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.

Background-Type 2 diabetes (DM-2) and impaired glucose metabolism (IGM) are associated with an increased cardiovascular disease risk. In nondiabetic individuals, increased arterial stiffness is an important cause of cardiovascular disease. Associations between DM-2 and IGM and arterial stiffness have not been systematically investigated. Methods and Results-In a population-based cohort (nϭ747; 278 with normal glucose metabolism, 168 with IGM, and 301 with DM-2; mean age, 68.5 years), arterial stiffness was ultrasonically estimated by distensibility and compliance of the carotid, femoral, and brachial arteries and by the carotid elastic modulus. After adjustment for age, sex, and mean arterial pressure, DM-2 was associated with increased carotid, femoral, and brachial stiffness, whereas IGM was associated only with increased femoral and brachial stiffness. .63) for femoral compliance. The brachial artery followed a pattern similar to that of the femoral artery. Increases in stiffness indices were explained by decreases in distension, increases in pulse pressure, an increase in carotid intima-media thickness, and, for the femoral artery, a decrease in diameter. Hyperglycemia or hyperinsulinemia explained only 30% of the arterial changes associated with glucose tolerance. Adjustment for conventional cardiovascular risk factors did not affect these findings. Conclusions-IGM and DM-2 are associated with increased arterial stiffness. An important part of the increased stiffness occurs before the onset of DM-2 and is explained neither by conventional cardiovascular risk factors nor by hyperglycemia or hyperinsulinemia.

Short sleepers, especially those with poor sleep quality, have an increased risk of total CVD and CHD incidence. Future investigations should not only focus on sleep duration, but should also take sleep quality into account.

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.