Impact of fasting & ketogenic interventions on the NLRP3 inflammasome: A narrative review
Why this work is in the frame
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Bibliographic record
Abstract
Overactivation of the NLRP3 inflammasome is implicated in chronic low-grade inflammation associated with various disease states, including obesity, type 2 diabetes, atherosclerosis, Alzheimer's disease, and Parkinson's disease. Emerging evidence, mostly from cell and animal models of disease, supports a role for ketosis in general, and the main circulating ketone body beta-hydroxybutyrate (BHB) in particular, in reducing NLRP3 inflammasome activation to improve chronic inflammation. As a result, interventions that can induce ketosis (e.g., fasting, intermittent fasting, time-restricted feeding/eating, very low-carbohydrate high-fat ketogenic diets) and/or increase circulating BHB (e.g., exogenous ketone supplementation) have garnered increasing interest for their therapeutic potential. The purpose of the present review is to summarize our current understanding of the literature on how ketogenic interventions impact the NLRP3 inflammasome across human, rodent and cell models. Overall, there is convincing evidence that ketogenic interventions, likely acting through multiple interacting mechanisms in a cell-, disease- and context-specific manner, can reduce NLRP3 inflammasome activation. The evidence supports a direct effect of BHB, although it is important to consider the myriad of other metabolic responses to fasting or ketogenic diet interventions (e.g., elevated lipolysis, low insulin, stable glucose, negative energy balance) that may also impact innate immune responses. Future research is needed to translate promising findings from discovery science to clinical application.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it