Pro12Ala polymorphism of the peroxisome proliferator-activated receptor-γ2 (PPARγ-2) gene is associated with greater insulin sensitivity and decreased risk of type 2 diabetes in an Iranian population
Bibliographic record
Abstract
BACKGROUND: The Pro12Ala polymorphism of the peroxisome proliferator-activated receptor-gamma2 (PPARgamma-2) gene has been variably associated with insulin resistance, obesity and type 2 diabetes in several populations. However, this association has not been studied in Iranian subjects and we hypothesized that this variation might be associated with insulin resistance, type 2 diabetes and related metabolic traits in this population. METHODS: The Pro12Ala genotypes were determined by PCR-restriction fragment length polymorphism in 696 unrelated subjects including 412 non-diabetic controls and 284 type 2 diabetic patients. RESULTS: The frequency of the Ala allele was 9.4% and 5.9% in controls and type 2 diabetic subjects, respectively [adjusted odds ratio (OR) 0.457, p=0.005]. The Ala allele did not show a significant effect on anthropometric and biochemical parameters in the type 2 diabetic group, whereas in non-diabetic subjects, carriers of the Ala allele had significantly lower fasting insulin (p=0.007) and homeostasis model assessment of insulin resistance (HOMA-IR) (p=0.009) levels compared to Pro/Pro subjects. Multivariate logistic regression analysis showed that Pro12Ala polymorphism was an independent determinant of type 2 diabetes in this population. CONCLUSIONS: Our results for a sample of Iranian type 2 diabetes cases and controls provide evidence that the Pro/Ala genotype of the PPARgamma-2 gene is associated with insulin sensitivity and may also have protective role against type 2 diabetes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".