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Background: Consumption of whole-grain, coffee, and red meat were consistently related to the risk of developing type 2 diabetes in prospective cohort studies, but potentially underlying biological mechanisms are not well understood. Metabolomics profiles were shown to be sensitive to these dietary exposures, and at the same time to be informative with respect to the risk of type 2 diabetes. Moreover, graphical network-models were demonstrated to reflect the biological processes underlying high-dimensional metabolomics profiles.
Aim: The aim of this study was to infer hypotheses on the biological mechanisms that link consumption of whole-grain bread, coffee, and red meat, respectively, to the risk of developing type 2 diabetes. More specifically, it was aimed to consider network models of amino acid and lipid profiles as potential mediators of these risk-relations.
Study population: Analyses were conducted in the prospective EPIC-Potsdam cohort (n = 27,548), applying a nested case-cohort design (n = 2731, including 692 incident diabetes cases). Habitual diet was assessed with validated semiquantitative food-frequency questionnaires. Concentrations of 126 metabolites (acylcarnitines, phosphatidylcholines, sphingomyelins, amino acids) were determined in baseline-serum samples. Incident type 2 diabetes cases were assed and validated in an active follow-up procedure. The median follow-up time was 6.6 years.
Analytical design: The methodological approach was conceptually based on counterfactual causal inference theory. Observations on the network-encoded conditional independence structure restricted the space of possible causal explanations of observed metabolomics-data patterns. Given basic directionality assumptions (diet affects metabolism; metabolism affects future diabetes incidence), adjustment for a subset of direct neighbours was sufficient to consistently estimate network-independent direct effects. Further model-specification, however, was limited due to missing directionality information on the links between metabolites. Therefore, a multi-model approach was applied to infer the bounds of possible direct effects. All metabolite-exposure links and metabolite-outcome links, respectively, were classified into one of three categories: direct effect, ambiguous (some models indicated an effect others not), and no-effect.
Cross-sectional and longitudinal relations were evaluated in multivariable-adjusted linear regression and Cox proportional hazard regression models, respectively. Models were comprehensively adjusted for age, sex, body mass index, prevalence of hypertension, dietary and lifestyle factors, and medication.
Results: Consumption of whole-grain bread was related to lower levels of several lipid metabolites with saturated and monounsaturated fatty acids. Coffee was related to lower aromatic and branched-chain amino acids, and had potential effects on the fatty acid profile within lipid classes. Red meat was linked to lower glycine levels and was related to higher circulating concentrations of branched-chain amino acids. In addition, potential marked effects of red meat consumption on the fatty acid composition within the investigated lipid classes were identified.
Moreover, potential beneficial and adverse direct effects of metabolites on type 2 diabetes risk were detected. Aromatic amino acids and lipid metabolites with even-chain saturated (C14-C18) and with specific polyunsaturated fatty acids had adverse effects on type 2 diabetes risk. Glycine, glutamine, and lipid metabolites with monounsaturated fatty acids and with other species of polyunsaturated fatty acids were classified as having direct beneficial effects on type 2 diabetes risk.
Potential mediators of the diet-diabetes links were identified by graphically overlaying this information in network models. Mediation analyses revealed that effects on lipid metabolites could potentially explain about one fourth of the whole-grain bread effect on type 2 diabetes risk; and that effects of coffee and red meat consumption on amino acid and lipid profiles could potentially explain about two thirds of the altered type 2 diabetes risk linked to these dietary exposures.
Conclusion: An algorithm was developed that is capable to integrate single external variables (continuous exposures, survival time) and high-dimensional metabolomics-data in a joint graphical model. Application to the EPIC-Potsdam cohort study revealed that the observed conditional independence patterns were consistent with the a priori mediation hypothesis: Early effects on lipid and amino acid metabolism had the potential to explain large parts of the link between three of the most widely discussed diabetes-related dietary exposures and the risk of developing type 2 diabetes.
Metabolites and lipids are the final products of enzymatic processes, distinguishing the different cellular functions and activities of single cells or whole tissues. Understanding these cellular functions within a well-established model system requires a systemic collection of molecular and physiological information. In the current report, the green alga Chlamydomonas reinhardtii was selected to establish a comprehensive workflow for the detailed multi-omics analysis of a synchronously growing cell culture system. After implementation and benchmarking of the synchronous cell culture, a two-phase extraction method was adopted for the analysis of proteins, lipids, metabolites and starch from a single sample aliquot of as little as 10-15million Chlamydomonas cells. In a proof of concept study, primary metabolites and lipids were sampled throughout the diurnal cell cycle. The results of these time-resolved measurements showed that single compounds were not only coordinated with each other in different pathways, but that these complex metabolic signatures have the potential to be used as biomarkers of various cellular processes. Taken together, the developed workflow, including the synchronized growth of the photoautotrophic cell culture, in combination with comprehensive extraction methods and detailed metabolic phenotyping has the potential for use in in-depth analysis of complex cellular processes, providing essential information for the understanding of complex biological systems.