Identification of a leucine-mediated threshold effect governing macrophage mTOR signalling and cardiovascular risk
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
High protein intake is common in western societies and is often promoted as part of a healthy lifestyle; however, amino-acid-mediated mammalian target of rapamycin (mTOR) signalling in macrophages has been implicated in the pathogenesis of ischaemic cardiovascular disease. In a series of clinical studies on male and female participants ( NCT03946774 and NCT03994367 ) that involved graded amounts of protein ingestion together with detailed plasma amino acid analysis and human monocyte/macrophage experiments, we identify leucine as the key activator of mTOR signalling in macrophages. We describe a threshold effect of high protein intake and circulating leucine on monocytes/macrophages wherein only protein in excess of ∼25 g per meal induces mTOR activation and functional effects. By designing specific diets modified in protein and leucine content representative of the intake in the general population, we confirm this threshold effect in mouse models and find ingestion of protein in excess of ∼22% of dietary energy requirements drives atherosclerosis in male mice. These data demonstrate a mechanistic basis for the adverse impact of excessive dietary protein on cardiovascular risk. Zhang et al. use human studies and mechanistic work in mouse models to describe how leucine serves as the key amino acid derived from dietary protein to drive deleterious macrophage mTORC1 signalling and promote cardiovascular disease.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it