Preliminary Study on the Association of Serum Branched-Chain Amino Acids With Lipid and Hepatic Markers
Bibliographic record
Abstract
Background: Serum levels of branched-chain amino acids (BCAAs) are associated with various vital physiological functions and thus elevation in circulating levels results in several metabolic disturbances. Serum levels of BCAAs are strong predictors of various metabolic disorders. Their association with cardiovascular health is uncertain. The study aimed to investigate the association of BCAAs with circulating levels of vital cardiovascular and hepatic markers. Methods: The study population of 714 individuals was included from the population tested for the vital cardio and hepatic biomarkers at the Vibrant America Clinical Laboratories. The subjects were stratified into four quartiles based on the serum levels of BCAAs, and their association with vital markers was studied using the Kruskal-Wallis test. Pearson's correlation analyzed the univariant relationship of BCAAs with selected cardio and hepatic markers. Results: BCAAs exhibited a strong negative correlation with serum HDL. Serum triglycerides were found to have a positive correlation with serum levels of leucine and valine. Univariant analysis exhibited a strong negative correlation between serum levels of BCAAs and HDL, and a positive correlation was observed between triglycerides and amino acids isoleucine and leucine. Among analyzed hepatic markers, alanine transaminase exhibited a considerable association with BCAAs. Conclusions: The elevated levels of serum BCAAs are strongly associated with serum HDL and triglycerides. Consumption of these supplements must be in coordination with healthcare providers to avoid metabolic and cardiovascular risk.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".