Circulating glutamate concentration as a biomarker of visceral obesity and associated metabolic alterations
Bibliographic record
Abstract
Visceral adipose tissue (VAT) area is a strong predictor of obesity-related cardiometabolic alterations, but its measurement is costly, time consuming and, in some cases, involves radiation exposure. Glutamate, a by-product of branched-chain-amino-acid (BCAA) catabolism, has been shown to be increased in visceral obese individuals. In this follow-up data analysis, we aimed to investigate the ability of plasma glutamate to identify individuals with visceral obesity and concomitant metabolic alterations. Measurements of adiposity, targeted blood metabolomics and cardiometabolic risk factors were performed in 59 healthy middle-aged women. Visceral and subcutaneous adipose tissue areas were measured by computed tomography (CT) whereas body fat and lean mass were assessed by dual-energy x-ray absorptiometry (DEXA). The univariate Pearson correlation coefficient between glutamate and VAT area was r = 0.46 ( p < 0.001) and it was r = 0.36 ( p = 0.006) when adjusted for total body fat mass. Glutamate allowed to identify individuals with VAT areas ≥100 cm 2 (ROC_AUC: 0.78, 95% CI: 0.66–0.91) and VAT ≥130 cm 2 (ROC_AUC: 0.71, 95% CI: 0.56–0.87). The optimal glutamate concentration threshold determined from the ROC curve (glutamate ≥34.6 μmol/L) had a greater sensitivity than the metabolic syndrome (MetS) and the hypertriglyceridemic waist (HTW) phenotype to identify individuals with VAT ≥100 cm 2 (83% for glutamate vs 52% for the MetS and 35% for the HTW). Variance analysis showed that women with a high circulating glutamate level (≥34.6 μmol/L) had an altered metabolic profile, particularly regarding total triglyceride levels and the amount of triglycerides and cholesterol in very-low-density lipoproteins (all p < 0.01). Circulating glutamate is strongly associated with VAT area and may represent a potential screening tool for visceral obesity and alterations of the metabolic profile.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".