Academic Journal
Manifold fitting reveals metabolomic heterogeneity and disease associations in UK Biobank populations.
| Τίτλος: | Manifold fitting reveals metabolomic heterogeneity and disease associations in UK Biobank populations. |
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| Συγγραφείς: | Li B; Department of Statistics and Data Science, National University of Singapore, Singapore 117546, Singapore., Su J; Department of Statistics and Data Science, National University of Singapore, Singapore 117546, Singapore., Lin R; Department of Statistics and Data Science, National University of Singapore, Singapore 117546, Singapore., Yau ST; Yau Mathematical Sciences Center, Tsinghua University, Jingzhai, Beijing 100084, China., Yao Z; Department of Statistics and Data Science, National University of Singapore, Singapore 117546, Singapore. |
| Πηγή: | Proceedings of the National Academy of Sciences of the United States of America [Proc Natl Acad Sci U S A] 2025 Jun 03; Vol. 122 (22), pp. e2500001122. Date of Electronic Publication: 2025 May 28. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: National Academy of Sciences Country of Publication: United States NLM ID: 7505876 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1091-6490 (Electronic) Linking ISSN: 00278424 NLM ISO Abbreviation: Proc Natl Acad Sci U S A Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Washington, DC : National Academy of Sciences |
| Ιατρικοί όροι (MeSH): | Metabolomics*/methods , Biological Specimen Banks* , Metabolome*, Biomarkers/metabolism ; Humans ; United Kingdom ; Male ; Female ; Middle Aged ; Aged ; Magnetic Resonance Spectroscopy ; UK Biobank |
| Περίληψη: | 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. |
| Competing Interests: | Competing interests statement:The authors declare no competing interest. |
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| Grant Information: | A-0008520-00-00 Singapore MOE Tier 2; A-8001562-00-00 Singapore MOE Tier 2; A-8000987-00-00 Singapore MOE Tier 1; A-8002931-00-00 Singapore MOE Tier 1 |
| Contributed Indexing: | Keywords: disease risk prediction; geometric decomposition; manifold fitting; metabolic manifolds; population heterogeneity |
| Substance Nomenclature: | 0 (Biomarkers) |
| Entry Date(s): | Date Created: 20250528 Date Completed: 20250528 Latest Revision: 20250611 |
| Update Code: | 20260130 |
| PubMed Central ID: | PMC12146735 |
| DOI: | 10.1073/pnas.2500001122 |
| PMID: | 40434639 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 1091-6490 |
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| DOI: | 10.1073/pnas.2500001122 |