0283 Sleep Fragmentation Predicts Risk of Congestive Heart Failure in Community-Based Older Adults
Bibliographic record
Abstract
Sleep disturbances are commonly associated with cardiovascular diseases. There is evidence that clinical heart failure predicts a reduction of sleep quality. We tested whether disturbances in sleep also predict the risk for congestive heart failure (CHF). We studied 1,102 older adults (age: 80.7±7.4 [SD]) in the Rush Memory and Aging Project who had no history of CHF at baseline and had been followed annually for up to 13 years. Motor activity data of up to 10 days were recorded at baseline and were used to quantify sleep fragmentation. Incident CHF were obtained based on self-report. A Cox proportional hazards model was performed to examine the association of sleep fragmentation at baseline with incident CHF while adjusted for age, sex, and years of education. The model was further augmented to adjust for potential confounders including baseline vascular risk factors and vascular diseases. CHF was reported in 72 individuals (6.4% of 1,102) averaged after 4.6±2.9 [SD] years from baseline. For 1-SD increase in sleep fragmentation (0.007), the risk of CHF increased by 1.23-fold (95% CI: 1.03-1.46, p=0.02) after controlled for age, sex, and education. This effect is equivalent to the effect of being ~3.8 years older of age. In addition, the association persisted after further adjusting for all the potential confounders mentioned above. Community-based older adults with more fragmented sleep had increased risk for developing CHF. Since previous studies also showed that clinical heart failure predicts poorer sleep quality, the relationship between heart failure and sleep disturbances is likely bidirectional. This work was supported by NIH grants R01AG048108, RF1AG059867, R01AG017917, and R01NS078009.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".