A Supplemented High-Fat Low-Carbohydrate Diet for the Treatment of Glioblastoma
Bibliographic record
Abstract
PURPOSE: Dysregulated energetics coupled with uncontrolled proliferation has become a hallmark of cancer, leading to increased interest in metabolic therapies. Glioblastoma (GB) is highly malignant, very metabolically active, and typically resistant to current therapies. Dietary treatment options based on glucose deprivation have been explored using a restrictive ketogenic diet (KD), with positive anticancer reports. However, negative side effects and a lack of palatability make the KD difficult to implement in an adult population. Hence, we developed a less stringent, supplemented high-fat low-carbohydrate (sHFLC) diet that mimics the metabolic and antitumor effects of the KD, maintains a stable nutritional profile, and presents an alternative clinical option for diverse patient populations. EXPERIMENTAL DESIGN: The dietary paradigm was tested in vitro and in vivo, utilizing multiple patient-derived gliomasphere lines. Cellular proliferation, clonogenic frequency, and tumor stem cell population effects were determined in vitro using the neurosphere assay (NSA). Antitumor efficacy was tested in vivo in preclinical xenograft models and mechanistic regulation via the mTOR pathway was explored. RESULTS: Reducing glucose in vitro to physiologic levels, coupled with ketone supplementation, inhibits proliferation of GB cells and reduces tumor stem cell expansion. In vivo, while maintaining animal health, the sHFLC diet significantly reduces the growth of tumor cells in a subcutaneous model of tumor progression and increases survival in an orthotopic xenograft model. Dietary-mediated anticancer effects correlate with the reduction of mTOR effector expression. CONCLUSIONS: We demonstrate that the sHFLC diet is a viable treatment alternative to the KD, and should be considered for clinical testing. Clin Cancer Res; 22(10); 2482-95. ©2015 AACR.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".