It has been a while now since the development of the first epigenetic clock, a weighted combination of DNA methylation sites that correlates tightly with chronological age. More interesting is that epigenetic ages higher than chronological ages correlate with age-related mortality, as well as risk and progression of numerous age-related diseases. This has inspired all sorts of similar efforts to produce clocks based on the wealth of data that can be assessed from blood, tissue, and other samples. Here, researchers discuss their work on a clock derived from the metabolome, the diverse collection of metabolites present in a biological sample. This research into biomarkers of aging is hoped to lead to a fast, cheap, and effective method to assess potential rejuvenation therapies: test shortly before and shortly after treatment, and compare. We’re not there yet, however.

Since aging is a process that affects almost all tissues and organs and involves crosstalk between multiple physiological systems, there has been increased research into composite markers of aging, involving multiple parameters. Biological age scores have been developed by combining established clinical biomarkers and have been associated with measures of functional decline such as cognitive ability.

Modern “omics” platforms have provided new opportunities for the systematic and agnostic assessment of biological aging. Analysis of genome-wide DNA methylation, mRNA, and miRNAs has allowed the development of multi-parameter “omic clocks,” built upon molecular changes that tick at an average rate consistent with chronological age. DNA methylation age acceleration, defined as having a greater DNA methylation age than chronological age (i.e., a faster than average “ticking rate”), is associated with multiple noncommunicable disease (NCD) risk factors and predictive of aging outcomes such as frailty, cognitive decline, and all-cause mortality.

Metabolomics, the profiling of small molecules, is a promising technology for the comprehensive assessment of biological aging. As the final product of cellular metabolism, metabolites may provide a more complete picture of biological processes and a stronger phenotypic representation than other “omic profiles.” Although metabolomic studies have reported strong associations between metabolites and age, these have been of limited sample size.

We developed a model of age based on untargeted metabolic profiling across multiple platforms, including nuclear magnetic resonance spectroscopy and liquid chromatography-mass spectrometry in urine and serum, within a large sample (N = 2,239) from the UK Airwave cohort. We validated a subset of model predictors in a Finnish cohort including repeat measurements from 2,144 individuals. We investigated the determinants of accelerated aging, including lifestyle and psychological risk factors for premature mortality.

The metabolomic age model was well correlated with chronological age (mean r = .86 across independent test sets). Increased metabolomic age acceleration (mAA) was associated after false discovery rate (FDR) correction with overweight/obesity, diabetes, heavy alcohol use, and depression. DNA methylation age acceleration measures were uncorrelated with mAA. Increased DNA methylation phenotypic age acceleration (N = 1,110) was associated after FDR correction with heavy alcohol use, hypertension, and low income. In conclusion, metabolomics is a promising approach for the assessment of biological age and appears complementary to established epigenetic clocks.