SummaryBackgroundConflicting evidence exists regarding the association between saturated fatty acids (SFAs) and type 2 diabetes. In this longitudinal case-cohort study, we aimed to investigate the prospective associations between objectively measured individual plasma phospholipid SFAs and incident type 2 diabetes in EPIC-InterAct participants.MethodsThe EPIC-InterAct case-cohort study includes 12 403 people with incident type 2 diabetes and a representative subcohort of 16 154 individuals who were selected from a cohort of 340 234 European participants with 3·99 million person-years of follow-up (the EPIC study). Incident type 2 diabetes was ascertained until Dec 31, 2007, by a review of several sources of evidence. Gas chromatography was used to measure the distribution of fatty acids in plasma phospholipids (mol%); samples from people with type 2 diabetes and subcohort participants were processed in a random order by centre, and laboratory staff were masked to participant characteristics. We estimated country-specific hazard ratios (HRs) for associations per SD of each SFA with incident type 2 diabetes using Prentice-weighted Cox regression, which is weighted for case-cohort sampling, and pooled our findings using random-effects meta-analysis.FindingsSFAs accounted for 46% of total plasma phospholipid fatty acids. In adjusted analyses, different individual SFAs were associated with incident type 2 diabetes in opposing directions. Even-chain SFAs that were measured (14:0 [myristic acid], 16:0 [palmitic acid], and 18:0 [stearic acid]) were positively associated with incident type 2 diabetes (HR [95% CI] per SD difference: myristic acid 1·15 [95% CI 1·09–1·22], palmitic acid 1·26 [1·15–1·37], and stearic acid 1·06 [1·00–1·13]). By contrast, measured odd-chain SFAs (15:0 [pentadecanoic acid] and 17:0 [heptadecanoic acid]) were inversely associated with incident type 2 diabetes (HR [95% CI] per 1 SD difference: 0·79 [0·73–0·85] for pentadecanoic acid and 0·67 [0·63–0·71] for heptadecanoic acid), as were measured longer-chain SFAs (20:0 [arachidic acid], 22:0 [behenic acid], 23:0 [tricosanoic acid], and 24:0 [lignoceric acid]), with HRs ranging from 0·72 to 0·81 (95% CIs ranging between 0·61 and 0·92). Our findings were robust to a range of sensitivity analyses.InterpretationDifferent individual plasma phospholipid SFAs were associated with incident type 2 diabetes in opposite directions, which suggests that SFAs are not homogeneous in their effects. Our findings emphasise the importance of the recognition of subtypes of these fatty acids. An improved understanding of differences in sources of individual SFAs from dietary intake versus endogenous metabolism is needed.FundingEU FP6 programme, Medical Research Council Epidemiology Unit, Medical Research Council Human Nutrition Research, and Cambridge Lipidomics Biomarker Research Initiative.

SummaryBackgroundStatins increase the risk of new-onset type 2 diabetes mellitus. We aimed to assess whether this increase in risk is a consequence of inhibition of 3-hydroxy-3-methylglutaryl-CoA reductase (HMGCR), the intended drug target.MethodsWe used single nucleotide polymorphisms in the HMGCR gene, rs17238484 (for the main analysis) and rs12916 (for a subsidiary analysis) as proxies for HMGCR inhibition by statins. We examined associations of these variants with plasma lipid, glucose, and insulin concentrations; bodyweight; waist circumference; and prevalent and incident type 2 diabetes. Study-specific effect estimates per copy of each LDL-lowering allele were pooled by meta-analysis. These findings were compared with a meta-analysis of new-onset type 2 diabetes and bodyweight change data from randomised trials of statin drugs. The effects of statins in each randomised trial were assessed using meta-analysis.FindingsData were available for up to 223 463 individuals from 43 genetic studies. Each additional rs17238484-G allele was associated with a mean 0·06 mmol/L (95% CI 0·05–0·07) lower LDL cholesterol and higher body weight (0·30 kg, 0·18–0·43), waist circumference (0·32 cm, 0·16–0·47), plasma insulin concentration (1·62%, 0·53–2·72), and plasma glucose concentration (0·23%, 0·02–0·44). The rs12916 SNP had similar effects on LDL cholesterol, bodyweight, and waist circumference. The rs17238484-G allele seemed to be associated with higher risk of type 2 diabetes (odds ratio [OR] per allele 1·02, 95% CI 1·00–1·05); the rs12916-T allele association was consistent (1·06, 1·03–1·09). In 129 170 individuals in randomised trials, statins lowered LDL cholesterol by 0·92 mmol/L (95% CI 0·18–1·67) at 1-year of follow-up, increased bodyweight by 0·24 kg (95% CI 0·10–0·38 in all trials; 0·33 kg, 95% CI 0·24–0·42 in placebo or standard care controlled trials and −0·15 kg, 95% CI −0·39 to 0·08 in intensive-dose vs moderate-dose trials) at a mean of 4·2 years (range 1·9–6·7) of follow-up, and increased the odds of new-onset type 2 diabetes (OR 1·12, 95% CI 1·06–1·18 in all trials; 1·11, 95% CI 1·03–1·20 in placebo or standard care controlled trials and 1·12, 95% CI 1·04–1·22 in intensive-dose vs moderate dose trials).InterpretationThe increased risk of type 2 diabetes noted with statins is at least partially explained by HMGCR inhibition.FundingThe funding sources are cited at the end of the paper.

