NMR-determined lipoprotein subclass profile predicts type 2 diabetes

diabetes research and clinical practice 83 (2009) 132–139

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NMR-determined lipoprotein subclass profile predicts type 2 diabetes§,§§ Allison M. Hodge a,*, Alicia J. Jenkins a, Dallas R. English b,c, Kerin O’Dea a, Graham G. Giles c a

University of Melbourne, Department of Medicine, Melbourne, Australia University of Melbourne, Centre for Molecular, Environmental, Genetic and Analytical Epidemiology, Melbourne, Australia c The Cancer Council Victoria, Cancer Epidemiology Centre, Melbourne, Australia b

article info

abstract

Article history:

Aims: To determine whether nuclear magnetic resonance (NMR)-determined lipoprotein

Received 13 May 2008

profiles predict type 2 diabetes.

Received in revised form

Methods: Subjects were 813 male and female participants in the Melbourne Collaborative

5 November 2008

Cohort Study, aged 40–69 years at baseline (1990–1994), and with a baseline fasting plasma

Accepted 6 November 2008

glucose <7.0 mmol/L. Incident type 2 diabetes was identified in 1994–1998 by self-report and

Published on line 16 December 2008

confirmation from doctors. Eligible cases and a random group of controls were selected, with NMR data available for 59 cases and 754 non-cases.

Keywords:

Results: Concentration of very low density lipoprotein (VLDL) particles (positive) and high

Diabetes mellitus

density lipoprotein (HDL) particle size (negative) were selected by stepwise regression as

Type 2

predictors of type 2 diabetes. These associations were independent of other non-lipid risk

Magnetic resonance spectroscopy

factors, but not plasma triglycerides. Factor analysis identified a factor from NMR variables,

Lipoproteins

explaining 47% of their variation, and characterized by a positive correlation with VLDL,

Predictive studies

particularly large and medium sized; more low density lipoprotein (LDL) that were smaller; and relatively smaller, but not more HDL particles. This factor was positively associated with diabetes incidence, but not independently of triglycerides. Conclusions: We identified an atherogenic NMR lipoprotein profile in people who developed diabetes, but this did not improve diabetes prediction beyond conventional triglyceride levels. # 2008 Elsevier Ireland Ltd. All rights reserved.

The use of nuclear magnetic resonance (NMR) spectroscopy enabling simultaneous measurement of very low density lipoprotein (VLDL), low density lipoprotein (LDL) and high density lipoprotein (HDL) subclass levels and particle sizes [2] has revealed that larger VLDL and smaller LDL and HDL tend to

be associated with cardiovascular disease risk and insulin resistance [3–8]. The Insulin Resistance Atherosclerosis Study (IRAS) was the first to examine whether NMR-lipoprotein subclass profile (LSP) predicts type 2 diabetes. Mean VLDL size and small HDL

§ Related data was previously presented at the Australasian Epidemiological Association Annual Conference, September 18–19, 2006, University of Melbourne, Melbourne, Australia. §§ This work was funded by VicHealth, The Cancer Council Victoria and the National Health and Medical Research Council (Grant Ids 124317, 126402, 126403, 180705, 180706, 194327, 209057, 251533). * Corresponding author at: University of Melbourne, Department of Medicine, St Vincent’s Hospital, PO Box 2900, Fitzroy 3065, Australia. Tel.: +61 3 9288 2676; fax: +61 3 9288 2581. E-mail address: [email protected] (A.M. Hodge). 0168-8227/$ – see front matter # 2008 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.diabres.2008.11.007

diabetes research and clinical practice 83 (2009) 132–139

concentration were positively associated with 5-year diabetes incidence, independently of triglyceride and HDL-cholesterol levels [9]. The association with VLDL size, but not small HDL concentration, was attenuated by adjustment for insulin levels. IRAS included 32% with impaired glucose tolerance (IGT). This prevalence is higher than in the general population, for example in AusDiab, 10.6% had IGT [10]. Similar results were found in IRAS for subjects with IGT and normal glucose tolerance, but the association between lipoprotein abnormalities and diabetes has not been examined in people with normal fasting glucose levels. The study aim was to assess whether baseline NMR-LSP predicts type 2 diabetes in people with normal fasting glucose.

