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Polygenic risk scores (PRS) provide a stable, lifelong measure of genetic predisposition to obesity. However, existing PRS have not been evaluated for their association with longitudinal weight change in adulthood and often lack generalizability across diverse genetic ancestry groups.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      We conducted ancestry-specific genome-wide association study meta-analyses of body mass index (BMI) in populations of European, African or African American, Admixed American, East Asian, and South Asian ancestries and developed ancestry-specific PRS. A multi-ancestry polygenic risk score (MAPRS) was trained using ancestry-specific PRS in a model selection dataset (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u200939,685) from the All of Us Research Program (AoU). We evaluated the MAPRS in an independent AoU model evaluation dataset (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u2009158,743) for BMI prediction and in a separate AoU test dataset (\n                      <jats:italic>N<\/jats:italic>\n                      \u2009=\u200978,219) with repeated measurements over 1.5\u20132.5 years for weight change prediction. The outcomes included change in BMI and\u2009\u2265\u200910% or\u2009\u2265\u20095% total body weight (TBW) gain. We further examined the relationship between MAPRS and 12 clinical risk factors commonly comorbid with obesity in relation to weight change.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The MAPRS captured 7.05% of the variance in measured BMI in the AoU model evaluation dataset and demonstrated improved generalizability across all non-European genetic ancestry groups. In the AoU test dataset, conditioned on baseline BMI at the second-to-last measurement, a one SD increase in MAPRS was associated with a 0.16 kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      increase in future BMI (standard error\u2009=\u20090.012 kg\/m\n                      <jats:sup>2<\/jats:sup>\n                      ;\n                      <jats:italic>p<\/jats:italic>\n                      -value\u2009=\u20092.2\u2009\u00d7\u200910\n                      <jats:sup>\u201339<\/jats:sup>\n                      ), 1.27-fold increased odds of experiencing\u2009\u2265\u200910% TBW gain (95% CI: 1.24\u20131.31;\n                      <jats:italic>p<\/jats:italic>\n                      -value\u2009=\u20091.4\u2009\u00d7\u200910\n                      <jats:sup>\u201355<\/jats:sup>\n                      ), and 1.15-fold increased odds of experiencing\u2009\u2265\u20095% TBW gain (95% CI: 1.13\u20131.18;\n                      <jats:italic>p<\/jats:italic>\n                      -value\u2009=\u20092.8\u2009\u00d7\u200910\n                      <jats:sup>\u201339<\/jats:sup>\n                      ). These associations were observed across all genetic ancestry groups and remained highly consistent after adjustment for any clinical risk factor. In contrast, most clinical risk factors demonstrated inconsistent or weaker associations with weight change outcomes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>We developed an MAPRS for BMI that represents a robust and generalizable risk factor for longitudinal weight gain in adulthood, providing a foundation for genetically informed risk stratification and earlier, more targeted obesity prevention strategies.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s13073-026-01741-8","type":"journal-article","created":{"date-parts":[[2026,8,5]],"date-time":"2026-08-05T02:34:49Z","timestamp":1785897289000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A multi-ancestry polygenic risk score for body mass index predicts longitudinal weight change"],"prefix":"10.1186","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5664-5698","authenticated-orcid":false,"given":"Tianyuan","family":"Lu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lily N.","family":"Stalter","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kate V.","family":"Lauer","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bret M.","family":"Hanlon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenmin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luke M.","family":"Funk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,8,5]]},"reference":[{"key":"1741_CR1","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1038\/s41574-019-0176-8","volume":"15","author":"M Bl\u00fcher","year":"2019","unstructured":"Bl\u00fcher M. 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A multi-ancestry polygenic risk score for body mass index predicts longitudinal weight change. figshare. 2026. https:\/\/doi.org\/10.6084\/m9.figshare.29553035.","DOI":"10.6084\/m9.figshare.29553035"}],"container-title":["Genome Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s13073-026-01741-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13073-026-01741-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s13073-026-01741-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T13:56:20Z","timestamp":1788443780000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s13073-026-01741-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,5]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1741"],"URL":"https:\/\/doi.org\/10.1186\/s13073-026-01741-8","relation":{},"ISSN":["1756-994X"],"issn-type":[{"value":"1756-994X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,5]]},"assertion":[{"value":"17 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"29 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 August 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This research was conducted using secondary data and has been determined to be exempt from IRB review under the University of Wisconsin-Madison Institutional Review Board policies with a formal IRB waiver. De-identified data were obtained from the NIH All of Us Research Program, which implements a broad informed consent model under rigorous ethical and regulatory oversight. The NIH All of Us Research Program collects biospecimens and electronic health records under an IRB-approved protocol that adheres to federal regulations including the Common Rule (45 CFR 46). Participants voluntarily enroll and provide electronic informed consent through a process that includes multimedia materials to enhance understanding, covering topics such as data sharing, privacy protections, and withdrawal rights. Information on this consent process and governance is available at\n                      \n                      .","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable, as the manuscript does not contain any identifiable individual person\u2019s data.","order":2,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"T.L. has been consulting for Five Prime Sciences Inc. for research programs unrelated to this study. W.Z. is an employee of Regeneron Pharmaceuticals, Inc., and the employer had no role in the design, conduct, or reporting of this research. The other authors declare no conflict of interest.","order":3,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"130"}}