AGE-RAGE Interaction Does Not Explain the Clinical Improvements after Therapeutic Fasting in Osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Therapeutic fasting improves joint pain in patients with osteoarthritis (OA), but the underlying mechanisms are unknown. Interactions of advanced glycation end products (AGEs) and their receptors (RAGE) play a role in OA pathogenesis. This study aimed to investigate whether the benefits of fasting in OA can be explained by changes in AGEs or RAGE. PATIENTS AND METHODS: 37 patients with OA underwent fasting for 8 days. Serum levels of an AGE (N-ε-(carboxymethyl)-lysine; CML) and the soluble RAGE (sRAGE) as well as clinical outcome parameters such pain characteristics (measured by visual analogue scale; VAS), joint function (determined by the Western Ontario and McMaster Universities Arthritis Index; WOMAC), and quality of life (via the 36-Item Short-Form Health Survey (SF-36) questionnaire) were assessed. The variables were measured at baseline, the end of fasting, and at follow-up at 4 weeks. RESULTS: The CML levels did not significantly change from baseline to the end of intervention (Δ = -25.6 ± 92.2 ng/ml; p = 0.10). In contrast, the sRAGE levels (Δ = -182.7 ± 171.4 ng/ml; p < 0.0001) and the sRAGE/CML ratio (Δ = -0.4 ± 0.6; p < 0.001) significantly decreased, but they returned to baseline levels 4 weeks after the end of fasting. The scores for pain, WOMAC, and the physical subscale of the SF-36 significantly improved during fasting. There was no correlation between the clinical outcomes and changes in serum levels of CML, sRAGE, or the sRAGE/CML ratio. CONCLUSIONS: Fasting resulted in a significant but non-sustained reduction of sRAGE levels and the sRAGE/CML ratio in OA, while the CML levels did not change. Improvement in clinical endpoints of OA does not correlate with changes in CML or sRAGE.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".