Tryptophan Metabolites Are Associated With Symptoms and Nigral Pathology in Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to determine whether neurotoxic kynurenine metabolites, induced by inflammation, in plasma and cerebrospinal fluid (CSF) are associated with symptom severity and nigral pathology in Parkinson's disease (PD). METHODS: Clinical and MRI data were obtained from 97 PD and 89 controls. We used ultra-performance liquid chromatography to quantify kynurenine metabolites and high-sensitivity multiplex assays to quantify inflammation in plasma and CSF. We evaluated group-wise differences as well as associations between the biomarkers, motor and nonmotor symptoms, and nigral R2* (MRI metric reflecting iron content). RESULTS: PD subjects had >100% higher 3-hydroxykynurenine and 14% lower 3-hydroxyanthranilic acid in plasma. The 3-HK in plasma was closely associated with both symptom severity and disease duration. PD subjects also had 23% lower kynurenic acid in the CSF. Higher CSF levels of the excitotoxin quinolinic acid were associated with more severe symptoms, whereas lower levels of the neuroprotective kynurenic acid were linked to olfactory deficits. An elevated quinolinic acid/picolinic acid ratio in the CSF correlated with higher R2* values in the substantia nigra in the entire cohort. Plasma C-reactive protein and serum amyloid alpha were associated with signs of increased kynurenine pathway activity in the CSF of PD patients, but not in controls. CONCLUSIONS: In PD, the kynurenine pathway metabolite levels are altered in both the periphery and the central nervous system, and these changes are associated with symptom severity. Replication studies are warranted in other cohorts, and these can also explore if kynurenine metabolites might be PD biomarkers and/or are involved in PD pathogenesis. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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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".