Obstructive Sleep Apnea and Cardiometabolic Disease: Obesity, Hypertension, and Diabetes
Bibliographic record
Abstract
Obstructive sleep apnea (OSA) is a highly prevalent sleep disorder, characterized by recurrent upper airway obstruction during sleep, resulting in intermittent hypoxia, increased sympathetic activation, and sleep deficiency. Over the past 2 decades, there has been a robust body of evidence to support a strong link between OSA and cardiometabolic diseases. Obesity is an important risk factor for OSA. Observational studies indicate that OSA is a strong risk factor for the development of hypertension and diabetes. Moreover, clinical and experimental studies support a causal role of OSA in hypertension and impairments in glucose metabolism, beyond excess weight. Notably, OSA is particularly underdiagnosed and undertreated in women, which may heighten the cardiometabolic risk. OSA is often overlooked during pregnancy and has been linked to adverse cardiometabolic outcomes in observational studies. In randomized clinical trials, treatment of OSA with continuous positive airway pressure reduces blood pressure in individuals with hypertension, but the beneficial effects of continuous positive airway pressure on glycemic outcomes are less convincing. Inconsistent cardiometabolic response to OSA treatment can be partly explained by failure to consider heterogeneity in OSA and variable continuous positive airway pressure adherence among diverse populations. In this review, we summarize the relationships between OSA and cardiometabolic conditions with a particular focus on obesity, hypertension, and diabetes. We review the current knowledge on the heterogeneity in OSA and discuss potential underlying mechanisms for impairments in blood pressure and glucose metabolism in OSA. We also provide a clinical perspective for OSA management considering current research gaps and emerging approaches for the prevention and treatment of cardiometabolic disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".