Association between omentin and obstructive sleep apnea: A meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Obstructive sleep apnea (OSA) can lead to multisystem and multiorgan damage, which has attracted widespread attention from scholars. The pathogenesis of OSA is complex, and obesity plays an important role. Adipokine is secreted by adipose tissue, and its abnormal expression may be closely related to OSA. The relationship between omentin (a novel adipokine) and OSA is controversial. This study focuses on the important role of omentin in OSA and explores whether it can be regarded as a new target for the diagnosis and treatment of OSA. METHOD: PubMed, Embase, Web of Science, the Cochrane library, WANFANG, VIP, and Chinese National Knowledge Infrastructure were systematically searched for retrieving eligible studies until May 2022. Documents were screened according to strict inclusion and exclusion criteria, and data were extracted using Excel spreadsheets. The quality of the literature was assessed using the Newcastle-Ottawa Scale. RevMan 5.3 and Stata 12.0 software were used in this meta-analysis for data synthesis. RESULT: A total of eight eligible studies with 23 databases involving 914 participants were included in this meta-analysis. Combined data indicated that omentin levels in OSA patients were lower than that in controls (standardized mean difference = -1.54, 95% confidence interval = -2.07 to -1.00, p < 0.001). According to the subgroup analysis results of different races, sample source, gender, and the severity of the disease, compared with that in the control group, the level of omentin in OSA patients was significantly lower. When conducting sensitivity analysis, the results of the study were less stable. Meta-analysis indicated that there was no publication bias in this study. The omentin levels were significantly lower in OSA patients. The findings suggest that omentin may be a potential marker for the diagnosis and treatment of OSA. However, the heterogeneity of this study is high, and more high-quality large-sample studies will be needed in the future.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".