0458 Association between Serum Adiponectin Levels and Obstructive Sleep Apnea: A Meta-Analysis
Bibliographic record
Abstract
Reduced adiponectin is associated with increased risk of obesity, insulin resistance and cardiovascular diseases as an anti-artheroscelrsosis factor; however, whether serum adiponectin level in obstructive sleep apnea (OSA) patients is lower than their counterparts remains controversial. Therefore, it is worthwhile to systematically evaluate the association between serum adiponectin levels and OSA. A computerized literature search of PubMed, EMBASE, and Chinese National Knowledge Infrastructure (CNKI) and Chinese Wanfang (Chinese) database was conducted to identify relevant publications by 2 independent reviewers. The study quality was assessed by the Newcastle-Ottawa scale (NOS). Pooled standard mean difference (SMD) with 95% confidence interval (CI) was calculated by the random-effect models. Cochrane Q test and I2 statistics were used to test heterogeneity. RevMan 5.3 and Stata 12.0 were applied in this meta-analysis for data synthesis. A total of 10 eligible studies including 690 patients were included in current meta-analysis. Results revealed that serum adiponectin levels were lower than controls [SMD=-1.40, 95% CI (-1.93, -0.88), P<0.001]. The removal of any independent study did not affect the pooled SMD in the following sensitivity analysis. Subgroup analysis indicated that the heterogeneity would decrease when the average BMI≥30, age>50 and as well as serum adiponectin assay with ELISA. The serum adiponectin levels in OSA patients were significantly lower than that in the control group. Serum adiponectin levels may play role in the development of OSA. International Science & Technology Cooperation Program of China (2015DFA30160).
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.061 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".