Obstructive Sleep Apnea and Aging Effects on Macrovascular and Microcirculatory Function
Bibliographic record
Abstract
STUDY OBJECTIVES: Many patients with obstructive sleep apnea (OSA) are obese, and whether obesity itself explains the increased prevalence of cardiovascular disease in OSA is unknown. We hypothesize that OSA, independent of obesity, contributes to abnormal vascular function. DESIGN: Physiology study. SETTING: Academic medical centers. PATIENTS: Obese subjects, free of known comorbidities, were enrolled. MEASUREMENTS AND RESULTS: Vascular function was assessed with brachial artery ultrasound for flow-mediated dilation (FMD) and in skin microcirculation by laser Doppler flowmetry. Arterial stiffness was measured by arterial tonometry. Seventy-two subjects (43/72 women, 38/72 with OSA) were studied. FMD was impaired in patients with OSA, compared with control subjects (5.7% +/- 3.8% vs 8.3% +/- 4.1%, P = 0.005). In step-forward regression analysis inclusive of age, sex, and body mass index, age (P = 0.013) was a significant independent predictor of FMD. In a subgroup of subjects younger than 50 years of age (n = 59), however, OSA was the only independent predictor of FMD (P = 0.04), adjusted for known covariates. OSA did not significantly influence vascular function in the skin microcirculation. The augmentation index, a measure of arterial stiffness, was similar between the OSA and control groups (16.2% +/- 11.4% vs 20.4% +/- 10.1%, respectively, P = 0.10). In step-forward regression analysis of younger men (< or = 50 years old, 23 subjects), OSA independently predicted the augmentation index in men only (P = 0.001). CONCLUSIONS: In obesity, both OSA and aging impair endothelial function and increase arterial stiffness. The influence of OSA on vascular function is most pronounced in young subjects. OSA, therefore, may be associated with functional impairment ("a premature aging effect") on the endothelium and on arterial stiffness (in men), although skin microcirculatory function appears preserved.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".