Glutamate and obesity – what is the link?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Many studies using metabolomics have tried to unravel the metabolic signature of obesity and understand the pathophysiology of this complex and heterogeneous disease. Circulating levels of the amino acid glutamate have been consistently associated with obesity and more specifically with measurements of abdominal fat accumulation. The purpose of this narrative review is to highlight recent studies documenting this association. RECENT FINDINGS: Circulating glutamate concentrations have been positively correlated with measurements of central fat accumulation such as waist circumference and visceral adipose tissue area. Moreover, elevated glutamate levels have been linked to a higher prevalence of type 2 diabetes, cardiovascular diseases and nonalcoholic fatty liver disease. The association with adiposity is detected in early life, and genetic predisposition does not appear as a major driver. Glutamate levels reflect in vivo synthesis rather than dietary intake. However, interventions generating metabolic improvements such as incretin receptor agonist treatment or dietary improvements may reduce plasma levels of this amino acid. SUMMARY: Recent findings confirm the consistent association between circulating glutamate and abdominal obesity and its cardiometabolic complications. The pathophysiological pathways underlying this phenomenon are still unclear. Furthermore, studies are needed to establish the usefulness of this analyte as a biomarker of abdominal obesity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".