
1.24 (0.68, 2.27)b
1.19 (1.04, 1.36)a
1.41 (0.81, 2.46)
b
a
1.29 (0.88, 1.91)
1.25 (0.46, 3.41)
1.26 (1.04, 1.53)
1.16 (0.40, 3.36)b 0.94 (0.32, 2.76)b
GL and high BMI GI and high BMI CHO
2.41 (0.77, 7.56)b
1.16 (0.92, 1.47)a
1.04 (0.67, 1.63)
High glycemic CHO Dietary fiber
1.53 (0.64, 3.67)b
1.19 (1.01, 1.42)a
1.31 (0.93, 1.86)
Cereal fiber Abbreviations: GI, glycemic index; GL, glycemic load; CHO, carbohydrate; BMI, body mass index; CHD, coronary heart disease; BMI, body mass index. Hazard Ratio used continuous form of dietary variable or highest vs lowest tertile/ quartile/quintile categories. a
Findings based on one study with more than one stratification.
b
Findings based on one study with a single entry.
Blank entries indicate data were not present in published studies. Bold: p value < 0.05 was used to judge statistical significance.
47
48 S1 Table 1. Heterogeneity of glycemic index and glycemic load pooled estimates in US studies Characteristic Coefficient Confidence Interval P Value Sex Study time Cases Sample size
-0.132 0.017285 0.00005 2.92e-06
Glycemic Index -0.253, -0.010 0.005, 0.030 0.00003, 0.00007 3.09e-07, 5.53e-06
.036 .012 <.001 .032
Glycemic Load Sex -0.048 -0.183, 0.086 .456 Study time 0.011 0.007, 0.015 < .001 Cases 0.00002 0.00002, 0.00003 < .001 Sample size 2.54e-06 1.64e-06, 3.44e-06 < .001 S1 Table 1. Meta regression analysis was used to determine study characteristics that caused heterogeneity. Bold: p value < 0.05 was used to judge statistical significance.
48
49 S1 Table 2. Assessment of risk of bias in the individual studies using the ROBIN-I tool. ROBIN-I Tool Study
Countr y US
Region
Dataset
US
Meyer et al. 2000 [66]
US
Stevens et al. (Whites) 2001 [67] Schultz et al. 2004 [11] Krishnan et al. 2007 [68] Hopping et al. 2010 (Whites) [10] Bhupathiraju et al. 2014 [3] AlEssa et al. 2015 [9] Barclay et al. 2007[84] Van Woudenbergh et al. 2011[69] Similä et al. 2011[70] Similä et al. 2012[71] Rossi et al. 2013 [72] Slujis et al. 2013 [73] Villegas et al. 2007 [75] Sakurai et al. 2011 [76] He et al. [77]
Salmeron et al. 1997(men) [13]
Liu et al. 2001 [12] Hardy et al. 2010 [42] Van Dam et al 2000 [32] Levitan et al. 2007 [44] Levitan et al. 2010 [43] Beulens et al. 2007 [18] Mursu et al. 2009 [19] Sieri et al. 2010 [31]
HPFS
Confoundi ng moderate
US
IWHS
US
US
US
Selection moderate
Informatio n serious
Reportin g serious
Total serious
moderate
high
serious
serious
serious
ARIC
moderate
serious
moderate
moderate
modera te
US
NHS II
moderate
moderate
serious
serious
serious
US
US
moderate
moderate
serious
serious
serious
US
US
Black Women’s Health study Hawaii MEC
moderate
serious
serious
serious
serious
US
US
US
moderate
moderate
serious
serious
serious
US
NHS, NHS11, HFPS NHS
moderate
moderate
serious
serious
serious
Australi a Netherl ands
Europe
Australian cohort
serious
moderate
moderate
moderate
Europe
Rotterdam study
moderate
moderate
moderate
moderate
modera te modera te
Findlan d Findlan d Greece
Europe
ATBC
low
low
moderate
moderate
Europe
ATBC
low
low
moderate
moderate
Europe
moderate
moderate
moderate
moderate
Europe
Europe
moderate
moderate
moderate
moderate
China
Asia
EPIC-Greek cohort EPIC-InterAct Study SWHS
moderate
moderate
moderate
moderate
Japan
Asia
China
Asia
US
modera te modera te modera te modera te modera te modera te modera te serious
moderate
moderate
moderate
moderate
serious
moderate
moderate
moderate
US
Japanese Factory study Guangzhou study NHS
moderate
moderate
serious
serious
US
US
ARIC
moderate
serious
moderate
moderate
Netherl ands Sweden
Europe
moderate
moderate
moderate
moderate
Europe
moderate
moderate
serious
serious
Sweden
Europe
Zutphen Elderly study Cohort of Swedish men Swedish MC
modera te modera te serious
moderate
serious
serious
serious
serious
Netherl ands Finland
Europe
Italy
modera te modera te modera te
