The role of one-carbon metabolism and homocysteine in Parkinson’s disease onset, pathology and mechanisms
Bibliographic record
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disorder. It is characterised by the progressive degeneration of dopaminergic (DA) neurons. The cause of degeneration is not well understood; however, both genetics and environmental factors, such as nutrition, have been implicated in the disease process. Deficiencies in one-carbon metabolism in particular have been associated with increased risk for PD onset and progression, though the precise relationship is unclear. The aim of the present review is to determine the role of one-carbon metabolism and elevated levels of homocysteine in PD onset and pathology and to identify potential mechanisms involved. A search of PubMed, Google Scholar and Web of Science was undertaken to identify relevant human and animal studies. Case-control, prospective cohort studies, meta-analyses and non-randomised trials were included in the present review. The results from human studies indicate that polymorphisms in one-carbon metabolism may increase risk for PD development. There is an unclear role for dietary B-vitamin intake on PD onset and progression. However, dietary supplementation with B-vitamins may be beneficial for PD-affected individuals, particularly those on l-DOPA (levodopa or l-3,4-dihydroxyphenylalanine) treatment. Additionally, one-carbon metabolism generates methyl groups, and methylation capacity in PD-affected individuals is reduced. This reduced capacity has an impact on expression of disease-specific genes that may be involved in PD progression. During B-vitamin deficiency, animal studies report increased vulnerability of DA cells through increased oxidative stress and altered methylation. Nutrition, especially folates and related B-vitamins, may contribute to the onset and progression of PD by making the brain more vulnerable to damage; however, further investigation is required.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".