OBJECTIVEDietary recommendations are focused mainly on relative dietary fat and carbohydrate content in relation to diabetes risk. Meanwhile, high-protein diets may contribute to disturbance of glucose metabolism, but evidence from prospective studies is scarce. We examined the association among dietary total, vegetable, and animal protein intake and diabetes incidence and whether consuming 5 energy % from protein at the expense of 5 energy % from either carbohydrates or fat was associated with diabetes risk.RESEARCH DESIGN AND METHODSA prospective cohort study was conducted among 38,094 participants of the European Prospective Investigation into Cancer and Nutrition (EPIC)-NL study. Dietary protein intake was measured with a validated food frequency questionnaire. Incident diabetes was verified against medical records.RESULTSDuring 10 years of follow-up, 918 incident cases of diabetes were documented. Diabetes risk increased with higher total protein (hazard ratio 2.15 [95% CI 1.77–2.60] highest vs. lowest quartile) and animal protein (2.18 [1.80–2.63]) intake. Adjustment for confounders did not materially change these results. Further adjustment for adiposity measures attenuated the associations. Vegetable protein was not related to diabetes. Consuming 5 energy % from total or animal protein at the expense of 5 energy % from carbohydrates or fat increased diabetes risk.CONCLUSIONSDiets high in animal protein are associated with an increased diabetes risk. Our findings also suggest a similar association for total protein itself instead of only animal sources. Consumption of energy from protein at the expense of energy from either carbohydrates or fat may similarly increase diabetes risk. This finding indicates that accounting for protein content in dietary recommendations for diabetes prevention may be useful.

Objective To identify existing prediction models for the risk of development of type 2 diabetes and to externally validate them in a large independent cohort. Data sources Systematic search of English, German, and Dutch literature in PubMed until February 2011 to identify prediction models for diabetes.Design Performance of the models was assessed in terms of discrimination (C statistic) and calibration (calibration plots and Hosmer-Lemeshow test).The validation study was a prospective cohort study, with a case cohort study in a random subcohort. Setting Models were applied to the Dutch cohort of the European Prospective Investigation into Cancer and Nutrition cohort study (EPIC-NL).Participants 38 379 people aged 20-70 with no diabetes at baseline, 2506 of whom made up the random subcohort.Outcome measure Incident type 2 diabetes. ResultsThe review identified 16 studies containing 25 prediction models. We considered 12 models as basic because they were based on variables that can be assessed non-invasively and 13 models as extended because they additionally included conventional biomarkers such as glucose concentration. During a median follow-up of 10.2 years there were 924 cases in the full EPIC-NL cohort and 79 in the random subcohort. The C statistic for the basic models ranged from 0.74 (95% confidence interval 0.73 to 0.75) to 0.84 (0.82 to 0.85) for risk at 7.5 years. For prediction models including biomarkers the C statistic ranged from 0.81 (0.80 to 0.83) to 0.93 (0.92 to 0.94). Most prediction models overestimated the observed risk of diabetes, particularly at higher observed risks. After adjustment for differences in incidence of diabetes, calibration improved considerably. ConclusionsMost basic prediction models can identify people at high risk of developing diabetes in a time frame of five to 10 years. Models including biomarkers classified cases slightly better than basic ones. Most models overestimated the actual risk of diabetes. Existing prediction models therefore perform well to identify those at high risk, but cannot sufficiently quantify actual risk of future diabetes.

Importance Low-density lipoprotein (LDL) cholesterol-lowering alleles in or near NPC1L1 or HMGCR, encoding the respective molecular targets of ezetimibe and statins, have previously been used as proxies to study the efficacy of these lipid-lowering drugs. Alleles near HMGCR are associated with a higher risk of type 2 diabetes, mimicking the increased incidence of new-onset diabetes associated with statin treatment in randomized clinical trials. It is unknown whether alleles near NPC1L1 are also associated with the risk of type 2 diabetes. Objective To investigate whether LDL-lowering alleles in or near NPC1L1 and other genes encoding current or prospective molecular targets of lipid-lowering therapy (i.e. HMGCR, PCSK9, ABCG5/G8, LDLR) are associated with the risk of type 2 diabetes. Design, Setting and Participants The associations with type 2 diabetes and coronary artery disease of LDL-lowering genetic variants were investigated in meta-analyses of genetic association studies. Meta-analyses included 50,775 individuals with type 2 diabetes and 270,269 controls including three studies and 60,801 individuals with coronary artery disease and 123,504 controls from a published meta-analysis. Data collection took place in Europe and the United States between 1991 and 2016. Exposure LDL-lowering alleles in or near NPC1L1, HMGCR, PCSK9, ABCG5/G8, LDLR. Main Outcomes and Measures Odds ratio of type 2 diabetes and coronary artery disease. Results LDL-lowering genetic variants at NPC1L1 were inversely associated with coronary artery disease (odds ratio for a genetically-predicted reduction of 1 mmol/L in LDL cholesterol, 0.61; 95% confidence interval, 0.42-0.88; p=0.008) and directly associated with type 2 diabetes (2.42, 1.70-3.43; p<0.001). The odds ratio of type 2 diabetes for PCSK9 genetic variants was 1.19 (95% confidence interval, 1.02-1.38, p=0.03). For a given reduction in LDL cholesterol, genetic variants were associated with a similar reduction in coronary artery disease risk (I-squared for heterogeneity in genetic associations=0.0%; p=0.93). However, associations with type 2 diabetes were heterogeneous (I-squared=77.2%; p=0.002), indicating gene-specific associations with metabolic risk for LDL-lowering alleles. Conclusions and Relevance In this meta-analysis, exposure to LDL-cholesterol lowering genetic variants in or near NPC1L1 and other genes was associated with a higher risk of type 2 diabetes. These data provide insights into potential adverse effects of LDL cholesterol-lowering therapy.