GDF15 is an exercise-induced hepatokine regulated by glucagon and insulin in humans
Bibliographic record
Abstract
Objective: Growth differentiation factor (GDF)-15 is implicated in regulation of metabolism and circulating GDF15 increases in response to exercise. The source and regulation of the exercise-induced increase in GDF15 is, however not known. Method: Plasma GDF15 was measured by ELISA under the following conditions: 1) Arterial-to-hepatic venous differences sampled before, during, and after exercise in healthy male subjects (n=10); 2) exogenous glucagon infusion compared to saline infusion in resting healthy subjects (n=10); 3) an acute exercise bout with and without a pancreatic clamp (n=6); 4) healthy subjects for 36 hours (n=17), and 5) patients with anorexia nervosa (n=25) were compared to healthy age-matched subjects (n=25). Tissue GDF15 mRNA content was determined in mice in response to exhaustive exercise (n=16). Results: The splanchnic bed released GDF15 to the circulation during exercise and increasing the glucagon-to-insulin ratio in resting humans led to a 2.7-fold (P<0.05) increase in circulating GDF15. Conversely, inhibiting the exercise-induced increase in the glucagon-to-insulin ratio blunted the exercise-induced increase in circulating GDF15. Fasting for 36 hours did not affect circulating GDF15, whereas resting patients with anorexia nervosa displayed elevated plasma concentrations (1.4-fold, P<0.05) compared to controls. In mice, exercise increased GDF15 mRNA contents in liver, muscle, and adipose tissue. Conclusion: In humans, GDF15 is a "hepatokine" which increases during exercise and is at least in part regulated by the glucagon-to-insulin ratio. Moreover, chronic energy deprivation is associated with elevated plasma GDF15, which supports that GDF15 is implicated in metabolic signalling in humans.
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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 | 0.000 |
| 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".