Integrating Pulmonology and Metabolism: Adipokines in COPD from a Systematic Review and Meta-analysis Perspective
Bibliographic record
Abstract
Background: Chronic Obstructive Pulmonary Disease (COPD) is increasingly recognized not just as a pulmonary disorder but as a complex systemic syndrome involving chronic inflammation, metabolic dysregulation, and immune imbalance. This systematic review and meta-analysis aim to evaluate circulating adipokine profiles in COPD patients, exploring their diagnostic significance, correlation with disease severity, and potential as biomarkers or therapeutic targets. Methods: Following PRISMA 2020 guidelines, a comprehensive literature search was conducted across PubMed, Scopus, and Web of Science. A random-effects meta-analysis assessed standardized mean differences (SMDs) for key adipokines. Risk of bias was evaluated using the Newcastle-Ottawa Scale, and evidence certainty was assessed using the GRADE framework. Results: Twelve eligible studies comprising 2,100 COPD patients and 2,100 controls were included. Leptin (1.57 [-0.02 to 3.16] double armed; 19.59 [14.01-25.18] single armed), resistin (0.67 [-0.27 to 1.60]), and adiponectin (-0.18 [-2.13 to 1.77] double armed; 7.83 [6.95 to 8.71] single armed) levels showed statistically significant differences between COPD patients and controls (p < 0.001 for all), with high heterogeneity (>60%). Elevated leptin and resistin levels were associated with systemic inflammation and poorer clinical outcomes, whereas adiponectin was often reduced despite its anti-inflammatory properties. Sensitivity and subgroup analyses reinforced these findings. The certainty of evidence ranged from low to moderate. Discussion: Adipokines reflect systemic inflammation and metabolic imbalance in COPD, highlighting their potential role in phenotypic stratification and disease monitoring. While promising, translation to clinical practice requires further standardized, longitudinal, and mechanistic research.
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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 | 0.036 | 0.081 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.026 |
| Bibliometrics | 0.012 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".