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 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 | 0.015 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".