Association of Plasma Branched-Chain Amino Acid With Biomarkers of Inflammation and Lipid Metabolism in Women
Bibliographic record
Abstract
Background: Branched-chain amino acids (BCAAs; isoleucine, leucine, and valine) correlate with insulin resistance and poor glucose control, which may in part explain associations between type 2 diabetes and cardiovascular disease. However, the relationships of BCAAs with other cardiometabolic pathways, including inflammation and dyslipidemia, are unclear. We hypothesized that plasma BCAAs would correlate with multiple pathways of cardiometabolic dysfunction. Methods: We conducted a cross-sectional analysis among 19 472 participants (mean age=54.9 years, SD=7.2 years) in the Women’s Health Study without a history of type 2 diabetes, cardiovascular disease, or cancer. We quantified the concentrations of individual biomarkers of inflammation and lipids, across quartiles of BCAAs, adjusting for age, smoking, body mass index, physical activity, and other established cardiovascular disease risk factors at blood draw. Results: Women in the highest versus lowest quartiles of plasma BCAAs had higher inflammatory markers including high-sensitivity C-reactive protein (multivariable-adjusted means: 1.96 versus 1.43 mg/L), fibrinogen (367 versus 362 mg/dL), soluble intercellular cell adhesion molecule-1 (361 versus 353 ng/mL), and glycoprotein acetylation (407 versus 371 µmol/L; P trend=0.0002 for fibrinogen; P <0.0001 for others). Similarly for lipids, women with higher BCAAs had lower HDL-C (high-density lipoprotein cholesterol; 49.0 versus 55.0 mg/dL), and higher triglycerides (143 versus 114 mg/dL), LDL-C (low-density lipoprotein cholesterol; 133 versus 124 mg/dL), and lipoprotein insulin resistance score (52.6 versus 37.3; all: P <0.0001). Similar associations with these biomarkers were observed in isoleucine, leucine, and valine, respectively. Conclusions: Higher circulating BCAA concentrations are associated with adverse profiles of biomarkers of inflammation and dyslipidemia independent of established cardiovascular disease risk factors, and thus, may reflect poorer cardiometabolic health through multiple pathways. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT00000479.
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.
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 | 0.000 | 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) | 0.001 | 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".