Genetic Polymorphisms and the Risk of Diabetic Foot: A Systematic Review and Meta-Analyses
Bibliographic record
Abstract
BACKGROUND: Diabetic foot (DF) is a dangerous complication of diabetes. The aim of the study was to synthesize all the published single nucleotide polymorphisms (SNPs) of DF to objectively evaluate the relationship of SNPs and DF risks. METHODS: The HuGE database and CNKI were searched for eligible publications on genetic polymorphisms and the risk of DF systematically. The quality of literatures was evaluated by the Newcastle-Ottawa scale. Pooled odds ratios with a 95% confidence interval for SNPs were evaluated through 3 genetic models. RESULTS: Citing 29 different polymorphisms from 24 articles and the study met our selection criteria. There were 24 polymorphisms summarized systematically, and 5 merged polymorphisms for a meta-analysis: 9 positively associated with DF: HIF-1α rs11549465, TNF-α rs1800629, TLR-9 rs5743836, FIB rs6056, HSP70-2437C/T, VDR rs2228570, LOX rs1800449, ITLN1 rs2274907, and OPG rs2073617, but OPG rs3134069 was not a risk factor in DF; 6 negatively associated with DF: VEGF rs833061 and rs2010963, MCP-1 rs1024611, SDF-1 rs1801157, SIRT1 rs12778366, and OPG rs2073617. In addition, 13 polymorphisms were not associated with DF: MMP-9 rs3918242, eNOS rs1799983, VEGF rs3025039, -7C/T, rs1570360, rs13207351, and rs699947, IL-6 rs1800795, HIF-1α rs11549467, TNF-α rs361525, TLR-2 rs3804100, SIRT1 rs3758391, and TIMP-1 rs2070584. CONCLUSIONS: The study provided some evidence for SNPs to the development of diabetic foot. The meta-analysis showed that rs1024611 of MCP-1 may be regarded as a protective factor, especially in Asian populations. Other loci indicated inconsistent results.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.026 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".