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Dietary fiber intake and risk of metabolic syndrome: A meta-analysis of observational studies
Clinical Nutrition xxx (2017) 1e8
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Clinical Nutrition journal homepage: http://www.elsevier.com/locate/clnu
Meta-analyses
Dietary fiber intake and risk of metabolic syndrome: A meta-analysis of observational studies Baozhu Wei a, 1, Yang Liu b, 1, Xuan Lin b, Ying Fang b, Jing Cui b, Jing Wan a, * a b
Department of Cardiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, China Department of Endocrinology, China Resources and WISCO General Hospital, Wuhan, China
a r t i c l e i n f o
s u m m a r y
Article history: Received 31 January 2017 Accepted 21 October 2017
Background & aims: Epidemiological studies show inconsistent findings on the association of dietary fiber intake with risk of metabolic syndrome (MetS). Herein, we aim to conduct a meta-analysis of published studies to determine the role of dietary fiber in prevention of MetS. Methods: A systematical search in PubMed and Embase databases through December 2016, together with reference scrutiny of relevant literature, was performed to identify studies for inclusion. We aggregated the odds ratios (ORs) with 95% confidence intervals (CIs) of MetS using a random effect model. Doseeresponse relationship between fiber intake and MetS was also evaluated. Results: This meta-analysis included 8 cross-sectional and 3 cohort studies, totaling 28,241 participants and 9140 MetS cases. The highest versus lowest fiber intake was associated with a reduced risk of MetS (OR: 0.85, 95% CI: 0.79e0.92; P ¼ 0.005), with moderate heterogeneity (I2 ¼ 64%, P ¼ 0.001) across studies. The benefit of fiber intake was significant among cross-sectional studies (OR: 0.85, 95% CI: 0.78 e0.92; P < 0.001) but not among cohort studies (OR: 0.86, 95% CI: 0.70e1.06; P ¼ 0.16). In doseeresponse analysis, we found a curvilinear relationship between fiber consumption and prevalence of MetS. Compared with non-fiber intake, the ORs (95% CIs) of MetS across fiber intake levels were 0.85 (0.79 e0.91), 0.76 (0.67e0.85), 0.73 (0.65e0.83), and 0.73 (0.65e0.82) for 10, 20, 30, and 40 g/d, respectively. Conclusions: Dietary fiber intake is associated with less likelihood of having MetS. Additional large, prospective studies are warranted to enhance our findings. © 2017 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.
Keywords: Dietary fiber Metabolic syndrome Meta-analysis Prevention
1. Introduction Metabolic syndrome (MetS) represents a concurrence of interrelated metabolic disorders, including abdominal obesity, dyslipidemia, hypertension, and hyperglycemia. The incidence of MetS has rapidly increased worldwide, with an estimated prevalence of 35% in the United States, 18%e34% in Europe, and 13%e41% in Asia [1]. Globally, there is approximately 20%e25% of the world's adult population suffering from this syndrome [2]. MetS has been found to confer elevated risks of cardiovascular disorders, type 2 diabetes mellitus, and all deaths [3,4]. In addition, some evidence also
Abbreviations: AHRQ, Agency for Healthcare Research Quality; ATP, Adult Treatment Panel; IDF, International Diabetes Federation; MetS, metabolic syndrome; NOS, Newcastle-Ottawa Scale. * Corresponding author. No. 169 Donghu Road, Wuchang District, Wuhan City 430071, Hubei Province, China. Fax: þ86 027 67813073. E-mail address: [email protected] (J. Wan). 1 Both authors contributed equally to this work.
pointed out that MetS is correlated with enhanced risks of common cancers, such as breast and colorectal cancers [5]. Due to these features, controlling the development of MetS is of great significance for public health. Among the various preventive strategies against MetS, lifestyle modifications, especially healthy diet, have been attracting great attentions. Dietary consumptions of vegetables, fruits, fish, dairy products, coffee, and tea have offered a protective role against MetS development [6e8]. Dietary fiber, an important ingredient of a healthy diet, is commonly obtained from consuming cereals, fruits, and vegetables. Accumulating evidence suggest that intake of dietary fiber can reduce risks of cardiovascular disease, type 2 diabetes mellitus, and some cancers [9]. More importantly, dietary fiber has salutary effects on individual components of MetS, including body weight regulation, lipid reduction, improved glucose metabolism, and blood pressure control [10]. Dietary fiber also suppresses oxidative stress and inflammation [11], both of which have been linked to the development of MetS [12]. However, whether this nutrient is associated with the overall risk of MetS remains unclear. A
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Please cite this article in press as: Wei B, et al., Dietary fiber intake and risk of metabolic syndrome: A meta-analysis of observational studies, Clinical Nutrition (2017), https://doi.org/10.1016/j.clnu.2017.10.019
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mounting body of studies has been performed to explore the relationship of fiber intake with MetS, but the results are inconsistent. Some of them showed that dietary fiber was inversely correlated with risk of MetS [13e18], while others not [19e23]. Herein, we performed this meta-analysis to comprehensively estimate the association of fiber intake with MetS. We hypothesized that high consumption of dietary fiber may reduce the prevalence of MetS. 2. Materials and methods 2.1. Search strategy This study was performed followed the Meta-analysis Of Observational Studies in Epidemiology guideline [24]. We systematically searched the PubMed and Embase databases from inception to December 2016 to identify publications for inclusion. The MeSH terms used included “dietary fiber” and “metabolic syndrome X”. We combined these terms with free text word searches that included “fiber”, “fibre”, “metabolic syndrome”, “MetS”, “cardiometabolic”, and “cardiovascular risk factor” (see detailed search strategy in Supplementary Table 1). The reference lists of retrieved literature were also checked for including additional studies. Only studies that were published in English with full-text form available were enrolled in this meta-analysis. The search process was performed by 2 independent investigators (B.W. and Y.L.). 2.2. Eligibility of studies Studies that met all of the following conditions were included: 1) the study design was cohort, caseecontrol, or cross-sectional; 2) the exposure of interest was dietary fiber intake; 3) the outcome of interest included MetS; 4) the risk estimates of MetS, such as odds ratios (ORs) or risk ratios (RRs), have been reported. We also planned to include randomized trials that investigated the impacts of fiber supplementation on risk of MetS; however, no randomized trials met this requirement. For doseeresponse analysis, the risk effect size should be provided for 3 quantitative categories of fiber intake, and the numbers of cases and participants or person-years for each category were also available (or details were provided to calculate them). Reviews, abstracts, comments, unpublished results, and studies of children or adolescents were excluded. When reports pertained to overlapping participants, we included only the study with larger population to avoid duplication of data.
