Serum sTREM2: A Potential Biomarker for Mild Cognitive Impairment in Patients With Obstructive Sleep Apnea
Bibliographic record
Abstract
Objective: Cognitive impairment is a common comorbidity in patients with obstructive sleep apnea (OSA) that leads to poor quality of life and a heavier medical burden. However, the assessment and longitudinal tracking of cognitive impairment in OSA is challenging. This study aimed to examine the alternation and related factors of serum soluble triggering receptor expressed on myeloid cells 2 (sTREM2) in patients with OSA, and to explore whether serum sTREM2 could be a biomarker for mild cognitive impairment in OSA patients. Methods: A total of 94 OSA patients and 13 snoring subjects were enrolled in this cross-sectional study. Demographic information, questionnaires, and polysomnography results were collected. Serum sTREM2 levels were quantified using an enzyme-linked immunosorbent assay. Multivariate linear regression was used to analyze the factors influencing sTREM2, and the receiver operating characteristic curve was used to assess the predictive value of serum sTREM2 for mild cognitive impairment in patients with OSA. Results: Patients with OSA had higher serum sTREM2 levels than the controls. Multivariate linear regression analysis showed that serum sTREM2 levels in patients with OSA were associated with the Montreal Cognitive Assessment score and oxygen depletion index levels. Additionally, serum sTREM2 levels were higher in OSA patients with mild cognitive impairment (MCI) than in those without. The receiver operating characteristic curve showed that at a cutoff value of >18,437 pg/ml, the sensitivity of serum sTREM2 to predict MCI in OSA was 64.62%, the specificity was 68.97%, and the area under the curve was 0.70 (95% CI: 0.58-0.81). Conclusion: Serum sTREM2 levels were elevated in patients with OSA, particularly in those with MCI. It therefore has the potential to be a biomarker for MCI in OSA patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".