P030 EFFECTS OF GENDER-RELATED FACTORS ON THE HYPERTENSION-WHITE MATTER HYPERINTENSITIES ASSOCIATION: A SEX-STRATIFIED ANALYSIS
Bibliographic record
Abstract
Background: Hypertension (HTN) is a global health issue affecting various vascular beds, including the brain. There is a gap in understanding how gender-related factors, such as roles, identity, relationships, and institutionalized gender, contribute to sex differences in the relationship between HTN and brain structure changes. This study aims to investigate the role of gender-related factors in the relationship between HTN and white matter hyperintensities (WMH) in both males and females. Methods: This cohort study used MRI data from 41,300 UK Biobank participants. HTN status was determined through hospital records and self-report, and WMH volumes were measured using MRI. Eight gender-related factors, including unpaid work, caregiving, unemployment, risk-taking behavior, living alone, education, income, and material deprivation, were examined. Generalized linear models were used to evaluate the interactions between each gender-related factor and HTN in males and females. Effect modification was evaluated using the Z-test to compare the beta coefficients of the HTN-WMH association across strata. Results: The study included 19,260 males and 22,040 females aged 40-69 years at baseline, with 27.3% hypertensive. Hypertensive males (7.7±9.2 mL) and females (7.2±8.8 mL) exhibited greater WMH volumes compared to their normotensive counterparts (males: 4.7±5.9 mL; females: 3.9±5.2 mL). The main effects model revealed that low income was significantly associated with greater WMHs in females, while no gender-related factors were linked to WMHs in males after adjusting for clinical, behavioral, and gender-related variables. No significant interactions between HTN and gender-related factors were found in either males or females after adjustment. In the stratified analysis, females with lower material deprivation showed a significantly greater increase in WMHs compared to those with higher material deprivation. No other gender-related factors in either group interacted with HTN to amplify the association with WMHs. Discussion: Socioeconomic factors, particularly in females, were associated with WMH. These findings highlight the importance of considering biological and social determinants to address hypertension risks effectively.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".