quality. Any disagreements in data abstraction and quality evaluation were resolved by consensus between the 2 reviewers or consulting with a third reviewer (X.L.). 2.4. Statistical analyses The summarized risk estimates of MetS were reported as OR with its 95% confidence interval (CI). We pooled the risk estimates of MetS for highest versus lowest total fiber and fiber subtypes intakes using a random effect model. The study by HosseinpourNiazi et al. [20] did not provide the separate risks of MetS for soluble and insoluble fibers; thus, we used the data of their previous report [27] instead, to evaluate the effect of fiber subtypes. Heterogeneity across the included studies was detected by the Cochrane Q test, with a P value < 0.1 suggesting significance. We also reported the heterogeneity as low, moderate, and high with I2 values of 25%, 50%, and 75%, respectively [28]. Subgroup analyses were performed according to sample size (2000 or <2000), study design (cross-sectional or cohort studies), location (America, Europe, or Asia), MetS definition (Adult Treatment Panel III [ATP III] or other criteria), degree of adjustment (10 or <10 variables), and mean age (60 or <60 years). Sensitivity analyses, including removals of individual study, low-quality studies, and studies of diabetic patients, were conducted to appraise the robustness of our results. Publication bias was evaluated by funnel plot analyses and further confirmed by Egger's tests. If there was publication bias, we used the “trim and fill” strategy to adjust the funnel plot and then re-computed the results [29]. To assess the potential doseeresponse pattern between fiber intake and MetS risk, we performed a 2-stage random-effects doseeresponse meta-analysis. In the first stage, we estimated a restricted cubic spline model with 3 knots at the 10th, 50th, and 90th percentiles of the fiber intake using generalized least-square regression, considering the correlation within each set of published ORs as proposed by Orsini et al. [30]. Then the study-specific ORs were pooled using the restricted maximum likelihood method in a random-effects meta-analysis [31]. A P-value for nonlinearity was calculated by testing the null hypothesis that the coefficient of the second spline is equal to 0. All data analyses were realized with STATA 12.0 (StataCorp, College Station, TX, USA) and R 3.2.5 (The R Foundation for Statistical Computing, Vienna, Austria) softwares, and P values < 0.05 were considered as significant. 3. Results
2.3. Data collection and quality evaluation 3.1. Search process Two reviewers (B.W. and Y.L.) independently extracted the information on study characteristics, including study author, publication year, study design and population, number of participants and cases, percentage of men, baseline age, MetS definition criteria, ascertainments of fiber intake and Mets, study location, and factors adjusted in multivariable models. The maximally adjusted risk estimates were also recorded for pooling analyses. When necessary, the corresponding authors of the included studies were e-mailed for missing data. To evaluate the methodological quality, we used an 11-item checklist recommended by Agency for Healthcare Research Quality (AHRQ) [25] for cross-sectional studies and Newcastle-Ottawa Scale (NOS) [26] for cohort studies. For each cross-sectional study, an item of AHRQ checklist can score 1 point if it was answered “yes”; otherwise, score 0 point. The NOS score for each cohort study was calculated according to 3 major aspects: selection of participants (0e4 points), adjustment for confounders (0e2 points), and ascertainment of outcomes (0e3 points). A study with an AHRQ score 8 or NOS score 7 was regarded as of high
We initially identified 1786 publications, of which 729 duplicates and 993 irrelevant studies were discarded. Among the remaining 64 articles selected for full-text reading, 53 articles that failed to meet the eligibility criteria were eliminated. Besides, the study by Moreno-Franco et al. [22] reported only the separate risks of MetS for soluble and insoluble fibers, thus it was treated as 2 individual reports. In total, 11 studies [13e23] with 12 reports were included in the final analyses (Fig. 1). 3.2. Study characteristics and quality evaluation The baseline characteristics of eligible studies are listed in Table 1. Briefly, the set of studies consists of 8 cross-sectional and 3 cohort studies, totaling 28,241 participants and 9140 MetS cases. The age range was 18e84 years, and men accounted for 54% of the total participants. Five studies were conducted in America, 3 in Europe, and 3 in Asia. With regard to MetS definition, 8 studies
Please cite this article in press as: Wei B, et al., Dietary fiber intake and risk of metabolic syndrome: A meta-analysis of observational studies, Clinical Nutrition (2017), https://doi.org/10.1016/j.clnu.2017.10.019
IdenƟficaƟon
B. Wei et al. / Clinical Nutrition xxx (2017) 1e8
Records idenƟfied from databases search (n = 1778)
3
Records idenƟfied from other sources (n=8)
Screening
Records aŌer duplicates removed (n=1057)
Records screened (n = 1057)
Eligibility
993 records were excluded based on Ɵtles or abstracts Records selected for full-text reading (n = 64)
Inclusion
53 records excluded: 4 review arƟcles 27 did not study fiber intake as an exposure 15 did not study MetS as an outcome 4 duplicated samples or cohorts 2 no risk esƟmates reported 1 study of adolescents Studies included in this meta-analysis (n = 11) Fig. 1. Flow diagram of study search process.
adopted the ATP III criteria or its adapted version [32,33], 3 studies adopted the International Diabetes Federation (IDF) criteria [34], and 1 study used the harmonized definition [35]. Of the included reports, all performed health examination to ascertain MetS conditions, and 6 assessed dietary factors using food frequency questionnaire. The most commonly adjusted confounders in the studies were age, gender, smoking, alcohol consumption, and total calorie intake. The detailed quality assessment is shown in Supplementary Tables 2 and 3. In general, there were 5 of the cross-sectional studies [13e16,21] and 2 of the cohort studies [19,20] having a high quality. The mean AHRQ score was 7.5 for cross-sectional studies and the mean NOS score was 7.3 for cohort studies. 3.3. High versus low analyses Data pooling indicated that compared to the lowest intake, the highest intake of dietary fiber was correlated with a decreased prevalence of MetS (OR: 0.85, 95% CI: 0.79e0.92, P ¼ 0.005; Fig. 2), with moderate heterogeneity across the studies (I2 ¼ 64%, P ¼ 0.001). Among fiber subtypes, the summarized OR (95% CI) of developing MetS was 0.80 (0.67e0.96) for cereal fiber, 0.85 (0.77e0.94) for fruit fiber, 1.07 (0.88e1.31) for vegetable fiber, 0.78 (0.62e0.98) for soluble fiber, and 0.55 (0.42e0.73) for insoluble fiber (Figs. 3 and 4). In the subgroup analysis for study locations, inverse associations of dietary fiber intake with MetS were seen in America (OR: 0.84, 95% CI: 0.73e0.97, P ¼ 0.01) and in Europe (OR: 0.62, 95% CI: 0.49e0.79, P < 0.001), but not in Asia (OR: 0.94, 95% CI: 0.88e1.01,
P ¼ 0.09). Also, the stratified analysis based on study design indicated that the association between fiber intake and prevalence of MetS was significant among cross-sectional studies (OR: 0.85, 95% CI: 0.78e0.92; P < 0.001) but not among cohort studies (OR: 0.86, 95% CI: 0.70e1.06, P ¼ 0.16). Other subanalyses, such as that for sample size, MetS definition, degree of adjustment, and mean age, showed consistent results. Relevant details are exhibited in Table 2. 3.4. Sensitivity analyses and publication bias Sensitivity analysis by excluding each study in sequence had no influence on the overall results. Exclusion of low-quality studies [17,18,22,23] or studies of diabetic patients [15,18,23] did not alter the inverse relationship between fiber intake and MetS. The funnel plot was visually asymmetric with the P value of Egger's test ¼ 0.001, suggesting the potential of publication bias. After introducing the “trim and fill” strategy to correct this bias, the overall risk estimate was still significant in favor of dietary fiber intake (OR: 0.90, 95% CI: 0.83e0.98, P ¼ 0.02; Supplementary Fig. 1). 3.5. Doseeresponse analyses Eight studies [13,14,16,17,19e22] were selected for the dosee response meta-analysis. We found a nonlinear association between dietary fiber intake and risk of MetS (P for nonlinearity < 0.001; Fig. 5). The summarized OR (95% CI) of MetS for a fiber intake of 10, 20, 30, and 40 g/d was 0.85 (0.79e0.91), 0.76 (0.67e0.85), 0.73 (0.65e0.83), and 0.73 (0.65e0.82), respectively. Additionally, we
Please cite this article in press as: Wei B, et al., Dietary fiber intake and risk of metabolic syndrome: A meta-analysis of observational studies, Clinical Nutrition (2017), https://doi.org/10.1016/j.clnu.2017.10.019
Brazil Brazil HE HE Dietary records Dietary records IDF IDF 61 (mean) 60 (mean) 50.9 48.6 109/175 174/214
Cohort
Cross-sectional Cross-sectional Silva 2011 [23] Steemburgo 2009 [18]
DM patients DM patients
Iran HE ATP III 62/160
62.5
18
FFQ
Spain HE FFQ ATP III 40e55 95.1 377/1592
Workers free of CVD Patients after RT Cross-sectional
Moreno-Franco 2014 [22] Noori 2010 [17]
USA HE FFQ ATP III 26e82 45.5 Healthy adults Cross-sectional
605/2834
USA Iran HE HE Dietary recalls FFQ ATP III ATP III 20e50 19e84 54.8 46.0 National population Healthy adults Cross-sectional Cohort
Grooms 2013 [16] Hosseinpour-Niazi 2015 [20] McKeown 2004 [21]
3780/10,473 240/1582
Japan HE Harmonized definition DM patients Cross-sectional Fujii 2013 [15]
2397/4399
56.7
20
BDHQ
USA HE Dietary history ATP III 18e30 44.6 Cohort
Adults at high risk of CVD Healthy adults Cross-sectional
Cabello-Saavedra 2014 [14] Carnethon 2004 [19]
575/4192
Spain HE FFQ ATP III IDF 55e80 44.9
Italy HE FFQ ATP III 45e64 47.2 384/1653 Healthy adults Cross-sectional Bo 2006 [13]
437/967
Adjustment Location Outcome Exposure
Ascertainment
MetS definition Age (years) Men (%) Participants (MetS/total) Population Design Study (author, year)
Table 1 Baseline characteristics of the included studies.
