Age-Dependent Control of Energy Homeostasis by Brown Adipose Tissue in Progeny Subjected to Maternal Diet–Induced Fetal Programming
Bibliographic record
Abstract
Epidemiological and animal studies show that deleterious maternal environments predispose aging offspring to metabolic disorders and type 2 diabetes. Young progenies in a rat model of maternal low-protein (LP) diet are normoglycemic despite collapsed insulin secretion. However, without further worsening of the insulin secretion defect, glucose homeostasis deteriorates in aging LP descendants. Here we report that normoglycemic and insulinopenic 3-month-old LP progeny shows increased body temperature and energy dissipation in association with enhanced brown adipose tissue (BAT) activity. In addition, it is protected against a cold challenge and high-fat diet (HFD)-induced obesity with associated insulin resistance and hyperglycemia. Surgical BAT ablation in 3-month-old LP offspring normalizes body temperature and causes postprandial hyperglycemia. At 10 months, BAT activity declines in LP progeny with the appearance of reduced protection to HFD-induced obesity; at 18 months, LP progeny displays a BAT activity comparable to control offspring and insulin resistance and hyperglycemia occur. Together our findings identify BAT as a decisive physiological determinant of the onset of metabolic dysregulation in offspring predisposed to altered β-cell function and hyperglycemia and place it as a critical regulator of fetal programming of adult metabolic disease.
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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 | 0.001 |
| 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".