Fatty Acids Increase GDF15 and Reduce Food Intake Through a GFRAL Signaling Axis
Bibliographic record
Abstract
In contrast to the well-defined biological feedback loops controlling glucose, the mechanisms by which the body responds to changes in fatty acid availability are less clearly defined. Growth differentiating factor 15 (GDF15) suppresses the consumption of diets high in fat but is paradoxically increased in obese mice fed a high-fat diet. Given this interrelationship, we investigated whether diets high in fat could directly increase GDF15 independently of obesity. We found that fatty acids increase GDF15 levels dose dependently, with the greatest response observed with linolenic acid. GDF15 mRNA expression was modestly increased in the gastrointestinal tract; however, kidney GDF15 mRNA was ∼1,000-fold higher and was increased by more than threefold, with subsequent RNAscope analysis showing elevated expression within the cortex and outer medulla. Treatment of wild-type mice with linolenic acid reduced food intake and body mass; however, this effect disappeared in mice lacking the GDF15 receptor GFRAL. An equal caloric load of glucose did not suppress food intake or reduce body mass in either wild-type or GFRAL-knockout mice. These data indicate that fatty acids such as linolenic acid increase GDF15 and suppress food intake through a mechanism requiring GFRAL. These data suggest that a primary physiological function of GDF15 may be as a fatty acid sensor designed to protect cells from fatty acid overload. ARTICLE HIGHLIGHTS: The mechanisms by which the body responds to changes in fatty acid availability are less clearly defined. We investigated whether diets high in fat could directly increase growth differentiating factor 15 (GDF15) independently of obesity. Fatty acids increase GDF15 and reduce food intake through a GFRAL signaling axis. GDF15 is a sensor of fatty acids that may have important implications for explaining increased satiety after consumption of diets high in fat.
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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 | 0.001 |
| 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) | 0.002 | 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".