Sleep Biomarkers, Health Comorbidities, and Neurocognition in Obstructive Sleep Apnea
Bibliographic record
Abstract
OBJECTIVES: Obstructive sleep apnea (OSA) is associated with cognitive impairment but the relationships between specific biomarkers and neurocognitive domains remain unclear. The present study examined the influence of common health comorbidities on these relationships. Adults with suspected OSA (N=60; 53% male; M age=52 years; SD=14) underwent neuropsychological evaluation before baseline polysomnography (PSG). Apneic syndrome severity, hypoxic strain, and sleep architecture disturbance were assessed through PSG. METHODS: Depression (Center for Epidemiological Studies Depression Scale, CESD), pain, and medical comorbidity (Charlson Comorbidity Index) were measured via questionnaires. Processing speed, attention, vigilance, memory, executive functioning, and motor dexterity were evaluated with cognitive testing. A winnowing approach identified 9 potential moderation models comprised of a correlated PSG variable, comorbid health factor, and cognitive performance. RESULTS: Regression analyses identified one significant moderation model: average blood oxygen saturation (AVO2) and depression predicting recall memory, accounting for 31% of the performance variance, p<.001. Depression was a significant predictor of recall memory, p<.001, but AVO2 was not a significant predictor. The interaction between depression and AVO2 was significant, accounting for an additional 10% of the variance, p<.001. The relationship between low AVO2 and low recall memory performance emerged when depression severity ratings approached a previously established clinical cutoff score (CESD=16). CONCLUSIONS: This study examined sleep biomarkers with specific neurocognitive functions among individuals with suspected OSA. Findings revealed that depression burden uniquely influence this pathophysiological relationship, which may aid clinical management. (JINS, 2018, 28, 864-875).
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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 | 0.003 |
| 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) | 0.001 | 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".