Leptin Receptor Gene Variation Predicts Weight Change in Subjects with Impaired Glucose Tolerance
Bibliographic record
Abstract
The leptin receptor (OB-R) gene is a promising candidate gene for type 2 diabetes, because leptin and its receptor play an important role in insulin secretion and the development of obesity. Therefore, we studied whether the pentanucleotide insertion polymorphism of the 3'-untranslated region (3'UTR) of the OB-R gene has an influence on the conversion from impaired glucose tolerance (IGT) to type 2 diabetes in the STOP-Noninsulin-Dependent Diabetes Mellitus trial. The STOP trial was a longitudinal, double-blind, placebo-controlled randomized trial that included 1429 subjects with IGT from high-risk populations. Using the restriction fragment length polymorphism method, we genotyped 770 subjects whose DNA was available for the insertion/deletion polymorphism of the 3'UTR of the OB-R gene. We did not find a relationship between the OB-R polymorphism and the conversion from IGT to type 2 diabetes (p = 0.747). However, the insertion allele was associated with a significant reduction in weight (p = 0.016), BMI (p = 0.009), and waist circumference (p = 0.006) in all subjects. Women carrying the I allele had a larger waist circumference change (p = 0.036), whereas men lost more weight and had a greater decrease in BMI. The pentanucleotide insertion/deletion polymorphism in the 3'UTR of the OB-R gene did not influence the conversion to type 2 diabetes in obese patients with IGT. However, this polymorphism was associated with a significant weight change, suggesting that it may potentially modulate the risk for 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".