moderate
moderate
moderate
moderate
Europe
Prospect-EPICBreastCcancer Kuopio KIHD
moderate
moderate
moderate
moderate
Europe
EPICOR
moderate
moderate
moderate
moderate
49
50 Burger et al. 2011 [29] Similä et al. 2013[78] Turati et al. 2015 [79] Yu et al. 2013 [27] AlEssa et al. 2018[41]
Netherl ands Findlan d Greece
Europe
EPIC-MORGEN
moderate
moderate
moderate
moderate
Europe
ATBC
low
low
moderate
moderate
Europe
moderate
moderate
moderate
moderate
Asia
EPIC Greek cohort SMHS
moderate
moderate
moderate
moderate
US
US
HPFS
moderate
serious
serious
moderate
modera te modera te modera te modera te serious
China
Oh et al. 2005 [16] Seri et al. 2013 [80]
US
US
NHS
moderate
moderate
serious
serious
serious
Italy
Europe
EPICOR
moderate
moderate
moderate
moderate
modera te
Rossi et al. 2015 [81]
Greece
Europe
EPIC-Greek cohort
moderate
moderate
moderate
moderate
modera te
Yu et al. 2016 [40]
China
Asia
SWHS
moderate
moderate
moderate
moderate
modera te
Kaushik et al. 2009 [15]
Australi a
Europe
Blue Mountains Eye Study
moderate
moderate
serious
serious
serious
Burger et al. 2012 [28]
Netherl ands
Europe
EPIC-MORGEN
moderate
moderate
moderate
moderate
modera te
Turati et al. 2014 [79]
Greece
Europe
EPIC Greek cohort
moderate
moderate
moderate
moderate
modera te
Castro-Quezada et al. 2014 [83]
Spain
Europe
PREDIMED
moderate
low
serious
moderate
modera te
Oba et al. 2010 [30]
Japan
Europe
Takayama study
moderate
moderate
serious
serious
serious
He et al. 2010[82]
US
US
NHS
moderate
moderate
serious
serious
serious
50
51 S1 Table 2. Assessment of risk of bias of individual studies using the ROBIN-I tool. Abbreviations: HPFS, Health Professionals Follow-Up Study; IWHS, Iowa Women’s Health Study; NHS, Nurses’ Health Study; NHSII, Nurses’ Health Study; ARIC, Atherosclerosis Risk in Communities Study; Hawaii MEC, Hawaii Multiethnic Cohort; EPIC, European Prospective Investigation into Cancer and Nutrition; EPICOR, European Prospective Investigation into Cancer and Nutrition in Italian cohorts; ATBC, The Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study; KIHD, Kuopio Ischaemic Heart Disease Risk Factor; SWHS, Shanghai Women’s Health Study; SMHS, Shanghai Men’s Health Study; EPIC-MORGEN, European Prospective Investigation into Cancer and Nutrition; EPICOR, European Prospective Investigation into Cancer and Nutrition in three Dutch populations (Amsterdam, Doetinchem, and Maastricht) in the Netherlands; PREDIMED, PREvencion con DIeta MEDiterranea. Risk of Bias was assessed using the ROBIN-I tool for non-randomized studies at https://training.cochrane.org/handbook/current/ chapter-25. The ROBIN-I tool includes domains for biases in relation to confounding, selection into the study, information and measurement, and reporting, to form an overall Risk of Bias score.
51
52 S1 Table 3. GRADE Assessment for rating of the meta-evidence. US Dietary Variable
Disease Outcome
No. of Studies
No. of Effect Estimates
Study Time (Y)
Cases
Sample Size
Pooled Hazard Ratio (Effect Size)
I2
P Value
GRADE Quality Rating
5
8
101
19422
367406
1.14 (1.06, 1.21)
94.4
0
very low
Glycemic index
Type 2 diabetes CHD
1
2
17
10894
247611
1.05 (0.95, 1.16)
71.9
0.059
very low
Glycemic index
Stroke
1
2
36
794
157558
1.05 (0.84, 1.33)
0
0.969
low
Glycemic index
Mortality
1
1
26
852
7822
1.09 (0.93, 1.29)
62
0.031
low
glycemic load
5
13
197
39348
434196
1.02 (1.01, 1.03)
92
0
very low
glycemic load
Type 2 diabetes CHD
2
3
44
2444
98867
1.28 (0.98, 1.67)
82
0.004
very low
glycemic load
Stroke
1
2
36
794
157558
1.16 (0.90, 1.50)
0
0.73
low
Glycemic index
glycemic load
Mortality
BMI high GI
4
5
16
2679
150249
1.28 (1.04, 1.59)
40
0.197
very low
BMI high GI
Type 2 diabetes CHD
BMI high GI
Stroke
1
2
36
794
157558
1.39 (1.25, 1.54)
0
0.807
moderate
BMI high GI
Mortality
1
1
9.2
791
6192
1.02 (0.90, 1.15)
.
