SOD2gene Val16Ala polymorphism is associated with macroalbuminuria in Mexican Type 2 Diabetes patients: a comparative study and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Several studies in type 2 diabetes patients have shown significant associations between the SOD2 gene Val16Ala polymorphism and albuminuria, but this association has not been explored in the Mexican population. METHODS: We evaluated the association between the SOD2 gene Val16Ala polymorphism (rs4880) and macroalbuminuria in a sample of 994 unrelated Mexican type 2 diabetes patients. The study included 119 subjects with urinary albumin >300 mg/dL and 875 subjects with urinary albumin ≤ 30 mg/dL. Genotyping of the SOD2 gene Val16Ala SNP was carried out with Real-Time Polymerase Chain Reaction (RT-PCR). RESULTS: The frequency of the TT genotype was 6.7% higher in participants with macroalbuminuria than in the normoalbuminuria group (16.8% vs. 10.1%). Using a logistic regression analysis, we observed that individuals with the CC genotype had significantly lower risks of macroalbuminuria than those with the TT genotype (OR=0.42, p=0.034). We carried out a meta-analysis combining our data with data from four previous studies and estimated an odds ratio (95% CI) for the C allele (with respect to the reference T allele) of 0.65 (0.52-0.80, p<0.001). CONCLUSIONS: A significant association was found between the SOD2 Val16Ala polymorphism and macroalbuminuria in a sample of Mexican type 2 diabetes patients.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.019 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".