{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T08:57:12Z","timestamp":1791881832516,"version":"4.3.4"},"reference-count":26,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2026,8,30]],"date-time":"2026-08-30T00:00:00Z","timestamp":1788048000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Alzheimer\u2019s Disease"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Late-life depression is robustly associated with subsequent cognitive impairment, yet the dose-response gradient and temporally validated, uncertainty-aware prediction tools remain understudied in Chinese older adults.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To characterize the severity-graded epidemiological association between depressive symptoms and low cognitive performance, and to develop and validate a prospective risk-prediction framework with explicit uncertainty estimation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed 31,570 observations from 12,494 participants aged \u226545 years in the China Health and Retirement Longitudinal Study (2011\u20132020). Depressive symptoms were measured with the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10); low cognitive performance was defined as total cognition score &lt; 10. Layer 1 reported severity-graded prevalence and adjusted odds ratios. Layer 2 implemented a BARNN-inspired prospective state-transition predictor via a diverse tree ensemble, yielding predictive probability and epistemic uncertainty. Both layers used strict participant-level temporal validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Clinically significant depressive symptoms (CESD-10\u2009\u2265\u200910) were associated with twofold higher odds of low cognitive performance (OR\u2009=\u20092.07, 95% CI 1.97\u20132.18), with a monotonic dose-response gradient across severity tiers (20.8% to 46.0%, p\u2009&lt;\u20090.001). In prospective validation, the ensemble achieved comparable discrimination (AUC-ROC 0.838 versus 0.839 for logistic regression, P\u2009=\u20090.85) but superior calibration (Brier score 0.099 versus 0.126), plus per-sample uncertainty estimates and an explicit algorithmic deferral tier. The frozen four-tier rule produced a steep risk gradient (Low 5.8%, Moderate 24.9%, High 63.0%; OR high versus low=27.8).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Depressive symptom severity shows a robust graded association with low cognitive performance in Chinese older adults. The framework supports reliable ordinal risk stratification with algorithmic deferral, providing a transferable template for population-level cognitive monitoring.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13872877261482053","type":"journal-article","created":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T03:28:01Z","timestamp":1788146881000},"page":"1797-1809","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Depressive symptoms and low cognitive performance in Chinese older adults: A severity-graded epidemiological analysis and a BARNN-inspired prospective risk-prediction framework with strict participant-level temporal validation"],"prefix":"10.1177","volume":"113","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-0368-6656","authenticated-orcid":false,"given":"Qiang","family":"Zhang","sequence":"first","affiliation":[{"name":"Jining Polytechnic"},{"name":"Shandong University of 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