Dietary patterns derived from principal component- and k-means cluster analysis: Long-term association with coronary heart disease and stroke
ABSTRACT BACKGROUND AND AIMS: Studies comparing dietary patterns derived from different a posteriori methods in view of predicting disease risk are scarce. We aimed to explore differences between dietary patterns derived from principal component- (PCA) and k-means cluster analysis (KCA) in relation to their food group composition and ability to predict CHD and stroke risk. METHODS AND RESULTS: The study was conducted in the EPIC-NL cohort that consists of 40,011 men and women. Baseline dietary intake was measured using a validated food-frequency questionnaire. Food items were consolidated into 31 food groups. Occurrence of CHD and stroke was assessed through linkage with registries. After 13 years of follow-up, 1,843 CHD and 588 stroke cases were documented. Both PCA and KCA extracted a prudent pattern (high intakes of fish, high-fiber products, raw vegetables, wine) and a western pattern (high consumption of French fries, fast food, low-fiber products, other alcoholic drinks, soft drinks with sugar) with small variation between components and clusters. The prudent component was associated with a reduced risk of CHD (HR for extreme quartiles: 0.87; 95%-CI: 0.75-1.00) and stroke (0.68; 0.53-0.88). The western component was not related to any outcome. The prudent cluster was related with a lower risk of CHD (0.91; 0.82-1.00) and stroke (0.79; 0.67-0.94) compared to the western cluster. CONCLUSION: PCA and KCA found similar underlying patterns with comparable associations with CHD and stroke risk. A prudent pattern reduced the risk of CHD and stroke.
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- "Using a targeted metabolomics approach we analysed the correlation between metabolite serum levels using the Biocrates Absolute-IDQ TM Kit p150 and the above five dietary patterns in 892–965 individuals. These patterns essentially cover all relevant dietary intake information available in the FFQs and are broadly comparable to those reported in a number of large-scale populationbased studies of nutrition (Stricker et al. 2012; Schulze et al. 2001; Adebamowo et al. 2005; Ouderaa et al. 2006). "
ABSTRACT: Nutrition plays an important role in human metabolism and health. Metabolomics is a promising tool for clinical, genetic and nutritional studies. A key question is to what extent metabolomic profiles reflect nutritional patterns in an epidemiological setting. We assessed the relationship between metabolomic profiles and nutritional intake in women from a large cross-sectional community study. Food frequency questionnaires (FFQs) were applied to 1,003 women from the TwinsUK cohort with targeted metabolomic analyses of serum samples using the Biocrates Absolute-IDQ™ Kit p150 (163 metabolites). We analyzed seven nutritional parameters: coffee intake, garlic intake and nutritional scores derived from the FFQs summarizing fruit and vegetable intake, alcohol intake, meat intake, hypo-caloric dieting and a “traditional English” diet. We studied the correlation between metabolite levels and dietary intake patterns in the larger population and identified for each trait between 14 and 20 independent monozygotic twins pairs discordant for nutritional intake and replicated results in this set. Results from both analyses were then meta-analyzed. For the metabolites associated with nutritional patterns, we calculated heritability using structural equation modelling. 42 metabolite nutrient intake associations were statistically significant in the discovery samples (Bonferroni P < 4 × 10−5) and 11 metabolite nutrient intake associations remained significant after validation. We found the strongest associations for fruit and vegetables intake and a glycerophospholipid (Phosphatidylcholine diacyl C38:6, P = 1.39 × 10−9) and a sphingolipid (Sphingomyeline C26:1, P = 6.95 × 10−13). We also found significant associations for coffee (confirming a previous association with C10 reported in an independent study), garlic intake and hypo-caloric dieting. Using the twin study design we find that two thirds the metabolites associated with nutritional patterns have a significant genetic contribution, and the remaining third are solely environmentally determined. Our data confirm the value of metabolomic studies for nutritional epidemiologic research. Electronic supplementary material The online version of this article (doi:10.1007/s11306-012-0469-6) contains supplementary material, which is available to authorized users.Metabolomics 04/2013; 9(2):506-514. DOI:10.1007/s11306-012-0469-6 · 3.97 Impact Factor
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ABSTRACT: Type 2 diabetes (T2D) can be prevented in pre-diabetic individuals with impaired glucose tolerance (IGT). Here, we have used a metabolomics approach to identify candidate biomarkers of pre-diabetes. We quantified 140 metabolites for 4297 fasting serum samples in the population-based Cooperative Health Research in the Region of Augsburg (KORA) cohort. Our study revealed significant metabolic variation in pre-diabetic individuals that are distinct from known diabetes risk indicators, such as glycosylated hemoglobin levels, fasting glucose and insulin. We identified three metabolites (glycine, lysophosphatidylcholine (LPC) (18:2) and acetylcarnitine) that had significantly altered levels in IGT individuals as compared to those with normal glucose tolerance, with P-values ranging from 2.4 × 10(-4) to 2.1 × 10(-13). Lower levels of glycine and LPC were found to be predictors not only for IGT but also for T2D, and were independently confirmed in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort. Using metabolite-protein network analysis, we identified seven T2D-related genes that are associated with these three IGT-specific metabolites by multiple interactions with four enzymes. The expression levels of these enzymes correlate with changes in the metabolite concentrations linked to diabetes. Our results may help developing novel strategies to prevent T2D.Molecular Systems Biology 09/2012; 8:615. DOI:10.1038/msb.2012.43 · 14.10 Impact Factor
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ABSTRACT: The role of differences in diet on the relationship between socioeconomic factors and cardiovascular diseases remains unclear. We studied the contribution of diet and other lifestyle factors to the explanation of socioeconomic inequalities in cardiovascular diseases. We prospectively examined the incidence of coronary heart disease (CHD) and stroke events amongst 33,106 adults of the EPIC-NL cohort. Education and employment status indicated socioeconomic status. We used Cox proportional models to estimate hazard ratios ((HR (95% confidence intervals)) for the association of socioeconomic factors with CHD and stroke and the contribution of diet and lifestyle. During 12years of follow-up, 1617 cases of CHD and 531 cases of stroke occurred. The risks of CHD and stroke were higher in lowest (HR=1.98 (1.67;2.35); HR=1.55 (1.15;2.10)) and lower (HR=1.50 (1.29;1.75); HR=1.42 (1.08;1.86)) educated groups than in the highest. Unemployed and retired subjects more often suffered from CHD (HR=1.37 (1.19;1.58); HR=1.20 (1.05;1.37), respectively), but not from stroke, than the employed. Diet and lifestyle, mainly smoking and alcohol, explained more than 70% of the educational differences in CHD and stroke and 65% of employment status variation in CHD. Diet explained more than other lifestyle factors of educational and employment status differences in CHD and stroke (36% to 67% vs. 9% to 27%). The socioeconomic distribution of diet, smoking and alcohol consumption largely explained the inequalities in CHD and stroke in the Netherlands. These findings need to be considered when developing policies to reduce socioeconomic inequalities in cardiovascular diseases.International journal of cardiology 07/2013; 168(6). DOI:10.1016/j.ijcard.2013.07.188 · 6.18 Impact Factor