Assessment of the Relationship Between Amino Acid Status and Parkinson’s Disease: A Comprehensive Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Parkinson's disease (PD) is characterized by the inability of dopamine production from amino acids. Therefore, changes in amino acid profile in PD patients are very critical for understanding disease development. Determination of amino acid levels in PD patients with a cumulative approach may enlighten the disease pathophysiology. METHODS: A systematic search was performed until February 2023, resulting in 733 articles in PubMed, Web of Science and Scopus databases to evaluate the serum amino acid profile of PD patients. Relevant articles in English with mean/standard deviation values of serum amino acid levels of patients and their healthy controls were included in the meta-analysis. RESULTS: Our results suggest that valine, proline, ornithine and homocysteine levels were increased, while aspartate, citrulline, lysine and serine levels were significantly decreased in PD patients compared to healthy controls. Homocysteine showed positive correlations with glutamate and ornithine levels. We also analyzed the disease stage parameters: Unified Parkinson's Disease Rating Scale III (UPDRS III) score, Hoehn-Yahr Stage Score, disease duration and levodopa equivalent daily dose (LEDD) of patients. It was observed that LEDD has a negative correlation with arginine levels in patients. UPDRS III score is negatively correlated with phenylalanine levels, and it also tends to show a negative correlation with tyrosine levels. Disease duration tends to be negatively correlated with citrulline levels in PD patients. CONCLUSION: This cumulative analysis shows evidence of the relation between the mechanisms underlying amino acid metabolism in PD, which may have a great impact on disease development and new therapeutic strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".