.
very low
BMI high GL
4
5
16
2679
150249
1.21 (1.00, 1.48)
0
0.732
moderate
BMI high GL
Type 2 diabetes CHD
1
1
10
761
75521
1.97 (1.31, 2.96)
.
.
low
BMI high GL
Stroke
1
2
36
794
157558
1.60 (1.06, 2.40)
0
0.859
low
BMI high GL
Mortality
Carbohydrate
3
3
38
8590
204033
0.96 (0.88, 1.04)
0
0.8
low
Carbohydrate
Type 2 diabetes CHD
1
2
54
8106
117885
1.05 (0.98, 1.13)
0
0.692
low
Carbohydrate
Stroke
1
2
36
794
157558
1.29 (0.55, 3.02)
79
0.028
very low
1
1
6
228
35988
0.87 (0.70, 1.08)
.
.
very low
1
1
10
761
75521
1.22 (0.94, 1.59)
.
.
low
Carbohydrate
Mortality
High glycemic CHO High glycemic CHO High glycemic CHO High glycemic
Type 2 diabetes CHD Stroke Mortality
52
53 CHO Dietary fiber
5
Dietary fiber
Type 2 diabetes CHD
24039
304047
0.92 (0.88, 0.96)
78
0
moderate*
1
2
Dietary fiber
Stroke
1
2
54
8106
117885
0.96 (0.89, 1.04)
0
0.442
low
36
794
157558
0.80 (0.61, 1.04)
0
0.792
low
Dietary fiber
Mortality
Cereal fiber
6
12
148
Cereal fiber
Type 2 diabetes CHD
25977
363047
0.83 (0.77, 0.90)
83
0
moderate*
1
2
Cereal fiber
Stroke
1
2
54
8106
117885
0.81 (0.76, 0.87)
0
0.491
moderate
36
794
157558
0.65 (0.42, 1.01)
62
0.107
moderate
Cereal fiber
Mortality
1
1
26
852
7822
0.86 (0.66, 1.12)
.
.
low
No. of Studies 5
No. of Entries
Glycemic index
Disease Outcome Type 2 diabetes CHD
Cases
Sample Size
112613
183913
Pooled Hazard Ratio (Effect Size) 1.03 (0.94, 1.13)
I2 (%) 20
P Value 0.248
GRADE Quality
13
Study Time (Y) 155
Glycemic index
Stroke
7
9
163.1
11203
364977
1.06 (0.97, 1.16)
50.4
0.034
very low
4
7
64.8
1607
196012
1.12 (1.00, 1.27)
0
0.724
very low
Glycemic index
Mortality
4
5
44.9
2952
51815
1.09 (0.93, 1.29)
62
0.031
very low
glycemic load
4
11
134.4
103108
187284
1.02 (0.92, 1.13)
0
0.709
very low
glycemic load
Type 2 diabetes CHD
8
10
114.3
9534
243894
1.04 (0.96, 1.12)
9.9
0.352
very low
glycemic load
Stroke
5
11
124.8
1839
275308
1.18 (0.98, 1.44)
18
0.272
low
glycemic load
Mortality
4
4
29.3
1861
58050
1.02 (0.89, 1.16)
23
0.272
low
BMI high GI BMI high GI
Type 2 diabetes CHD
2
2
25.1
1514
38215
1.31 (1.02, 1.68)
11
0.291
low
BMI high GI
Stroke
BMI high GI
Mortality
2
2
9.2
791
6192
1.02 (0.90, 1.15)
.
.
low
BMI high GL
1
2
11.34
2330
22295
1.26 (1.08, 1.47)
.
.
low
BMI high GL
Type 2 diabetes CHD
3
3
35.5
1931
58490
1.63 (1.28, 2.07)
0
0.496
low
BMI high GL
Stroke
Europe Dietary Variable Glycemic index
1
140
very low
53
54 BMI high GL
Mortality
2
2
19.6
953
18221
1.34 (0.70, 2.55)
75
0.047
very low
Carbohydrate
1
8
96
99224
134680
0.98 (0.86, 1.10)
0
0.779
very low
Carbohydrate
Type 2 diabetes CHD
4
6
75.2
6140
153719
1.08 (0.98, 1.19)
0
0.509
very low
Carbohydrate
Stroke
2
4
45.8
507
107806
1.02 (0.79, 1.32)
0
0.599
very low
Carbohydrate
Mortality
2
2
19.6
953
18221
1.04 (0.90, 1.19)
0
0.766
very low
High glycemic CHO High glycemic CHO High glycemic CHO High glycemic CHO Dietary fiber
Type 2 diabetes CHD
2
9
108
100322
160623
1.00 (0.91, 1.10)
0
0.83
very low
4
6
75.2
6140
153719
1.10 (0.99, 1.21)
30.7
0.205
very low
Stroke
2
4
45.8
507
107806
1.30 (0.86, 1.99)
59
0.065
very low
Mortality
2
2
19.6
953
18221
1.07 (0.92, 1.25)
10
0.292
low
1
1
4
12
11559
0.91 (0.81, 1.03)
0
0.902
low
Dietary fiber
Type 2 diabetes CHD
1
1
10
94
646
0.91 (0.81, 1.03)
.
