Genetic Polymorphisms and Kidney Stones Around the Globe: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective: This study explores associations between recurrent kidney stones and genetic polymorphisms. Methods: Meta-analysis of polymorphisms in renal stone cases versus control groups. Four electronic databases (PubMed, SCOPUS, EMBASE, and Web of Science) were searched up to 30 May 2021, using the keywords: “kidney stone” or “kidney calculi,” or “urolithiasis” or “nephrolithiasis” or “urinary calculi” and “genome” or “genetic” or “mutation” or “single nucleotide polymorphism.” Forrest plots, ORs, 95% CI, Chi-square (χ2)-test, and index of heterogeneity (I2) were calculated. Only studies with Newcastle–Ottawa scale (NOS) ≥ 6 were included for quality control, and Funnel, Begg’s, and Eager’s plots assessed publication bias. PROSPERO: CRD42022250427. Results: Among 7,671 searched articles, 72 were included. Polymorphisms in VDR (OR: 1.20; 95% CI: 1.06–1.36), CASR (OR = 1.24; 95% CI: 1.01–1.52), Osteopontin (OR = 1.38; 95% CI: 1.09–1.74), and Urokinase genes (OR = 1.52; 95% CI: 1.02–2.28) showed a significant association with risk of urinary stone formation, while Klotho gene showed a protective effect (OR = 0.75; 95% CI: 0.57–0.99). The VDR gene polymorphism was frequent in Asians, whereas CASR polymorphism was frequent in European and North American populations. Conclusion: Multifactorial nature of the stone formation, emphasizing the role of environmental factors, might explain contradictory results in the literature. While polymorphisms in VDR, CASR, Osteopontin, and Urokinase genes were associated with urinary stone formation, the Klotho gene showed a protective effect.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".