Candidate gene association study conditioning on individual ancestry in patients with type 2 diabetes and metabolic syndrome from Mexico City
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes (T2D) is influenced by diverse environmental and genetic risk factors. Metabolic syndrome (MS) increases the risk of cardiovascular disease and diabetes. We analysed 14 cases of polymorphisms located in 10 candidate loci, in a sample of patients with T2D and controls from Mexico City. METHODS: We analysed the association of 14 polymorphisms located within 10 genes (TCF7L2, ENPP1, ADRB3, KCNJ11, LEPR, PPARgamma, FTO, CDKAL1, SIRT1 and HHEX) with T2D and MS. The analysis included 519 subjects with T2D defined according to the ADA criteria, 389 with MS defined according to the AHA/NHLBI criteria and 547 controls. Association was tested with the program ADMIXMAP including individual ancestry, age, sex, education and in some cases body mass index (BMI), in a logistic regression model. RESULTS: The two markers located within the TCF7L2 gene showed strong associations with T2D (rs7903146, T allele, odd ratio (OR) = 1.76, p = 0.001 and rs12255372, T allele, OR = 1.78, p = 0.002), but did not show significant association with MS. The non-synonymous rs4994 polymorphism of the ADRB3 gene was associated with T2D (Trp allele, OR = 0.62, p = 0.001) and MS (Trp allele, OR = 0.74, p = 0.018). Nominally significant associations were also observed between T2D and the SIRT1 rs3758391 SNP and MS and the HHEX rs5015480 polymorphism. CONCLUSIONS: Variants located within the gene TCF7L2 are strongly associated with T2D but not with MS, providing support to previous evidence indicating that polymorphisms at the TCF7L2 gene increase T2D risk. In contrast, the non-synonymous ADRB3 rs4994 polymorphism is associated with T2D and MS.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".