Paper details Sapient’s novel rapid liquid chromatography-mass spectrometry (rLC-MS) system and its application to predict clinically relevant physiological states at scale in human populations

Sapient, a leader in multi-omics data generation for biomarker discovery and clinical insight delivery, has published a pioneering study detailing the application of its rLC-MS system for non-targeted metabolomics analysis in more than 26,000 plasma samples, which led to the discovery of key metabolic phenotypes (“metabotypes”) that correlate with common diseases and enabled development of a machine learning (ML)-based metabolic aging clock found to accurately predict accelerated aging in various chronic diseases, with dynamic reversal of aging following definitive therapy.

For this study, a subset of samples from Sapient’s DynamiQ™ biorepository — comprised of more than 62,000 total plasma samples — were selected from 6,935 individuals to represent diverse demographic backgrounds and disease profiles. The samples were analyzed by rLC-MS to capture over 15,000 metabolites and lipids per sample, providing the first deep view into the comprehensive landscape of human small molecule chemistry. These molecules are derived from both endogenous and exogenous sources and therefore reflect dynamic physiological processes and environmental influences, making them important biomarkers to understand and predict clinically relevant physiological states. Across individuals, biological variation was markedly higher than technical variation, indicating good power to discern biological effects.

Using the large-scale dataset generated via rLC-MS, Sapient was able to identify several distinct subpopulations with metabotypes that correlate with heterogeneous disease phenotypes, including strong associations with cardiometabolic disorders. These findings suggest that plasma metabolomics can be used to metabolically define subgroups, predict disease states, and develop molecular diagnostic tools.

The rLC-MS dataset was also used to train an ML model to predict biological age and capture individual differences in rate of aging, in particular exploring how chronic human diseases may shorten lifespan. This metabolic aging clock successfully predicted accelerated aging in chronic human disorders as well as reversal of aging in kidney disease patients following transplantation, demonstrating that metabolomics may better capture dynamic changes in aging than biomedical data or epigenetic markers alone.

“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, PhD, first author on the paper and 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.”

“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,” said Saumya Tiwari, PhD, co-first author and Co-Founder and Head of Computational R&D Operations at Sapient. “We built AI-powered custom software and a library of over 13,000 chemical standards to transform raw signal into meaningful biology. The result is a dynamic, high-resolution portrait of human individuality shaped by both biology and environment.”

“We’ve shown that non-targeted metabolomics data can be very valuable to train predictive models of complex physiological states,” said Tao Long, PhD, MBA, Co-Founder and Head of Data Science at Sapient. “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.”

The full paper, entitled “Rapid Liquid Chromatography-Mass Spectrometry (rLC-MS) for Deep Metabolomics Analysis of Population Scale Studies”, can be accessed here.

About Sapient

Sapient is a leader in multi-omics data generation and insight delivery, providing bespoke services for proteomics, metabolomics, and lipidomics that enable biopharma sponsors to go beyond the genome to accelerate precision drug development.

Utilizing cutting-edge, high-throughput mass spectrometry and biocomputational frameworks, Sapient enables comprehensive biomarker-phenotype mapping across thousands of biosamples for discovery of robust protein, metabolite, and lipid biomarkers, drug targets, and clinical signatures of drug response. The company’s DynamiQ™ Insights Engine — a longitudinal database of integrated multi-omics and real-world data collected from tens of thousands of samples — enables rapid drug target identification, biomarker discovery and validation, and translational and clinical insights across all stages of drug development. For more information, visit sapient.bio.

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The samples were analyzed by rLC-MS to capture over 15,000 metabolites and lipids per sample, providing the first deep view into the comprehensive landscape of human small molecule chemistry.