Impact of Cerebrovascular Disease on Gender‐Specific Cognitive and Biomarker Profiles: Insights from the Southeast Asian BIOCIS Cohort
Bibliographic record
Abstract
BACKGROUND: Emerging evidence suggests that biological sex influences cognitive performance across various domains. However, data specific to Southeast Asian populations remain scarce. This study aims to delineate gender-specific cognitive profiles and cerebrovascular risk among cognitively unimpaired individuals within the Southeast Asian BIOCIS cohort, thereby identifying potential disparities pertinent to this demographic. METHOD: The study included 714 cognitively unimpaired, age-matched participants (357 males and 357 females) from the BIOCIS cohort. Each participant underwent a comprehensive neuropsychological assessment, which included the Montreal Cognitive Assessment (MoCA), the Visual Cognitive Assessment Test (VCAT), and tests evaluating episodic memory, executive function, processing speed, visuospatial abilities, and language. Demographic differences were analyzed using independent t-tests and chi-square tests. Gender differences in cognitive performance and interaction effects with cerebrovascular risk (CAIDE scores) were examined using multilinear regression models, adjusting for CAIDE scores. RESULT: The mean age of participants was 56.46±10.29 years, with an average education level of 15.53±3.22 years. Males had a significantly higher prevalence of a CAIDE score of 5 or above (p <0.001), hypertension (p = 0.001), and higher BMI (p <0.001), while females had a higher prevalence of hyperlipidaemia (p = 0.004). Neuropsychological assessments showed females scored higher in episodic memory and language (p <0.001), and males scored higher in visuospatial abilities (p <0.001). Multivariable regression, adjusted for CAIDE score, revealed significant gender differences in episodic memory (p = 0.007), executive function (p = 0.042), visuospatial (p < 0.001), and language (p <0.001). Gender differences varied with CAIDE score ≥6 (Table 2, Figure 1). CONCLUSION: This study identifies distinct gender-specific cognitive, neuroimaging, and biomarker profiles in a Southeast Asian population. Females outperformed males in episodic memory and language, while males excelled in visuospatial abilities. Biomarker differences, such as elevated GFAP in females and higher p-tau 181 in males, suggest underlying neurobiological mechanisms. Neuroimaging showed males having higher WMH volumes and females with greater gray matter volumes. Among those with a CAIDE score ≥6, females demonstrated smaller declines in episodic memory, language, and visuospatial abilities. These findings highlight the need for research to develop tailored assessments and interventions addressing gender-specific cognitive and biological profiles.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".