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Manifold fitting reveals metabolomic heterogeneity and disease associations in UK Biobank populations.

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Τίτλος: Manifold fitting reveals metabolomic heterogeneity and disease associations in UK Biobank populations.
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.
References: Nat Commun. 2023 Feb 3;14(1):604. (PMID: 36737450)
Circ Cardiovasc Genet. 2015 Feb;8(1):192-206. (PMID: 25691689)
Int J Epidemiol. 2008 Apr;37(2):234-44. (PMID: 18381398)
Am J Epidemiol. 2017 Nov 1;186(9):1084-1096. (PMID: 29106475)
Stat Med. 1996 Feb 28;15(4):361-87. (PMID: 8668867)
Am J Hum Genet. 2010 Apr 9;86(4):581-91. (PMID: 20346437)
Cell Metab. 2017 Jan 10;25(1):43-56. (PMID: 28094011)
Proc Natl Acad Sci U S A. 2024 Jan 30;121(5):e2311436121. (PMID: 38266050)
Bioinformatics. 2003 May 22;19(8):1019-26. (PMID: 12761066)
Bioinformatics. 2016 Jun 15;32(12):i28-i36. (PMID: 27307628)
Brief Bioinform. 2021 Mar 22;22(2):1531-1542. (PMID: 32940335)
Proc Natl Acad Sci U S A. 2024 Sep 10;121(37):e2400002121. (PMID: 39226348)
Bioinformatics. 2007 Jun 1;23(11):1378-85. (PMID: 17344242)
IEEE Trans Pattern Anal Mach Intell. 1979 Feb;1(2):224-7. (PMID: 21868852)
Ophthalmology. 2024 Dec;131(12):1436-1446. (PMID: 38972358)
BMJ Open. 2014 Nov 19;4(11):e006286. (PMID: 25410606)
Am J Hum Genet. 2001 Jul;69(1):138-47. (PMID: 11404819)
Cardiovasc Diabetol. 2021 Dec 7;20(1):231. (PMID: 34876126)
Nat Rev Genet. 2005 Jun;6(6):451-64. (PMID: 15883588)
Nat Rev Drug Discov. 2003 Aug;2(8):668-76. (PMID: 12904817)
Trends Biotechnol. 2009 Jan;27(1):37-44. (PMID: 19010556)
Analyst. 2009 Sep;134(9):1781-5. (PMID: 19684899)
Am J Physiol Endocrinol Metab. 2012 Aug 15;303(4):E542-50. (PMID: 22713506)
Int J Obes (Lond). 2023 Jun;47(6):453-462. (PMID: 36823293)
Nat Med. 2022 Nov;28(11):2309-2320. (PMID: 36138150)
Nat Rev Genet. 2012 Nov;13(11):759-69. (PMID: 23032255)
PLoS Med. 2015 Mar 31;12(3):e1001779. (PMID: 25826379)
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
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  Data: Manifold fitting reveals metabolomic heterogeneity and disease associations in UK Biobank populations.
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  Data: 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.
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  Data: Competing interests statement:The authors declare no competing interest.
