Metabolomic epidemiology offers insights into disease aetiology

Abstract

Metabolomic epidemiology is the high-throughput study of the relationship between metabolites and health-related traits. This emerging and rapidly growing field has improved our understanding of disease aetiology and contributed to advances in precision medicine. As the field continues to develop, metabolomic epidemiology could lead to the discovery of diagnostic biomarkers predictive of disease risk, aiding in earlier disease detection and better prognosis. In this Review, we discuss key advances facilitated by the field of metabolomic epidemiology for a range of conditions, including cardiometabolic diseases, cancer, Alzheimer's disease and COVID-19, with a focus on potential clinical utility. Core principles in metabolomic epidemiology, including study design, causal inference methods and multi-omic integration, are briefly discussed. Future directions required for clinical translation of metabolomic epidemiology findings are summarized, emphasizing public health implications. Further work is needed to establish which metabolites reproducibly improve clinical risk prediction in diverse populations and are causally related to disease progression.

Figure 1.

Summary of the process and applications of metabolomic epidemiology studies.

Figure 2.. Number of metabolomic publications across commonly studied health-related traits over the years.

Results are based on a PubMed database search conducted on April 27, 2023. Search terms included the condition of interest along with metabolom* and NMR, GC-MS, or LC-MS. Each search was conducted separately; as such, it is possible that studies using more than one technology were counted twice. Search terms used for “mental health” included mental health, mental wellness, psychological distress, depression, anxiety, posttraumatic stress disorder, or PTSD. Search terms for “adiposity” included adiposity, BMI, body-mass index, WHR, waist-hip ratio, or obesity. Search terms for “COVID-19” included COVID-19, 2019-nCoV, SARS-CoV-2, Coronavirus-2, or coronavirus 19. Search terms for “Cardiovascular” included cardiovascular, stroke, heart failure, coronary artery disease, coronary heart disease, venous thromboembolism, pulmonary embolism, myocardial infarction, cardiovascular mortality, or major cardiovascular event.

Figure 3.. Summary of consistently reported circulating metabolites associated with traits discussed in this review.

Metabolites reported are significantly associated with the trait of interest (FDR adjusted p-values <0.05) in a metabolomic investigation, with replication in at least one independent study. Due to the large number of metabolomic studies conducted in cardiovascular disease (CVD), only CVD studies exceeding >100 participants were included for the purpose of creating this figure. These UpSet plots indicate the number of metabolites uniquely associated with one indicated trait (e.g., 31 are associated with adiposity and no other traits shown here) and the number of metabolites associated with multiple traits (e.g., seven metabolites are associated with both adiposity and CVD). The inset plot depicts metabolites associated with sub-conditions of CVD. ClassyFire super classes were assigned to metabolites using RaMP-DB 2.0.

Figure 4.. Significant associations identified in circulating metabolomic GWAS.

Studies were identified by conducting a literature review, including the studies cited in Supplementary Table 8 of Chen et al (2018)^,–,,,. When the same metabolite was identified more than once within a 500kb region, the strongest single association was included. A. Significant associations identified in individuals of European ancestry. B. Significant associations identified in individuals with significant non-European ancestry. Pie charts in A and B indicate the proportion of significant associations identified by population. C. Lipid metabolite associations identified in the FADS region. “Other Organic Compounds” class includes prenol lipids, alkaloids and derivatives, phenylpropanoic acids, hydroxycinnamic acids, and peptides. Pie charts represent the number of significant metabolite-variant associations identified in each population, with associations based on the metabolomic GWAS references provided in the “Metabolomics and Genetics” section. Note that lipids and amino acids are much more commonly measured and numerous than other metabolite classes. Figure 4 was created with PhenoGram.