Biomarkers in patients with suspected obstructive sleep apnea and obstructive lung disease: Associations among polysomnographic, demographic and spirometric parameters
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to characterize the associations between inflammatory and oxidative stress biomarkers, demographic, polysomnographic and spirometric parameters in patients with suspected obstructive sleep apnea (OSA) and/or obstructive airway disease.METHODS: This was a cross-sectional exploratory study of patients referred to the University of British Columbia Sleep Clinic who had a diagnostic polysomnogram for suspected OSA. All patients had samples collected for measurements of IL-10, IL-6, e-selectin, endostatin, VCAM-1, ICAM-1, PDGF, thrombospondin-2, 8-OHdG, 8-isoprostane and superoxide dismutase. Spearman correlation and multiple linear regression were used to identify predictors of biomarkers.RESULTS: A total of 63 patients were included: 65% male, mean age 53 years and body mass index (BMI) 33 kg/m2. Inflammatory biomarkers were associated with female sex (IL-6, coefficient 0.51, p = 0.032), FEV1 (IL-6, coefficient −0.02, p = 0.013) and BMI (VCAM-1, coefficient 0.009, p = 0.051). The oxidative stress marker, 8-OHdG, was associated with hypoxemia in rapid eye movement (REM) sleep (coefficient 0.006, p = 0.02). Age and BMI were both independently associated with percentage of time spent below SpO2 90%. REM sleep and patients with overlap conditions and OSA had greater degree of REM and nonrapid eye movement (NREM) sleep hypoxemia than control group. Lastly, there were no differences in oxidative or inflammatory biomarkers between control, OSA, obstructive airway disease and overlap groups though the number of patients in each group were small.CONCLUSION: Female sex, lower FEV1 and increased BMI were independent predictors of increased inflammatory biomarker levels. The oxidative stress marker 8-OHdG was associated with hypoxemia indices of REM. Larger studies are warranted to delineate biomarker profiles in patients with overlap conditions.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".