O1‐02‐03: Metabolic defects in the 3xTg‐AD model of Alzheimer's disease are further enhanced by diet‐induced obesity
Bibliographic record
Abstract
Alzheimer's disease (AD) has been associated with type II diabetes and obesity in several epidemiological studies. To determine whether AD neuropathology and diet-induced obesity interact to cause peripheral metabolic impairments, we investigated metabolic parameters in the 3xTg-AD mouse model of AD after exposure to an obesity-inducing high-fat diet (HFD). 3xTg-AD and Non-transgenic (NonTg) controls were fed with a high-fat diet (HFD, 60% kCal) or control diet (CD, 15% kCal) from 6 to 14 months of age. The HFD induced a weight gain (+21 g vs CD) and reduced insulin sensitivity (area under the curve (AUC) -76% vs CD). We found a significant deterioration of glucose tolerance in 3xTg-AD mice (AUC +118 % vs NonTg mice), which was aggravated by diet-induced obesity (AUC +28 % vs CD). Consequences of 3xTg-AD transgenes on glucose tolerance were worse than HFD (AUC 3xTg-AD chow +63% vs NonTg HFD). Furthermore, 3xTg-AD mice fed the HFD displayed minimal insulin response to glucose injection (change from fasting value -0.001 mg/L vs +1.178 mg/L for NonTg HFD). As previously shown, diet-induced obesity was associated with increased Aβ accumulation in the brain cortex (+920% soluble Aβ42 vs CD) and worsened memory deficit in 3xTg-AD mice (-13% object recognition index). As deterioration of metabolic perturbations coincides with behavioral deficit in the 3xTg-AD mouse, we suggest that AD pathology aggravates metabolic consequences of diet-induced obesity leading to a pathological self-amplifying loop.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".