Genomics of perivascular space burden unravels early mechanisms of cerebral small vessel disease
Bibliographic record
Abstract
Abstract Perivascular space burden (PVS) is an emerging and possibly the earliest magnetic resonance imaging (MRI)-marker of cerebral small vessel disease (cSVD), a leading cause of stroke and dementia. Its molecular underpinnings are unknown. Genome-wide and whole-exome association studies in 40,095 participants (21 population-based cohorts, 66.3±8.6 years) revealed 24 genome-wide significant PVS risk loci. These showed association with white matter PVS already at age 20, suggesting an important role of early-life factors. PVS loci were enriched in genes causing early-onset leukodystrophies and genes expressed in fetal brain endothelial cells. Mendelian randomization analyses supported causal associations of high blood pressure with basal ganglia (BG) and hippocampal PVS, and of BG PVS with stroke. Transcriptome-wide association studies suggest causal implication of 11 genes, to prioritize for experimental follow-up as putative biotargets for cSVD. Two-thirds of PVS loci point to novel pathways, involving extracellular matrix, membrane transport, and developmental processes, with enrichment in targets of existing drugs for vascular/cognitive disorders.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".