.
low
Dietary fiber
Stroke
1
2
22
278
88198
0.99 (0.33, 3.01)
69
0.074
very low
Dietary fiber
Mortality
1
1
9.2
791
6192
0.83 (0.75, 0.91)
.
.
low
Cereal fiber
1
1
4
12
11559
0.95 (0.83, 1.08)
.
.
low
Cereal fiber
Type 2 diabetes CHD
1
1
10
94
646
0.95 (0.83, 1.08)
.
.
low
Cereal fiber
Stroke
Cereal fiber
Mortality
1
2
26
1253
5794
0.74 (0.33, 1.63)
84
0.013
very low
Dietary Variable
Disease Outcome
No. of Studies
No. of Entries
Study Time (Y)
Cases
Sample Size
Pooled Hazard Ratio (Effect Size)
I2
P Value
GRADE Quality
Glycemic index
2
2
15.6
1868
66862
1.25 (1.02, 1.53)
12.8
0.318
low
Glycemic index
Type 2 diabetes CHD
1
2
15.2
309
117366
1.17 (0.81, 1.69)
0
0.811
low
Glycemic index
Stroke
2
2
8
2991
128656
1.19 (1.04, 1.36)
0
0.693
low
Glycemic index
Mortality
3
3
18
856
92190
1.19 (0.77, 1.84)
50
0.134
very low
Glycemic load
2
2
10.6
1741
66222
1.02 (1.01, 1.04)
86
0
low
Glycemic load
Type 2 diabetes CHD
1
2
15.2
309
117366
1.87 (0.98, 3.55)
3
0.31
low
glycemic load
Stroke
1
2
8
2991
128656
1.26 (1.04, 1.53)
0
0.707
moderate
Asia
54
55 glycemic load
Mortality
2
3
40
1099
35684
1.23 (0.87, 1.73)
0
0.811
moderate
BMI high GI
2
2
10.6
1741
66222
1.28 (1.05, 1.56)
0
0.648
low
BMI high GI
Type 2 diabetes CHD
BMI high GI
Stroke
BMI high GI
Mortality
1
2
14
247
27862
0.88 (0.41, 1.91)
0
0.863
low
BMI high GL
1
1
4.6
1608
64227
1.30 (1.17, 1.45)
.
.
moderate
BMI high GL
Type 2 diabetes CHD
BMI high GL
Stroke
BMI high GL
Mortality
1
2
14
247
27862
0.98 (0.48, 1.98)
0
0.669
very low
Carbohydrate
1.28 (1.09, 1.50)
.
.
low
Carbohydrate
Type 2 diabetes CHD
1
2
15.2
309
117366
2.88 (1.44, 5.78)
0
0.7
very low
Carbohydrate
Stroke
1
2
8
2991
128656
1.16 (0.92, 1.47)
0
0.692
very low
Carbohydrate
Mortality
2
3
18
856
92190
1.07 (0.91, 1.19)
0
0.863
very low
High glycemic CHO High glycemic CHO High glycemic CHO High glycemic CHO Dietary fiber
Type 2 diabetes CHD
1
1
4.6
1608
64227
1.78 (1.48, 2.15)
.
.
low
1
2
15.2
309
117366
1.79 (1.02, 3.16)
0
0.641
low
Stroke
1
2
8
2991
128656
1.19 (1.01, 1.42)
0
0.649
low
Mortality
2
3
18
856
92190
1.19 (0.96, 1.22)
0
0.568
very low
1
2
6
133
1995
0.99 (0.59, 1.66)
.
.
low
Dietary fiber
Type 2 diabetes CHD
Dietary fiber
Stroke
Dietary fiber
Mortality
Cereal fiber Cereal fiber
Type 2 diabetes CHD
Cereal fiber
Stroke
Cereal fiber
Mortality
S1 Table 3. GRADE assessment for rating of the quality of the meta-evidence.
