Hepatic steatosis, inflammation, and ER stress in mice maintained long term on a very low-carbohydrate ketogenic diet
Bibliographic record
Abstract
Low-carbohydrate diets are used to manage obesity, seizure disorders, and malignancies of the central nervous system. These diets create a distinctive, but incompletely defined, cellular, molecular, and integrated metabolic state. Here, we determine the systemic and hepatic effects of long-term administration of a very low-carbohydrate, low-protein, and high-fat ketogenic diet, serially comparing these effects to a high-simple-carbohydrate, high-fat Western diet and a low-fat, polysaccharide-rich control chow diet in C57BL/6J mice. Longitudinal measurement of body composition, serum metabolites, and intrahepatic fat content, using in vivo magnetic resonance spectroscopy, reveals that mice fed the ketogenic diet over 12 wk remain lean, euglycemic, and hypoinsulinemic but accumulate hepatic lipid in a temporal pattern very distinct from animals fed the Western diet. Ketogenic diet-fed mice ultimately develop systemic glucose intolerance, hepatic endoplasmic reticulum stress, steatosis, cellular injury, and macrophage accumulation, but surprisingly insulin-induced hepatic Akt phosphorylation and whole-body insulin responsiveness are not impaired. Moreover, whereas hepatic Pparg mRNA abundance is augmented by both high-fat diets, each diet confers splice variant specificity. The distinctive nutrient milieu created by long-term administration of this low-carbohydrate, low-protein ketogenic diet in mice evokes unique signatures of nonalcoholic fatty liver disease and whole-body glucose homeostasis.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".