The relationship of inflammatory cytokines with asthma and obesity
Bibliographic record
Abstract
OBJECTIVES: Obesity is considered a risk factor for asthma. However, the mechanism that connects the two is not well understood. In this study we investigated the relationship between inflammatory cytokines and acute phase reactants in obesity, and asthma. METHOD: Asthmatic and control subjects were divided into 2 sub-groups: obese and non-obese. Anthropomorphic parameters, erythrocyte sedimentation rate (ESR), C-reactive protein (CRP), leptin, tumour necrosis factor-alpha (TNF-alpha), and interleukin-6 (IL-6) were compared between obese, asthmatics and control subjects of normal weight. Respiratory function tests and allergy skin tests were also performed in the patients with asthma. RESULTS: ESR, CRP, TNF-alpha, IL-6, and leptin levels in obese asthma patients were higher than in the healthy controls (P < 0.01). TNF-alpha, IL-6, and leptin levels were higher in obese asthma patients than in non-obese asthma patients (P < 0.01). Inflammatory markers were related to parameters of obesity. No association was found between allergy test results and obesity (P > 0.05). CONCLUSION: We identified a relationship between acute phase reactants and inflammatory cytokines, and the criteria for obesity in obese asthma patients. Inflammation markers were at their highest levels in obese asthma patients. Leptin levels were considerably higher in obese patients than in normal weight controls. Like obesity, leptin is suggested to play a role in the pathogenesis of asthma.
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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 | 0.001 |
| 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) | 0.002 | 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".