1.

Subjects and methods

1.1.

Subjects

The Melbourne Collaborative Cohort Study (MCCS) was established to study prospectively cancer and other lifestyle related diseases [11]. The MCCS recruited 17,049 males and 24,479 females, aged between 27 and 75 years at baseline, 99.3% of whom were aged 40–69 years. The study participants were recruited from the Melbourne metropolitan area from 1990 to 1994 via the Electoral Rolls, advertisements and community announcements in local media. Southern European migrants to Australia were deliberately over-sampled to extend the range of lifestyle exposures and to increase genetic variation. Participants for the NMR sub-study were selected from people who had developed diabetes at 4 years follow-up, plus controls from within a randomly selected sub-cohort. Only those with baseline fasting plasma glucose <7.0 mmol/L, with a spare plasma sample, and not missing data were eligible. A total of 103 cases and 804 controls were eligible. The Cancer Council Victoria’s Human Research Ethics Committee approved the study and subjects gave written consent to participate and for researchers to obtain access to their medical records.

1.2.

Baseline plasma assays

Plasma glucose and lipid levels were measured by routine assays. Glucose and cholesterol were measured on a Kodak Ektachem analyzer (Rochester, NY, USA) Total triglycerides were measured enzymatically using a Hitachi 917 instrument (Boehringer Mannheim Corp., Indianapolis, IN, USA). HDLcholesterol was measured using the HDL-C plus 2nd generation, Roche kit (Roche Diagnostics Australia Pty Ltd., Castle Hill, NSW, Australia) on a Hitachi 917 instrument (Boehringer Mannheim Corp, Indianapolis, IN, USA). LDL-cholesterol was estimated by the Friedewald formula if triglycerides <4.52 mmol/L; otherwise LDL was left as ‘‘missing’’. Plasma insulin was measured by ELISA (AxSYM Microparticle Enzyme Immunoassay, Abbott, North Ryde, NSW, Australia).

1.3.

NMR analysis of lipoproteins

The lipoprotein subclass profile was determined using NMR as previously described in detail [12]. Briefly: (1) a Bruker WM-250

133

spectrometer was used to acquire a 250-MHz proton NMR spectra of plasma at 45 8C, which then underwent; (2) deconvolution of the lipid methyl group signal envelope in these spectra at 0.8 ppm, yielding signal amplitudes broadcast by the lipoproteins; and (3) conversion of the signal amplitudes to lipoprotein subclass concentrations by using experimentally determined standards that relate signal amplitudes of isolated subfraction standards to their chemically measured lipid concentrations. To simplify data analysis, VLDL subclasses were grouped as large (previously called V6 and 5), medium (previously called V4 and 3) and small VLDL (previously called V2 and 1). LDL subclasses were grouped as Large (previously called L3), Medium (previously called L2) and Small LDL (previously called L1) subclasses. HDL subfractions were regrouped as Large (cardioprotective (H5 + H4)) Medium HDL subclass (previously called H3), and Small (less cardioprotective (H2 + H1)) HDL subclasses. It has previously been demonstrated that non-enzymatic glycation of lipoproteins does not alter their detection by NMR (data not shown).

1.4.

Measurement of other risk factors

At baseline, a structured interview obtained information on country of birth, smoking, alcohol consumption, physical activity, education, weight change over the last 5 years, and family history of diabetes. Standard anthropometric methods were used to measure body habitus, from which BMI (kg/m2) and WHR were calculated.

1.5.

Ascertainment of diabetes status

Diabetes was identified from a mailed self-administered questionnaire about 4 years after baseline. Participants were asked: ‘‘Has a doctor ever told you that you have had diabetes?’’ and, if yes, for the year of diagnosis. For all cases, except those who reported a diagnosis date before baseline and were excluded, confirmation of diagnosis was sought from participants’ doctors. Doctors were asked if the participant had diabetes, and if so to indicate whether it was type 1 or 2. For the 76% of doctors who could be contacted, diagnosis of type 2 diabetes was confirmed for 76% of participants.

