Clustering Patients With Gout Based on Comorbidities and Biomarkers: A Cross-Sectional Study
Bibliographic record
Abstract
OBJECTIVE: This single-center clinical study identifies clusters of different phenotypes and pathophysiology subtypes of patients with gout and associated comorbidities. METHODS: Patients clinically diagnosed with gout were enrolled between January 2018 and December 2019. Hierarchical cluster analyses were performed using clinical data or biological markers, inflammatory markers, and oxidative stress pathway metabolites assayed from serum and plasma samples. Subgroup clusters were compared using ANOVA for continuous data and chi-square tests for categorical data. RESULTS: Hierarchical cluster analysis identified 3 clusters. Cluster 1 (C1; n = 24) comprised dyslipidemia, hypertension, and early-onset gout, without tophi. Cluster 2 (C2; n = 25) comprised hypertension, dyslipidemia, nephrolithiasis, and obesity. Cluster 3 (C3; n = 39) comprised multiple comorbidities and tophi. Post hoc comparisons of data obtained from samples of patients in C1, C2, and C3 revealed significant differences in the levels of oxidative stress and inflammation-related markers, including 3-nitrotyrosine, tumor necrosis factor, C-reactive protein, interleukin (IL) 1β, IL-6, platelet-derived growth factor (PDGF)-AA, and PDGF-BB. Reclustering patients based on all markers as well as on the biological markers that significantly differed among the initial clusters identified similar clusters. CONCLUSION: Oxidative stress and inflammatory marker levels may affect the development and clinical manifestations (ie, clinical phenotypes) of gout. Measuring oxidative stress and levels of inflammatory cytokines is a potential adjunctive tool and biomarker for early identification and management of gout.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".