{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:51:08Z","timestamp":1792785068739,"version":"4.5.0"},"reference-count":0,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T00:00:00Z","timestamp":1791763200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T00:00:00Z","timestamp":1791763200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"Science Innovation Brain Science and Brain-like Research from the Ministry of National Science and Technology","award":["2022ZD0211805"],"award-info":[{"award-number":["2022ZD0211805"]}]},{"name":"Clinical and Translational Medicine Research Program from Chinese Academy of Medical Sciences","award":["2022-I2M-C&T-B-116"],"award-info":[{"award-number":["2022-I2M-C&T-B-116"]}],"id":[{"id":"https:\/\/ror.org\/02drdmm93","id-type":"ROR","asserted-by":"crossref"}]},{"name":"risk factors and assessment techniques for brain aging","award":["HX\u2010A\u20102023037"],"award-info":[{"award-number":["HX\u2010A\u20102023037"]}]},{"name":"Beijing Municipal Health Commission\u2019s Excellence Clinical Research Program for Research Oriented Wards Projec","award":["BRWEP2024W022040100"],"award-info":[{"award-number":["BRWEP2024W022040100"]}],"id":[{"id":"https:\/\/ror.org\/0374a5s68","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Beijing Scholar","award":["097"],"award-info":[{"award-number":["097"]}]},{"name":"National Key Research and Development Program of China","award":["2022YFC3501100; 2022YFC3501102"],"award-info":[{"award-number":["2022YFC3501100; 2022YFC3501102"]}]},{"name":"Noncommunicable Chronic Diseases National Science and Technology Major Project","award":["2023ZD0504700"],"award-info":[{"award-number":["2023ZD0504700"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Sci Rep"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Brain age gap is an emerging neuroimaging marker that captures interindividual variation in brain structural aging. Identifying accessible peripheral biomarkers associated with accelerated brain aging may improve early risk recognition and provide insight into systemic biological processes linked to brain health. This study investigated the associations between routine serum laboratory biomarkers and MRI-derived brain age gap in a health examination cohort. We included 852 participants with available structural MRI and routine laboratory data. Brain age was estimated from structural MRI using a deep learning model, and corrected brain age gap (CorrectedBAG) was derived to reduce age-related prediction bias. We evaluated 25 serum laboratory biomarkers spanning metabolic, immune-inflammatory, hepatic, renal, and bone\/nutrition-related domains. Multivariable linear regression was used to examine associations with CorrectedBAG, and restricted cubic spline models were applied to assess potential nonlinear dose-response relationships. Binary logistic regression was further used to evaluate accelerated brain aging defined by brain age gap thresholds. Among the 25 biomarkers examined, serum alkaline phosphatase (ALP) and neutrophil-to-lymphocyte ratio (NLR) showed independent positive associations with CorrectedBAG. Higher ALP was associated with greater CorrectedBAG (\u03b2\u2009=\u20090.4073, 95% CI 0.1534\u20130.6613;\n                    <jats:italic>P<\/jats:italic>\n                    \u2009=\u20090.0017), as was higher NLR (\u03b2\u2009=\u20090.2558, 95% CI 0.0164\u20130.4952;\n                    <jats:italic>P<\/jats:italic>\n                    \u2009=\u20090.0365). In logistic regression analyses, ALP was consistently associated with greater odds of accelerated brain aging defined as BAG\u2009&gt;\u20090 years (OR\u2009=\u20091.011, 95% CI 1.003\u20131.019;\n                    <jats:italic>P<\/jats:italic>\n                    \u2009=\u20090.010) and BAG\u2009&gt;\u20093 years (OR\u2009=\u20091.013, 95% CI 1.004\u20131.023;\n                    <jats:italic>P<\/jats:italic>\n                    \u2009=\u20090.006). NLR was also associated with BAG\u2009&gt;\u20093 years (OR\u2009=\u20091.333;\n                    <jats:italic>P<\/jats:italic>\n                    \u2009=\u20090.005). Restricted cubic spline analyses supported approximately linear associations of ALP and NLR with CorrectedBAG. In this Asian health examination cohort, higher serum ALP and NLR were associated with greater MRI-derived brain age gap. These findings suggest that routinely available serum biomarkers may capture peripheral biological processes related to accelerated brain structural aging. Further studies are warranted to clarify the temporal relationships and biological pathways underlying these associations.\n                  <\/jats:p>","DOI":"10.1038\/s41598-026-73753-8","type":"journal-article","created":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T17:33:45Z","timestamp":1791826425000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Peripheral serum biomarkers and MRI-derived accelerated brain aging: a cross-sectional study using deep learning-based brain age estimation"],"prefix":"10.1038","author":[{"given":"Yao","family":"Zhong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"Chai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiwei","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunyi","family":"Hao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fei","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yi","family":"Ju","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yaou","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huaguang","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingquan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,10,12]]},"container-title":["Scientific Reports"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-73753-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,10,12]],"date-time":"2026-10-12T17:33:46Z","timestamp":1791826426000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41598-026-73753-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,10,12]]},"references-count":0,"alternative-id":["73753"],"URL":"https:\/\/doi.org\/10.1038\/s41598-026-73753-8","relation":{},"ISSN":["2045-2322"],"issn-type":[{"value":"2045-2322","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,10,12]]},"assertion":[{"value":"23 June 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 September 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 October 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}]}}