Prediction of Cardiovascular and Renal Complications of Diabetes by a multi-Polygenic Risk Score in Different Ethnic Groups
Bibliographic record
Abstract
Abstract We developed a multi-Polygenic risk score (multiPRS) to predict the risk of nephropathy, stroke, and myocardial infarction in people with type 2 diabetes of European descent. The underrepresentation of non-European populations remains a major challenge in genomics research. Objective : To evaluate the ability of our multiPRS model to accurately predict these complications in patients of African and South Asian descents. Method : The multiPRS was developed using 4098 participants with type 2 diabetes of European origin from the ADVANCE trial. Its predictive performance was tested on 17,574 White British, 1,145 South Asian and 749 African participants with type 2 diabetes from the UK Biobank using different machine learning prediction models, including techniques tailored for imbalanced datasets. Results : Globally, linear discriminant analysis and logistic regression had the best performance to predict the risk of nephropathy, stroke, and myocardial infarction in people with type 2 diabetes for the three ethnic groups. Mondrian Cross-Conformal Prediction method when added to logistic regression improved the AUROC values and case detection, particularly in South Asians and Africans, while in White British, performance varied by phenotype. Conclusion : Logistic regression, when used as the underlying model within the Modrian Cross-Conformal Prediction framework, improved the prediction performance, with a confidence level, of diabetes complications and allows better translation of a multiPRS derived from European populations to other ethnic groups.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".