The Interplay of Type 2 Diabetes Status, Cardiorespiratory Fitness Level, and Sudden Cardiac Death: A Prospective Cohort Study
Bibliographic record
Abstract
Background: To evaluate the individual and joint effects of type 2 diabetes (T2D) status and cardiorespiratory fitness (CRF) level with sudden cardiac death (SCD) risk. Methods: Prevalent T2D was defined based on guideline recommendations, and CRF level was assessed using a respiratory gas-exchange analyzer during exercise testing at baseline, in 2308 men aged 42-61 years. T2D status was classified as either "Yes" or "No," and CRF level was classified as low, medium, or high. Cox regression analysis was used to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) for SCD. Results: A total of 264 SCDs occurred during a median follow-up of 28.1 years. Comparing Yes vs No history of T2D, the multivariable-adjusted HR (95% CI) for SCD was 1.79 (1.19-2.72). Comparing low vs high CRF levels, the corresponding adjusted HR (95% CI) for SCD was 1.77 (1.21-2.58). The HRs persisted when T2D status was further adjusted for CRF level, and vice versa. Compared with No-T2D & medium-high CRF level, men with No-T2D & low CRF and those with Yes-T2D & low CRF had an increased SCD risk: (HR = 1.87, 95% CI, 1.38-2.55) and (HR = 3.34, 95% CI, 2.00-5.57), respectively. No significant association occurred between men with Yes-T2D & medium-high CRF and SCD risk (HR = 1.46, 95% CI, 0.46-4.65). Modest evidence indicated the presence of additive and multiplicative interactions between T2D status and CRF level, in relation to SCD. Conclusions: An interplay exists between T2D status, CRF level, and SCD risk in middle-aged and older men. Higher CRF levels may mitigate the increased SCD risk observed in men with T2D.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".