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Background <p>Patients with diabetic kidney disease (DKD) exhibit a markedly elevated risk of developing gout. The coexistence of these conditions severely compromises health. Consequently, risk for gout necessitates evaluation within this patient cohort. This study aimed to identify risk factoes of gout among DKD patients and to establish and validate a clinically usable nomogram for estimating individual gout risk.</p> Methods <p>A two-center, retrospective analysis enrolling DKD patients managed at the Metabolic Management Centers of Taizhou Central Hospital and Yuhuan Second People’s Hospital between September 2017 and February 2025 was conducted. Key risk factors were selected using an integrated approach combining Best Subset Regression and Least Absolute Shrinkage and Selection Operator analysis, followed by multivariate logistic regression to identify independent correlates of gout. A risk nomogram was then generated. Model performance was assessed using Area Under the Receiver Operating Characteristic Curve (AUC) values, calibration plots, Hosmer-Lemeshow goodness-of-fit tests, and a decision curve analysis (DCA) approach.</p> Results <p>Overall, 5,670 DKD patients were analyzed. Of these, 4,617 participants from Yuhuan Second People’s Hospital were divided into training and internal validation cohorts, while 1,088 patients from Taizhou Central Hospital formed an external validation cohort. Variables incorporated into the final model included sex, body mass index, fasting C-peptide, estimated glomerular filtration rate, and serum urate. The nomogram displayed strong discrimination with respective AUCs of 0.784, 0.782, and 0.770 in the training, internal validation, and external validation cohorts. Calibration plots showed close alignment between predicted and observed outcomes (Hosmer-Lemeshow p-values 0.769, 0.332, and 0.520 for the respective cohorts). DCA demonstrated net benefits across various threshold probabilities deemed clinically useful.</p> Conclusions <p>The prognostic nomogram developed in validated in this study has been specifically tailored to estimate gout risk in DKD. The tool can help clinicians identify high-risk individuals and implement early, targeted preventive strategies.</p> Clinical trial number <p>Not applicable.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月30日

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  • Ningyu Cai,
  • Mengdie Chen,
  • Yiyun Wang,
  • Ping Feng,
  • Xianping Zhu,
  • Qidong Zheng

摘要

Background

Patients with diabetic kidney disease (DKD) exhibit a markedly elevated risk of developing gout. The coexistence of these conditions severely compromises health. Consequently, risk for gout necessitates evaluation within this patient cohort. This study aimed to identify risk factoes of gout among DKD patients and to establish and validate a clinically usable nomogram for estimating individual gout risk.

Methods

A two-center, retrospective analysis enrolling DKD patients managed at the Metabolic Management Centers of Taizhou Central Hospital and Yuhuan Second People’s Hospital between September 2017 and February 2025 was conducted. Key risk factors were selected using an integrated approach combining Best Subset Regression and Least Absolute Shrinkage and Selection Operator analysis, followed by multivariate logistic regression to identify independent correlates of gout. A risk nomogram was then generated. Model performance was assessed using Area Under the Receiver Operating Characteristic Curve (AUC) values, calibration plots, Hosmer-Lemeshow goodness-of-fit tests, and a decision curve analysis (DCA) approach.

Results

Overall, 5,670 DKD patients were analyzed. Of these, 4,617 participants from Yuhuan Second People’s Hospital were divided into training and internal validation cohorts, while 1,088 patients from Taizhou Central Hospital formed an external validation cohort. Variables incorporated into the final model included sex, body mass index, fasting C-peptide, estimated glomerular filtration rate, and serum urate. The nomogram displayed strong discrimination with respective AUCs of 0.784, 0.782, and 0.770 in the training, internal validation, and external validation cohorts. Calibration plots showed close alignment between predicted and observed outcomes (Hosmer-Lemeshow p-values 0.769, 0.332, and 0.520 for the respective cohorts). DCA demonstrated net benefits across various threshold probabilities deemed clinically useful.

Conclusions

The prognostic nomogram developed in validated in this study has been specifically tailored to estimate gout risk in DKD. The tool can help clinicians identify high-risk individuals and implement early, targeted preventive strategies.

Clinical trial number

Not applicable.