Association of Objective Sleep Characteristics and Incident Angina Pectoris: A Longitudinal Analysis from the Sleep Heart Health Study
Bibliographic record
Abstract
Purpose: While prior research has highlighted a significant association between sleep characteristics and angina pectoris (AP) incidence, the link between sleep efficiency (SE) and angina remains unexplored. This study seeks to elucidate the relationship between AP and objectively quantified SE. Patients and Methods: We examined a cohort of 2990 participants (1320 males and 1670 females; mean age 63.69 ± 13.2 years) from the Sleep Heart Health Study. The main exposure variable was SE, as determined by baseline home polysomnography, while the primary outcome was the first incidence of angina pectoris (AP) during the period between the baseline polysomnography and the end of follow-up. A multivariate Cox regression model was utilized, controlling for factors such as age, gender, BMI, smoking and alcohol consumption habits, diabetes, hypertension, sleep duration, triglycerides, cholesterol, high-density lipoprotein, apnea-hypopnea index, nocturnal oxygen saturation, to analyze the relationship between SE and AP. Results: During an average follow-up of 11 years, 284 patients developed AP. The unadjusted Kaplan-Meier analysis identified the 2nd quartile of SE as having the lowest AP risk. The multivariate Cox proportional hazards model demonstrated a higher risk of AP in quartile 1 (HR, 1.679; 95% CI, 1.109-2.542; P <0.014) and quartile 3 (HR, 1.503; 95% CI, 1.037-2.179; P <0.031), compared to quartile 2 of SE. Upon stratified analysis, this relationship was particularly pronounced in hypertensive individuals. Conclusion: Our results highlight the critical role of optimal sleep efficiency in mitigating the risk of angina pectoris, especially among hypertensive individuals.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".