ATP, Adult Treatment Panel; BDHQ, brief diet history questionnaire; BMI, body mass index; CVD, cardiovascular disease; DM, diabetes mellitus; FFQ, food frequency questionnaire; HE, health examination; IDF, International Diabetes Federation; MetS, metabolic syndrome; NA, not applicable; RT, renal transplantation.
B. Wei et al. / Clinical Nutrition xxx (2017) 1e8
Age, sex, BMI, smoking, drinking, physical activity, and intake of total energy, fat, and magnesium Age, sex, DM, smoking, drinking, physical activity, education, marital status, and total energy intake Age, sex, race, education, BMI, smoking, drinking, physical activity, and intake of carbohydrate and fat Age, sex, DM duration, smoking, drinking, leisure time, physical activity, hypoglycemic agents, and intake of total energy and fat Age, sex, race, smoking, education, and total energy intake Age, sex, BMI, smoking, physical activity, intake of total energy, protein, fat, cholesterol, dairy products, meat, poultry, and fish Age, sex, smoking, drinking, physical activity, and intake of total energy, fat, and multivitamin. Age, sex, smoking, drinking, studies completed level, physical activity, work type, and total energy intake Age, sex, BMI, smoking, physical activity, dialysis mode and duration, steroids dose, menopausal status, family history of diabetes and stroke, and total energy intake Sex, BMI, hypertension, and total energy intake Sex, DM duration, physical activity, and total energy intake
4
also assumed a linear relationship between dietary fiber and MetS. The result suggested that the odds of MetS decreased by 11% (OR: 0.89, 95% CI: 0.82e0.96) for each 10-g/d increment in total fiber intake. 4. Discussion There are limited studies that have examined the role of dietary fiber in preventing MetS. The findings from our meta-analysis reveal an inverse relationship between consumption of dietary fiber and risk of MetS when comparing the highest versus lowest categories. Moreover, we also observe a curvilinear correlation of fiber intake with prevalence of MetS. Previous studies have suggested a protective role of fiber-rich food or diet in the development of MetS. Whole-grain consumption was reported to be associated with a lower risk of having MetS, while intake of refined-grain (containing low fiber) appeared to increase the prevalence of MetS [36]. A recent randomized trial also showed that dietary fiber enriched rice cakes decreased the number of MetS components [37]. Similarly, adherence to Mediterranean diet, which is high in whole-grains, fresh fruits, vegetables, and legumes, may reduce the incidence of MetS [38]. In addition, Carlson et al. performed a cross-sectional analysis enrolling 2128 adolescents, and found that high intakes of dietary fiber might lower the risk of MetS [39]. We did not include this study in our analysis because of the potential metabolic differences between adults and adolescents. There are several plausible explanations for the favorable impacts of dietary fiber on MetS development. Firstly, it is found that fiber intake confers improvements in individual components of MetS [10]. These effects may be attributed to the fact that fiber intake induces increased satiety, delayed gastric emptying, reduced absorption of macronutrients, and improved insulin sensitivity [10]. Secondly, dietary fiber has been shown to exert protective roles against oxidative stress by suppress of ROS generation [11]. It is well known that oxidative stress is involved in the pathogenesis of MetS [40]. Thirdly, fiber supplementation can favorably affect the phylogenetic structure and functional capacity of gut microbiome [41]. Previous studies have indicated that alterations in microbial community and functional shifts of gut microbiome lead to systemic inflammation and insulin resistance, thus promoting the development of MetS [42]. Therefore, the observed benefits of fiber intake may be, at least in part, mediated through its regulation of gut microbiome. Among fiber subtypes, we observed reduced risks of MetS for cereal and fruit fibers, but not for vegetable fiber. Consistent with our finding, some included studies also revealed the neutral effects of vegetable fiber on insulin resistance and MetS [20,21]. Besides, no association was detected of vegetable fiber intake with incident coronary heart disease [43] and type 2 diabetes mellitus [44]. The potential biological mechanisms for the lack of significant finding for vegetable fiber remain unclear. It is likely that the common nutrient-poor starchy vegetables, such as potatoes and peas, may lead to high glycemic load, which has been shown to increase the incidence of MetS [45]. Thus, the benefits of vegetable fiber may be offset by some adverse impact of starchy vegetables. However, because of the limited studies available for analysis of vegetable fiber, this result should be interpreted with caution. Future studies concerning these issues are needed. The quality of included studies was relatively high, but moderate heterogeneity was detected across studies. We found that the heterogeneity was decreased among studies using either the ATP III criteria or other criteria, suggesting that different definition methods of MetS may account for the heterogeneity in our study. Nevertheless, an inverse correlation between fiber intake and risk
Please cite this article in press as: Wei B, et al., Dietary fiber intake and risk of metabolic syndrome: A meta-analysis of observational studies, Clinical Nutrition (2017), https://doi.org/10.1016/j.clnu.2017.10.019
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Fig. 2. Odds ratio of MetS for the highest versus lowest intake of dietary fiber.
of MetS was identified in both subsets of the diagnostic criteria. Although the prevalence of MetS was lower when using the ATP III criteria than the IDF criteria and harmonized definition, there is generally good concordance between all these 3 MetS criteria [46]. This fact may explain why moderate heterogeneity was observed
but not significantly affect our findings. Because the age ranges of participants in the studies differ so much from each other, we also performed a subanalysis according to the mean age. The beneficial effects of fiber intake on MetS were present in adults older or younger than 60 years, suggesting that our result is less likely to be
Fig. 3. Odds ratio of MetS for the highest versus lowest intake of cereal, fruit, and vegetable fibers.
Please cite this article in press as: Wei B, et al., Dietary fiber intake and risk of metabolic syndrome: A meta-analysis of observational studies, Clinical Nutrition (2017), https://doi.org/10.1016/j.clnu.2017.10.019
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Fig. 4. Odds ratio of MetS for the highest versus lowest intake of soluble and insoluble fibers.
influenced by the age difference. In addition to adults, previous studies have also confirmed the favorable impacts of dietary fiber on physiologic activities in elderly persons, including metabolic properties and microbiome functions [47]. Thus, dietary fiber can function well in both adults and the elderly, although potential differences in metabolism and microbial composition are seen between these 2 age groups. In doseeresponse meta-analysis, there appears to be no additional reduction in odds of MetS if the fiber intake is increased over 30 g/d. A possible explanation is that excessive fiber intake may negatively affect the salutary nutrients intake by extending satiety and binding up minerals through fiber-related phytates and oxalates [48]. It should be noted that there are few risk estimates available for the fiber intake levels of more than 30 g/d, potentially leading to mis-modeling of the exact doseeresponse relationship. Therefore, we also assumed a linear correlation between dietary
fiber and MetS, and each 10 g/d increase in fiber intake was found to be associated with a decreased risk of MetS. Our work has several strengths. To date, this is the first metaanalysis that explored the relationship between dietary fiber intake and prevalence of MetS. In addition, the inverse association of fiber intake with MetS was observed not only considering fiber intake as a categorical variable but also in a doseeresponse pattern. However, we should not ignore the limitations of the current study. First of all, the recall and selection bias cannot be eliminated owning to the observational nature of included studies. Cohort studies are less susceptible to such bias, but only 3 cohort studies were included in our study, and the summary estimate of these studies was not significant. Secondly, there are few studies available to perform stratified analyses by fiber subtypes and other variables, which may cause inaccurate evaluations of their effects on risk of MetS. Likewise, due to the lack of relevant data, we cannot conduct subanalyses by some important covariates, such as gender.
