1645-P: Polygenic Risk Score for Prediction of Complications in Men and Women with Type 2 Diabetes
Bibliographic record
Abstract
Introduction: We assessed the performance of our newly developed polygenic risk score (PRS) to predict microvascular and macrovascular complications of type 2 diabetes (T2D) in men and women. The PRS is composed of 600 common genomic variants associated to diabetes, cardiovascular and renal diseases and their key risk factors selected from summary statistics of meta-analyses of published genome-wide association studies performed in over 1.2 million of individuals. The performance of the polygenic model was assessed by c-statistics in 4098 genotyped participants of European descent of the ADVANCE trial (46.4% women) followed during a period of five years. Methods: The logistic regression model that included the PRS adjusted for the principal component (PC1) of genetically determined ancestry, age at diagnosis and T2D duration, and treatment assignment, did not include any clinical or outcome data. Results: The discrimination between cases (having a specific complication) from controls (free of this complication) at entry in ADVANCE had AUCs for microvascular complications of 0.63 (0.60-0.65) in men and 0.66 (0.63-0.69) in women, sex differences p = 0.07. AUCs for macrovascular complications were 0.56 (0.54-0.58) in men and 0.57 (0.54-0.61) in women; p = 0.41. The AUCs for prediction of incident cases, defined as having an outcome during the ADVANCE trial (free of outcome at baseline) compared to controls that did not have a specific outcome at any time during the study, were for microvascular events 0.66 (0.63-0.70) in men and 0.71 (0.66-0.77) in women; p = 0.15. AUCs for macrovascular events were 0.65 (0.62-0.68) and 0.72 (0.68-0.76) respectively, p = 0.01. AUC for prediction of cardiovascular death occurring during the trial was 0.71 (0.67-0.75) in men and 0.77 (0.72-0.82) in women; p = 0.04. Conclusion: Our polygenic model demonstrated an overall better performance in women than in men and a better prediction capacity in individuals free of previous events in both sexes. Disclosure J. Tremblay: Research Support; Self; Servier. Stock/Shareholder; Self; OPTITHERA. R. Attaoua: None. M. Haloui: None. R. Tahir: None. C. Long: None. C. Hizel: None. J. Chalmers: None. S. Harrap: None. M. Woodward: Consultant; Self; Amgen, Kyowa Hakko Kirin Co., Ltd. P. Hamet: Research Support; Self; Servier. Stock/Shareholder; Self; OPTITHERA. Funding Genome Quebec; Canadian Institutes of Health Research; MEIE; CQDM; Opti Thera; Servier, Canada Research Chair in Predictive Genomics
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".