The metabolomics of human aging: Advances, challenges, and opportunities

Abstract

As the global population becomes older, understanding the impact of aging on health and disease becomes paramount. Recent advancements in multiomic technology have allowed for the high-throughput molecular characterization of aging at the population level. Metabolomics studies that analyze the small molecules in the body can provide biological information across a diversity of aging processes. Here, we review the growing body of population-scale metabolomics research on aging in humans, identifying the major trends in the field, implicated biological pathways, and how these pathways relate to health and aging. We conclude by assessing the main challenges in the research to date, opportunities for advancing the field, and the outlook for precision health applications.

Fig. 1.. Timeline of human aging metabolomics population studies.

A timeline showing the sample size of aging metabolomics studies (by number of participants on a logarithmic scale) in humans is shown, including the metabolomics technology used and the sample type. When multiple technologies or sample types were used, the study is considered a “combination” study. There has been no strong trend in sample size over time; instead, the field is dominated by occasional studies with much larger sample sizes than the rest. There has been a shift toward more MS-based technologies, although NMR studies are still being conducted and published. Most studies have used blood or urine, although in the 2020s, a greater diversity of sample types (namely, CSF, saliva, and muscle) have started to be seen in the literature.

Fig. 2.. Overview of metabolomic associations with aging.

An overview of the major metabolite changes associated with aging in human cohort studies is provided. The changes are grouped into seven major categories: lipids, steroid hormones, excretion, amino acids, diet, oxidative stress, and inflammation. These themes are broadly grouped by chemical structure (themes on the left) and biological pathway (themes on the right). Within each theme are some of the most consistently observed changes, with the change with age noted as an up (increased with older age) or down (decreased with older age) arrowhead. Some of the icons used in this figure were created with BioRender.com.

Fig. 3.. Complex interplay of aging and metabolism underneath population-level associations.

The age-metabolite associations observed in population studies may reflect a number of underlying processes. One interpretation is that metabolic changes initiate or exacerbate aging processes or affect survival, leading to observed age-metabolite associations in the population. In the reverse conceptualization, biological changes occurring as part of aging processes lead to changes in metabolite level. In both cases, confounding and interaction effects with high-level demographic and lifestyle characteristics—including exercise, social determinants of health, sex, environment, BMI, health conditions, diet, and race and ethnicity—may be modifying the observed associations. Teasing apart these different effects from observational and often cross-sectional data is challenging, requiring careful study design and interpretation.

Fig. 4.. Distribution of aging metabolomics population studies worldwide.

(A) The distribution of participants in the reviewed aging metabolomics studies by country is shown. Most studies have been located in North America, Europe, or East Asia, leaving a substantial gap in our understanding of aging across the world. (B) The regional breakdown of aging metabolomics human population studies by study count and (C) participant count is shown. In both cases, Europe has been represented to a greater extent than other regions. The category “Multiple” includes studies that had cohorts from more than one of the regions listed. Transparent regions were not represented among the human aging metabolomics studies reviewed here.