Table 2 Subgroup analyses for the association between fiber intake and MetS. Subgroup
Sample size 2000 < 2000 Study design Cross-sectional Cohort Location America Europe Asia MetS definition ATP III Other Degree of adjustment 10 confounders <10 confounders Mean age 60 year <60 year
No. of reports
Heterogeneity
Pooled estimates
I2
P
OR (95% CI)
P
4 8
74.7 62.3
0.01 0.01
0.86 (0.74, 0.99) 0.72 (0.57, 0.89)
0.03 0.003
9 3
72.1 0
<0.001 0.50
0.85 (0.78, 0.92) 0.86 (0.70, 1.06)
<0.001 0.16
5 4 3
70.2 3.5 2.8
0.01 0.38 0.36
0.84 (0.73, 0.97) 0.62 (0.49, 0.79) 0.94 (0.88, 1.01)
0.01 <0.001 0.09
9 4
10.3 49.4
0.35 0.12
0.76 (0.68, 0.85) 0.94 (0.89, 0.98)
<0.001 0.01
4 8
0 74.5
0.52 <0.001
0.95 (0.91, 0.99) 0.74 (0.63, 0.88)
0.01 <0.001
4 7
60.6 10.9
0.05 0.35
0.93 (0.88, 0.99) 0.76 (0.65, 0.90)
0.02 0.001
ATP, Adult Treatment Panel; CI, confidence interval; MetS, metabolic syndrome; OR, odds ratio.
Fig. 5. Doseeresponse analysis for the association between fiber intake and MetS.
Please cite this article in press as: Wei B, et al., Dietary fiber intake and risk of metabolic syndrome: A meta-analysis of observational studies, Clinical Nutrition (2017), https://doi.org/10.1016/j.clnu.2017.10.019
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Thirdly, publication bias is present in our work because small studies with null findings is less likely to be published. However, the result remained significant in favor of fiber intake after introducing the “trim and fill” method to adjust publication bias. Fourthly, the results of doseeresponse analysis may be unstable when some biases are present, including publication bias, as mentioned by Orsini et al. [30]. In conclusion, dietary fiber intake is associated with a decreased risk of MetS. The result supports the current recommendation of fiber intake for preventing chronic disease. Considering the limitations of this meta-analysis, additional large, prospective studies are warranted to enhance our findings, especially regarding the effect of vegetable fiber and the doseeresponse relationship between fiber intake and MetS. Statement of authorship J.W. conceived and designed the study. B.W. and Y.L. acquired the data. B.W., Y.L., and X.L. did statistic analysis. B.W. drafted the manuscript. Y.F. and J.C. made critical revision of the manuscript for key intellectual content, with support from J.W. All authors approved the final version of this article. Funding sources This work was supported by a grant from the National Natural Science Foundation of China to Jing Wan [NSFC, Grant No. 81670409]. Conflict of interest The authors declare no conflict of interest. Acknowledgements The authors thank Qr. Cheng Qian (Department of Cardiology, Zhongnan Hospital of Wuhan University, Wuhan, China) for providing statistical assistance. Appendix A. Supplementary data Supplementary data related to this article can be found at https://doi.org/10.1016/j.clnu.2017.10.019. References [1] Dai L, Goncalves CM, Lin Z, Huang J, Lu H, Yi L, et al. Exploring metabolic syndrome serum free fatty acid profiles based on GC-SIM-MS combined with random forests and canonical correlation analysis. Talanta 2015;135:108e14. [2] Eckel RH, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet 2005;365: 1415e28. [3] O'Neill S, O'Driscoll L. Metabolic syndrome: a closer look at the growing epidemic and its associated pathologies. Obes Rev 2015;16:1e12. [4] Li Z, Yang X, Yang J, Yang Z, Wang S, Sun F, et al. The cohort study on prediction of incidence of all-cause mortality by metabolic syndrome. PLoS One 2016;11:e0154990. [5] Mendonca FM, de Sousa FR, Barbosa AL, Martins SC, Araujo RL, Soares R, et al. Metabolic syndrome and risk of cancer: which link? Metabolism 2015;64: 182e9. [6] Cheraghi Z, Mirmiran P, Mansournia MA, Moslehi N, Khalili D, Nedjat S. The association between nutritional exposures and metabolic syndrome in the Tehran Lipid and Glucose Study (TLGS): a cohort study. Public Health 2016;140:163e71. [7] Kim YS, Xun P, He K. Fish consumption, long-chain omega-3 polyunsaturated fatty acid intake and risk of metabolic syndrome: a meta-analysis. Nutrients 2015;7:2085e100. [8] Marventano S, Salomone F, Godos J, Pluchinotta F, Del Rio D, Mistretta A, et al. Coffee and tea consumption in relation with non-alcoholic fatty liver and metabolic syndrome: a systematic review and meta-analysis of observational studies. Clin Nutr 2016;35:1269e81.
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[18] Steemburgo T, Dall'Alba V, Almeida JC, Zelmanovitz T, Gross JL, de Azevedo MJ. Intake of soluble fibers has a protective role for the presence of metabolic syndrome in patients with type 2 diabetes. Eur J Clin Nutr 2009;63: 127e33. [19] Carnethon MR, Loria CM, Hill JO, Sidney S, Savage PJ, Liu K, et al. Risk factors for the metabolic syndrome: the coronary artery risk development in young adults (CARDIA) study, 1985-2001. Diabetes Care 2004;27:2707e15. [20] Hosseinpour-Niazi S, Mirmiran P, Mirzaei S, Azizi F. Cereal, fruit and vegetable fibre intake and the risk of the metabolic syndrome: a prospective study in the Tehran Lipid and Glucose Study. J Hum Nutr Diet 2015;28:236e45. [21] McKeown NM, Meigs JB, Liu S, Saltzman E, Wilson PW, Jacques PF. Carbohydrate nutrition, insulin resistance, and the prevalence of the metabolic syndrome in the Framingham offspring cohort. Diabetes Care 2004;27: 538e46. [22] Moreno Franco B, Leon Latre M, Andres Esteban EM, Ordovas JM, Casasnovas JA, Penalvo JL. Soluble and insoluble dietary fibre intake and risk factors for metabolic syndrome and cardiovascular disease in middle-aged adults: the AWHS cohort. Nutr Hosp 2014;30:1279e88. [23] Silva FM, Steemburgo T, de Mello VD, Tonding SF, Gross JL, Azevedo MJ. High dietary glycemic index and low fiber content are associated with metabolic syndrome in patients with type 2 diabetes. J Am Coll Nutr 2011;30:141e8. [24] Stroup DF, Berlin JA, Morton SC, Olkin I, Williamson GD, Rennie D, et al. Metaanalysis of observational studies in epidemiology: a proposal for reporting. Meta-analysis of Observational Studies in Epidemiology (MOOSE) group. JAMA 2000;283:2008e12. [25] Zeng X, Zhang Y, Kwong JS, Zhang C, Li S, Sun F, et al. The methodological quality assessment tools for preclinical and clinical studies, systematic review and meta-analysis, and clinical practice guideline: a systematic review. J Evid Based Med 2015;8:2e10. [26] Wells GA, Shea B, O'Connell D, Peterson J, Welch V, Losos M, et al. The Newcastle-Ottawa Scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses. (Accessed 