Interaction between type 2 diabetes polygenic risk and physical activity on cardiovascular outcomes
Bibliographic record
Abstract
AIMS: The beneficial effects of exercise on reducing the risk of cardiovascular disease are established. However, the potential interaction between genetic risk for type 2 diabetes and physical activity on cardiovascular outcomes remains elusive. We aimed to investigate the effect of type 2 diabetes genetic risk-physical activity interaction on cardiovascular outcomes in individuals with diabetes. METHODS AND RESULTS: Using the UK Biobank cohort, we investigated the effect of type 2 diabetes genetic risk-physical activity interaction on three-point and four-point major adverse cardiovascular events (MACE), in 25 701 diabetic participants. We used a polygenic risk score for type 2 diabetes (PRS_T2D) as a measure of genetic risk for type 2 diabetes. We observed a significant interaction between PRS_T2D and physical activity on cardiovascular outcomes (three-point MACE: P trend for interaction = 0.0081; four-point MACE: P trend for interaction = 0.0037). Among participants whose PRS_T2D was in the first or second quartile, but not in the third or fourth quartile, each 10 metabolic equivalents (METs) hours per week of physical activity decreased the risk of three-point or four-point MACE. Furthermore, restricted cubic spline analysis indicated that intense physical activity (>80 METs hours per week, which was self-reported by 12.7% of participants) increased the risk of cardiovascular outcomes among participants whose PRS_T2D was in the fourth quartile. Sub-group analysis suggested that negative impact of intense physical activity was observed only in non-insulin users. CONCLUSION: The beneficial effect of physical activity on cardiovascular outcomes disappeared among those with high genetic risk for type 2 diabetes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".