High‐Fat Diet Modulates Hepatic Amyloid β and Cerebrosterol Metabolism in the Triple Transgenic Mouse Model of Alzheimer’s Disease
Bibliographic record
Abstract
Obesity and diabetes are strongly associated not only with fatty liver but also cognitive dysfunction. Moreover, their presence, particularly in midlife, is recognized as a risk factor for Alzheimer’s disease (AD). AD, the most common cause of dementia, is increasingly considered as a metabolic disease, although underlying pathogenic mechanisms remain unclear. The liver plays a major role in maintaining glucose and lipid homeostasis, as well as in clearing the AD neuropathogenic factor amyloid‐β (Aβ) and in metabolizing cerebrosterol, a cerebral‐derived oxysterol proposed as an AD biomarker. We hypothesized that liver impairment induced by obesity contributes to AD pathogenesis. We show that the AD triple transgenic mouse model (3xTg‐AD) fed a chow diet presents a hepatic phenotype similar to nontransgenic controls (NTg) at 15 months of age. A high‐fat diet (HFD), started at the age of 6 months and continued for 9 months, until sacrifice, induced hepatic steatosis in NTg, but not in 3xTg‐AD mice, whereas HFD did not induce changes in hepatic fatty acid oxidation, de novo lipogenesis, and gluconeogenesis. HFD‐induced obesity was associated with a reduction of insulin‐degrading enzyme, one of the main hepatic enzymes responsible for Aβ clearance. The hepatic rate of cerebrosterol glucuronidation was lower in obese 3xTg‐AD than in nonobese controls (P < 0.05) and higher compared with obese NTg (P < 0.05), although circulating levels remained unchanged. Conclusion: Modulation of hepatic lipids, Aβ, and cerebrosterol metabolism in obese 3xTg‐AD mice differs from control mice. This study sheds light on the liver–brain axis, showing that the chronic presence of NAFLD and changes in liver function affect peripheral AD features and should be considered during development of biomarkers or AD therapeutic targets.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".