Genetic ancestry markers and difference in A1c between African-American and White in the Diabetes Prevention Program
Bibliographic record
Abstract
Purpose: HbA1c levels are higher in blacks than non-Hispanic whites (NHWs). We investigated whether genetics could explain this difference in Diabetes Prevention Program (DPP) participants. Methods: We tested (i) genetic variants causing hemoglobinopathies, (ii) a genetic risk score (GRS) based on 60 variants associated with HbA1c from genome-wide association meta-analysis, and (iii) principal component (PC) factors that capture continental ancestry derived from genetic markers distributed across the genome. Results: Of 2658 eligible DPP participants, 537 (20%) self-identified as black and 1476 (56%) as NHW. Despite comparable fasting and 2-hour glucose levels, blacks had higher HbA1c (mean ± SD = 6.2 ± 0.6%) compared with NHWs (5.8 ± 0.4%; P < 0.001). In blacks, the genetic variant causing sickle cell trait was associated with higher HbA1c [β (SE) = +0.44 (0.08)%; P = 2.1 × 10-4]. The GRS was associated with HbA1c in both blacks and NHWs. Self-identified blacks were distributed along the first PC axis, as expected in mixed ancestry populations. The first PC explained 60% of the 0.4% difference in HbA1c between blacks and NHWs, whereas the sickle cell variant explained 16% and GRS explained 14%. Conclusions: A large proportion of HbA1c difference between blacks and NHWs was associated with the first PC factor, suggesting that unidentified genetic markers influence HbA1c in blacks in addition to nongenetic factors.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".