Academic Journal
Age-adjusted machine learning identifies facial skin microbes associated with skin quality among Korean women.
| Τίτλος: | Age-adjusted machine learning identifies facial skin microbes associated with skin quality among Korean women. |
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| Συγγραφείς: | Park S; Department of Life Sciences, Pohang University of Science and Technology, Pohang, Korea., Kim H-B; Research and Innovation Center, COSMAX BTI, Seongnam, Republic of Korea.; Department of Precision Medicine, Kyung Hee University, Seoul, South Korea., Ahn H; Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang, Republic of Korea., Lee G; Division of Interdisciplinary Bioscience & Bioengineering, Pohang University of Science and Technology, Pohang, Republic of Korea., Song W; Department of Life Sciences, Pohang University of Science and Technology, Pohang, Korea., Kim M; Research and Innovation Center, COSMAX BTI, Seongnam, Republic of Korea., Park E; Research and Innovation Center, COSMAX BTI, Seongnam, Republic of Korea., Yu BS; Department of Biomedical Sciences, College of Bio-convergence, Dankook University, Cheonan, Republic of Korea., Han M; Department of Biomedical Sciences, College of Bio-convergence, Dankook University, Cheonan, Republic of Korea., Mun S; Department of Microbiology, College of Science & Technology, Dankook University, Cheonan, Republic of Korea.; Center for Bio-Medical Engineering Core Facility, Dankook University, Cheonan, Republic of Korea.; Department of Cosmedical Materials, College of Bio-convergence, Dankook University, Cheonan, Republic of Korea., Lee D-G; Research and Innovation Center, COSMAX BTI, Seongnam, Republic of Korea., Park CH; Research and Innovation Center, COSMAX BTI, Seongnam, Republic of Korea., Kang S; Research and Innovation Center, COSMAX, Seongnam, Republic of Korea., Jo H; Research and Innovation Center, COSMAX BTI, Seongnam, Republic of Korea., Kim S; Department of Life Sciences, Pohang University of Science and Technology, Pohang, Korea.; Graduate School of Artificial Intelligence, Pohang University of Science and Technology, Pohang, Republic of Korea.; Division of Interdisciplinary Bioscience & Bioengineering, Pohang University of Science and Technology, Pohang, Republic of Korea. |
| Πηγή: | MSystems [mSystems] 2026 Sep 22; Vol. 11 (9), pp. e0084026. Date of Electronic Publication: 2026 Aug 20. |
| Τύπος έκδοσης: | Journal Article |
| Γλώσσα: | English |
| Στοιχεία περιοδικού: | Publisher: American Society for Microbiology Country of Publication: United States NLM ID: 101680636 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2379-5077 (Electronic) Linking ISSN: 23795077 NLM ISO Abbreviation: mSystems Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Washington, DC : American Society for Microbiology, [2016]- |
| Ιατρικοί όροι (MeSH): | Skin*/microbiology , Face*/microbiology , Skin Microbiome* , Machine Learning*, Corynebacterium/isolation & purification ; Corynebacterium/genetics ; Humans ; Female ; Republic of Korea ; Middle Aged ; Adult ; Age Factors ; Aged ; Young Adult ; Microbiota |
| Περίληψη: | Recognizing specific microbes that significantly influence skin quality is becoming an essential aspect of personalized skincare. However, conventional large-scale cohort skin microbiome studies often overlook important confounders, such as age, leading to missing meaningful microbe-skin relationships. In this study, we developed an age-adjusted machine learning (AAML) framework to identify microbial candidates associated with skin quality by determining optimal age ranges that enhance age-independent signals of skin microbes. It allowed the identification of distinct age groups that clearly explain specific skin microbial effects, as well as potential microbes showing notable age-independent links to skin quality, which were not observed in analyses across the entire age spectrum. In particular, Corynebacterium propinquum (C. propinquum) was recognized as a key species that positively impacts the middle-aged group, especially regarding skin tone. We further validated its dermatological significance using functional assays in human skin cell lines, performed a gene-level functional analysis, and suggested a potential mechanism. Our AAML method can be adapted to other microbiome analyses to precisely measure factors unaffected by age-related confounding factors.IMPORTANCEAge is a crucial but often intractable confounder in microbiome studies, obscuring how specific microbes affect human traits. We developed an age-adjusted machine learning (AAML) framework that automatically finds age ranges where the microbiome best predicts skin quality, rather than relying on arbitrary age groups. In a Korean facial skin cohort, AAML revealed three biologically meaningful age windows and uncovered microbial effects that are invisible in whole-age analyses. AAML identified Corynebacterium propinquum as a previously unrecognized commensal microbe that improves skin tone in the middle-aged group, and we mechanistically linked this effect to resveratrol production. Our framework provides a general, confounder-aware strategy for discovering age-independent microbiome-host relationships. |
| Competing Interests: | The authors declare no conflict of interest. |
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| Grant Information: | RS-2025-16070008, RS-2026-25472972 National Research Foundation of Korea; RS-2019-II191906 Institute for Information and Communications Technology Promotion |
| Contributed Indexing: | Keywords: age; bioinformatics; cosmetology; skin microbiome; skin quality |
| Entry Date(s): | Date Created: 20260820 Date Completed: 20260922 Latest Revision: 20260924 |
| Update Code: | 20260924 |
| PubMed Central ID: | PMC13595949 |
| DOI: | 10.1128/msystems.00840-26 |
| PMID: | 42622436 |
| Βάση Δεδομένων: | MEDLINE |
| ISSN: | 2379-5077 |
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| DOI: | 10.1128/msystems.00840-26 |