10 January 2017, at http://www.ohri.ca/ programs/clinical_epidemiology/oxford.asp). [27] Hosseinpour-Niazi S, Mirmiran P, Sohrab G, Hosseini-Esfahani F, Azizi F. Inverse association between fruit, legume, and cereal fiber and the risk of metabolic syndrome: tehran lipid and glucose study. Diabetes Res Clin Pract 2011;94:276e83. [28] Chen LH, Wang YF, Xu QH, Chen SS. Omega-3 fatty acids as a treatment for non-alcoholic fatty liver disease in children: a systematic review and metaanalysis of randomized controlled trials. Clin Nutr 2016. https://doi.org/ 10.1016/j.clnu.2016.12.009. published online December 23. [29] Duval S, Tweedie R. A nonparametric “trim and fill” method of accounting for publication bias in meta-analysis. J Am Stat Assoc 2000;95:89e98. [30] Orsini N, Li R, Wolk A, Khudyakov P, Spiegelman D. Meta-analysis for linear and nonlinear dose-response relations: examples, an evaluation of approximations, and software. Am J Epidemiol 2012;175:66e73. [31] Jackson D, White IR, Thompson SG. Extending DerSimonian and Laird's methodology to perform multivariate random effects meta-analyses. Stat Med 2010;29:1282e97. [32] Expert panel on detection, evaluation, and treatment of high blood cholesterol in adults. Executive summary of the third report of the national cholesterol education program (NCEP) expert Panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment Panel III). JAMA 2001;285:2486e97. [33] Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, et al. Diagnosis and management of the metabolic syndrome: an American heart
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[41] Holscher HD, Caporaso JG, Hooda S, Brulc JM, Fahey Jr GC, Swanson KS. Fiber supplementation influences phylogenetic structure and functional capacity of the human intestinal microbiome: follow-up of a randomized controlled trial. Am J Clin Nutr 2015;101:55e64. [42] Festi D, Schiumerini R, Eusebi LH, Marasco G, Taddia M, Colecchia A. Gut microbiota and metabolic syndrome. World J Gastroenterol 2014;20: 16079e94. [43] Wu Y, Qian Y, Pan Y, Li P, Yang J, Ye X, et al. Association between dietary fiber intake and risk of coronary heart disease: a meta-analysis. Clin Nutr 2015;34: 603e11. [44] 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;29:79e88. [45] Juanola-Falgarona M, Salas-Salvado J, Buil-Cosiales P, Corella D, Estruch R, Ros E, et al. Dietary glycemic index and glycemic load are positively associated with risk of developing metabolic syndrome in middle-aged and elderly adults. J Am Geriatr Soc 2015;63:1991e2000. [46] Erasmus RT, Soita DJ, Hassan MS, Blanco-Blanco E, Vergotine Z, Kegne AP, et al. High prevalence of diabetes mellitus and metabolic syndrome in a South African coloured population: baseline data of a study in Bellville, Cape Town. S Afr Med J 2012;102:841e4. [47] Donini LM, Savina C, Cannella C. Nutrition in the elderly: role of fiber. Arch Gerontol Geriatr 2009;49(Suppl 1):61e9. [48] Comerford KB, Ayoob KT, Murray RD, Atkinson SA. The role of avocados in complementary and transitional feeding. Nutrients 2016. https://doi.org/ 10.3390/nu8050316. published online May 21.
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Clinical Nutrition journal homepage: http://www.elsevier.com/locate/clnu
Meta-analyses
Dietary fiber intake and risk of metabolic syndrome: A meta-analysis of observational studies Baozhu Wei a, 1, Yang Liu b, 1, Xuan Lin b, Ying Fang b, Jing Cui b, Jing Wan a, * a b
Department of Cardiology, Zhongnan Hospital of Wuhan University, Wuhan University, Wuhan, China Department of Endocrinology, China Resources and WISCO General Hospital, Wuhan, China
a r t i c l e i n f o
s u m m a r y
Article history: Received 31 January 2017 Accepted 21 October 2017
Background & aims: Epidemiological studies show inconsistent findings on the association of dietary fiber intake with risk of metabolic syndrome (MetS). Herein, we aim to conduct a meta-analysis of published studies to determine the role of dietary fiber in prevention of MetS. Methods: A systematical search in PubMed and Embase databases through December 2016, together with reference scrutiny of relevant literature, was performed to identify studies for inclusion. We aggregated the odds ratios (ORs) with 95% confidence intervals (CIs) of MetS using a random effect model. Doseeresponse relationship between fiber intake and MetS was also evaluated. Results: This meta-analysis included 8 cross-sectional and 3 cohort studies, totaling 28,241 participants and 9140 MetS cases. The highest versus lowest fiber intake was associated with a reduced risk of MetS (OR: 0.85, 95% CI: 0.79e0.92; P ¼ 0.005), with moderate heterogeneity (I2 ¼ 64%, P ¼ 0.001) across studies. The benefit of fiber intake was significant among cross-sectional studies (OR: 0.85, 95% CI: 0.78 e0.92; P < 0.001) but not among cohort studies (OR: 0.86, 95% CI: 0.70e1.06; P ¼ 0.16). In doseeresponse analysis, we found a curvilinear relationship between fiber consumption and prevalence of MetS. Compared with non-fiber intake, the ORs (95% CIs) of MetS across fiber intake levels were 0.85 (0.79 e0.91), 0.76 (0.67e0.85), 0.73 (0.65e0.83), and 0.73 (0.65e0.82) for 10, 20, 30, and 40 g/d, respectively. Conclusions: Dietary fiber intake is associated with less likelihood of having MetS. Additional large, prospective studies are warranted to enhance our findings. © 2017 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.
Keywords: Dietary fiber Metabolic syndrome Meta-analysis Prevention
1. Introduction Metabolic syndrome (MetS) represents a concurrence of interrelated metabolic disorders, including abdominal obesity, dyslipidemia, hypertension, and hyperglycemia. The incidence of MetS has rapidly increased worldwide, with an estimated prevalence of 35% in the United States, 18%e34% in Europe, and 13%e41% in Asia [1]. Globally, there is approximately 20%e25% of the world's adult population suffering from this syndrome [2]. MetS has been found to confer elevated risks of cardiovascular disorders, type 2 diabetes mellitus, and all deaths [3,4]. In addition, some evidence also
Abbreviations: AHRQ, Agency for Healthcare Research Quality; ATP, Adult Treatment Panel; IDF, International Diabetes Federation; MetS, metabolic syndrome; NOS, Newcastle-Ottawa Scale. * Corresponding author. No. 169 Donghu Road, Wuchang District, Wuhan City 430071, Hubei Province, China. Fax: þ86 027 67813073. E-mail address: [email protected] (J. Wan). 1 Both authors contributed equally to this work.