1.6.

Statistical analysis

As NMR measures were not normally distributed, medians with 25th and 75th percentiles were reported, and the Wilcoxon rank sum test used to compare values in cases and controls. Spearman correlation coefficients for NMR variables with lipids, body size (BMI and WHR) and glucose were calculated. To allow for the case-cohort design, data from people who were randomly selected in the sub-cohort, and who were also cases, were included twice in regressions, once with an outcome of ‘case’ and once as a ‘control’. Stepwise logistic regression with loge transformed NMR variables as listed in Table 1, was used to identify which were associated with the incidence of type 2 diabetes. Odds ratios for diabetes incidence in quintiles of the selected NMR variables were estimated, adjusting for age, sex, country of birth (Australia/UK, Greece, Italy), physical activity (4 levels based on time spent walking, in less vigorous activity and 2 time

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diabetes research and clinical practice 83 (2009) 132–139

Table 1 – Baseline characteristics by diabetes status 4 years later. Variable

Control (n = 754)

Case (n = 59)

Percentages Aus born (%) UK born (%) Greek born (%) Italian born (%) Hi physical activity category (%) Family history of diabetes (%) Current smoker (%) Women (%)

67.2 1.33 14.7 16.7 18.6 18.2 11.3 52.8

45.8 0 23.7 30.5 16.9 27.1 16.9 42.4

Medians (25–75%)a Age (years) Alcohol intake (g/day) BMI (kg/m2) WHR Fasting glucose (mmol/L)

55.8 (46.5–62.5) 4.1 (0–18.1) 26.6 (24.4–29.6) 0.85 (0.78–0.91) 5.5 (5.1–5.8)

59.0 (51.2–65.2) 3.8 (0–18.0) 30.5 (27.6–33.2) 0.93 (0.87–1.01) 6.2 (5.8–6.4)

Lipidsb Cholesterol (mmol/L) Triglycerides (mmol/L)c HDL-chol (mmol/l)d LDL-chol (mmol/L)e NMR Lipoprotein subclasses VLDL size (nm) LDL size (nm) HDL size (nm) VLDL particles (nmol/L) Large VLDL (nmol/L) Medium VLDL (nmol/L) Small VLDL (nmol/L) IDL (nmol/L) LDL particles (nmol/L) Large LDL (nmol/L) Medium small LDL (nmol/L) Very small LDL (nmol/L) HDL particles (mmol/L) Large HDL (mmol/L) Medium HDL (mmol/L) Small HDL (mmol/L)

5.5 1.1 1.4 3.5

(4.8–6.2) (0.8–1.4) (1.1–1.6) (3.0–4.2)

5.7 1.6 1.1 3.9

47.1 (43.4–51.1) 21.3 (20.8–21.8) 9.0 (8.6–9.4) 62.7 (41.7–88.0) 1.2 (0.3–3.2) 15.4 (6.4–27.1) 43.7 (31.6–57.2) 16.0 (0–44.0) 1426 (1174–1750) 660.0 (484.0–807.0) 140.0 (84.0–220.0) 576.5 (352.0–903.0) 34.2 (30.6–38.0) 7.3 (4.4–10.0) 0.6 (0–3.6) 24.9 (21.0–28.5)

p 0.004

0.530 0.090 0.404 0.123

(5.2–6.2) (1.1–2.2) (0.9–1.4) (3.2–4.3)

51.2 (44.9–55.7) 20.7 (20.1–21.4) 8.7 (8.4–9.0) 81.7 (63.5–94.1) 4.4 (1.8–7.8) 23.3 (14.6–37.9) 49.7 (40.7–58.1) 37.0 (0–58.0) 1758 (1441–21003) 528.0 (396.0–708.0) 218.0 (148.0–335.0) 903.0 (617.0–1347) 34.3 (31.5–38.9) 4.0 (3.0–7.3) 0.9 (0–6.6) 27.4 (23.2–30.4)

