Framework Analyzes Metabolic Data and Predicts Disease Risk

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Researchers at the National University of Singapore (NUS) have introduced a novel framework for analyzing large-scale metabolomic data, marking a major advancement in the precision and depth of metabolic profiling.

This approach enhances the precision and clarity of metabolic profiling, with potential applications in personalised healthcare and preventive medicine by improving the accuracy and interpretability of metabolic analyses.

Their research is published in the Proceedings of the National Academy of Sciences of the United States of America.

Using manifold fitting to interpret metabolic heterogeneity

Metabolomic profiling offers rich insights into human metabolism. However, the complexity and dimensionality of the data produced by traditional approaches have long challenged conventional analytical techniques.

The new framework, developed by associate professor Zhigang Yao and colleagues from the Department of Statistics and Data Science at the NUS Faculty of Science, utilises advanced mathematical techniques to fit low-dimensional manifolds within the high-dimensional space of nuclear magnetic resonance (NMR) metabolic biomarkers. This method reduces noise in the data and uncovers meaningful patterns linked to metabolic variation.

The researchers applied the method to data from more than 210,000 participants in the UK Biobank, focusing on 251 metabolic biomarkers measured by NMR. These biomarkers were grouped into seven biologically relevant categories representing different aspects of human metabolism. Manifold fitting was then applied to each category to reveal smooth, low-dimensional structures capturing essential metabolic variations.

Metabolic biomarkers

Biological molecules measured in the body that indicate metabolic processes, which can be linked to health or disease states.

The framework models individuals’ metabolic profiles as points distributed on these manifolds, providing a geometric representation that enhances interpretability by identifying coherent metabolic patterns. In three of the seven categories, the manifolds stratified the population into two subgroups associated with different risks for metabolic disorders, cardiovascular disease and autoimmune conditions.

This approach outperformed traditional methods by preserving biological signals and identifying disease-relevant subgroups that align with demographic, clinical and lifestyle factors. These features make it a valuable tool for metabolic research and precision health.

“The new approach allows us to identify meaningful metabolic subgroups by fitting low-dimensional manifolds to high-dimensional biomarker data. This will significantly improve our ability to relate metabolic states to susceptibility to disease,” said Yao.

Future research: genetic integration and longitudinal analysis

Building on this framework, the team plans to integrate genetic data with the metabolic subgroups. Genome-wide association studies (GWAS) within each manifold-defined subgroup could reveal genetic variants linked to specific metabolic patterns, providing insight into the hereditary basis of metabolic diversity and disease susceptibility.

Genome-wide association study (GWAS)

An approach used to scan the genome for genetic variants associated with specific traits or diseases across many individuals.

They also aim to conduct longitudinal analyses to examine the stability of metabolic manifolds over time and their predictive value. By analyzing time-series metabolomic data, the researchers hope to track how individuals move between metabolic states and whether these shifts correlate with disease development or progression. This could inform early detection and more targeted preventive strategies.

The study represents a step forward in population-scale metabolic profiling, providing a robust platform for further research into metabolic health and precision medicine.

Reference: Li B, Su J, Lin R, Yau ST, Yao Z. Manifold fitting reveals metabolomic heterogeneity and disease associations in UK Biobank populations. Proc Natl Acad Sci USA. 2025;122(22):e2500001122. doi: 10.1073/pnas.2500001122

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