Feasibility and metabolic outcomes of a well-formulated ketogenic diet as an adjuvant therapeutic intervention for women with stage IV metastatic breast cancer: The Keto-CARE trial
Bibliographic record
Abstract
PURPOSE: Ketogenic diets may positively influence cancer through pleiotropic mechanisms, but only a few small and short-term studies have addressed feasibility and efficacy in cancer patients. The primary goals of this study were to evaluate the feasibility and the sustained metabolic effects of a personalized well-formulated ketogenic diet (WFKD) designed to achieve consistent blood beta-hydroxybutyrate (βHB) >0.5 mM in women diagnosed with stage IV metastatic breast cancer (MBC) undergoing chemotherapy. METHODS: Women (n = 20) were enrolled in a six month, two-phase, single-arm WFKD intervention (NCT03535701). Phase I was a highly-supervised, ad libitum, personalized WFKD, where women were provided with ketogenic-appropriate food daily for three months. Phase II transitioned women to a self-administered WFKD with ongoing coaching for an additional three months. Fasting capillary βHB and glucose were collected daily; weight, body composition, plasma insulin, and insulin resistance were collected at baseline, three and six months. RESULTS: Capillary βHB indicated women achieved nutritional ketosis (Phase I mean: 0.8 mM (n = 15); Phase II mean: 0.7 mM (n = 9)). Body weight decreased 10% after three months, primarily from body fat. Fasting plasma glucose, plasma insulin, and insulin resistance also decreased significantly after three months (p < 0.01), an effect that persisted at six months. CONCLUSIONS: Women diagnosed with MBC undergoing chemotherapy can safely achieve and maintain nutritional ketosis, while improving body composition and insulin resistance, out to six months.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".