0.012 0.851 <0.0001 <0.0001 <0.0001

0.034 <0.0001 <0.0001 0.097

0.0008 <0.0001 <0.0001 0.0001 <0.0001 0.0001 0.021 0.007 <0.0001 0.002 <0.0001 <0.0001 0.304 <0.0001 0.076 0.006

p-values < 0.05 have been identified in bold. p from Wilcoxon ranksum test. b Chemically determined lipids, using standard assays in the Clinical Chemistry Department. c Triglycerides controls n = 705, cases n = 59. d HDL cholesterol controls n = 618, cases = 45. e LDL-cholesterol by Friedewald formula if TG < 4.52 mmol/L, controls n = 613, cases n = 44. a

spent in vigorous activity [13]), family history of diabetes (yes/ no), smoking (never, former, current), and alcohol intake (3 levels with different cut-offs for men and women [14]) (Model 1), and additionally for BMI and WHR (Model 2). Analyses were repeated in strata of sex, BMI (<27.2 kg/m2, 27.2 kg/m2), age (<56.6 years, 56.6 years), family history of diabetes (yes, no), country of birth (Australia/UK, Greece/Italy), and interaction terms for each stratifying variable with the selected NMR variables were tested. Because of the reduced numbers within strata these models only included age, sex and country of birth was reduced to 2 classes. Triglycerides were the only conventional lipid measure that predicted diabetes in a stepwise model including total cholesterol, HDL cholesterol, LDL cholesterol and triglycerides. The NMR variables selected by stepwise regression (large VLDL concentration and HDL particle size) were regressed against

triglyceride concentration. The residuals from these models represent the variation in NMR variables not explained by triglycerides. Both large VLDL concentration (r = 0.80, p < 0.001) and HDL particle size (r = 0.59, p < 0.001), were correlated with triglycerides. To determine whether the NMR variables improved diabetes prediction beyond triglycerides, logistic regression models predicting diabetes status, and including triglycerides and the confounders listed previously, were fitted with and without the saved residuals, and the likelihood ratio test used to determine whether their inclusion improved the fit. For comparison with the IRAS analysis we used stepwise regression to determine whether any of the NMR variables would enter a model with the conventional lipid variables. Models were reanalysed with the addition of the QUICKI insulin-sensitivity check index (QUICKI = 1/[log(fasting insulin) + log(fasting glucose)]) [15], and in people who had fasting

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glucose < 6.1 mmol/L, i.e. excluding subjects with impaired fasting glucose using the original American Diabetes Association criterion [16]. Factor analysis, using the principal factor method [17], was performed on the 16 NMR variables in Table 1. It was hypothesized that an atherogenic lipoprotein profile (large VLDL, small LDL and small HDL) associated with diabetes risk would be identified. Five factors with eigen values greater than 1.0 were retained. Orthogonal rotation was used to obtain factors that were not correlated with each other; then factor scores based on the rotated factor loadings were calculated. The associations between quintiles of factor scores and diabetes were assessed, adjusting for the same covariates as above, both with and without the inclusion of BMI and WHR. The first factor, which was strongly associated with diabetes risk, was assessed in the same way as were the two separate NMR variables.

2.

Results

2.1.

Subject characteristics

Data are available for 59 cases (57%) and 754 controls (94%). The only significant difference between people with NMR data and those without was that the controls with NMR data had a median age 5 years less than those without data. No differences by inclusion status were observed for BMI, insulin or conventional lipids within cases or controls. Southern European migrants were over-represented among people who developed diabetes, who were also older, had higher fasting glucose and were more obese (Table 1). Triglyceride and total cholesterol concentrations were higher, and HDL-cholesterol levels lower. Mean VLDL diameter was greater, and HDL and LDL smaller in cases compared with controls. Cases had higher concentrations of total VLDL and all VLDL subclasses, LDL particles, and favored smaller LDL and HDL particles compared with the controls.

2.2.

