Evidence mapPaperPMID 42494674Full record

ArticleCerebral circulation - cognition and behavior2026

Comparison of cognitive decline rates and risk factors between stroke and non - stroke populations: An analysis of data from the China health and retirement longitudinal study (CHARLS).

Jiayin Zhang, Zhe Wang, Yuhang Wu, Xiaolong Ren, Ke Li, Taiming Zhang, Long Yan

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Article in Cerebral circulation - cognition and behavior, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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7 authors.

Jiayin ZhangDepartment of Neurosurgery, The Second Hospital of Jilin University, Changchun, Jilin, China.
Zhe WangDepartment of Geriatrics, The First Hospital of Jilin University, Changchun, Jilin, China.
Yuhang WuDepartment of Neurosurgery, The Second Hospital of Jilin University, Changchun, Jilin, China.
Xiaolong RenDepartment of Neurosurgery, The Second Hospital of Jilin University, Changchun, Jilin, China.
Ke LiDepartment of Neurosurgery, The Second Hospital of Jilin University, Changchun, Jilin, China.
Taiming ZhangJilin University, Changchun, Jilin, China.
Long YanDepartment of Neurosurgery, The Second Hospital of Jilin University, Changchun, Jilin, China.

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6 · The paper itself

Abstract

Objective: This study aimed to longitudinally compare cognitive decline trajectories between stroke and non-stroke populations, and identify key risk factors associated with accelerated cognitive deterioration. Methods: Data were extracted from the 2011 baseline and 2020 follow-up waves of the China Health and Retirement Longitudinal Study (CHARLS). After excluding participants with missing cognitive assessment data, stroke status information, or follow-up data, a total of 6810 eligible participants were included. Cognitive function was evaluated using a composite score integrating episodic memory and executive function, ranging from 0 to 21. Univariate and multivariate logistic regression models were used to identify risk factors for cognitive decline, and a nomogram was constructed to visualize the combined effect of key variables. Results: At baseline (2011), there was no significant difference in cognitive function scores between the stroke and non-stroke groups (median: 13 vs. 13.5, P = 0.307). However, after 10 years of follow-up, the stroke group exhibited a significantly faster cognitive decline rate (median change: 6% vs. 3.1%, P = 0.031), with a higher proportion of cognitive decline (62.4%vs. 51.1%, P < 0.05). Multivariate logistic regression analysis revealed that Cystatin C (CysC; β=0.461, OR=1.585, 95% CI: 1.154-2.177, P < 0.05), low-density lipoprotein (LDL; β=0.002, OR=1.002, 95% CI: 1.000-1.004, P < 0.05) and blood urea nitrogen (BUN; β=0.018, OR=1.018, 95% CI: 1.002-1.034, P < 0.05)were independent risk factors for cognitive decline in the total population. In the stroke subgroup, hypertension was associated with cognitive decline (OR = 4.250, 95% CI: 1.169-15.454, P = 0.028). Conclusion: Compared with stroke-free individuals, stroke patients experienced a faster rate of cognitive decline. CysC, LDL, and BUN are reliable risk predictors of cognitive deterioration in the general elderly population, while hypertension correlates with post-stroke cognitive decline.

Indexed as

CHARLS cohortCognitive declineCystatin CHypertensionLow-density lipoproteinStroke

Identifiers

PMID42494674
PMCPMC13393735

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.