Serum Uric Acid in LRRK2 Related Parkinson’s Disease: Longitudinal Data from the PPMI Study
Bibliographic record
Abstract
BACKGROUND: Previous studies have highlighted serum uric acid as a putative idiopathic Parkinson's disease (iPD) biomarker. Only one study, so far, showed higher levels of serum uric acid in leucine-rich repeat kinase 2 (LRRK + 2) carriers compared to those who developed PD, however a longitudinal comparison between LRRK2 + PD and healthy controls (HC) has not been performed. OBJECTIVE: The aim of this study was to determine whether there are longitudinal differences in serum uric acid between iPD, LRRK2 + PD and HC and their association with motor and non-motor features. METHODS: Longitudinal data of uric acid of 282 de novo iPD, 144 LRRK2 + PD patients, and 195 age-matched HC were obtained from the Parkinson's Progression Markers Initiative (PPMI) database. We also used longitudinal Montreal Cognitive Assessment (MoCA), Movement Disorder Society-Unified Parkinson's Disease Rating Scale part III (MDS-UPDRS-III), Geriatric Depression Scale (GDS) scores, and DaTSCAN striatal binding ratios (SBRs). RESULTS: Longitudinal uric acid measurements were significantly lower in LRRK2 + PD patients compared to HC up to 5 years follow-up. There was no significant impact or correlation of adjusted or unadjusted uric acid levels with MoCA, MDS-UPDRS III, or GDS scores, the presence of RBD or DAT-SCAN SBRs. CONCLUSION: LRRK2 + PD group had significantly lower uric acid concentrations compared to HC after adjusting for age, sex and baseline BMI up to 5 years follow-up. There were no significant associations between uric acid levels and indices of disease severity. These findings identify serum uric acid as a marker linked to LRRK2 + PD.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".