NMR subclasses

Concentration of large VLDL particles (positive) and HDL particle size (negative) predicted diabetes independently of other NMR variables. In the multivariate models, large VLDL concentration was positively associated with increased risk of diabetes, while HDL particle size showed an inverse association (Table 2). Associations were attenuated by adjustment for BMI and WHR, but remained statistically significant. These associations were generally consistent over strata of BMI, age, sex, country of birth and family history of diabetes (Table 3). Only 10 incident cases were identified with a BMI < 27.2 kg/m2, resulting in wide confidence intervals. Interactions for NMR variables and effect modifiers all had p values > 0.10, although the study was not powered to look for interactions. Addition of the residuals from the regression of large VLDL concentration on triglycerides improved the prediction of diabetes incidence in model 1 ( p = 0.015). In model 2 when BMI and WHR were included, large VLDL concentration was less important ( p = 0.221). The residual for regression of HDL particle size on triglycerides also improved the model before ( p = 0.017) but not after ( p = 0.078), adjustment for body size. If

Table 2 – Odds ratios and 95% confidence intervals for quintiles of large VLDL concentration (nmol/L) and HDL particle size (nm) as risk factors for incident type 2 diabetes. Lipid variable

Odds ratio (95% confidence interval) Model 1a

Model 2b

Large VLDL concentration Q1 1.00 Q2 2.29 (0.42–12.55) Q3 2.98 (0.59–14.99) Q4 4.23 (0.85–21.07) Q5 9.46 (1.99–44.89) p trend <0.0001

1.00 1.81 (0.32–10.07) 1.71 (0.33–8.93) 2.19 (0.43–11.15) 4.49 (0.93–21.68) 0.004

HDL particle size Q1 Q2 Q3 Q4 Q5 p trend

1.00 0.62 (0.27–1.42) 1.72 (0.33–4.58) 0.24 (0.05–1.21) 0.28 (0.05–1.55) 0.044

1.00 0.68 (0.30–1.51) 0.74 (0.34–1.57) 0.21 (0.04–1.00) 0.27 (0.05–1.39) 0.019

p-values < 0.05 have been identified in bold. Adjusted for sex, country of birth (Aus/UK, Italy/Greece), age, physical activity, family history of diabetes, smoking, and alcohol intake. b As above with BMI and WHR. a

conventional lipids were included in a stepwise regression model, none of the NMR variables entered. In 754 participants for whom insulin data was available (including 59 incident cases), the addition of the QUICKI insulin sensitivity index attenuated the associations with diabetes risk for large VLDL concentration and HDL particle size. In model 1 the odds ratios for quintile 5 versus quintile 1 were 9.8 (95% CI 2.1–46.0, p trend < 0.0001) and 0.3 (0.06–1.4, p trend = 0.025) before QUICKI was included, and with QUICKI, 6.0 (1.2–28.1, p trend = 0.005) and 0.5 (0.09–2.7, p trend = 0.204) for large VLDL concentration and HDL particle size, respectively. Similar effects were observed when BMI and WHR were included (Model 2). For the 720 participants (29 cases) with fasting glucose <6.1 mmol/L at baseline, the odds ratios in the top 20% compared with the bottom 20% in model 1 were 6.4 (95% CI 1.2– 33.6, p trend = 0.003) and 0.3 (95% CI 0.03–3.4, p trend = 0.395) for large VLDL concentration and HDL particle size, respectively, reflecting the associations in the whole sample. Five factors with eigen values greater than 1 were identified, explaining 88% of the variation in the 16 NMR variables. Factor 1 was characterized by a high concentration of VLDL, particularly those of large and medium particle size, more LDL that were smaller, and relatively more smaller HDL particles than large, but not more HDL particles in total (Table 4), consistent with an atherogenic lipoprotein pattern. Factor 2 had negative loadings for average VLDL and HDL size and large HDL concentration, and positive loadings for VLDL particle number, medium and small VLDL concentration, and LDL particle number. Factor 3 was characterized by higher total and small HDL concentrations. Factor 4 was characterized by higher VLDL particle size, large VLDL and medium HDL concentrations and HDL particle number. Factor 5 was characterized by higher LDL and IDL particle numbers (Table 4).

