Clinical and lifestyle related factors influencing whole blood metabolite levels – A comparative analysis of three large cohorts

Abstract

ObjectiveHuman blood metabolites are influenced by a number of lifestyle and environmental factors. Identification of these factors and the proper quantification of their relevance provides insights into human biological and metabolic disease processes, is key for standardized translation of metabolite biomarkers into clinical applications, and is a prerequisite for comparability of data between studies. However, so far only limited data exist from large and well-phenotyped human cohorts and current methods for analysis do not fully account for the characteristics of these data. The primary aim of this study was to identify, quantify and compare the impact of a comprehensive set of clinical and lifestyle related factors on metabolite levels in three large human cohorts. To achieve this goal, we improve current methodology by developing a principled analysis approach, which could be translated to other cohorts and metabolite panels.Methods63 Metabolites (amino acids, acylcarnitines) were quantified by liquid chromatography tandem mass spectrometry in three cohorts (total N = 16,222). Supported by a simulation study evaluating various analytical approaches, we developed an analysis pipeline including preprocessing, identification, and quantification of factors affecting metabolite levels. We comprehensively identified uni- and multivariable metabolite associations considering 29 environmental and clinical factors and performed metabolic pathway enrichment and network analyses.ResultsInverse normal transformation of batch corrected and outlier removed metabolite levels accompanied by linear regression analysis proved to be the best suited method to deal with the metabolite data. Association analyses revealed numerous uni- and multivariable significant associations. 15 of the analyzed 29 factors explained >1% of variance for at least one of the metabolites. Strongest factors are application of steroid hormones, reticulocytes, waist-to-hip ratio, sex, haematocrit, and age. Effect sizes of factors are comparable across studies.ConclusionsWe introduced a principled approach for the analysis of MS data allowing identification, and quantification of effects of clinical and lifestyle factors with metabolite levels. We detected a number of known and novel associations broadening our understanding of the regulation of the human metabolome. The large heterogeneity observed between cohorts could almost completely be explained by differences in the distribution of influencing factors emphasizing the necessity of a proper confounder analysis when interpreting metabolite associations.

Serum Metabolite Profiles in Participants of Lung Cancer Screening Study; Comparison of Two Independent Cohorts

“…However, our study revealed large heterogeneity of serum metabolomes that resulted in low accuracy of classification models based on specific metabolites. This heterogeneity could result from various lifestyle-related factors, a phenomenon that was addressed in recent large metabolomics studies [ 40 , 41 ]. This aspect was not addressed specifically in the current study, yet putative lifestyle-related differences between populations (represented by Polish and Italian cohorts in our study) reduced the accuracy of serum metabolome-based signatures.…”

Relation of Whole Blood Amino Acid and Acylcarnitine Metabolome to Age, Sex, BMI, Puberty, and Metabolic Markers in Children and Adolescents

“…From an initial 62 metabolites, 30 passed quality control criteria for further analysis (Table S7). Furthermore, to stabilize the analyses, outliers were removed by applying a cutoff of ±5 standard deviations based on the mean of the logarithmized nonzero data [32,33].…”

Circulating Metabolites Differentiate Acute Ischemic Stroke from Stroke Mimics

“…Compared to other metabolite sets previously reported to be associated with IS within 24 hours after symptom onset, 14–18 our set showed the highest accuracy for separating patients with IS from patients with SMs and NCs in our samples. Differences in diagnostic accuracies and in the sets of circulating metabolites may be routed in (1) different recruitment strategies resulting in sample differences with regard to age, sex, comorbidities, and medication, which are known to influence circulating metabolite levels 33 ; (2) different metabolomic profiling approaches (untargeted vs targeted) and technologies (eg, nuclear magnetic resonance spectroscopy vs mass spectrometry); (3) small sample sizes resulting in insufficient statistical power to capture true differences of hundreds of measured metabolites; (4) lack of a validation step; and (5) lack of statistical adjustments for potential confounders and multiple testing. Among 11 metabolites that overlapped between previously published studies and our discovery analysis after adjusting for age and sex (“basic model,” stage 1), only 4 also met the prespecified criteria for statistical significance after adjustment for additional confounders (“full model”) and were validated in an independent sample (stage 2).…”