Serum Uric Acid Levels in Neurodegenerative Disorders: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Excessive oxidative stress may contribute to neurodegeneration by leading to protein aggregation and mitochondrial dysfunction. Uric acid (UA) is an important endogenous antioxidant that protects against oxidative stress, yet its exact role in neurodegeneration remains unclear. OBJECTIVE: To explore the performance of serum UA in neurodegenerative disorders. METHODS: A total of 839 controls and 840 patients, including Alzheimer's disease (AD), Parkinson's disease (PD), multiple system atrophy (MSA), progressive supranuclear palsy (PSP), frontotemporal dementia (FTD), dementia with Lewy bodies (DLB), motor neuron disease (MND), Creutzfeldt-Jakob disease (CJD), and mixed dementia (MixD) were enrolled. Fasting serum UA levels were measured in all participants and compared between patients and controls. Linear regression models were utilized to explore possible relationships of serum UA with cognition, disease duration, age, and age of onset. RESULTS: Compared to controls (355.48 ± 85.38 μmol/L), serum UA was significantly lower in AD (291.29 ± 83.49 μmol/L, p < 0.001), PD (286.95 ± 81.78 μmol/L, p < 0.001), PSP (313.32 ± 88.19 μmol/L, p < 0.001), FTD (313.89 ± 71.18 μmol/L, p = 0.001), and DLB (279.23 ± 65.51 μmol/L, p < 0.001), adjusting for confounding factors including age, gender, education, etc. In addition, serum UA was positively correlated with cognitive levels in all patients (Mini-Mental State Examination: r = 0.136, p = 0.001; and Montreal Cognitive Assessment Scale: r = 0.108, p = 0.009). CONCLUSION: Decreased levels of serum UA were correlated with AD, PD, PSP, FTD, and DLB, offering significant potential as a promisingly relevant, less-invasive marker of multiple neurodegenerative disorders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".