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Table 3 – Odd ratiosa for 1 S.D. increments in natural log transformed large VLDL concentration (nmol/L) and HDL particle size (nm) as risk factors for incident type 2 diabetes, within strata of sex, BMI age and country of birth. Lipid variable

Odds ratio

95% confidence interval

Men (cases = 34, controls = 356) Large VLDL HDL particle size

1.97 0.67

1.35–2.87 0.37–1.18

Women (cases = 25, controls = 398) Large VLDL HDL particle size

1.69 0.53

1.04–2.77 0.27–1.03

BMI < 27.2 kg/m2 (cases = 10, controls = 407) Large VLDL HDL particle size

2.53 1.21

1.29–4.94 0.53–2.77

BMI  27.2 kg/m2 (cases = 49, controls = 347) Large VLDL HDL particle size

1.78 0.55

1.26–2.51 0.33–0.91

Age < 56.6 years (cases = 23, controls = 394) Large VLDL HDL particle size

2.05 0.39

1.28–3.30 0.17–0.86

Age  56.6 years (cases = 36, controls = 360) Large VLDL HDL particle size

1.85 0.75

1.27–2.72 0.45–1.24

Australian/UK born (cases = 27, controls = 517) Large VLDL 2.35 HDL particle size 1.07

1.47–3.75 0.64–1.81

Greek/Italian born (cases = 32, controls = 237) Large VLDL 1.66 HDL particle size 0.42

1.09–2.53 0.22–0.80

No family history of diabetes (cases = 43, controls = 617) Large VLDL 1.91 HDL particle size 0.69

1.34–2.72 0.43–1.12

Family history of diabetes (cases = 16, controls = 137) Large VLDL 2.03 HDL particle size 0.37

1.15–3.58 0.15–0.91

a

p interaction 0.683 0.726

0.385 0.112

0.698 0.275

0.120 0.214

0.816 0.543

Adjusted for age, sex, country of birth (Aust/UK Greece/Italy), except where these variables are the stratifying variable.

Table 4 – Factor loadings for NMR lipoprotein variables. Variable

Factor 1

VLDL size (nm) LDL size (nm) HDL size (nm) VLDL particles (nmol/L) Large VLDL (nmol/L) Medium VLDL (nmol/L) Small VLDL (nmol/L) IDL (nmol/L) LDL particles (nmol/L) Large LDL (nmol/L) Small LDL (nmol/L) Medium small LDL (nmol/L) Very small LDL (nmol/L) HDL particles (mmol/L) Large HDL (mmol/l) Medium HDL (mmol/L) Small HDL (mmol/L) Proportion of variance explained a

0.28 S0.95 S0.80 0.36 0.48 0.44 0.13 0.21 0.67 S0.80 0.91 0.89 0.91 0.19 S0.83 0.05 0.32 0.50

2 S0.37 0.12 S0.30 0.91 0.18 0.73 0.89 0.10 0.39 0.13 0.25 0.25 0.25 0.03 0.29 0.03 0.20 0.13

3 a

0.22 0.006 0.18 0.12 0.23 0.06 0.11 0.04 0.21 0.16 0.10 0.13 0.09 0.84 0.02 0.07 0.89 0.11

Factor loadings 0.30 and  0.30 are bolded, these variables are considered to characterise factors.

4 S0.54 0.108 0.08 0.09 0.68 0.19 0.13 0.06 0.02 0.18 0.10 0.18 0.08 S0.40 0.02 0.90 0.22 0.07

5 0.03 0.07 0.04 0.07 0.004 0.05 0.16 0.89 0.46 0.18 0.24 0.22 0.24 0.13 0.17 0.06 0.03 0.06

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Table 5 – Odds ratios and 95% confidence intervals for quintiles of factor scores as risk factors for incident type 2 diabetes. Variable