pointed out that MetS is correlated with enhanced risks of common cancers, such as breast and colorectal cancers [5]. Due to these features, controlling the development of MetS is of great significance for public health. Among the various preventive strategies against MetS, lifestyle modifications, especially healthy diet, have been attracting great attentions. Dietary consumptions of vegetables, fruits, fish, dairy products, coffee, and tea have offered a protective role against MetS development [6e8]. Dietary fiber, an important ingredient of a healthy diet, is commonly obtained from consuming cereals, fruits, and vegetables. Accumulating evidence suggest that intake of dietary fiber can reduce risks of cardiovascular disease, type 2 diabetes mellitus, and some cancers [9]. More importantly, dietary fiber has salutary effects on individual components of MetS, including body weight regulation, lipid reduction, improved glucose metabolism, and blood pressure control [10]. Dietary fiber also suppresses oxidative stress and inflammation [11], both of which have been linked to the development of MetS [12]. However, whether this nutrient is associated with the overall risk of MetS remains unclear. A
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mounting body of studies has been performed to explore the relationship of fiber intake with MetS, but the results are inconsistent. Some of them showed that dietary fiber was inversely correlated with risk of MetS [13e18], while others not [19e23]. Herein, we performed this meta-analysis to comprehensively estimate the association of fiber intake with MetS. We hypothesized that high consumption of dietary fiber may reduce the prevalence of MetS. 2. Materials and methods 2.1. Search strategy This study was performed followed the Meta-analysis Of Observational Studies in Epidemiology guideline [24]. We systematically searched the PubMed and Embase databases from inception to December 2016 to identify publications for inclusion. The MeSH terms used included “dietary fiber” and “metabolic syndrome X”. We combined these terms with free text word searches that included “fiber”, “fibre”, “metabolic syndrome”, “MetS”, “cardiometabolic”, and “cardiovascular risk factor” (see detailed search strategy in Supplementary Table 1). The reference lists of retrieved literature were also checked for including additional studies. Only studies that were published in English with full-text form available were enrolled in this meta-analysis. The search process was performed by 2 independent investigators (B.W. and Y.L.). 2.2. Eligibility of studies Studies that met all of the following conditions were included: 1) the study design was cohort, caseecontrol, or cross-sectional; 2) the exposure of interest was dietary fiber intake; 3) the outcome of interest included MetS; 4) the risk estimates of MetS, such as odds ratios (ORs) or risk ratios (RRs), have been reported. We also planned to include randomized trials that investigated the impacts of fiber supplementation on risk of MetS; however, no randomized trials met this requirement. For doseeresponse analysis, the risk effect size should be provided for 3 quantitative categories of fiber intake, and the numbers of cases and participants or person-years for each category were also available (or details were provided to calculate them). Reviews, abstracts, comments, unpublished results, and studies of children or adolescents were excluded. When reports pertained to overlapping participants, we included only the study with larger population to avoid duplication of data.
quality. Any disagreements in data abstraction and quality evaluation were resolved by consensus between the 2 reviewers or consulting with a third reviewer (X.L.). 2.4. Statistical analyses The summarized risk estimates of MetS were reported as OR with its 95% confidence interval (CI). We pooled the risk estimates of MetS for highest versus lowest total fiber and fiber subtypes intakes using a random effect model. The study by HosseinpourNiazi et al. [20] did not provide the separate risks of MetS for soluble and insoluble fibers; thus, we used the data of their previous report [27] instead, to evaluate the effect of fiber subtypes. Heterogeneity across the included studies was detected by the Cochrane Q test, with a P value < 0.1 suggesting significance. We also reported the heterogeneity as low, moderate, and high with I2 values of 25%, 50%, and 75%, respectively [28]. Subgroup analyses were performed according to sample size (2000 or <2000), study design (cross-sectional or cohort studies), location (America, Europe, or Asia), MetS definition (Adult Treatment Panel III [ATP III] or other criteria), degree of adjustment (10 or <10 variables), and mean age (60 or <60 years). Sensitivity analyses, including removals of individual study, low-quality studies, and studies of diabetic patients, were conducted to appraise the robustness of our results. Publication bias was evaluated by funnel plot analyses and further confirmed by Egger's tests. If there was publication bias, we used the “trim and fill” strategy to adjust the funnel plot and then re-computed the results [29]. To assess the potential doseeresponse pattern between fiber intake and MetS risk, we performed a 2-stage random-effects doseeresponse meta-analysis. In the first stage, we estimated a restricted cubic spline model with 3 knots at the 10th, 50th, and 90th percentiles of the fiber intake using generalized least-square regression, considering the correlation within each set of published ORs as proposed by Orsini et al. [30]. Then the study-specific ORs were pooled using the restricted maximum likelihood method in a random-effects meta-analysis [31]. A P-value for nonlinearity was calculated by testing the null hypothesis that the coefficient of the second spline is equal to 0. All data analyses were realized with STATA 12.0 (StataCorp, College Station, TX, USA) and R 3.2.5 (The R Foundation for Statistical Computing, Vienna, Austria) softwares, and P values < 0.05 were considered as significant. 3. Results
2.3. Data collection and quality evaluation 3.1. Search process Two reviewers (B.W. and Y.L.) independently extracted the information on study characteristics, including study author, publication year, study design and population, number of participants and cases, percentage of men, baseline age, MetS definition criteria, ascertainments of fiber intake and Mets, study location, and factors adjusted in multivariable models. The maximally adjusted risk estimates were also recorded for pooling analyses. When necessary, the corresponding authors of the included studies were e-mailed for missing data. To evaluate the methodological quality, we used an 11-item checklist recommended by Agency for Healthcare Research Quality (AHRQ) [25] for cross-sectional studies and Newcastle-Ottawa Scale (NOS) [26] for cohort studies. For each cross-sectional study, an item of AHRQ checklist can score 1 point if it was answered “yes”; otherwise, score 0 point. The NOS score for each cohort study was calculated according to 3 major aspects: selection of participants (0e4 points), adjustment for confounders (0e2 points), and ascertainment of outcomes (0e3 points). A study with an AHRQ score 8 or NOS score 7 was regarded as of high
We initially identified 1786 publications, of which 729 duplicates and 993 irrelevant studies were discarded. Among the remaining 64 articles selected for full-text reading, 53 articles that failed to meet the eligibility criteria were eliminated. Besides, the study by Moreno-Franco et al. [22] reported only the separate risks of MetS for soluble and insoluble fibers, thus it was treated as 2 individual reports. In total, 11 studies [13e23] with 12 reports were included in the final analyses (Fig. 1). 3.2. Study characteristics and quality evaluation The baseline characteristics of eligible studies are listed in Table 1. Briefly, the set of studies consists of 8 cross-sectional and 3 cohort studies, totaling 28,241 participants and 9140 MetS cases. The age range was 18e84 years, and men accounted for 54% of the total participants. Five studies were conducted in America, 3 in Europe, and 3 in Asia. With regard to MetS definition, 8 studies
Please cite this article in press as: Wei B, et al., Dietary fiber intake and risk of metabolic syndrome: A meta-analysis of observational studies, Clinical Nutrition (2017), https://doi.org/10.1016/j.clnu.2017.10.019
IdenƟficaƟon
B. Wei et al. / Clinical Nutrition xxx (2017) 1e8
Records idenƟfied from databases search (n = 1778)
3
Records idenƟfied from other sources (n=8)
Screening
Records aŌer duplicates removed (n=1057)
Records screened (n = 1057)
Eligibility
993 records were excluded based on Ɵtles or abstracts Records selected for full-text reading (n = 64)
Inclusion
53 records excluded: 4 review arƟcles 27 did not study fiber intake as an exposure 15 did not study MetS as an outcome 4 duplicated samples or cohorts 2 no risk esƟmates reported 1 study of adolescents Studies included in this meta-analysis (n = 11) Fig. 1. Flow diagram of study search process.
