Neuroprotective Effects of Intermittent Fasting in Animal Models of Peripheral Neuropathy: A Systematic Review of Prevention and Disease Progression
Bibliographic record
Abstract
This systematic review protocol outlines a comprehensive evaluation of the neuroprotective effects of intermittent fasting (IF) in animal models of peripheral neuropathy. Peripheral neuropathy, a debilitating complication of metabolic disorders (e.g., diabetes) and chemotherapy, lacks universally effective treatments. Preclinical evidence suggests IF may mitigate neuropathy progression by modulating autophagy, oxidative stress, and mitochondrial function, but findings remain fragmented across heterogeneous models.Objectives:Assess IF’s efficacy in preventing or slowing neuropathy progression using behavioral (e.g., allodynia), functional (e.g., nerve conduction velocity), and structural (e.g., intraepidermal nerve fiber density) outcomes.Compare effects of different IF regimens (e.g., time-restricted feeding vs. alternate-day fasting).Synthesize mechanistic evidence (e.g., oxidative stress, inflammation) to bridge gaps between preclinical and clinical research.Methods:Eligibility: In vivo animal studies of metabolic, toxic, or genetic neuropathy with IF interventions.Data Sources: PubMed, Scopus, Embase, Cochrane Library, and grey literature.Risk of Bias: Assessed via Cochrane Tool and Newcastle-Ottawa Scale.Synthesis: Meta-analysis (if feasible) or narrative synthesis, following PRISMA 2020 guidelines.Significance: This review will consolidate preclinical evidence to inform translational research, identify optimal IF protocols, and highlight limitations in current models (e.g., sex bias, chronicity). Results may guide future clinical trials for neuropathy management.Keywords: Intermittent fasting, peripheral neuropathy, neuroprotection, animal models, systematic review, oxidative stress, autophagy.
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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 | 0.012 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".