55
56 GRADE[37] assessment included evaluation of the risk of bias, inconsistency, indirectness, imprecision, and publication bias in the confidence-rating of each dietary variabledisease outcome as either low or very low GRADE quality. GRADE quality was upgraded from very low to moderate* due to dose-response relationship of total
dietary fiber or cereal fiber and type 2 diabetes.
56
57 Figure Legends Figure 1. Selection of published studies for meta-analysis There were 40 studies. Some studies had >1 dietary variable and >1 disease outcome. Therefore, the studies may add up to more than 40 studies.
57
58 S1 Figure 1. Association of glycemic index and risk of type 2 diabetes within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; African Am, African Americans. Online Supporting Material
S1 Figure 2. Association of glycemic index and risk of coronary heart disease within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; African Am, African Americans. Online Supporting Material
S1 Figure 3. Association of glycemic index and risk of stroke within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females. Online Supporting Material
S1 Figure 4. Association of glycemic index and risk of mortality within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females. Online Supporting Material
S1 Figure 5. Association of glycemic load and risk of type 2 diabetes within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; African Am, African Americans. Online Supporting Material
58
59 S1 Figure 6. Association of glycemic load and risk of coronary heart disease within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females. Online Supporting Material
S1 Figure 7. Association of glycemic load and risk of stroke within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females. Online Supporting Material
S1 Figure 8. Association of glycemic load and risk of mortality within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females. Online Supporting Material
S1 Figure 9. Association of glycemic index with high BMI and risk of type 2 diabetes within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; GI, glycemic index; BMI, body mass index. Online Supporting Material
S1 Figure 10. Association of glycemic index with high BMI and risk of coronary heart disease within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; GI, glycemic index; BMI, body mass index. Online Supporting Material
59
60
S1 Figure 11. Association of glycemic index with high BMI and risk of stroke within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; GI, glycemic index; BMI, body mass index. Online Supporting Material
S1 Figure 12. Association of glycemic index with high BMI and mortality within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; GI, glycemic index; BMI, body mass index. Online Supporting Material
S1 Figure 13. Association of glycemic load with high BMI and risk of type 2 diabetes within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S1 Figure 14. Association of glycemic load with high BMI and risk of coronary heart disease within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
60
61
S1 Figure 15. Association of glycemic load with high BMI and risk of stroke within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S1 Figure 16. Association of glycemic load with high BMI and risk of mortality within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S1 Figure 17. Association of carbohydrate and risk of type 2 diabetes within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S1 Figure 18. Association of carbohydrate intake and risk of coronary heart disease within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects metaanalysis.
61
62
S1 Figure 19. Association of carbohydrate intake and risk of stroke within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Abbreviations: M, male; F, female; MF, males plus females; T2DM, type 2 diabetes; CHD, coronary heart disease. Online Supporting Material
S1 Figure 20. Association of carbohydrate intake and risk of mortality within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Abbreviations: M, male; F, female; MF, males plus females; T2DM, type 2 diabetes; CHD, coronary heart disease. Online Supporting Material
S1 Figure 21. Association of high glycemic carbohydrate and risk of type 2 diabetes within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females; GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S1 Figure 22. Association of high glycemic carbohydrate intake and risk of coronary heart disease within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis.
62
63 S1 Figure 23. Association of high glycemic carbohydrate intake and risk of stroke within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects metaanalysis. Abbreviations: M, male; F, female; MF, males plus females; T2DM, type 2 diabetes; CHD, coronary heart disease. Online Supporting Material
S1 Figure 24. Association of high glycemic carbohydrate intake and risk of mortality within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects metaanalysis. Abbreviations: M, male; F, female; MF, males plus females. Online Supporting Material
S1 Figure 25. Association of total dietary fiber and risk of type 2 diabetes within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females. Blank entries indicate data were not present in published studies. Online Supporting Material
S1 Figure 26. Association of total dietary fiber intake and risk of coronary heart disease within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis.
63
64 S1 Figure 27. Association of total dietary fiber intake and risk of stroke within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Abbreviations: M, male; F, female; MF, males plus females. Online Supporting Material
S1 Figure 28. Association of total dietary fiber intake and risk of mortality within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Abbreviations: M, male; F, female; MF, males plus females. Online Supporting Material
S1 Figure 28. Association of cereal fiber and risk of type 2 diabetes within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: M, male; F, female; MF, males plus females. Blank entries indicate data were not present in published studies. Online Supporting Material
S1 Figure 30. Association of cereal fiber intake and risk of coronary heart disease within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects metaanalysis.
