Sapient has published a large-scale metabolomics study using its rapid liquid chromatography-mass spectrometry (rLC-MS) platform, analysing more than 26,000 plasma samples to identify metabolic phenotypes and develop a machine learning (ML)-based aging clock.

The research, published under the title Rapid Liquid Chromatography-Mass Spectrometry (rLC-MS) for Deep Metabolomics Analysis of Population Scale Studies, demonstrates how metabolomics can be used to define subgroups of patients, predict disease states, and track biological aging.

Samples from 6,935 individuals were drawn from Sapient’s DynamiQ biorepository, which contains more than 62,000 plasma samples representing diverse populations. Each sample was analysed for more than 15,000 metabolites and lipids, providing an unprecedented view of human small molecule chemistry.

The dataset enabled Sapient researchers to identify subpopulations with distinct metabolic profiles linked to common disease phenotypes, particularly cardiometabolic disorders. It also powered the development of an ML-based metabolic aging clock that predicted accelerated aging in chronic conditions, and even showed reversal of aging in patients with kidney disease following transplantation.

“These findings demonstrate the discovery power of the rLC-MS platform to capture a broad and dynamic landscape of chemical variation in human plasma, across a large population,” said Jeramie Watrous, co-founder and head of analytical R&D at Sapient. “The robust, large-scale datasets that can be generated with rLC-MS will substantially increase the ability to identify robust small molecule biomarkers, elucidate novel disease mechanisms, and predict biomedically relevant physiological states.”

Co-first author Saumya Tiwari, co-founder and head of computational R&D operations at Sapient, added: “To answer our ambitious questions about shared and unique features of human biology at scale, we had to reimagine the entire technology stack, from our rLC-MS to an AI-driven pipeline for automated feature detection, annotation, and quality control. The result is a dynamic, high-resolution portrait of human individuality shaped by both biology and environment.”

Tao Long, co-founder and head of data science at Sapient, said: “With our ML-based analyses, we find circulating metabolites hold close association with key human health and disease phenotypes, and can predict and read out complex, dynamic biological processes including biological aging, disease onset, and therapeutic response.”