Impact of fasting & ketogenic interventions on the NLRP3 inflammasome: A narrative review
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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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".