Genetic Information and the Prediction of Incident Type 2 Diabetes in a High-Risk Multiethnic Population
Bibliographic record
Abstract
OBJECTIVE: To determine if 16 single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2DM) in Europeans are also associated with T2DM in South Asians and Latinos and if they can add to the prediction of incident T2DM in a high-risk population. RESEARCH DESIGN AND METHODS: In the EpiDREAM prospective cohort study, physical measures, questionnaires, and blood samples were collected from 25,063 individuals at risk for dysglycemia. Sixteen SNPs that have been robustly associated with T2DM in Europeans were genotyped. Among 15,466 European, South Asian, and Latino subjects, we examined the association of these 16 SNPs alone and combined in a gene score with incident cases of T2DM (n = 1,016) that developed during 3.3 years of follow-up. RESULTS: Nine of the 16 SNPs were significantly associated with T2DM, and their direction of effect was consistent across the three ethnic groups. The gene score was significantly higher among subjects who developed incident T2DM (cases vs. noncases: 16.47 [2.50] vs. 15.99 [2.56]; P = 0.00001). The gene score remained an independent predictor of incident T2DM, with an odds ratio of 1.08 (95% CI 1.05-1.11) per additional risk allele after adjustment for T2DM risk factors. The gene score in those with no family history of T2DM was 16.02, whereas it was 16.19 in those with one parent with T2DM and it was 16.32 in those with two parents with T2DM (P trend = 0.0004). The C statistic of T2DM risk factors was 0.708 (0.691-0.725) and increased only marginally to 0.714 (0.698-0.731) with the addition of the gene score (P for C statistic change = 0.0052). CONCLUSIONS: T2DM genetic associations are generally consistent across ethnic groups, and a gene score only adds marginal information to clinical factors for T2DM prediction.
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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 | 0.004 |
| 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 | 0.000 | 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".