Higher Plasma Kynurenine to Tryptophan Correlates with an Increased Incidence of Mild Cognitive Impairment in Treated Metabolic Syndrome Patients
Bibliographic record
Abstract
An increase in cognitive impairment was observed in metabolic syndrome (MetS) patients. Although alterations in metabolomic profiles have been identified as potential plasma/serum biomarkers of mild cognitive impairment (MCI) and MetS, findings remain inconsistentprobably due to the heterogeneity among MetS patients and the lack of subsequent validation using targeted analysis after the initial untargeted analysis. In this study, we validated mass spectrometry-based quantitation methods and quantified amino acids, fatty acids, and tryptophan metabolites in the kynurenine pathway in the plasma of 95 treated MetS patients with and without MCI assessed by the Montreal Cognitive Assessment. We found that MCI was positively associated with the kynurenine-to-tryptophan ratio (KTR) after the adjustment for age, gender, and BMI, as well as negatively associated with C20:3 [all-Z-8,11,14] and lysine. A one-unit increase in KTR resulted in an increased probability of developing MCI by 371%. In contrast, one-unit increases in C20:3 and lysine were associated with decreased odds of developing MCI by 81 and 78%, respectively. Our finding underscores prominent neuroinflammation, beyond normal aging, in MetS patients, even under ongoing clinical treatment. It also points to the potential of KTR as a risk marker for MCI, offering a valuable complement to the existing cognitive assessments that may be influenced by the educational background. In addition, the validated metabolite data serve as an invaluable resource for future research. They can facilitate comparisons across different studies, contribute to large-scale analyses, and be used in machine learning models for discovering and validating new biomarkers.
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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 | 0.002 |
| 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".