Fit-Fat Index and the risk of sudden cardiac death in Finnish men
Bibliographic record
Abstract
Background Emerging evidence suggests that the Fit-Fat Index (FFI), which combines measures of fitness and fatness, may offer a more accurate assessment of cardiometabolic risk than either component alone. We aimed to investigate the prospective associations of cardiorespiratory fitness (CRF), fatness indices, and FFI variants with the risk of sudden cardiac death (SCD), and to evaluate their utility in SCD risk prediction. Methods Baseline assessments of CRF (measured using a respiratory gas exchange analyzer during exercise testing) and fatness indices (body mass index (BMI), waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR)) were conducted in 1662 men aged 42–61 years. FFI variants (FFI BMI , FFI WHR , and FFI WHtR ) were calculated by dividing CRF by each corresponding fatness measure. Hazard ratios (HRs) with 95 % confidence intervals (CIs) were estimated using Cox regression, and improvements in risk prediction were assessed using measures of discrimination and reclassification. Results Over a median follow-up of 28.3 years, 172 SCDs occurred. After adjustment for confounders and potential mediators, each 1 SD increase in CRF and FFI variants was associated with a significantly lower risk of SCD: HRs (95 % CI) were 0.68 (0.56–0.82) for CRF, 0.65 (0.53–0.79) for FFI BMI , 0.66 (0.54–0.81) for FFI WHR , and 0.65 (0.53–0.80) for FFI WHtR . BMI and WHtR, but not WHR, remained associated with SCD after full adjustment. CRF and all FFI variants significantly improved model fit, net reclassification, and discrimination ( p < .001 for all). Conclusions CRF and FFI variants were robustly associated with lower SCD risk and offered comparable improvements in prediction. These findings support their potential utility in clinical and research settings for SCD risk stratification.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| 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".