Uric Acid Levels Correlate with Sensory Nerve Function in Healthy Subjects
Bibliographic record
Abstract
BACKGROUND: High levels of uric acid (UA) are associated with various peripheral neuropathies. Furthermore, uric acid levels have been found to correlate with both the clinical and electrophysiological severity of diabetic sensorimotor polyneuropathy, mainly with sensory functions. OBJECTIVES: To determine whether higher UA levels are associated negatively with nerve function in healthy subjects. METHODS: A total of 126 healthy subjects recruited prospectively for another study were included. We extracted demographic data, body mass index (BMI), blood pressure, Toronto Clinical Neuropathy Score (TCNS), electrophysiological findings, vibration perception thresholds (VPT), and laboratory test results including UA, hemoglobin A1c (HbA1c), estimated glomerular filtration rate (eGFR), and lipid levels. RESULTS: The mean age of the cohort was 56 ± 17 years with 56% females. Males had higher UA values compared with females. Univariate beta regression coefficient analysis between UA levels and demographic, clinical, electrophysiological, and laboratory findings showed significant positive correlations with male gender, components of the metabolic syndrome, and with VPT, while an inverse correlation was found with electrophysiological sensory parameters. A multivariate regression model showed positive correlations only with BMI, finger VPT, and triglycerides. CONCLUSION: Higher UA levels correlate with lower sensory nerve function in healthy subjects, expanding the evidence of possible negative influence of UA on peripheral nerves, although a causative role has not yet established.
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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) | 0.002 | 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".