Combining genetic markers and clinical risk factors improves the risk assessment of impaired glucose metabolism
Bibliographic record
Abstract
BACKGROUND: Although several candidate gene polymorphisms (SNPs) have been associated with increased risk of type 2 diabetes mellitus (T2DM), relatively few studies have assessed the ability of T2DM candidate genes to assess the risk of impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and T2DM beyond the information provided by clinical risk factors. OBJECTIVE: To test whether the inclusion of genetic markers in a regression model provides a better assessment of the risk of IFG, IGT, and T2DM than a model based only on non-genetic risk factors commonly assessed in clinical settings. METHODS: Subjects (n = 485; 213 parents, 272 offspring) from the Quebec Family Study, not known to haveT2DM, were measured for several risk factors and underwent an oral glucose tolerance test. Thirty-eight SNPs in 25 susceptibility/ candidate genes previously reported to be associated with T2DM were genotyped. In order to identify risk factors associated with IFG/IGT/T2DM, two logistic regression models were tested: a full model (FM) including age, sex, body mass index (BMI), systolic and diastolic blood pressure, smoking status, and the 38 SNPs; and a reduced model (RM), in which the SNPs were dropped, which allowed us to test the null-hypothesis that the markers are not associated with the risk of IFG/IGT/T2DM. Performances of the models were compared by using a likelihood ratio test and the receiver-operating characteristic curves (ROC).The area under the curve (AUC) was calculated from the ROC curve. RESULTS: The analyses showed that age (P < 0.0001), BMI (P < 0.0001), and six variants (IGF2BP2 rs4402960, P = 0.002; ADIPOQ+276 G>T, P = 0.004; UCP2Ala55Val, P = 0.01; CDKN2AI2B rs3731201, P = 0.02; rs495490, P = 0.02, and rsl 0811661, P = 0.03) were significantly associated with the risk of IFG/IGT/T2DM. Dropping genetic markers from the analysis significantly reduced the fit of the model to the data (chi-square = 38.98, P < 0.00001 contrasting RM to FM), suggesting that the genetic markers are significantly associated with the risk of IFG/IGT/T2DM. Furthermore, the AUC was higher for FM than for RM (0.85 (95% CI 0.81-0.89) versus 0.81 (95% CI 0.76-0.85), P = 0.004). CONCLUSION: Our results suggest that combining genetic markers with traditional clinical risk factors has the potential to improve our ability to assess the risk of complex diseases such as T2DM.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".