Sleep apnea as a risk factor for hypertension
Bibliographic record
Abstract
PURPOSE OF REVIEW: High blood pressure and obstructive sleep apnea are closely related, and the latter is considered to induce hypertension. The primary underlying mechanism is sympathetic activation triggered by apneic episodes. This type of hypertension is difficult to treat. The purpose of this review is (1) to evaluate the epidemiological data in view of the current focus on preclinical sleep apnea and prehypertension, (2) to examine additional factors that might contribute to high blood pressure, and (3) to indicate the best therapeutic strategy for treatment of hypertension in these patients. RECENT FINDINGS: Cardiovascular effects of sleep apnea can be detected early in the course of the disease, and young subjects are particularly susceptible to its deleterious effect. Blood pressure profiles in these patients show higher diastolic blood pressure and no nocturnal dipping. The renin-angiotensin axis in conjunction with other vasoactive hormones add to the sympathetic activation in elevating blood pressure in sleep apnea. Pro-inflammatory cytokines further contribute to the atherosclerotic consequences that primarily affect the heart and brain, and spare the kidneys. Mounting evidence indicates that treatment of sleep apnea using positive airway pressure, palato-nasal surgery and weight reduction correct the associated hypertension. Conversely, antihypertensive therapy is less effective. SUMMARY: Even the early stages of sleep apnea are associated with high blood pressure and cardiovascular consequences. Despite our knowledge of the role of the sympathetic activation and vasoactive hormones, no specific antihypertensive therapy is superior, and the optimal way of controlling hypertension is to treat sleep apnea and associated obesity.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".