Relationship between sleep apnea severity and mild cognitive impairment in people with obstructive sleep apnea
Bibliographic record
Abstract
BACKGROUND: It is becoming increasingly acknowledged that obstructive sleep apnea (OSA) is a variable factor influencing cognitive health. The aims of this study were to explore whether the severity of OSA is related to the occurrence of mild cognitive impairment (MCI) in people with OSA and whether different degrees of daytime sleepiness and nighttime sleep quality are related to MCI. METHODS: The study was cross-sectional. For our subjects, we selected individuals who visited the Sleep Medicine Center of Jinzhou Medical University's First Affiliated Hospital between May 2023 and October 2024, underwent polysomnography (PSG) or the home sleep apnea test (HSAT), and were diagnosed with OSA. The patients were split into two groups: one for normal cognitive function (NC) and the other for MCI. MCI was defined as Montreal Cognitive Assessment (MOCA) < 26 points. The apnea-hypopnea index (AHI) and the oxygen desaturation index (ODI) were used to assess the severity of sleep apnea. The Epworth Sleepiness Scale (ESS) was used to assess patients' daytime sleepiness, and the Pittsburgh Sleep Quality Index Scale (PSQI) was used to assess their sleep quality. Multivariate logistic regression analysis was used to evaluate the correlation between the variables. RESULTS: In this study, 387 patients with OSA (45.3 ± 12.6 years, 82.4% male) were included, of whom 38% had MCI (52.4 ± 11.9 years, 74.1% male). In the unadjusted model, the sleep apnea severity, daytime sleepiness severity, and different sleep quality at night were positively related to MCI. After controlling for confounding factors, this correlation was no longer significant. Only severe sleep apnea (AHI ≥ 30/h, p < 0.001), poor nighttime sleep quality (PSQI ≥ 9, p = 0.020), and sleepiness (ESS ≥ 11, p < 0.05) were associated with increased risk of MCI. CONCLUSIONS: Severe sleep apnea, poor sleep quality, and sleepiness were relevant to increased risk of MCI. It provides a basis for a more comprehensive understanding of the relationship between OSA and MCI.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".