Correlational Analysis of Serum Metabolites and Cognitive Function in Moyamoya Disease
Bibliographic record
Abstract
BACKGROUND: Cognitive dysfunction is a common symptom in moyamoya disease (MMD). However, the mechanisms underlying this impairment, particularly the changes in serum metabolites, have not been thoroughly investigated. The primary objective of this study was to utilize untargeted metabolomics technology to analyze serum metabolic profiles in MMD patients and to explore the potential relationship between metabolic alterations and cognitive function. METHOD: Thirty patients with MMD and ten healthy controls were enrolled in our study. Cognitive assessment composing Montreal Cognitive Assessment (MoCA) and Hopkins Verbal Learning Test-Revised (HVLT-R) were used in this study to appraise general cognition and specific cognitive domains, including immediate recall, delayed recall, and delayed recognition. Serum samples were collected for metabolomics analysis that through using liquid chromatography-tandem mass spectrometry (LC-MS/MS) to quantify and identify metabolomics. Differentially expressed metabolites were identified based on fold changes and statistical significance. Additionally, correlation analyses were performed to evaluate the associations between identified differential metabolites and cognitive function. RESULT: Our results found that overall cognitive function and the subdomains of cognitive function include immediate memory, delayed recall, and recognition in MMD patients were significantly impaired. The untargeted metabolomics analysis identified 142 significantly altered metabolites in the serum of MMD patients compared to controls. Of these, 22 metabolites were downregulated, while 120 metabolites were upregulated. We identified significant alterations in bile acid, amino acid, peptide, and purine metabolism, along with changes in lipid metabolism, inflammation, hormonal regulation, and protein metabolism in the MMD group, suggesting a complex metabolic dysregulation in MMD. Furthermore, correlation analysis indicated that the metabolomic changes in MMD patients were strongly associated with cognitive dysfunction, implying their potential involvement in the pathophysiology of cognitive impairment. CONCLUSION: In conclusion, our study provides evidence that cognitive dysfunction in MMD are associated with significant metabolic changes. The altered serum metabolites highlight the needs to comprehensively understand and manage cognitive dysfunction in MMD patients.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".