Model 1a

Cases

Model 2b

Odds ratio

95% confidence interval

Odds ratio

95% confidence interval

Factor 1 Quintile Quintile Quintile Quintile Quintile p trend

1 2 3 4 5

1 15 2 13 28

1.00 15.80 2.60 18.28 30.00 <0.0001

2.00–124.9 0.22–30.28 2.20–151.6 3.70–243.1

1.00 17.58 2.48 16.45 23.57 0.003

2.09–147.8 0.20–31.10 1.86–145.3 2.79–198.7

Factor 2 Quintile Quintile Quintile Quintile Quintile p trend

1 2 3 4 5

5 11 12 20 11

1.00 2.07 2.11 3.41 1.67 0.364

0.63–6.78 0.66–6.80 1.13–10.33 0.49–5.70

1.00 2.20 2.65 3.35 1.87 0.443

0.60–8.05 0.75–9.37 0.99–11.31 0.50–7.01

Factor 3 Quintile Quintile Quintile Quintile Quintile p trend

1 2 3 4 5

9 8 11 10 18

1.00 1.57 2.06 1.70 3.81 0.029

0.54–4.53 0.70–6.09 0.56–5.15 1.32–11.02

1.00 1.70 2.00 1.41 3.58 0.120

0.55–5.24 0.64–6.23 0.43–4.58 1.14–11.23

Factor 4 Quintile Quintile Quintile Quintile Quintile p trend

1 2 3 4 5

10 9 12 6 22

1.00 1.32 2.20 0.76 3.39 0.015

0.48–3.63 0.82–5.92 0.24–2.37 1.39–8.29

1.00 1.22 2.01 0.64 2.89 0.040

0.43–3.47 0.72–5.65 0.20–2.07 1.14–7.34

Factor 5 Quintile Quintile Quintile Quintile Quintile p trend

1 2 3 4 5

11 9 8 17 14

1.00 1.42 1.09 2.64 1.68 0.312

0.52–3.92 0.38–3.11 1.05–6.63 0.66–4.31

1.00 1.88 1.18 2.50 1.97 0.340

0.63–5.60 0.38–3.64 0.92–6.79 0.72–5.34

p-values < 0.05 have been identified in bold. Adjusted for age, sex, family history of diabetes, country of birth, smoking, alcohol intake, physical activity and quintiles of other factor scores. b Adjusted for same as 1 with the addition of BMI and WHR. a

Factors 1, 3 and 4 showed positive associations with diabetes in model 1, while factors 2, and 5 showed no association (Table 5). The association with factor 1 was still strong after adjustment for body size. Because factor 1 reflects an overall atherogenic lipoprotein pattern and includes aspects of each of the major lipoprotein classes (VLDL, LDL and HDL), further analyses were limited to this factor. Factor 1 was positively correlated with fasting glucose, BMI, WHR, triglycerides, LDL-cholesterol and insulin, and inversely with HDL-cholesterol concentration (data not shown). The factor score did not add to the prediction of diabetes incidence beyond triglyceride concentration ( p from likelihood ratio test = 0.30 in model 1 and p = 0.33 in model 2).

3.

Discussion

Our results are consistent with the IRAS study, showing atherogenic lipoprotein abnormalities in people who subse-

quently developed diabetes, and extend the findings to a group that were not deliberately selected for a high diabetes risk. Concentration of large VLDL particles and HDL particle size predicted diabetes incidence. For both, the associations were independent of non-lipid risk factors, but neither improved diabetes prediction beyond conventional triglyceride concentration adjusting for BMI and WHR. There was no evidence of effect modification by age, sex, BMI, country of birth or family history of diabetes. An atherogenic NMR-LSP factor that was strongly associated with type 2 diabetes was identified. However, the factor score did not improve the assessment of diabetes risk provided by triglyceride concentration. Plasma insulin explained some of the association between NMR variables and diabetes risk. In IRAS, 830 people without diabetes were followed for 5 years, and 130 developed diabetes [9]. The differences in NMRLSP between those who developed diabetes and those who did not, were consistent with the differences we report. Stepwise regression identified mean VLDL size and concentration of

