Association of <i>TCF7L2</i> polymorphisms with type 2 diabetes in Mexico City
Bibliographic record
Abstract
Polymorphisms within the transcription factor 7-like 2 gene (TCF7L2) have been associated with type 2 diabetes (T2D) in several recent studies. We characterized three of these polymorphisms (rs12255372, rs7903146 and the microsatellite DG10S478) in an admixed sample of 286 patients with T2D and 275 controls from Mexico City. We also analyzed three samples representative of the relevant parental populations: Native Americans from the state of Guerrero (Mexico), Spanish from Valencia and Nigerians (Bini from the Edo region). In order to minimize potential confounding because of the presence of population stratification in the sample, we evaluated the association of the three TCF7L2 polymorphisms with T2D by using the program admixmap to fit a logistic regression model incorporating individual ancestry, sex, age, body mass index and education. The markers rs12255372, rs7903146 and DG10S478 are in tight disequilibrium in the Mexican sample. We observed a significant association between the single-nucleotide polymorphism (SNP) rs12255372 and the microsatellite DG10S478 with T2D in the Mexican sample [rs12255372, odds ratio (OR) = 1.78, p = 0.017; DG10S478, OR = 1.62, p = 0.041]. The SNP rs7903146 shows similar trends, but its association with T2D is not as strong (OR = 1.39, p = 0.152). Analysis of the parental samples, as well as other available data, indicates that there are substantial population frequency differences for these polymorphisms: The frequencies of the T2D risk factors are more than 20% higher in European and West African populations than in East Asian and Native American 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".