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 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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.035 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".