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  Data: Nat Commun. 2023 Feb 3;14(1):604. (PMID: <searchLink fieldCode="PM" term="%2236737450%22">36737450)</searchLink><br />Circ Cardiovasc Genet. 2015 Feb;8(1):192-206. (PMID: <searchLink fieldCode="PM" term="%2225691689%22">25691689)</searchLink><br />Int J Epidemiol. 2008 Apr;37(2):234-44. (PMID: <searchLink fieldCode="PM" term="%2218381398%22">18381398)</searchLink><br />Am J Epidemiol. 2017 Nov 1;186(9):1084-1096. (PMID: <searchLink fieldCode="PM" term="%2229106475%22">29106475)</searchLink><br />Stat Med. 1996 Feb 28;15(4):361-87. (PMID: <searchLink fieldCode="PM" term="%228668867%22">8668867)</searchLink><br />Am J Hum Genet. 2010 Apr 9;86(4):581-91. (PMID: <searchLink fieldCode="PM" term="%2220346437%22">20346437)</searchLink><br />Cell Metab. 2017 Jan 10;25(1):43-56. (PMID: <searchLink fieldCode="PM" term="%2228094011%22">28094011)</searchLink><br />Proc Natl Acad Sci U S A. 2024 Jan 30;121(5):e2311436121. (PMID: <searchLink fieldCode="PM" term="%2238266050%22">38266050)</searchLink><br />Bioinformatics. 2003 May 22;19(8):1019-26. (PMID: <searchLink fieldCode="PM" term="%2212761066%22">12761066)</searchLink><br />Bioinformatics. 2016 Jun 15;32(12):i28-i36. (PMID: <searchLink fieldCode="PM" term="%2227307628%22">27307628)</searchLink><br />Brief Bioinform. 2021 Mar 22;22(2):1531-1542. (PMID: <searchLink fieldCode="PM" term="%2232940335%22">32940335)</searchLink><br />Proc Natl Acad Sci U S A. 2024 Sep 10;121(37):e2400002121. (PMID: <searchLink fieldCode="PM" term="%2239226348%22">39226348)</searchLink><br />Bioinformatics. 2007 Jun 1;23(11):1378-85. (PMID: <searchLink fieldCode="PM" term="%2217344242%22">17344242)</searchLink><br />IEEE Trans Pattern Anal Mach Intell. 1979 Feb;1(2):224-7. (PMID: <searchLink fieldCode="PM" term="%2221868852%22">21868852)</searchLink><br />Ophthalmology. 2024 Dec;131(12):1436-1446. (PMID: <searchLink fieldCode="PM" term="%2238972358%22">38972358)</searchLink><br />BMJ Open. 2014 Nov 19;4(11):e006286. (PMID: <searchLink fieldCode="PM" term="%2225410606%22">25410606)</searchLink><br />Am J Hum Genet. 2001 Jul;69(1):138-47. (PMID: <searchLink fieldCode="PM" term="%2211404819%22">11404819)</searchLink><br />Cardiovasc Diabetol. 2021 Dec 7;20(1):231. (PMID: <searchLink fieldCode="PM" term="%2234876126%22">34876126)</searchLink><br />Nat Rev Genet. 2005 Jun;6(6):451-64. (PMID: <searchLink fieldCode="PM" term="%2215883588%22">15883588)</searchLink><br />Nat Rev Drug Discov. 2003 Aug;2(8):668-76. (PMID: <searchLink fieldCode="PM" term="%2212904817%22">12904817)</searchLink><br />Trends Biotechnol. 2009 Jan;27(1):37-44. (PMID: <searchLink fieldCode="PM" term="%2219010556%22">19010556)</searchLink><br />Analyst. 2009 Sep;134(9):1781-5. (PMID: <searchLink fieldCode="PM" term="%2219684899%22">19684899)</searchLink><br />Am J Physiol Endocrinol Metab. 2012 Aug 15;303(4):E542-50. (PMID: <searchLink fieldCode="PM" term="%2222713506%22">22713506)</searchLink><br />Int J Obes (Lond). 2023 Jun;47(6):453-462. (PMID: <searchLink fieldCode="PM" term="%2236823293%22">36823293)</searchLink><br />Nat Med. 2022 Nov;28(11):2309-2320. (PMID: <searchLink fieldCode="PM" term="%2236138150%22">36138150)</searchLink><br />Nat Rev Genet. 2012 Nov;13(11):759-69. (PMID: <searchLink fieldCode="PM" term="%2223032255%22">23032255)</searchLink><br />PLoS Med. 2015 Mar 31;12(3):e1001779. (PMID: <searchLink fieldCode="PM" term="%2225826379%22">25826379)</searchLink>
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