adopted the ATP III criteria or its adapted version [32,33], 3 studies adopted the International Diabetes Federation (IDF) criteria [34], and 1 study used the harmonized definition [35]. Of the included reports, all performed health examination to ascertain MetS conditions, and 6 assessed dietary factors using food frequency questionnaire. The most commonly adjusted confounders in the studies were age, gender, smoking, alcohol consumption, and total calorie intake. The detailed quality assessment is shown in Supplementary Tables 2 and 3. In general, there were 5 of the cross-sectional studies [13e16,21] and 2 of the cohort studies [19,20] having a high quality. The mean AHRQ score was 7.5 for cross-sectional studies and the mean NOS score was 7.3 for cohort studies. 3.3. High versus low analyses Data pooling indicated that compared to the lowest intake, the highest intake of dietary fiber was correlated with a decreased prevalence of MetS (OR: 0.85, 95% CI: 0.79e0.92, P ¼ 0.005; Fig. 2), with moderate heterogeneity across the studies (I2 ¼ 64%, P ¼ 0.001). Among fiber subtypes, the summarized OR (95% CI) of developing MetS was 0.80 (0.67e0.96) for cereal fiber, 0.85 (0.77e0.94) for fruit fiber, 1.07 (0.88e1.31) for vegetable fiber, 0.78 (0.62e0.98) for soluble fiber, and 0.55 (0.42e0.73) for insoluble fiber (Figs. 3 and 4). In the subgroup analysis for study locations, inverse associations of dietary fiber intake with MetS were seen in America (OR: 0.84, 95% CI: 0.73e0.97, P ¼ 0.01) and in Europe (OR: 0.62, 95% CI: 0.49e0.79, P < 0.001), but not in Asia (OR: 0.94, 95% CI: 0.88e1.01,
P ¼ 0.09). Also, the stratified analysis based on study design indicated that the association between fiber intake and prevalence of MetS was significant among cross-sectional studies (OR: 0.85, 95% CI: 0.78e0.92; P < 0.001) but not among cohort studies (OR: 0.86, 95% CI: 0.70e1.06, P ¼ 0.16). Other subanalyses, such as that for sample size, MetS definition, degree of adjustment, and mean age, showed consistent results. Relevant details are exhibited in Table 2. 3.4. Sensitivity analyses and publication bias Sensitivity analysis by excluding each study in sequence had no influence on the overall results. Exclusion of low-quality studies [17,18,22,23] or studies of diabetic patients [15,18,23] did not alter the inverse relationship between fiber intake and MetS. The funnel plot was visually asymmetric with the P value of Egger's test ¼ 0.001, suggesting the potential of publication bias. After introducing the “trim and fill” strategy to correct this bias, the overall risk estimate was still significant in favor of dietary fiber intake (OR: 0.90, 95% CI: 0.83e0.98, P ¼ 0.02; Supplementary Fig. 1). 3.5. Doseeresponse analyses Eight studies [13,14,16,17,19e22] were selected for the dosee response meta-analysis. We found a nonlinear association between dietary fiber intake and risk of MetS (P for nonlinearity < 0.001; Fig. 5). The summarized OR (95% CI) of MetS for a fiber intake of 10, 20, 30, and 40 g/d was 0.85 (0.79e0.91), 0.76 (0.67e0.85), 0.73 (0.65e0.83), and 0.73 (0.65e0.82), respectively. Additionally, we
Please cite this article in press as: Wei B, et al., Dietary fiber intake and risk of metabolic syndrome: A meta-analysis of observational studies, Clinical Nutrition (2017), https://doi.org/10.1016/j.clnu.2017.10.019
Brazil Brazil HE HE Dietary records Dietary records IDF IDF 61 (mean) 60 (mean) 50.9 48.6 109/175 174/214
Cohort
Cross-sectional Cross-sectional Silva 2011 [23] Steemburgo 2009 [18]
DM patients DM patients
Iran HE ATP III 62/160
62.5
18
FFQ
Spain HE FFQ ATP III 40e55 95.1 377/1592
Workers free of CVD Patients after RT Cross-sectional
Moreno-Franco 2014 [22] Noori 2010 [17]
USA HE FFQ ATP III 26e82 45.5 Healthy adults Cross-sectional
605/2834
USA Iran HE HE Dietary recalls FFQ ATP III ATP III 20e50 19e84 54.8 46.0 National population Healthy adults Cross-sectional Cohort
Grooms 2013 [16] Hosseinpour-Niazi 2015 [20] McKeown 2004 [21]
3780/10,473 240/1582
Japan HE Harmonized definition DM patients Cross-sectional Fujii 2013 [15]
2397/4399
56.7
20
BDHQ
USA HE Dietary history ATP III 18e30 44.6 Cohort
Adults at high risk of CVD Healthy adults Cross-sectional
Cabello-Saavedra 2014 [14] Carnethon 2004 [19]
575/4192
Spain HE FFQ ATP III IDF 55e80 44.9
Italy HE FFQ ATP III 45e64 47.2 384/1653 Healthy adults Cross-sectional Bo 2006 [13]
437/967
Adjustment Location Outcome Exposure
Ascertainment
MetS definition Age (years) Men (%) Participants (MetS/total) Population Design Study (author, year)
Table 1 Baseline characteristics of the included studies.
ATP, Adult Treatment Panel; BDHQ, brief diet history questionnaire; BMI, body mass index; CVD, cardiovascular disease; DM, diabetes mellitus; FFQ, food frequency questionnaire; HE, health examination; IDF, International Diabetes Federation; MetS, metabolic syndrome; NA, not applicable; RT, renal transplantation.
B. Wei et al. / Clinical Nutrition xxx (2017) 1e8
Age, sex, BMI, smoking, drinking, physical activity, and intake of total energy, fat, and magnesium Age, sex, DM, smoking, drinking, physical activity, education, marital status, and total energy intake Age, sex, race, education, BMI, smoking, drinking, physical activity, and intake of carbohydrate and fat Age, sex, DM duration, smoking, drinking, leisure time, physical activity, hypoglycemic agents, and intake of total energy and fat Age, sex, race, smoking, education, and total energy intake Age, sex, BMI, smoking, physical activity, intake of total energy, protein, fat, cholesterol, dairy products, meat, poultry, and fish Age, sex, smoking, drinking, physical activity, and intake of total energy, fat, and multivitamin. Age, sex, smoking, drinking, studies completed level, physical activity, work type, and total energy intake Age, sex, BMI, smoking, physical activity, dialysis mode and duration, steroids dose, menopausal status, family history of diabetes and stroke, and total energy intake Sex, BMI, hypertension, and total energy intake Sex, DM duration, physical activity, and total energy intake
4
also assumed a linear relationship between dietary fiber and MetS. The result suggested that the odds of MetS decreased by 11% (OR: 0.89, 95% CI: 0.82e0.96) for each 10-g/d increment in total fiber intake. 4. Discussion There are limited studies that have examined the role of dietary fiber in preventing MetS. The findings from our meta-analysis reveal an inverse relationship between consumption of dietary fiber and risk of MetS when comparing the highest versus lowest categories. Moreover, we also observe a curvilinear correlation of fiber intake with prevalence of MetS. Previous studies have suggested a protective role of fiber-rich food or diet in the development of MetS. Whole-grain consumption was reported to be associated with a lower risk of having MetS, while intake of refined-grain (containing low fiber) appeared to increase the prevalence of MetS [36]. A recent randomized trial also showed that dietary fiber enriched rice cakes decreased the number of MetS components [37]. Similarly, adherence to Mediterranean diet, which is high in whole-grains, fresh fruits, vegetables, and legumes, may reduce the incidence of MetS [38]. In addition, Carlson et al. performed a cross-sectional analysis enrolling 2128 adolescents, and found that high intakes of dietary fiber might lower the risk of MetS [39]. We did not include this study in our analysis because of the potential metabolic differences between adults and adolescents. There are several plausible explanations for the favorable impacts of dietary fiber on MetS development. Firstly, it is found that fiber intake confers improvements in individual components of MetS [10]. These effects may be attributed to the fact that fiber intake induces increased satiety, delayed gastric emptying, reduced absorption of macronutrients, and improved insulin sensitivity [10]. Secondly, dietary fiber has been shown to exert protective roles against oxidative stress by suppress of ROS generation [11]. It is well known that oxidative stress is involved in the pathogenesis of MetS [40]. Thirdly, fiber supplementation can favorably affect the phylogenetic structure and functional capacity of gut microbiome [41]. Previous studies have indicated that alterations in microbial community and functional shifts of gut microbiome lead to systemic inflammation and insulin resistance, thus promoting the development of MetS [42]. Therefore, the observed benefits of fiber intake may be, at least in part, mediated through its regulation of gut microbiome. Among fiber subtypes, we observed reduced risks of MetS for cereal and fruit fibers, but not for vegetable fiber. Consistent with our finding, some included studies also revealed the neutral effects of vegetable fiber on insulin resistance and MetS [20,21]. Besides, no association was detected of vegetable fiber intake with incident coronary heart disease [43] and type 2 diabetes mellitus [44]. The potential biological mechanisms for the lack of significant finding for vegetable fiber remain unclear. It is likely that the common nutrient-poor starchy vegetables, such as potatoes and peas, may lead to high glycemic load, which has been shown to increase the incidence of MetS [45]. Thus, the benefits of vegetable fiber may be offset by some adverse impact of starchy vegetables. However, because of the limited studies available for analysis of vegetable fiber, this result should be interpreted with caution. Future studies concerning these issues are needed. The quality of included studies was relatively high, but moderate heterogeneity was detected across studies. We found that the heterogeneity was decreased among studies using either the ATP III criteria or other criteria, suggesting that different definition methods of MetS may account for the heterogeneity in our study. Nevertheless, an inverse correlation between fiber intake and risk
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Fig. 2. Odds ratio of MetS for the highest versus lowest intake of dietary fiber.
