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

Abstract

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.

Figure 1

Serum metabolite network of the EPIC-Potsdam subcohort (n=2380). Each node represents one metabolite and each edge between two nodes represents the partial correlation between two metabolites mutually adjusted for the other metabolites. Solid line represents positive correlation and dotted line negative correlation. The thickness of the line indicates the strength of the correlation.

Figure 2

Association between BMI (a) and waist circumference (b) and the serum metabolite network of the EPIC-Potsdam subcohort. Presented are partial correlation coefficients adjusted for age, sex, education, alcohol consumption, smoking and physical activity, adapted from Bachlechner et al. Red color implies positive association and blue color inverse association between exposure and metabolite. Intensity of the color reflects the strength of association.

Figure 3

Association between cardiorespiratory fitness (a) and physical activity energy expenditure (b), and the serum metabolite network of the EPIC-Potsdam subcohort. Presented are β-coefficients adjusted for age, sex, education, alcohol consumption, smoking, BMI, waist circumference and measurement occasion, adopted from Wientzek et al. Red color implies positive association and blue color inverse association between exposure and metabolite. Intensity of the color reflects the strength of association.

Figure 4

Association between intake of whole-grain bread (a), coffee (b), cake and cookies (c), and the serum metabolite network of the EPIC-Potsdam subcohort. Presented are partial correlation coefficients adjusted for age, sex, education, alcohol consumption, smoking, physical activity, BMI, waist circumference, prevalent hypertension and prevalent diabetes. Red color implies positive association and blue color inverse association between exposure and metabolite. Intensity of the color reflects the strength of association.