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Development and validation of a risk models for gout in patients with diabetic kidney disease

Ningyu Cai1, Mengdie Chen2, Yiyun Wang3

  • 1Department of Orthopedics, Taizhou Central Hospital (Taizhou University Hospital), Taizhou, Zhejiang, 318000, China.

BMC Endocrine Disorders
|July 1, 2026
PubMed

Insights

This study developed a nomogram to predict gout risk in patients with diabetic kidney disease (DKD). The tool helps clinicians identify high-risk individuals for early intervention.

Area of Science:

  • Nephrology and Endocrinology
  • Metabolic Diseases
  • Rheumatology

Background:

  • Diabetic kidney disease (DKD) significantly increases the risk of developing gout.
  • The co-occurrence of DKD and gout negatively impacts patient health.
  • Accurate gout risk assessment is crucial for DKD patients.

Purpose of the Study:

  • To identify risk factors for gout in patients with DKD.
  • To develop and validate a clinical nomogram for estimating individual gout risk in DKD patients.

Main Methods:

  • Retrospective analysis of 5,670 DKD patients from two centers.
  • Utilized Best Subset Regression, LASSO, and multivariate logistic regression for risk factor selection.
  • Generated and validated a nomogram using AUC, calibration plots, and decision curve analysis.

Main Results:

  • Identified sex, BMI, fasting C-peptide, eGFR, and serum urate as key risk factors.
  • The nomogram demonstrated strong predictive performance with AUCs ranging from 0.770 to 0.784 across cohorts.
  • Calibration plots confirmed good agreement between predicted and observed gout risk.

Conclusions:

  • A validated prognostic nomogram can accurately estimate gout risk in DKD patients.
  • This tool aids clinicians in identifying high-risk individuals.
  • Facilitates early and targeted preventive strategies for gout in the DKD population.
Abstract

Keywords:
Diabetic kidney diseaseGoutNomogramRisk factorsRisk model

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