Associations of anthropometric markers with serum metabolites using a targeted metabolomics approach: results of the EPIC-potsdam study.

BACKGROUND/OBJECTIVES: The metabolic consequences of type of body shape need further exploration. Whereas accumulation of body mass in the abdominal area is a well-established metabolic risk factor, accumulation in the gluteofemoral area is controversially debated. We evaluated the associations of anthropometric markers of overall body mass and body shape with 127 serum metabolites within a sub-sample of the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort. SUBJECTS/METHODS: The cross-sectional analysis was conducted in 2270 participants, randomly drawn from the EPIC-Potsdam cohort. Metabolites were measured by targeted metabolomics. To select metabolites related with both waist circumference (WC) (abdominal subcutaneous and visceral fat) and hip circumference (HC) (gluteofemoral fat, muscles and bone structure) correlations (r) with body mass index (BMI) as aggregating marker of body mass (lean and fat mass) were calculated. Relations with body shape were assessed by median metabolite concentrations across tertiles of WC and HC, mutually adjusted to each other. RESULTS: Correlations revealed 23 metabolites related to BMI (r⩾I0.20 I). Metabolites showing relations with BMI were showing similar relations with HC adjusted WC (WCHC). In contrast, relations with WC adjusted HC (HCWC) were less concordant with relations of BMI and WCHC. In both sexes, metabolites with concordant relations regarding WCHC and HCWC included tyrosine, diacyl-phosphatidylcholine C38:3, C38:4, lyso-phosphatidylcholine C18:1, C18:2 and sphingomyelin C18:1; metabolites with opposite relations included isoleucine, diacyl-phosphatidylcholine C42:0, acyl-alkyl-phosphatidylcholine C34:3, C42:4, C42:5, C44:4 and C44:6. Metabolites specifically related to HCWC included acyl-alkyl-phosphatidylcholine C34:2, C36:2, C38:2 and C40:4, and were solely observed in men. Other metabolites were related to WCHC only. CONCLUSIONS: The study revealed specific metabolic profiles for HCWC as marker of gluteofemoral body mass differing from those for BMI and WCHC as markers of overall body mass and abdominal fat, respectively. Thus, the study suggests that gluteofemoral mass may have less-adverse metabolic implications than abdominal fat.

Linking diet, physical activity, cardiorespiratory fitness and obesity to serum metabolite networks: findings from a population-based study.

OBJECTIVE: It is not yet resolved how lifestyle factors and intermediate phenotypes interrelate with metabolic pathways. We aimed to investigate the associations between diet, physical activity, cardiorespiratory fitness and obesity with serum metabolite networks in a population-based study. METHODS: The present study included 2380 participants of a randomly drawn subcohort of the European Prospective Investigation into Cancer and Nutrition-Potsdam. Targeted metabolomics was used to measure 127 serum metabolites. Additional data were available including anthropometric measurements, dietary assessment including intake of whole-grain bread, coffee and cake and cookies by food frequency questionnaire, and objectively measured physical activity energy expenditure and cardiorespiratory fitness in a subsample of 100 participants. In a data-driven approach, Gaussian graphical modeling was used to draw metabolite networks and depict relevant associations between exposures and serum metabolites. In addition, the relationship of different exposure metabolite networks was estimated. RESULTS: In the serum metabolite network, the different metabolite classes could be separated. There was a big group of phospholipids and acylcarnitines, a group of amino acids and C6-sugar. Amino acids were particularly positively associated with cardiorespiratory fitness and physical activity. C6-sugar and acylcarnitines were positively associated with obesity and inversely with intake of whole-grain bread. Phospholipids showed opposite associations with obesity and coffee intake. Metabolite networks of coffee intake and obesity were strongly inversely correlated (body mass index (BMI): r = -0.57 and waist circumference: r = -0.59). A strong positive correlation was observed between metabolite networks of BMI and waist circumference (r = 0.99), as well as the metabolite networks of cake and cookie intake with cardiorespiratory fitness and intake of whole-grain bread (r = 0.52 and r = 0.50; respectively). CONCLUSIONS: Lifestyle factors and phenotypes seem to interrelate in various metabolic pathways. A possible protective effect of coffee could be mediated via counterbalance of pathways of obesity involving hepatic phospholipids. Experimental studies should validate the biological mechanisms.

Variation of serum metabolites related to habitual diet: a targeted metabolomic approach in EPIC-Potsdam.

BACKGROUND/OBJECTIVE: Serum metabolites have been linked to higher risk of chronic diseases but determinants of serum metabolites are not clear. We aimed to investigate the association between habitual diet as a modifiable risk factor and relevant serum metabolites. SUBJECTS/METHODS: This cross-sectional study comprised 2380 EPIC-Potsdam participants. Intake of 45 food groups was assessed by food frequency questionnaire and concentrations of 127 serum metabolites were measured by targeted metabolomics. Reduced rank regression was used to find dietary patterns that explain the maximum variation of metabolites. RESULTS: In the multivariable-adjusted model, the proportion of explained variation by habitual diet was ranked as follows: acyl-alkyl-phosphatidylcholines (5.7%), sphingomyelins (5.1%), diacyl-phosphatidylcholines (4.4%), lyso-phosphatidylcholines (4.1%), acylcarnitines (3.5%), amino acids (2.2%) and hexose (1.6%). A pattern with high intake of butter and low intake of margarine was related to acylcarnitines, acyl-alkyl-phosphatidylcholines, lyso-phosphatidylcholines and hydroxy-sphingomyelins, particularly with saturated and monounsaturated fatty acid side chains. A pattern with high intake of red meat and fish and low intake of whole-grain bread and tea was related to hexose and phosphatidylcholines. A pattern consisting of high intake of potatoes, dairy products and cornflakes particularly explained methionine and branched chain amino acids. Dietary patterns related to type 2 diabetes-relevant metabolites included high intake of red meat and low intake of whole-grain bread, tea, coffee, cake and cookies, canned fruits and fish. CONCLUSIONS: Dietary patterns characterized by intakes of red meat, whole-grain bread, tea and coffee were linked to relevant metabolites and could be potential targets for chronic disease prevention.