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Ketone Therapy Reduces Systemic and Organ Inflammation in Sepsis

2022· article· en· W4225376173 on OpenAlexafffund
Shubham Soni, Matthew D. Martens, Zaid H. Maayah, Shingo Takahara, Heidi Silver, Aja M. Rieger, Mourad Ferdaoussi, Jason R.B. Dyck

Bibliographic record

VenueThe FASEB Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsSystemic inflammationSepsisInflammationMedicineInternal medicine

Abstract

fetched live from OpenAlex

Introduction Sepsis is the body's reaction to an infection that often causes lasting multi‐organ injury due to a dysregulated inflammatory response. Currently, there are no effective treatments to reduce inflammation during sepsis and assist in preventing the lasting inflammation‐mediated damage and thus, sepsis accounts for approximately 20% of all global causes of death. In addition, many patients who recover from sepsis have permanent damage to many organ systems which makes them more susceptible to future injury that likely affects them more severely. Thus, therapeutic strategies to reduce the inflammatory response in sepsis are needed to save lives and improve the outcomes and quality of life of those who survive sepsis. Herein, we tested the efficacy of a ketone therapy that increases circulating ketones via ketone ester supplementation. Ketones are small molecules that are normally produced by the liver and are elevated during carbohydrate‐deprived states, such as fasting or exercise. While ketones are classically known to be a metabolic source of energy, they also have non‐metabolic effects, such as inhibiting inflammation, which can be of therapeutic importance. We hypothesized that ketones have potent anti‐inflammatory effects in a mouse model of lipopolysaccharide (LPS)‐induced sepsis and that ketones can be used to mitigate the inflammation‐mediated organ damage. Objective To determine if ketone supplementation can effectively reduce inflammation and organ dysfunction/damage in a model of LPS‐induced sepsis. Methods 8‐week‐old mice orally received either vehicle or a clinically tested ketone ester (KE) for 3 days. On day 3, mice were injected with saline or LPS to induce systemic inflammation and organ damage. 24 hours post‐injection, cardiac function was assessed and then mice were euthanized to assess systemic and organ inflammation from the 3 groups (control, LPS, LPS+KE). Results LPS‐treated mice had higher blood ketones compared to controls, suggesting that ketones may be important as an innate defense mechanism, and this response was further augmented in KE‐treated septic mice. While LPS‐treated mice had an induction of systemic pro‐inflammatory cytokines (e.g., IL‐1β, IL‐6, interferon‐γ) these cytokines were significantly lower in KE‐treated septic mice. Similarly, LPS induced numerous inflammatory markers in the heart, kidney, and liver, a large majority of which were reduced in the KE‐treated septic mice. LPS‐induced cardiac dysfunction and renal fibrosis was also significantly lower in KE‐treated septic mice. Finally, there was either no change or a reduction of ketolytic enzymes in various organs, suggesting that these protective and anti‐inflammatory effects do not depend on ketone metabolism for energy production. Conclusion Together, these data are the first to show that ketone therapy may be a novel approach to reducing systemic and organ inflammation and dysfunction, and subsequent organ damage in a model of LPS‐induced sepsis.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.021
GPT teacher head0.266
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2022
Admission routes2
Has abstractyes

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