Glucagon receptor signaling is indispensable for the healthspan effects of caloric restriction in aging male mice
Bibliographic record
Abstract
Abstract Obesity and type 2 diabetes mellitus accelerate aging, shortening the duration of healthspan. Conversely, chronic calorie restriction (CR) extends healthspan. Research aimed at understanding the mechanism by which CR slows aging has focused heavily on insulin and downstream signaling cascades. Glucagon, a hormone that counter-regulates insulin, is commonly affected by these same interventions. To investigate the role of glucagon in aging, we used dietary manipulation, global and liver-specific glucagon receptor knockout, and pharmacological glucagon receptor activation. We found that globally eliminating glucagon receptor signaling (Gcgr KO) decreases median lifespan by 35% in lean mice. Extending these findings to metabolic health, we found that glucagon receptor signaling is indispensable to the metabolic response to chronic CR in young and aged mice. While CR decreased liver fat, serum triglyceride, and serum cholesterol in WT mice, these metabolic benefits were absent in Gcgr KO mice. In line with these observations, we found that critical nutrient-sensing pathways known to improve aging are dysregulated in mice lacking glucagon receptor signaling at the liver (Gcgr hep−/− ). Liver-specific deletion of the glucagon receptor decreases hepatic AMP kinase activation in aging mice, regardless of diet. Further, CR decreases hepatic mTOR activity in WT mice but not in Gcgr hep−/− mice. Together, these findings propose that glucagon signaling plays a critical role in both normal aging and the lifespan and healthspan extension driven by caloric restriction. Graphical Abstract
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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 | 0.000 | 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) | 0.024 | 0.003 |
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".