Dynamic changes in metabolites of the kynurenine pathway in Alzheimer’s disease, Parkinson’s disease, and Huntington’s disease: A systematic Review and meta-analysis
Bibliographic record
Abstract
Background: ), and metabolites of this pathway may have protective or degenerative effects on the nervous system. Thus, the KP may be involved in neurodegenerative diseases. Objectives: The purpose of this systematic review and meta-analysis is to assess the changes in KP metabolites such as TRP, kynurenine (KYN), kynurenic acid (KYNA), Anthranilic acid (AA), 3-hydroxykynurenine (3-HK), 5-Hydroxyindoleacetic acid (5-HIAA), and 3-Hydroxyanthranilic acid (3-HANA) in Alzheimer's disease (AD), Parkinson's disease (PD), and Huntington's disease (HD) patients compared to the control group. Methods: We conducted a literature search using PubMed/Medline, Scopus, Google Scholar, Web of Science, and EMBASE electronic databases to find articles published up to 2022. Studies measuring TRP, KYN, KYNA, AA, 3-HK, 5-HIAA, 3-HANA in AD, PD, or HD patients and controls were identified. Standardized mean differences (SMDs) were used to determine the differences in the levels of the KP metabolites between the two groups. Results: A total of 30 studies compromising 689 patients and 774 controls were included in our meta-analysis. Our results showed that the blood levels of TRP was significantly lower in the AD (SMD=-0.68, 95% CI=-0.97 to -0.40, p=0.000, I2 = 41.8%, k=8, n=382), PD (SMD=-0.77, 95% CI=-1.24 to -0.30, p=0.001, I2 = 74.9%, k=4, n=352), and HD (SMD=-0.90, 95% CI=-1.71 to -0.10, p=0.028, I2 = 91.0%, k=5, n=369) patients compared to the controls. Moreover, the CSF levels of 3-HK in AD patients (p=0.020) and the blood levels of KYN in HD patients (p=0.020) were lower compared with controls. Conclusion: Overall, the findings of this meta-analysis support the hypothesis that the alterations in the KP may be involved in the pathogenesis of AD, PD, and HD. However, additional research is needed to show whether other KP metabolites also vary in AD, PD, and HD patients. So, the metabolites of KP can be used for better diagnosing these diseases.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.033 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".