Abstract 072: Sex Differences In The Relationship Between The Age Of Hypertension Diagnosis And Brain Structure In Midlife And Older Population: New Insights From The Uk Biobank
Bibliographic record
Abstract
Background: Hypertension is an established leading risk factor for morbidity and mortality from vascular diseases across the lifespan. Early adulthood to midlife high blood pressure has been linked to later reduced brain volume and white matter hyperintensities, both of which are hallmarks of cerebral small vessel disease (CSVD). However, whether the association of age at diagnosis of hypertension with CSVD-related brain atrophy differs between females and males is not fully understood. Therefore, our objective was to assess for sex differences in the association between the age at diagnosis of hypertension and CSVD-related brain structural changes. Method: We used data from the UK Biobank. We selected participants with a known age of diagnosis of hypertension who had also obtained a brain MRI between 2014 and 2016 (n = 9,410) and stratified by age of diagnosis of hypertension (<35 years, 35-44 years, 45-54 years, 55-64 years, ≥65 years old) and sex. Using propensity score matching a control participant with MRI data but no hypertension was chosen at random. Generalized linear models were used to determine changes in brain structure as a function of age of hypertension diagnosis and sex while controlling for vascular risk factors and demographic covariates. Results: There was a trend for lower brain grey matter volume (adjusted for head size) in males diagnosed with hypertension at a younger age of diagnosis (45-54 years) (β –8448.5 (95%CI, [–2876.7 to –5662.6]mm 3 ), (55-64 years) (β –7977.0 (95%CI, [–3283.0 to –5630.0 ]mm 3 ) as well as lower volume of white matter (<35 years) (β –25788.3 (95%CI, [– 1965.7 to –13877.0 ]mm 3 ), but not in females. The volume of white matter hyperintensities was greater in both males (β (95%CI, 1488.1 [1149.2 to 1827]mm 3 ) and females with hypertension (1700.1 [1351.9 to 2048.3]mm 3 ) than those without hypertension; more so in females (P interaction =0.03). Discussion: Hypertension diagnosed in mid-life was associated with smaller brain grey matter and white matter volumes in males. Even though both males and females with hypertension had a higher volume of white matter hyperintensities, hypertensive females had the highest volume. Therefore, sex-specific analyses are necessary to uncover effect of hypertension related to CSVD.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".