S1 Figure 31. Association of cereal fiber intake and risk of stroke within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the
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65 DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Abbreviations: M, male; F, female; MF, males plus females. Online Supporting Material
S1 Figure 32. Association of cereal fiber intake and risk of mortality within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Abbreviations: M, male; F, female; MF, males plus females. Online Supporting Material
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66 S2 Figure 1. Association of glycemic index and risk of type 2 diabetes among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: African Am, African Americans. Online Supporting Material
S2 Figure 2. Association of glycemic index and risk of coronary heart disease among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate Abbreviations: African Am, African Americans. Online Supporting Material
S2 Figure 3. Association of glycemic index and risk of stroke among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Online Supporting Material
S2 Figure 4. Association of glycemic index and risk of mortality among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Online Supporting Material
S2 Figure 5. Association of glycemic load and risk of type 2 diabetes among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: African Am, African Americans.
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67 Online Supporting Material
S2 Figure 6. Association of glycemic load and risk of coronary heart disease among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Online Supporting Material
S2 Figure 7. Association of glycemic load and risk of stroke among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Online Supporting Material
S2 Figure 8. Association of glycemic load and risk of mortality among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Online Supporting Material
S2 Figure 9. Association of glycemic index with high BMI and risk of type 2 diabetes among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GI, glycemic index; BMI, body mass index. Online Supporting Material
S2 Figure 10. Association of glycemic index with high BMI and risk of coronary heart disease among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate.
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68 Abbreviations: GI, glycemic index; BMI, body mass index. Online Supporting Material
S2 Figure 11. Association of glycemic index with high BMI and mortality among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GI, glycemic index; BMI, body mass index. Online Supporting Material
S2 Figure 12. Association of glycemic load with high BMI and risk of coronary heart disease among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S2 Figure 13. Association of glycemic load with high BMI and risk of mortality among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S2 Figure 14. Association of carbohydrate and risk of type 2 diabetes among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate.
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69 Abbreviations: GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S2 Figure 15. Association of carbohydrate intake and risk of coronary heart disease among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
S2 Figure 16. Association of carbohydrate intake and risk of stroke among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
S2 Figure 17. Association of carbohydrate intake and risk of mortality among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects metaanalysis. Online Supporting Material
S2 Figure 18. Association of high glycemic carbohydrate intake and risk of coronary heart disease among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
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S2 Figure 19. Association of high glycemic carbohydrate intake and risk of stroke among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
S2 Figure 20. Association of high glycemic carbohydrate intake and risk of mortality among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
S2 Figure 21. Association of total dietary fiber and risk of type 2 diabetes among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Blank entries indicate data were not present in published studies. Online Supporting Material
S2 Figure 22. Association of total dietary fiber intake and risk of coronary heart disease among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
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71 S2 Figure 23. Association of cereal fiber and risk of type 2 diabetes among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Blank entries indicate data were not present in published studies. Online Supporting Material
S2 Figure 24. Association of cereal fiber intake and risk of coronary heart disease among males within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
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72 S3 Figure 1. Association of glycemic index and risk of type 2 diabetes among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate Abbreviations: African Am, African Americans. Online Supporting Material
S3 Figure 2. Association of glycemic index and risk of coronary heart disease among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate Abbreviations: African Am, African Americans. Online Supporting Material
S3 Figure 3. Association of glycemic index and risk of stroke among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Online Supporting Material
S3 Figure 4. Association of glycemic index and risk of mortality among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Online Supporting Material
S3 Figure 5. Association of glycemic load and risk of type 2 diabetes among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: African Am, African Americans.
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73 Online Supporting Material
S3 Figure 6. Association of glycemic load and risk of coronary heart disease among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Online Supporting Material
S3 Figure 7. Association of glycemic load and risk of stroke among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Online Supporting Material
S3 Figure 8. Association of glycemic load and risk of mortality among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Online Supporting Material
S3 Figure 9. Association of glycemic index with high BMI and risk of type 2 diabetes among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GI, glycemic index; BMI, body mass index. Online Supporting Material
S3 Figure 10. Association of glycemic index with high BMI and risk of coronary heart disease among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate.
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74 Abbreviations: GI, glycemic index; BMI, body mass index. Online Supporting Material
S3 Figure 11. Association of glycemic index with high BMI and risk of stroke among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GI, glycemic index; BMI, body mass index. Online Supporting Material
S3 Figure 12. Association of glycemic index with high BMI and mortality among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GI, glycemic index; BMI, body mass index. Online Supporting Material
S3 Figure 13. Association of glycemic load with high BMI and risk of type 2 diabetes among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S3 Figure 14. Association of glycemic load with high BMI and risk of coronary heart disease among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies.
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75 Online Supporting Material
S3 Figure 15. Association of glycemic load with high BMI and risk of stroke among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S3 Figure 16. Association of glycemic load with high BMI and risk of mortality among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S3 Figure 17. Association of carbohydrate and risk of type 2 diabetes among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S3 Figure 18. Association of carbohydrate intake and risk of coronary heart disease among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis.
