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Aim <p>To identify risk factors for chronic kidney disease (CKD) in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) and to develop a risk prediction model for CKD comorbidity within this population, thereby facilitating targeted prevention and early screening.</p> Methods <p>We retrospectively enrolled 8,975 MASLD patients diagnosed by color Doppler ultrasonography at the Affiliated Hospital of Nantong University between 2021 and 2023. Participants were categorized into CKD and non-CKD groups based on standard diagnostic criteria. Baseline biochemical parameters, including markers of glucose, lipid metabolism, inflammation, and organ function, were compared between two groups. Independent predictors were identified using multivariable logistic regression and incorporated into a nomogram. Model performance was temporally validated in an independent MASLD cohort from 2024 via receiver operating characteristic (ROC) curve analysis, calibration plots, and decision curve analysis (DCA).</p> Results <p>Compared with the non-CKD group, the CKD group exhibited significantly higher levels of systolic blood pressure, creatinine, triglycerides, and uric acid to high-density lipoprotein cholesterol ratio, but lower hemoglobin compared with the non-CKD group (<i>P</i> &lt; 0.05). Age, systolic blood pressure, hemoglobin, triglycerides, and uric acid to high-density lipoprotein cholesterol ratio were identified as independent predictors of MASLD with CKD. The developed nomogram exhibited strong discriminative power, consistent calibration during temporal validation, and demonstrated favorable net benefit across clinically relevant threshold probabilities in the DCA.</p> Conclusion <p>The accurate estimation of CKD risk in patients with MASLD is effectively achieved via a nomogram incorporating routinely available clinical indicators, thereby facilitating early screening and individualized interventions for high-risk patients.</p>

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Risk factors and development of a predictive model for chronic kidney disease in patients with metabolic dysfunction-associated steatotic liver disease

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月20日

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  • Jiaojiao Zhu,
  • Yiwen Liu,
  • Siqian Lu,
  • Yiwen Han,
  • Wangpeng Zhao,
  • Lishuai Qu,
  • Jinxia Liu

摘要

Aim

To identify risk factors for chronic kidney disease (CKD) in patients with metabolic dysfunction-associated steatotic liver disease (MASLD) and to develop a risk prediction model for CKD comorbidity within this population, thereby facilitating targeted prevention and early screening.

Methods

We retrospectively enrolled 8,975 MASLD patients diagnosed by color Doppler ultrasonography at the Affiliated Hospital of Nantong University between 2021 and 2023. Participants were categorized into CKD and non-CKD groups based on standard diagnostic criteria. Baseline biochemical parameters, including markers of glucose, lipid metabolism, inflammation, and organ function, were compared between two groups. Independent predictors were identified using multivariable logistic regression and incorporated into a nomogram. Model performance was temporally validated in an independent MASLD cohort from 2024 via receiver operating characteristic (ROC) curve analysis, calibration plots, and decision curve analysis (DCA).

Results

Compared with the non-CKD group, the CKD group exhibited significantly higher levels of systolic blood pressure, creatinine, triglycerides, and uric acid to high-density lipoprotein cholesterol ratio, but lower hemoglobin compared with the non-CKD group (P < 0.05). Age, systolic blood pressure, hemoglobin, triglycerides, and uric acid to high-density lipoprotein cholesterol ratio were identified as independent predictors of MASLD with CKD. The developed nomogram exhibited strong discriminative power, consistent calibration during temporal validation, and demonstrated favorable net benefit across clinically relevant threshold probabilities in the DCA.

Conclusion

The accurate estimation of CKD risk in patients with MASLD is effectively achieved via a nomogram incorporating routinely available clinical indicators, thereby facilitating early screening and individualized interventions for high-risk patients.