Association of a diabetes risk score with risk of myocardial infarction, stroke, specific types of cancer, and mortality: a prospective study in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort

To evaluate the impact of a recently developed, non-invasive risk score predictive for type 2 diabetes on the incidence and mortality of cardiovascular diseases and specific types of cancer. A total of 23,455 participants from the population-based European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study aged 35-65 years and free of diabetes and major chronic diseases at baseline (1994-1998) were followed through 2006 for incident myocardial infarction, stroke, types of cancer, and death. Risk score points were assigned to each participant based on age, waist circumference, height, physical activity, history of hypertension, smoking, alcohol consumption, and intake of red meat, whole-grain bread, and coffee. Hazard ratios (HRs) were estimated by Cox regression models. In age- and sex-adjusted analyses, participants with a high risk score (5-year probability to develop diabetes > or = 10%) had significantly higher risks of myocardial infarction (HR 2.7, 95% CI 1.5-5.0) and stroke (1.9, 1.0-3.6), but not of colon, breast or prostate cancer incidence, than those with a low score (5-year probability < 1%). In addition, participants with a high risk score had considerably higher risks of cardiovascular (HR 4.6, 95% CI 2.3-9.4), cancer (1.7, 1.1-2.7), and total mortality (2.4, 1.8-3.4), the latter being equivalent to a difference in life expectancy of 13 years. These data indicate that a risk score predictive for type 2 diabetes is also related to elevated risks of myocardial infarction, stroke, and premature death in apparently healthy individuals and emphasize the need for early intervention in high-risk individuals

The validation of cardiovascular risk scores for patients with type 2 diabetes mellitus

Objective Various cardiovascular prediction models have been developed for patients with type 2 diabetes. Their predictive performance in new patients is mostly not investigated. This study aims to quantify the predictive performance of all cardiovascular prediction models developed specifically for diabetes patients. Design and methods Follow-up data of 453, 1174 and 584 type 2 diabetes patients without pre-existing cardiovascular disease (CVD) in the EPIC-NL, EPIC-Potsdam and Secondary Manifestations of ARTerial disease cohorts, respectively, were used to validate 10 prediction models to estimate risk of CVD or coronary heart disease (CHD). Discrimination was assessed by the c-statistic for time-to-event data. Calibration was assessed by calibration plots, the Hosmer-Lemeshow goodness-of-fit statistic and expected to observed ratios. Results There was a large variation in performance of CVD and CHD scores between different cohorts. Discrimination was moderate for all 10 prediction models, with c-statistics ranging from 0.54 (95% CI 0.46 to 0.63) to 0.76 (95% CI 0.67 to 0.84). Calibration of the original models was poor. After simple recalibration to the disease incidence of the target populations, predicted and observed risks were close. Expected to observed ratios of the recalibrated models ranged from 1.06 (95% CI 0.81 to 1.40) to 1.55 (95% CI 0.95 to 2.54), mainly driven by an overestimation of risk in high-risk patients. Conclusions All 10 evaluated models had a comparable and moderate discriminative ability. The recalibrated, but not the original, prediction models provided accurate risk estimates. These models can assist clinicians in identifying type 2 diabetes patients who are at low or high risk of developing CVD

Serum metabolomic profiling highlights pathways associated with liver fat content in a general population sample

BACKGROUND/OBJECTIVES: Fatty liver disease (FLD) is an important intermediate trait along the cardiometabolic disease spectrum and strongly associates with type 2 diabetes. Knowledge of biological pathways implicated in FLD is limited. An untargeted metabolomic approach might unravel novel pathways related to FLD. SUBJECTS/METHODS: In a population-based sample (n = 555) from Northern Germany, liver fat content was quantified as liver signal intensity using magnetic resonance imaging. Serum metabolites were determined using a non-targeted approach. Partial least squares regression was applied to derive a metabolomic score, explaining variation in serum metabolites and liver signal intensity. Associations of the metabolomic score with liver signal intensity and FLD were investigated in multivariable-adjusted robust linear and logistic regression models, respectively. Metabolites with a variable importance in the projection 41 were entered in in silico overrepresentation and pathway analyses. RESULTS: In univariate analysis, the metabolomics score explained 23.9% variation in liver signal intensity. A 1-unit increment in the metabolomic score was positively associated with FLD (n = 219; odds ratio: 1.36; 95% confidence interval: 1.27-1.45) adjusting for age, sex, education, smoking and physical activity. A simplified score based on the 15 metabolites with highest variable importance in the projection statistic showed similar associations. Overrepresentation and pathway analyses highlighted branched-chain amino acids and derived gamma-glutamyl dipeptides as significant correlates of FLD. CONCLUSIONS: A serum metabolomic profile was associated with FLD and liver fat content. We identified a simplified metabolomics score, which should be evaluated in prospective studies

Food intake of individuals with and without diabetes across different countries and ethnic groups

Background/Objectives: Given the importance of nutrition therapy in diabetes management, we hypothesized that food intake differs between individuals with and without diabetes. We investigated this hypothesis in two large prospective studies including different countries and ethnic groups. Subjects/Methods: Study populations were the European Prospective Investigation into Cancer and Nutrition Study (EPIC) and the Multiethnic Cohort Study (MEC). Dietary intake was assessed by food frequency questionnaires, and calibrated using 24h-recall information for the EPIC Study. Only confirmed self-reports of diabetes at cohort entry were included: 6192 diabetes patients in EPIC and 13 776 in the MEC. For the cross-sectional comparison of food intake and lifestyle variables at baseline, individuals with and without diabetes were matched 1: 1 on sex, age in 5-year categories, body mass index in 2.5 kg/m(2) categories and country. Results: Higher intake of soft drinks (by 13 and 44% in the EPIC and MEC), and lower consumption of sweets, juice, wine and beer (> 10% difference) were observed in participants with diabetes compared with those without. Consumption of vegetables, fish and meat was slightly higher in individuals with diabetes in both studies, but the differences were <10%. Findings were more consistent across different ethnic groups than countries, but generally showed largely similar patterns. Conclusions: Although diabetes patients are expected to undergo nutritional education, we found only small differences in dietary behavior in comparison with cohort members without diabetes. These findings suggest that emphasis on education is needed to improve the current behaviors to assist in the prevention of complications. European Journal of Clinical Nutrition (2011) 65, 635-641; doi: 10.1038/ejcn.2011.11; published online 23 February 201