Proc Natl Acad Sci U S A. 2025 Jun 3;122(22):e2500001122. doi: 10.1073/pnas.2500001122. Epub 2025 May 28.
ABSTRACT
NMR-based metabolic biomarkers provide comprehensive insights into human metabolism; however, extracting biologically meaningful patterns from such high-dimensional data remains a significant challenge. In this study, we propose a manifold-fitting-based framework to analyze metabolic heterogeneity within the UK Biobank population, utilizing measurements of 251 NMR biomarkers from 212,853 participants. Initially, our method clusters these biomarkers into seven distinct metabolic categories that reflect the modular organization of human metabolism. Subsequent manifold fitting to each category unveils underlying low-dimensional structures, elucidating fundamental variations from basic energy metabolism to hormone-mediated regulation. Importantly, three of these manifolds clearly stratify the population, identifying subgroups with distinct metabolic profiles and associated disease risks. These subgroups exhibit consistent links with specific diseases, including severe metabolic dysregulation and its complications, as well as cardiovascular and autoimmune conditions, highlighting the intricate relationship between metabolic states and disease susceptibility. Supported by strong correlations with demographic factors, clinical measurements, and lifestyle variables, these findings validate the biological relevance of the identified manifolds. By utilizing a geometrically informed approach to dissect metabolic heterogeneity, our framework enhances the accuracy of population stratification and deepens our understanding of metabolic health, potentially guiding personalized interventions and preventive healthcare strategies.
PMID:40434639 | DOI:10.1073/pnas.2500001122
