Altered branched-chain α-keto acid metabolism is a feature of NAFLD in individuals with severe obesity
Bibliographic record
Abstract
ABSTRACT Hepatic de novo lipogenesis is influenced by the branched-chain α-keto acid dehydrogenase (BCKDH) kinase (BCKDK). We aimed to determine whether circulating levels of the immediate substrates of BCKDH, the branched-chain α-ketoacids (BCKAs) and hepatic BCKDK expression are associated with the presence and severity of non-alcoholic fatty liver disease (NAFLD).Eighty metabolites (3 BCKA, 14 amino acids, 43 acylcarnitines, 20 ceramides) were quantified in plasma from 288 bariatric surgery patients with severe obesity (BMI > 35 kg/m 2 ) with scored liver biopsy samples. Metabolite principal component analysis (PCA) factors, BCKA, branched-chain amino acids (BCAA), and the BCKA:BCAA ratio were tested for associations with steatosis grade and presence of non-alcoholic steatohepatitis (NASH). Of all analytes tested, only the valine-derived BCKA, α-ketoisovalerate, and the BCKA:BCAA ratio were associated with both steatosis grade and NASH. Gene expression analysis in liver samples from two independent bariatric surgery cohorts showed that hepatic BCKDK mRNA expression correlates with steatosis, ballooning, and levels of the lipogenic transcription factor SREBP1 . Experiments in AML12 hepatocytes showed that SREBP1 inhibition lowers BCKDK mRNA expression. These findings demonstrate that higher plasma levels of BCKA and hepatic expression of BCKDK are features of human NAFLD/NASH and identify SREBP1 as a transcriptional regulator of BCKDK .
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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 | 0.001 |
| 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".