2025-05-30 シンガポール国立大学(NUS)
<関連情報>
- https://news.nus.edu.sg/analysing-large-scale-metabolomic-data/
- https://www.pnas.org/doi/10.1073/pnas.2500001122
多様体フィッティングにより、英国バイオバンク集団における代謝異質性と疾患の関連性が明らかに Manifold fitting reveals metabolomic heterogeneity and disease associations in UK Biobank populations
Bingjie Li, Jiaji Su, Runyu Lin, +1 , and Zhigang Yao
Proceedings of the National Academy of Sciences Published:May 28, 2025
DOI:https://doi.org/10.1073/pnas.2500001122
Significance
This study utilizes a manifold-fitting framework within NMR-based metabolomics to explore metabolic heterogeneity in the UK Biobank population. Our method clusters 251 metabolic biomarkers into seven distinct categories that reflect the modular organization of human metabolism. Applying manifold fitting reveals low-dimensional structures in each category, capturing crucial metabolic variations associated with diverse disease risks. Notably, fitted manifolds in three categories distinctly stratify the population, each identifying two subgroups with unique metabolic profiles linked to a broad spectrum of diseases, from metabolic complications to cardiovascular and autoimmune disorders. This nuanced stratification enhances our understanding of the interactions between metabolism and disease, potentially guiding personalized health interventions and advancing preventive medicine strategies.
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.
