Unravelling cysteine-deficiency-associated rapid weight loss
Bibliographic record
Abstract
Abstract Around 40% of the US population and 1 in 6 individuals worldwide have obesity, with the incidence surging globally 1,2 . Various dietary interventions, including carbohydrate, fat and, more recently, amino acid restriction, have been explored to combat this epidemic 3–6 . Here we investigated the impact of removing individual amino acids on the weight profiles of mice. We show that conditional cysteine restriction resulted in the most substantial weight loss when compared to essential amino acid restriction, amounting to 30% within 1 week, which was readily reversed. We found that cysteine deficiency activated the integrated stress response and oxidative stress response, which amplify each other, leading to the induction of GDF15 and FGF21, partly explaining the phenotype 7–9 . Notably, we observed lower levels of tissue coenzyme A (CoA), which has been considered to be extremely stable 10 , resulting in reduced mitochondrial functionality and metabolic rewiring. This results in energetically inefficient anaerobic glycolysis and defective tricarboxylic acid cycle, with sustained urinary excretion of pyruvate, orotate, citrate, α-ketoglutarate, nitrogen-rich compounds and amino acids. In summary, our investigation reveals that cysteine restriction, by depleting GSH and CoA, exerts a maximal impact on weight loss, metabolism and stress signalling compared with other amino acid restrictions. These findings suggest strategies for addressing a range of metabolic diseases and the growing obesity crisis.
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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) | 0.001 | 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".