A systematic review and meta-regression of exogenous ketone infusion rates and resulting ketosis—A tool for clinicians and researchers
Bibliographic record
Abstract
Introduction:Ketone bodies such as beta-hydroxybutyrate (BHB) have pleiotropic functional benefits as fuel and signaling metabolites and may have multiple clinical applications. An alternative to inducing ketosis by dietary modification is intravenous delivery of exogenous sources of ketones. It is unknown whether there is a strong relationship between BHB infusion rate and blood BHB concentrations in the published literature; this information is vital for clinical studies investigating therapeutic effects of ketosis. This systematic review aimed to aggregate available data and address this gap. Methods:The PubMed and EMBASE databases were searched, and data were extracted from 23 manuscripts where BHB was infused and maximum and/or steady state BHB levels assessed. Infusion rate was adjusted when racemic BHB was infused but onlyD-BHB was measured. Results:Using a random effects meta-regression, strong linear relationships between BHB infusion rate and maximal (y = 0.060 + 0.870x,R2= 87.2%,p< 0.0001) and steady state (y = −0.022 + 0.849x,R2= 86.9%,p< 0.0001) blood BHB concentrations were found. Sensitivity analysis found this relationship was stronger when studies in non-healthy populations were excluded (y = 0.059 + 0.831x,R2= 96.3%,p< 0.0001). Conclusion:There is a strong relationship between BHB infusion rate and blood BHB concentrations; the regressions described here can be used by clinicians or researchers to determine ketone delivery required for a target blood concentration.
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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 | 0.037 | 0.108 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".