Genomics of perivascular space burden unravels early mechanisms of cerebral small vessel disease
Bibliographic record
Abstract
Abstract Background Perivascular space (PVS) burden is an emerging MRI‐marker of cerebral small vessel disease (cSVD), a leading cause of stroke and dementia. Underlying mechanisms of PVS are unknown. PVS are thought to be related to the glymphatic system, involved in brain clearance of molecules such as amyloid beta. We aimed to decipher the genetic underpinnings of PVS burden. Method We conducted genome‐wide and whole‐exome association studies in N = 39,823 participants for white matter (WM) PVS, N = 40,000 for basal ganglia (BG) PVS and N = 40,095 for hippocampal (HIP) PVS (21 population‐based cohorts, 66.3±8.6 years). As PVS were rated with different scales across cohorts, we tested association of genetic variants with the top quartile of PVS burden distribution in each cohort followed by a sample‐size weighted meta‐analysis. We searched for shared genetic variation with related vascular and neurological phenotypes using linkage disequilibrium‐score regression, explored causality of associations with putative risk factors using Mendelian randomization and conducted extensive functional exploration of identified PVS loci using multiple bioinformatics approaches, including transcriptome‐wide association studies. Result We identified 24 genome‐wide significant PVS risk loci. These showed association with WM PVS already at age 20 in 1,748 young healthy adults, 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 BG and HIP PVS, and of BG PVS with stroke. Transcriptome‐wide association and colocalization analyses suggest causal implication of 11 genes, that could be prioritized for experimental follow‐up. 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, and infectious disorders. Conclusion In this first gene‐mapping study of PVS, one of the earliest MRI‐markers of cSVD, we describe 24 genome‐wide significant risk loci. Our findings provide completely novel insight into the biology of PVS across the adult lifespan and its contribution to cSVD pathophysiology, with potential for genetically informed prioritization of drug targets for prevention trials of cSVD, a major cause of stroke and dementia worldwide.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".