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76 Online Supporting Material
S3 Figure 19. Association of carbohydrate intake and risk of stroke among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects metaanalysis. Online Supporting Material
S3 Figure 20. Association of carbohydrate intake and risk of mortality among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects metaanalysis. Online Supporting Material
S3 Figure 21. Association of high glycemic carbohydrate and risk of type 2 diabetes among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Abbreviations: GL, glycemic load; BMI, body mass index. Blank entries indicate data were not present in published studies. Online Supporting Material
S3 Figure 22. Association of high glycemic carbohydrate intake and risk of coronary heart disease among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
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S3 Figure 23. Association of high glycemic carbohydrate intake and risk of stroke among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
S3 Figure 24. Association of high glycemic carbohydrate intake and risk of mortality among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
S3 Figure 25. Association of total dietary fiber and risk of type 2 diabetes among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Blank entries indicate data were not present in published studies. Online Supporting Material
S3 Figure 26. Association of total dietary fiber intake and risk of coronary heart disease among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
S3 Figure 27. Association of total dietary fiber intake and risk of stroke among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed
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78 using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects metaanalysis. Online Supporting Material
S3 Figure 28. Association of cereal fiber and risk of type 2 diabetes among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. Blank entries indicate data were not present in published studies. Online Supporting Material
S3 Figure 29. Association of cereal fiber intake and risk of coronary heart disease among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
S3 Figure 30. Association of cereal fiber intake and risk of stroke among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects meta-analysis. Online Supporting Material
S3 Figure 31. Association of cereal fiber intake and risk of mortality among females within world geographic locations of the US, Europe, and Asia (highest hazard ratio compared with lowest hazard ratio). The pooled estimate was computed using the DerSimonian and Laird random-effects meta-analysis method. I-squared indicates the proportion of heterogeneity between studies. The dashed vertical line represents the pooled estimate. The pooled estimate was based on random-effects metaanalysis. Online Supporting Material
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79 S4 Figure 1. Dose-response trend for total dietary fiber. A parametric dose-response model based on summarized data for linear dose-response relationships for total dietary fiber and T2DM, performed in drmeta. The Hamling’s method of covariance structure, random-effects, and maximum likelihood features were specified. Effect measures for risk ratios were calculated. Online Supporting Material
S4 Figure 2. Dose-response trend for cereal fiber. A parametric dose-response model based on summarized data for linear dose-response relationships for cereal fiber and T2DM, performed in drmeta. The Hamling’s method of covariance structure, random-effects, and maximum likelihood features were specified. Effect measures for risk ratios were calculated. Online Supporting Material
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80 S5 Figure 1. GI T2DM Europe. Trim and filled data with 12 estimates (as indicated by the circles on the graph. The ‘trim and fill’ method is a rank data imputation technique that estimates the number and outcomes of missing studies. This method adjusts the meta-analysis to incorporate the imputed data to re-estimate the overall meta-analytic effect using the random-effects method. In this analysis, no studies were imputed. Publication bias test results from Begg (z=1.03; p=0.304) and Egger (Slope=-0.107; 95% Confidence Interval (CI): -0.374, 0.159; p=0.391) showed that publication bias was not present. As noted in the Eggers test, bias was not significant (Bias: 0.966; -0.876, 2.808). Abbreviation: GI, glycemic index; T2DM, type 2 diabetes. Online Supporting Material
S5 Figure 2. GI CHD Europe. Trim and filled data with 10 estimates (as indicated by the circles on the graph) and one imputed study (as depicted by the grey circles on the graph). The ‘trim and fill’ method is a rank data imputation technique that estimates the number and outcomes of missing studies. This method adjusts the meta-analysis to incorporate the imputed data to re-estimate the overall meta-analytic effect using the random-effects method. In this analysis, no studies were imputed. Publication bias test results from Begg (z=1.25; p=0.210) and Egger (Slope=-0116; 95% Confidence Interval (CI): -0.295, 0.064; p=0.175) showed that publication bias was not present. As noted in the Eggers test, bias was not significant (Bias: 1.75; -0.270, 3.773). Abbreviation: GI, glycemic index; CHD, coronary heart disease. Online Supporting Material
S5 Figure 3. GL T2DM Europe. Trim and filled data with 10 estimates for nine observed studies (as indicated by the circles on the graph). There were no imputed studies. The ‘trim and fill’ method is a rank data imputation technique that estimates the number and outcomes of missing studies. This method adjusts the meta-analysis to incorporate the imputed data to re-estimate the overall meta-analytic effect using the random-effects method. Publication bias test results from Begg (z=0.54 p=0.592) and Egger (Slope=-0.089; 0.468, 0.290; p=0.602). As noted in the Eggers test, bias was not significant (Bias: 0.687; -1.608, 2.983; p=0.509). Abbreviation: GL, glycemic load; T2DM, type 2 diabetes. Online Supporting Material
S5 Figure 4. Total dietary fiber T2DM US.