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small HDL as predictors of diabetes incidence in IRAS [9]. Both IRAS and the MCCS point to larger VLDL and smaller HDL being associated with diabetes risk, although we found concentration of large VLDL rather than a greater average VLDL particle size, and smaller HDL size rather than concentration of small HDL associated with diabetes. In contrast to our findings, IRAS showed that NMR measures provided additional information on diabetes risk beyond chemically measured triglycerides and HDL-cholesterol [9]. Correlations between conventional lipids and NMR variables are not presented for IRAS, but in our data they were strongly correlated. Other studies have indicated that NMR-LSP characterized by larger VLDL, and smaller LDL and HDL particles and higher concentrations of total VLDL and LDL, with small or no differences in total HDL concentration are associated with the Metabolic Syndrome [5], insulin resistance [8], CAD in men [4], and pre-diabetes [9], consistent with our findings. In the IRAS, scores for a factor showing very similar characteristics to our ‘factor 1’ were inversely associated with insulin sensitivity [18]. The existence of an atherogenic lipoprotein profile in people at increased diabetes risk is consistent with their increased CVD risk [19], and appears related to insulin resistance [8,9,18]. The dyslipoproteinemia in people at risk of diabetes may occur as a consequence of insulin resistance [3,8], and the observed association with diabetes may be due to confounding by insulin resistance. However, our results indicated that the associations of the concentration of large VLDL and an atherogenic lipoprotein pattern with diabetes risk were not fully explained by an index of insulin sensitivity. Several relevant mechanisms exist, for example: LDL particles are toxic to isolated rat b-cells [20], and VLDL are hydrolysed to non-esterified fatty acids (NEFA), which are associated with insulin resistance [9]. Oxidised LDL can contribute to b-cell failure, and the effect is attenuated by HDL [21]. Triglyceride levels were a strong risk factor for type 2 diabetes, suggesting that interventions such as weight loss and exercise, which can reduce the risk of developing diabetes [22,23], or at least delay the onset, as well as lowering triglycerides [24] would be important. Pharmacological therapies for triglyceride lowering may also reduce diabetes risk [25]. Study strengths are the longitudinal design, the sample was not selected on the basis of glucose tolerance, and included southern European migrants who are known in Australia for low CVD mortality rates [26], and possibly lower risk of dyslipidemia. A study weakness is the relatively small number of cases, which limits stratified analyses. Because we had only fasting glucose at baseline, some people with elevated 2-h glucose may have been included. Within AusDiab only 9% of people with a fasting glucose <7.0 mmol/L, had a 2h glucose 7.8 mmol/L [10]. Our exclusion criteria for IFG was based on the earlier figure of 6.1 mmol/L, which has now been dropped to 5.6 mmol/L. Had we used this criteria, only 11 of the incident cases would have been eligible. Because we did not screen for diabetes at follow-up, and identification of cases relied on clinical diagnosis, it is possible that people with dyslipidemia at baseline were more likely to be diagnosed than those with normal lipid levels, which could exaggerate the association between dyslipidemia and type 2 diabetes.

A higher concentration of large VLDL and smaller HDL particle size predicted diabetes incidence independent of established risk factors. An atherogenic NMR-LSP factor score was also associated with diabetes incidence. However NMR variables did not add to triglyceride concentration as predictors of type 2 diabetes once body size (expressed as BMI and WHR) was accounted for. It is possible, but not yet proven, that lipoprotein abnormalities have a direct aetiological link with type 2 diabetes, and that lipid modulation could delay or prevent type 2 diabetes. Our results support a relationship between insulin resistance and an atherogenic lipoprotein profile, and support identification and treatment of lipid abnormalities in people at risk of type 2 diabetes to reduce their risk of CVD. Whilst the NMR-LSP does not enhance diabetes prediction, it may facilitate research studies to identify lipoprotein subclasses representing therapeutic targets.

Conflict of interest statement None.

Acknowledgements This study was made possible by the contribution of many people, including the original investigators and the diligent team who recruited the participants and completed follow-up. We would like to express our gratitude to the many thousands of Melbourne residents who continue to participate in the study. We would particularly like to acknowledge Dr. Jim Otvos for the analysis of the NMR lipoprotein profiles.

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