of MetS was identified in both subsets of the diagnostic criteria. Although the prevalence of MetS was lower when using the ATP III criteria than the IDF criteria and harmonized definition, there is generally good concordance between all these 3 MetS criteria [46]. This fact may explain why moderate heterogeneity was observed
but not significantly affect our findings. Because the age ranges of participants in the studies differ so much from each other, we also performed a subanalysis according to the mean age. The beneficial effects of fiber intake on MetS were present in adults older or younger than 60 years, suggesting that our result is less likely to be
Fig. 3. Odds ratio of MetS for the highest versus lowest intake of cereal, fruit, and vegetable fibers.
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Fig. 4. Odds ratio of MetS for the highest versus lowest intake of soluble and insoluble fibers.
influenced by the age difference. In addition to adults, previous studies have also confirmed the favorable impacts of dietary fiber on physiologic activities in elderly persons, including metabolic properties and microbiome functions [47]. Thus, dietary fiber can function well in both adults and the elderly, although potential differences in metabolism and microbial composition are seen between these 2 age groups. In doseeresponse meta-analysis, there appears to be no additional reduction in odds of MetS if the fiber intake is increased over 30 g/d. A possible explanation is that excessive fiber intake may negatively affect the salutary nutrients intake by extending satiety and binding up minerals through fiber-related phytates and oxalates [48]. It should be noted that there are few risk estimates available for the fiber intake levels of more than 30 g/d, potentially leading to mis-modeling of the exact doseeresponse relationship. Therefore, we also assumed a linear correlation between dietary
fiber and MetS, and each 10 g/d increase in fiber intake was found to be associated with a decreased risk of MetS. Our work has several strengths. To date, this is the first metaanalysis that explored the relationship between dietary fiber intake and prevalence of MetS. In addition, the inverse association of fiber intake with MetS was observed not only considering fiber intake as a categorical variable but also in a doseeresponse pattern. However, we should not ignore the limitations of the current study. First of all, the recall and selection bias cannot be eliminated owning to the observational nature of included studies. Cohort studies are less susceptible to such bias, but only 3 cohort studies were included in our study, and the summary estimate of these studies was not significant. Secondly, there are few studies available to perform stratified analyses by fiber subtypes and other variables, which may cause inaccurate evaluations of their effects on risk of MetS. Likewise, due to the lack of relevant data, we cannot conduct subanalyses by some important covariates, such as gender.
Table 2 Subgroup analyses for the association between fiber intake and MetS. Subgroup
Sample size 2000 < 2000 Study design Cross-sectional Cohort Location America Europe Asia MetS definition ATP III Other Degree of adjustment 10 confounders <10 confounders Mean age 60 year <60 year
No. of reports
Heterogeneity
Pooled estimates
I2
P
OR (95% CI)
P
4 8
74.7 62.3
0.01 0.01
0.86 (0.74, 0.99) 0.72 (0.57, 0.89)
0.03 0.003
9 3
72.1 0
<0.001 0.50
0.85 (0.78, 0.92) 0.86 (0.70, 1.06)
<0.001 0.16
5 4 3
70.2 3.5 2.8
0.01 0.38 0.36
0.84 (0.73, 0.97) 0.62 (0.49, 0.79) 0.94 (0.88, 1.01)
0.01 <0.001 0.09
9 4
10.3 49.4
0.35 0.12
0.76 (0.68, 0.85) 0.94 (0.89, 0.98)
<0.001 0.01
4 8
0 74.5
0.52 <0.001
0.95 (0.91, 0.99) 0.74 (0.63, 0.88)
0.01 <0.001
4 7
60.6 10.9
0.05 0.35
0.93 (0.88, 0.99) 0.76 (0.65, 0.90)
0.02 0.001
ATP, Adult Treatment Panel; CI, confidence interval; MetS, metabolic syndrome; OR, odds ratio.
Fig. 5. Doseeresponse analysis for the association between fiber intake and MetS.
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Thirdly, publication bias is present in our work because small studies with null findings is less likely to be published. However, the result remained significant in favor of fiber intake after introducing the “trim and fill” method to adjust publication bias. Fourthly, the results of doseeresponse analysis may be unstable when some biases are present, including publication bias, as mentioned by Orsini et al. [30]. In conclusion, dietary fiber intake is associated with a decreased risk of MetS. The result supports the current recommendation of fiber intake for preventing chronic disease. Considering the limitations of this meta-analysis, additional large, prospective studies are warranted to enhance our findings, especially regarding the effect of vegetable fiber and the doseeresponse relationship between fiber intake and MetS. Statement of authorship J.W. conceived and designed the study. B.W. and Y.L. acquired the data. B.W., Y.L., and X.L. did statistic analysis. B.W. drafted the manuscript. Y.F. and J.C. made critical revision of the manuscript for key intellectual content, with support from J.W. All authors approved the final version of this article. Funding sources This work was supported by a grant from the National Natural Science Foundation of China to Jing Wan [NSFC, Grant No. 81670409]. Conflict of interest The authors declare no conflict of interest. Acknowledgements The authors thank Qr. Cheng Qian (Department of Cardiology, Zhongnan Hospital of Wuhan University, Wuhan, China) for providing statistical assistance. Appendix A. Supplementary data Supplementary data related to this article can be found at https://doi.org/10.1016/j.clnu.2017.10.019. References [1] Dai L, Goncalves CM, Lin Z, Huang J, Lu H, Yi L, et al. Exploring metabolic syndrome serum free fatty acid profiles based on GC-SIM-MS combined with random forests and canonical correlation analysis. Talanta 2015;135:108e14. [2] Eckel RH, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet 2005;365: 1415e28. [3] O'Neill S, O'Driscoll L. Metabolic syndrome: a closer look at the growing epidemic and its associated pathologies. Obes Rev 2015;16:1e12. [4] Li Z, Yang X, Yang J, Yang Z, Wang S, Sun F, et al. The cohort study on prediction of incidence of all-cause mortality by metabolic syndrome. PLoS One 2016;11:e0154990. [5] Mendonca FM, de Sousa FR, Barbosa AL, Martins SC, Araujo RL, Soares R, et al. Metabolic syndrome and risk of cancer: which link? Metabolism 2015;64: 182e9. [6] Cheraghi Z, Mirmiran P, Mansournia MA, Moslehi N, Khalili D, Nedjat S. The association between nutritional exposures and metabolic syndrome in the Tehran Lipid and Glucose Study (TLGS): a cohort study. Public Health 2016;140:163e71. [7] Kim YS, Xun P, He K. Fish consumption, long-chain omega-3 polyunsaturated fatty acid intake and risk of metabolic syndrome: a meta-analysis. Nutrients 2015;7:2085e100. [8] Marventano S, Salomone F, Godos J, Pluchinotta F, Del Rio D, Mistretta A, et al. Coffee and tea consumption in relation with non-alcoholic fatty liver and metabolic syndrome: a systematic review and meta-analysis of observational studies. Clin Nutr 2016;35:1269e81.
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