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81 Trim and filled data with 12 observed studies. There were six imputed studies. The ‘trim and fill’ method is a rank data imputation technique that estimates the number and outcomes of missing studies. This method adjusts the meta-analysis to incorporate the imputed data to re-estimate the overall meta-analytic effect using the random-effects method. Publication bias test results from Begg (z=0.48; p=0.631) and Egger (Slope=0.010; -0.007, 0.028; p=0.219) showed that publication bias was not present. As noted in the Eggers test, the assemetry was due to small study effects that was statistically significant (p=0.005) . Bias was significant (Bias= -1.78; -2.905, -0.660; p=0.005). Abbreviation: T2DM, type 2 diabetes. Online Supporting Material
S5 Figure 5. Cereal Fiber T2DM US. Trim and filled data with 13 estimates for 7 observed studies and nine imputed studies. The ‘trim and fill’ method is a rank data imputation technique that estimates the number and outcomes of missing studies. This method adjusts the meta-analysis to incorporate the imputed data to re-estimate the overall meta-analytic effect using the random-effects method. Publication bias test results from Begg (z=1.77; p=0.077) and Egger (Slope=-0.004; -0.078, 0.073; p=0.918 ) showed that publication bias was not present. As noted in the Eggers test, the assemetry was due to small study effects that was statistically significant at p=0.013. (Bias= -2.32; -4.056, -0.588; p=0.013). Abbreviation: T2DM, type 2 diabetes. Online Supporting Material
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PRISMA 2009 Flow Diagram
Identification
Records identified through database searching from Medline, Ovid Medline, PubMed, EBSCOhost (n = 7,012)
Additional records identified through other sources (n = 6)
• • •
Duplicates removed (n= 418)
• •
Screening
Records screened based on title and abstract (n = 6,558)
Full-text articles assessed for eligibility (n = 75)
Eligibility
Studies included in quantitative synthesis (meta-analysis) (n = 40)
Included
US studies (n = 13)
Included dietary variables: GI (n = 11) GL (n = 18) High BMI & GI (n = 4) High BMI & GL (n = 5) CHO (n = 8) High glycemic CHO (n = 2) Dietary fiber (n = 16) Cereal fiber (n = 18)
Included disease outcomes: Type 2 diabetes (n = 16) Coronary heart disease (n = 5) Stroke (n = 2) Mortality (n = 1)
•
• • • • •
Records excluded (n = 6,483) Abstract (n = 23) Clinical trials: phase 1-VI (n = 2204) Other experimental studies, different disease outcomes, e.g. cancer (n = 2,294) Case reports (n = 66) Reviews, meta-analysis, commentaries, letters, genotype studies (n = 1,892) Books/documents (n = 4)
Full-text articles excluded, with reasons (n = 35) Cross-sectional or case control studies (n=7) Studies with estimates besides hazard ratios such as odds ratios, linear regression (n=16) Studies that used correlations and residuals (n=5) Studies that used similar outcomes such as cardiovascular disease that was difficult to tease apart for analysis (n=4) Older studies that used the same dataset on the same outcomes and same dietary exposure (n= 3)
European studies (n = 22)
Asian studies (n = 5)
Included dietary variables: GI (n = 34) GL (n = 36) High BMI & GI (n = 3) High BMI & high GL (n = 6) CHO (n = 21) High glycemic CHO (n = 22) Dietary fiber (n = 6) Cereal fiber (n = 5)
Included dietary variables: GI (n = 10) GL (n = 10) GI and high BMI (n = 4) GL and high BMI (n = 3) CHO (n = 8) High glycemic CHO (n = 8) Dietary fiber (n = 1) Cereal fiber (n = 0)
Included disease outcomes: Type 2 diabetes (n = 15) Coronary heart disease (n = 11) Stroke (n = 11) Mortality (n = 6)
Included disease outcomes: Type 2 diabetes (n = 3) Coronary heart disease (n = 2) Stroke (n = 2) Mortality (n = 3)
From: Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group (2009). Preferred Reporting Items for Systematic Reviews and MetaAnalyses: The PRISMA Statement. PLoS Med 6(7): e1000097. doi:10.1371/journal.pmed1000097
For more information, visit www.prisma-statement.org.
Highlights •
High Glycemic index & glycemic load increased risk of type 2 diabetes in US studies
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High glycemic load increased risk of coronary heart disease in European female studies
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Glycemic index and glycemic load had the highest risks in overweight/obese persons
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Dietary and cereal fibers were protective against type 2 diabetes in US studies
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Cereal fiber dose-response was protective against type 2 diabetes in US studies