{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T13:59:28Z","timestamp":1790085568152,"version":"4.0.1"},"reference-count":115,"publisher":"American Society for Microbiology","issue":"9","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.asm.org\/non-commercial-tdm-license"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2025-16070008, RS-2026-25472972"],"award-info":[{"award-number":["RS-2025-16070008, RS-2026-25472972"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010418","name":"Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","award":["RS-2019-II191906"],"award-info":[{"award-number":["RS-2019-II191906"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.asm.org"],"crossmark-restriction":true},"short-container-title":["mSystems"],"published-print":{"date-parts":[[2026,9,22]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title\/>\n                    <jats:p>\n                      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,\n                      <jats:italic toggle=\"yes\">Corynebacterium propinquum<\/jats:italic>\n                      (\n                      <jats:italic toggle=\"yes\">C. propinquum<\/jats:italic>\n                      ) 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.\n                    <\/jats:p>\n                    <jats:sec>\n                      <jats:title>IMPORTANCE<\/jats:title>\n                      <jats:p>\n                        Age 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\n                        <jats:italic toggle=\"yes\">Corynebacterium propinquum<\/jats:italic>\n                        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.\n                      <\/jats:p>\n                    <\/jats:sec>\n                  <\/jats:sec>","DOI":"10.1128\/msystems.00840-26","type":"journal-article","created":{"date-parts":[[2026,8,20]],"date-time":"2026-08-20T13:00:19Z","timestamp":1787230819000},"update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":0,"title":["Age-adjusted machine learning identifies facial skin microbes associated with skin quality among Korean women"],"prefix":"10.1128","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5323-9209","authenticated-orcid":false,"given":"Sangbeen","family":"Park","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/04xysgw12","id-type":"ROR","asserted-by":"publisher"}],"name":"Department of Life Sciences, Pohang University of Science and 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