{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T06:05:08Z","timestamp":1789538708620,"version":"build-2803163510"},"reference-count":40,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T00:00:00Z","timestamp":1789516800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study investigates the mechanistic link between age-related osteoporosis (OP) and cognitive decline, with a particular emphasis on the neuroprotective role of undercarboxylated osteocalcin (ucOC)\u2014the biologically active form of osteocalcin\u2014and its influence on brain structural integrity and cognitive performance. We further explored the translational potential of integrating neuroimaging features, and machine learning algorithms to develop predictive models for early identification of cognitive deterioration in OP patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We conducted a comprehensive voxel-based morphometry (VBM) analysis across two independent datasets. Voxel-wise comparisons between OP patients and healthy controls were performed to characterize grey matter volume (GMV) alterations. Correlation and mediation analyses were subsequently employed to elucidate the relationships among serum ucOC levels, GMV, and cognitive function. Brain regions exhibiting significant mediation effects were identified as ucOC-associated neuroimaging biomarkers in Dataset 1, and their GMV values were used as fixed features to develop machine-learning models for predicting 1-year cognitive decline in OP patients in the independent Dataset 2, evaluated through a leakage-free leave-one-out cross-validation procedure.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>OP patients exhibited significantly lower Montreal Cognitive Assessment (MoCA) scores and reduced GMV in the medial prefrontal cortex (mPFC) relative to healthy controls. Notably, serum ucOC, but not carboxylated osteocalcin (cOC), demonstrated significant positive correlations with both MoCA scores and mPFC GMV in OP patients. Mediation analysis further confirmed that mPFC GMV statistically mediates the relationship between ucOC and cognitive function. Machine-learning models integrating mPFC GMV achieved good discriminative performance in leakage-free validation in Dataset 2 (XGBoost: AUC\u202f=\u202f0.84, 95% CI [0.79, 0.88]), classifying OP patients at elevated risk of cognitive decline over a 1-year follow-up period.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Our study establishes undercarboxylated osteocalcin (ucOC) as a mechanistically relevant, circulating biomarker of cognitive resilience in osteoporosis, and identifies medial prefrontal cortex grey matter volume (mPFC GMV) as its structural neuroimaging correlate for ucOC. Importantly, XGBoost yields a promising predictive framework that achieved good discriminative accuracy for 1-year cognitive decline risk.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fneur.2026.1950788","type":"journal-article","created":{"date-parts":[[2026,9,16]],"date-time":"2026-09-16T05:34:41Z","timestamp":1789536881000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Integrative multimodal framework combining undercarboxylated osteocalcin and medial prefrontal cortex morphometry for prediction of cognitive decline in osteoporosis: a machine learning study"],"prefix":"10.3389","volume":"17","author":[{"given":"Xiaomeng","family":"Tong","sequence":"first","affiliation":[{"name":"Department of Laboratory Medicine, China-Japan Friendship Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, China-Japan Friendship Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongtong","family":"Cao","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, China-Japan Friendship Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengwu","family":"Han","sequence":"additional","affiliation":[{"name":"Department of Laboratory Medicine, China-Japan Friendship Hospital","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,16]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"3553","DOI":"10.3390\/ijms22073553","article-title":"Skeletal aging and osteoporosis: mechanisms and 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