A Replication Study of the IRS1, CAPN10, TCF7L2, and PPARG Gene Polymorphisms Associated with Type 2 Diabetes in Two Different Populations of Mexico
Bibliographic record
Abstract
Type 2 diabetes (T2D) is a chronic degenerative disease that involves the participation of several genetic and environmental factors. The objective of the study was to determine the association of the IRS1 (rs1801278), CAPN10 (rs3792267), TCF7L2 (rs7903146 and rs12255372), and PPARG (rs1801282) gene polymorphisms with T2D, in two different Mexican populations. We conducted a case-control replication study in the state of Guerrero and in Mexico City, with 400 subjects from Guerrero and 1065 from Mexico City. Data were analyzed by logistic regression, adjusting by ancestry, age, gender, and BMI, to determine the association with T2D. Heterozygosity for the Gly972Arg variant of the IRS1 gene showed the strongest association for T2D in both analyzed samples (OR = 2.43, 95% CI 1.12-5.26 and 2.64, 95% CI 1.37-5.10, respectively). In addition, an association of two SNPs of the TCF7L2 gene with T2D was observed in both cities: rs7903146, (for Guerrero OR = 1.98 CI95% 1.02-3.89 and for Mexico OR = 1.94 CI95% 1.31-2.88) and rs12255372 (OR = 1.79 CI95% 1.08-2.97, OR = 1.78 CI95% 1.17-2.71 respectively). We suggest that our results provide strong evidence that variation in the IRS1 and TCF7L2 genes confers susceptibility to T2D in our studied populations.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".