Sleep duration, cardiovascular disease, and proinflammatory biomarkers
Bibliographic record
Abstract
Habitual sleep duration has been associated with cardiometabolic disease, via several mechanistic pathways, but few have been thoroughly explored. One hypothesis is that short and/or long sleep duration is associated with a proinflammatory state, which could increase risk for cardiovascular and metabolic diseases. This hypothesis has been largely explored in the context of experimental sleep deprivation studies which have attempted to demonstrate changes in proinflammatory markers following acute sleep loss in the laboratory. Despite the controlled environment available in these studies, samples tend to lack generalization to the population at large and acute sleep deprivation may not be a perfect analog for short sleep. To address these limitations, population based studies have explored associations between proinflammatory markers and habitual sleep duration. This review summarizes what is known from experimental and cross-sectional studies about the association between sleep duration, cardiovascular disease, and proinflammatory biomarkers. First, the association between sleep duration with both morbidity and mortality, with a focus on cardiovascular disease, is reviewed. Then, a brief review of the potential role of proinflammatory markers in cardiovascular disease is presented. The majority of this review details specific findings related to specific molecules, including tumor necrosis factor-α, interleukins-1, -6, and -17, C-reactive protein, coagulation molecules, cellular adhesion molecules, and visfatin. Finally, a discussion of the limitations of current studies and future directions is provided.
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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 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".