Nrg4 promotes fuel oxidation and a healthy adipokine profile to ameliorate diet-induced metabolic disorders
Bibliographic record
Abstract
OBJECTIVE: Brown and white adipose tissue exerts pleiotropic effects on systemic energy metabolism in part by releasing endocrine factors. Neuregulin 4 (Nrg4) was recently identified as a brown fat-enriched secreted factor that ameliorates diet-induced metabolic disorders, including insulin resistance and hepatic steatosis. However, the physiological mechanisms through which Nrg4 regulates energy balance and glucose and lipid metabolism remain incompletely understood. The aims of the current study were: i) to investigate the regulation of adipose Nrg4 expression during obesity and the physiological signals involved, ii) to elucidate the mechanisms underlying Nrg4 regulation of energy balance and glucose and lipid metabolism, and iii) to explore whether Nrg4 regulates adipose tissue secretome gene expression and adipokine secretion. METHODS: We examined the correlation of adipose Nrg4 expression with obesity in a cohort of diet-induced obese mice and investigated the upstream signals that regulate Nrg4 expression. We performed metabolic cage and hyperinsulinemic-euglycemic clamp studies in Nrg4 transgenic mice to dissect the metabolic pathways regulated by Nrg4. We investigated how Nrg4 regulates hepatic lipid metabolism in the fasting state and explored the effects of Nrg4 on adipose tissue gene expression, particularly those encoding secreted factors. RESULTS: Adipose Nrg4 expression is inversely correlated with adiposity and regulated by pro-inflammatory and anti-inflammatory signaling. Transgenic expression of Nrg4 increases energy expenditure and augments whole body glucose metabolism. Nrg4 protects mice from diet-induced hepatic steatosis in part through activation of hepatic fatty acid oxidation and ketogenesis. Finally, Nrg4 promotes a healthy adipokine profile during obesity. CONCLUSIONS: Nrg4 exerts pleiotropic beneficial effects on energy balance and glucose and lipid metabolism to ameliorate obesity-associated metabolic disorders. Biologic therapeutics based on Nrg4 may improve both type 2 diabetes and non-alcoholic fatty liver disease (NAFLD) in patients.
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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) | 0.000 | 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) | 0.003 | 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".