The Impact of Ramadan Fasting on Interleukin-6, Tumor Necrosis Factor-α, and C-Reactive Protein in Overweight and/or Obese and Non-Obese Individuals: A Systematic Review with Meta-Analysis
Bibliographic record
Abstract
Introduction: Ramadan fasting (RF) has been suggested to modulate inflammation. Inflammatory biomarkers such as interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), and C-reactive protein (CRP) play pivotal roles in various metabolic and immune-related diseases. Given their clinical relevance, this study aims to evaluate the impact of RF on IL-6, TNF-α, and CRP. Methods: A systematic search of the literature evaluating the impact of RF on IL-6, TNF-α, and CRP/high sensitivity CRP (hsCRP) published in PubMed, ScienceDirect, Scopus, and Google Scholar was employed. Study quality was evaluated using the Newcastle-Ottawa Scale. Fixed- or random-effects model meta-analyses were performed using RevMan version 5.4 software. Subgroup analysis was performed based on BMI status, in overweight and/or obese (BMI ≥25) and non-obese (BMI ≥18.5) individuals. Meta-analysis was presented in standardized mean difference (SMD) with 95% confidence interval (CI). Fourteen studies involving 522 participants were finally included. Results: The finding showed that RF significantly reduced IL-6 (SMD = 0.68; 95% CI = 0.50, 0.87; p <0.00001) and TNF-α (SMD = 0.69; 95% CI = 0.34, 1.04; p = 0.0001), with stronger effects in overweight and/or obese individuals for IL-6 (SMD = 0.71; 95% CI = 0.52, 0.90; p <0.00001) and TNF-α (SMD = 0.79; 95% CI = 0.35, 1.23; p = 0.0005). Meanwhile, the reduction of CRP/hsCRP was significant only in non-obese individuals (SMD = 1.03; 95% CI = 0.04, 2.02; p = 0.04). Conclusion: RF may serve as a natural strategy to reduce systemic inflammation, particularly in individuals with elevated BMI. These findings may have clinical relevance for populations at risk of inflammation-related conditions such as obesity and metabolic syndrome. However, the heterogeneity in this meta-analysis requires the interpretation of the results with caution.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".