{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T16:11:06Z","timestamp":1790784666104,"version":"4.1.0"},"reference-count":47,"publisher":"SAGE Publications","license":[{"start":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T00:00:00Z","timestamp":1790640000000},"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"],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Cognitive impairment (including Alzheimer's disease), frailty, and hypertension are interlinked challenges in older adults. Among non-frail adults, prefrail older adults represent a crucial window for prevention. However, whether systolic blood pressure (SBP) affects cognitive decline differently across age-frailty strata, or what constitutes an optimal age-frailty-stratified target, remains unclear.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To investigate the association of SBP with annual cognitive aging rate in non-frail adults (robust\/prefrail, \u226545years), focusing on nonlinearity and modification by age-frailty status, and to explore optimal SBP thresholds, especially in prefrail older adults.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This longitudinal analysis included 4723 non-frail adults from China Health and Retirement Longitudinal Study (2011\u20132018). SBP and annual cognitive aging rate (Z-score) were measured, with age and frailty categorized as middle-aged (45\u201364 years)\/older (\u226565 years), robust\/prefrail. Linear regression and restricted cubic splines assessed associations, with interaction effect tested. Exploratory threshold and piecewise linear regression identified optimal SBP ranges and breakpoints.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Besides linearity, a significant inverted U-shaped relationship existed between SBP and annual global cognition aging rate (p-nonlinear=0.049), with a peak at 120.1 mmHg overall, jointly modified by age-frailty status. A steep, distinct curve with a higher peak (130.8 mmHg; p-nonlinear=0.005) was found in prefrail older adults. The SBP-by-age-frailty nonlinear interaction was significant (p\u2009=\u20090.010) and improved model fit (p\u2009=\u20090.001). Divergent coefficients across breakpoints corroborated the robustness of the nonlinear SBP-cognition association in both populations. Episodic memory analyses showed highly concordant patterns above.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>To optimize cognitive aging prevention, personalized SBP targets should be set based on age-frailty status, with relaxed thresholds for vulnerable prefrail older adults.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/13872877261492695","type":"journal-article","created":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T11:44:21Z","timestamp":1790682261000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Age-frailty status jointly modifies the nonlinear association between systolic blood pressure and annual cognitive aging rate: A community-based cohort study"],"prefix":"10.1177","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7179-2984","authenticated-orcid":false,"given":"Qing","family":"Tian","sequence":"first","affiliation":[{"name":"The Third Affiliated Hospital of Sun Yat-sen University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chengsong","family":"Deng","sequence":"additional","affiliation":[{"name":"Third Affiliated Hospital of Sun Yat-Sen University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinchi","family":"Liao","sequence":"additional","affiliation":[{"name":"The Third Affiliated Hospital of Sun Yat-sen University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yongqiang","family":"Dai","sequence":"additional","affiliation":[{"name":"The Third Affiliated Hospital of Sun Yat-sen University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tingting","family":"Lu","sequence":"additional","affiliation":[{"name":"The Third Affiliated Hospital of Sun Yat-sen University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,9,29]]},"reference":[{"key":"e_1_3_3_2_2","article-title":"Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019","volume":"7","author":"GBD 2019 Dementia Forecasting Collaborators","year":"2022","unstructured":"GBD 2019 Dementia Forecasting Collaborators. 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