Fight Aging!

Obtaining enormous amounts of data on the human metabolome now costs little. Databases of metabolomic data available for analysis have become vast, and continue to grow. Productive analysis trails far behind the production of data, unfortunately, as is true for all of the omics technologies. In this paper, researchers discuss the present state of metabolomic knowledge in the context of aging, and the path forward to producing useful understanding from this deluge of human data, contributing perhaps to the better development of treatments for aging.

Aging is a fundamental part of the human experience, and it has long been understood to be a crucial component of health and disease. Population estimates of mortality fundamentally incorporate and adjust for age, which is widely considered the most important predictor of mortality. Defining the biology that drives aging is challenging, but theories of aging have coalesced around several key hallmarks, ranging from cellular senescence and stem cell exhaustion to mitochondrial, proteostasis, and genomic dysfunction. As the world's population ages, with one in six expected to be 60 or older by the end of 2030, understanding these physiological pathways and how to intervene in them will be critical to the prevention and management of the major drivers of morbidity and mortality.

In recent decades, technological advancements have opened up new possibilities for obtaining molecular data at a population scale. One of these technologies is metabolomics, which refers to the study of small molecules in the body. The Recon3D resource has mapped over 4,000 unique metabolites in a model of human metabolism, comprising over 13,000 metabolic reactions, and the Human Metabolome Database (HMDB) has annotated over 200,000 metabolites that may potentially be found in humans. Metabolites span a diversity of physiological processes, including the building blocks of the major macromolecules (e.g., amino acids, nucleic acids, carbohydrates, and fatty acids), functional nutrients (e.g., vitamins and cofactors), and compounds such as sex hormones, drug intermediates, and toxins. While this molecular diversity makes chemical identification more challenging, it also makes the metabolome an attractive dataset for application to many biomedical problems.

The decreasing cost and increasing scalability of metabolomics platforms have led to a proliferation of cohorts and biobanks adding metabolomics to their studies. For instance, the U.K. Biobank, one of the largest population cohorts to date, announced a project in 2018 to measure over 200 metabolites in half a million blood samples; the Trans-Omics for Precision Medicine (TOPMed) program has funded metabolomics collection in over 60,000 samples from diverse populations to pair with deep phenotyping and whole-genome sequencing data; and the Consortium of Metabolomics Studies (COMETS) has been working since 2014 to combine blood metabolomics data from dozens of cohorts worldwide for large-scale biomedical research. These studies represent a new era of population health research and molecular epidemiology that has enabled an unprecedented molecular view of aging processes with profound implications for precision health applications.

Link: https://doi.org/10.1126/sciadv.add6155