Signature White Matter Hyperintensity Locations Associated With Vascular Risk Factors Derived From 15 653 Individuals
Bibliographic record
Abstract
BACKGROUND: White matter hyperintensities (WMHs) of presumed vascular origin are common in the elderly and are associated with vascular risk factors. There is evidence that vascular risk factors, in particular hypertension, are associated with WMH in particular locations of the white matter. However, it remains unclear whether this is true for all risk factors and whether signature WMH locations differ between risk factors. We aimed to identify WMH locations associated with vascular risk factors in community-dwelling individuals. METHODS: We pooled cross-sectional data from 16 population-based cohorts (15 653 individuals; mean age, 64.2±11.8 years; 52.2% female) through the Meta VCI Map Consortium. We quantified associations between WMH volumes in 50 white matter regions and 6 vascular risk factors using linear mixed models. Analyses were corrected for age, sex, study site, and total WMH volume. RESULTS: =0.531) were not. After correcting for total WMH volume, hypertension was associated with WMH volume in 10 regions (ie, bilateral external capsule, superior longitudinal fasciculus, superior corona radiata, anterior limb of the internal capsule, left anterior corona radiata, and left superior fronto-occipital fasciculus), smoking (body corpus callosum), diabetes (genu corpus callosum), and obesity (left inferior fronto-occipital fasciculus), each with one region. CONCLUSIONS: Hypertension has a signature WMH pattern, whereas associations between other vascular risk factors and regional WMH volumes seem to be mainly explained by a global increase in WMH rather than region-specific effects.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".