Publication | August 12, 2025
Rapid Liquid Chromatography-Mass Spectrometry (rLC-MS) for Deep Metabolomics Analysis of Population Scale Studies
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Read the PaperMass spectrometry (MS)-based metabolomics is a key technology for the interrogation of exogenous and endogenous small molecule mediators that influence human health and disease. To date, however, low throughput of MS systems have largely precluded large-scale metabolomics studies of human populations, limiting power to discover physiological roles of metabolites.
In this paper, Sapient details its fully automated rLC-MS system coupled to an AI-enabled computational pipeline that enables high-throughput, reproducible, nontargeted metabolite measurements across tens of thousands of samples. To demonstrate the discovery power of the rLC-MS platform, 26,042 plasma samples with matched real-world data (RWD), acquired across time from 6,935 diverse individuals, were selected for deep analysis by rLC-MS.
Through this large-scale, nontargeted metabolomics study, Sapient uncovered several subpopulations with distinct metabolic phenotypes (“metabotypes”) that correlate with heterogeneous disease phenotypes, which may reveal altered risk for development of common diseases.
The rLC-MS dataset was also used to train an machine learning model to predict biological age and capture individual differences in rate of aging. This metabolic aging clock was able to accurately predict accelerated aging in various chronic diseases, with dynamic reversal of metabolic aging following definitive therapy.
These data demonstrate that the rLC-MS platform enables prediction of clinically relevant physiological states from plasma metabolomics at scale in human populations. The ability to map metabolites, both known and unknown, across tens of thousands of samples at a time provides the statistical power for robust discovery. Given the dynamic nature of plasma metabolites, which derive from endogenous, dietary, and environmental and integrate information on biological processes across multiple body tissues, the study finds that population-scale metabolomics data can be used to predict complex physiological traits like biological age – as exemplified by the metabolic aging clock.
In fact, this data may better capture dynamic changes in aging than biomedical data or epigenetic markers alone, as the metabolic aging clock demonstrated when applied to a set of individuals with end-stage renal disease that underwent kidney transplantation. The biological age predicted from plasma samples of these individuals decreased markedly after transplantation, suggesting that pro-aging factors affecting systemic aging were normalized with definitive treatment.
To learn more, read the full paper and findings.