Polygenic Overlap Between Kidney Function and Large Artery Atherosclerotic Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Epidemiological studies show strong associations between kidney dysfunction and risk of ischemic stroke (IS), the mechanisms of which are incompletely understood. We investigated whether these associations may reflect shared heritability because of a common polygenic basis and whether this differed for IS subtypes. METHODS: Polygenic models were derived using genome-wide association studies meta-analysis results for 3 kidney traits: estimated glomerular filtration rate using serum creatinine (eGFRcrea: n=73 998), eGFR using cystatin C (eGFRcys: n=22 937), and urinary albumin to creatinine ratio (n=31 580). For each, single nucleotide polymorphisms passing 10 P value thresholds were used to form profile scores in 4561 IS cases and 7094 controls from the United Kingdom, Germany, and Australia. Scores were tested for association with IS and its 3 aetiological subtypes: large artery atherosclerosis, cardioembolism, and small vessel disease. RESULTS: Polygenic scores correlating with higher eGFRcrea were associated with reduced risk of large artery atherosclerosis, with 5 scores reaching P<0.05 (peak P=0.004) and all showing the epidemiologically expected direction of effect. A similar pattern was observed for polygenic scores reflecting higher urinary albumin to creatinine ratio, of which 3 associated with large artery atherosclerosis (peak P=0.01) and all showed the expected directional association. One urinary albumin to creatinine ratio-based score also associated with small vessel disease (P=0.03). The global pattern of results was unlikely to have occurred by chance (P=0.02). CONCLUSIONS: This study suggests possible polygenic correlation between renal dysfunction and IS. The shared genetic components may be specific to stroke subtypes, particularly large artery atherosclerotic stroke. Further study of the genetic relationships between these disorders seems merited.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".