Effects of Ramadan intermittent fasting on inflammatory and biochemical biomarkers in males with obesity
Bibliographic record
Abstract
BACKGROUND: To determine the effects of Ramadan intermittent fasting (RIF) on inflammatory (C-reactive protein (CRP), interleukin-6 (IL-6) and tumor necrosis factor-alpha (TNF-α)) and biochemical markers of liver-renal function (aspartate aminotransferase (AST), alanine amino transferase (ALT), bilirubin, lactate dehydrogenase (LDH), urea and creatinine) in males with obesity. MATERIALS AND METHODS: Twenty-eight males with obesity were randomly allocated to an experimental group (EG, n = 14) or a control group (CG, n = 14). The EG group completed their fasting rituals for the entire month of Ramadan (30 days) whereas the CG group continued with their normal daily habits. Blood samples were collected 24 h before the start of Ramadan (T0), on the 15th day of Ramadan (T1), the day after the end of Ramadan (T2), and 21 days after the end of Ramadan (T3). Resting plasma volume variation between pre and post-RIF (ΔPV) was calculated. RESULTS: Decreases were noted for interleukin-6 (p = 0.02, d = 1.4) and tumor necrosis factor-alpha (p = 0.01, d = 0.7), with no changes for C-reactive protein (p = 0.3; d = 0.1) in the EG compared to CG group. There were no changes (P > 0.05) in ΔPV recorded after RIF for either EG (-0.035 ± 0.02%) and CG (0.055 ± 0.06%). CONCLUSION: This study demonstrates that RIF improves systemic inflammation biomarkers in males with obesity. Moreover, RIF did not negatively affect biomarkers of liver and renal function.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".