Age Moderates Associations of Hypertension, White Matter Hyperintensities, and Cognition
Bibliographic record
Abstract
BACKGROUND: Hypertension and white matter hyperintensities (WMH) are mutually associated risk factors for cognitive impairment. However, age may modify the associations between hypertension and WMH, and their links to cognitive impairment. OBJECTIVE: We evaluated the interaction between age and hypertension on WMH, and the age-stratified associations of hypertension and WMH with cognition. METHODS: Key measures include systolic blood pressure (SBP), WMH (modified Fazekas visual ratings of cranial MRI), and the Montreal Cognitive Assessment (MoCA). Participants (N = 488) with prodromal and mild dementia were age-stratified (≤49, 50-59, 60-69,≥70), and considered hypertensive if their SBP≥140 mmHg. The interaction between age strata and hypertension on WMH, and age-stratified associations of hypertension and WMH with cognition, were evaluated using multiple linear regression analyses. Analyses controlled for other risk factors for WMH and cognitive impairment. RESULTS: Age moderated the association between SBP and WMH. Hypertension was associated with higher WMH only in those aged 60-69, and WMH trends across age bands differed between those with and without hypertension. Finally, WMH and SBP≥140 were independently associated with lower MoCA scores within the 50-59 age band, while WMH alone was associated with poorer MoCA scores in the≥70 age band. CONCLUSION: In adults with prodromal or mild dementia, hypertension was associated with WMH specifically in the 60-69 age strata. Associations between hypertension and WMH with poorer cognition also differed across age bands. Future studies will be needed to investigate whether blood pressure management to slow cognitive decline by targeting WMH may be age dependent.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".