Butyric Acid Influences How β-Hydroxybutyrate Modulates Fatty Acid Oxidation and Ketogenesis To Support Ketosis
Bibliographic record
Abstract
Background: The ketogenic diet has been applied since the early 1900's for therapy.Although, the restrictive dietary application has proved to be effective in the treatment of neurologic disease we are seeing a more recent trend for the successful application of the ketogenic diet to treat metabolic disorders.The restrictive nature of the diet and the flu-like symptoms associated with its inception, however, make it intolerable for most patients.Objectives: Here we report the mechanism by which the combination of an exogenous ketone, β-hydroxybutyrate (BHB) and butyric acid (BA) supplement (BHB-BA) can help facilitate ketosis with moderate and more tolerable dietary restriction by inducing ketogenesis and fatty acid oxidation.Methods: HepG2 cell line was assayed for ketogenesis and fatty acid oxidation activity after treating the cells with equimolar dosing of BHB alone and BHB-BA in fed-and starved-conditions.HepG2, T98G, BV2 and HEK293 cells were analysed for Nrf2 expression through Western blotting after treatment with different BHB-BA concentrations.Results: The results support incremental fatty acid oxidation and ketogenesis by BHB-BA over equimolar dosing of BHB on its own.We also show that the BHB-BA complex up-regulates expression of Nrf2, a master regulator of endogenous antioxidant proteins such as hemeoxygenase-1, glutathione peroxidase, superoxide dismutase-1 and catalase, in a cell and dose specific manner. Conclusion:Although more research needs to be done to explore the full metabolic and signalling activities by BHB and BA, the reported results provide an indication of how the short chain fatty acid, BA, could synergise the effects of the exogenous ketone body, BHB, and support the elevation of ketone status independent of or as an addition to the ketogenic diet.
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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 | 0.000 | 0.000 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".