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Background <p>The factors influencing nonobese Metabolic dysfunction-associated steatotic liver disease are discussed, and an online dynamic nomogram model is constructed.</p> Methods <p>A retrospective analysis was conducted on the clinical data of 216 non-obese patients with MASLD and 322 non-obese healthy controls from the Second Affiliated Hospital of Soochow University. The data were randomly divided into a training set and a validation set in a 7:3 ratio. Potential variables were initially screened through univariate analysis, correlation analysis, and LASSO regression (including cross-validation). Subsequently, binary logistic regression and SHAP methods were employed to identify independent risk factors, and an online dynamic ROC curve was constructed. Model performance was evaluated using the area under the curve (AUC), sensitivity, specificity, calibration curves, and recalibration methods.</p> Results <p>LASSO regression initially screened 11 potential variables. Binary logistic regression ultimately identified six independent risk factors which is past history (odds ratio (OR): 2.399, <i>P</i> = 0.0008), triglyceride(TG) (OR: 1.176, <i>P</i> = 0.008), high density lipoprotein cholesterol(HDL-C) (OR: 0.173, <i>P</i> = 0.014), low density lipoprotein cholesterol(LDL-C) (OR: 3.916, <i>P</i> = 0.001), fat mass(Fat) (OR: 4.299, <i>P</i> = 0.0009) and dPhaseAngle(OR: 3.174, <i>P</i> = 0.022). SHAP analysis revealed that TG was the most influential feature, while HDL-C showed a negative contribution. All variance inflation factor (VIF) values were below 1.5, indicating no significant multicollinearity. The AUC values were 0.869 in the training cohort and 0.802 in the validation cohort. The calibration curve demonstrates that the prediction results of the line graph for the outcome closely approximate the ideal curve, aligning with the actual outcomes.</p> Conclusion <p>An online dynamic nomogram for nonobese MASLD patients with good identified performance was constructed, which can be used as a practical approach for personalized early screening and auxiliary diagnosis of potential risk factors and can assist physicians in making personalized diagnoses and treatments for patients.</p>

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Development and validation of an online dynamic nomogram for nonobese metabolic dysfunction-associated steatotic liver disease based on body composition analysis

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月16日

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  • Tingting Zheng,
  • Haizhen Yang,
  • Sijia Wang,
  • Xiaoqin Shi,
  • Qingxiu Zhang,
  • Zhongmin Wen

摘要

Background

The factors influencing nonobese Metabolic dysfunction-associated steatotic liver disease are discussed, and an online dynamic nomogram model is constructed.

Methods

A retrospective analysis was conducted on the clinical data of 216 non-obese patients with MASLD and 322 non-obese healthy controls from the Second Affiliated Hospital of Soochow University. The data were randomly divided into a training set and a validation set in a 7:3 ratio. Potential variables were initially screened through univariate analysis, correlation analysis, and LASSO regression (including cross-validation). Subsequently, binary logistic regression and SHAP methods were employed to identify independent risk factors, and an online dynamic ROC curve was constructed. Model performance was evaluated using the area under the curve (AUC), sensitivity, specificity, calibration curves, and recalibration methods.

Results

LASSO regression initially screened 11 potential variables. Binary logistic regression ultimately identified six independent risk factors which is past history (odds ratio (OR): 2.399, P = 0.0008), triglyceride(TG) (OR: 1.176, P = 0.008), high density lipoprotein cholesterol(HDL-C) (OR: 0.173, P = 0.014), low density lipoprotein cholesterol(LDL-C) (OR: 3.916, P = 0.001), fat mass(Fat) (OR: 4.299, P = 0.0009) and dPhaseAngle(OR: 3.174, P = 0.022). SHAP analysis revealed that TG was the most influential feature, while HDL-C showed a negative contribution. All variance inflation factor (VIF) values were below 1.5, indicating no significant multicollinearity. The AUC values were 0.869 in the training cohort and 0.802 in the validation cohort. The calibration curve demonstrates that the prediction results of the line graph for the outcome closely approximate the ideal curve, aligning with the actual outcomes.

Conclusion

An online dynamic nomogram for nonobese MASLD patients with good identified performance was constructed, which can be used as a practical approach for personalized early screening and auxiliary diagnosis of potential risk factors and can assist physicians in making personalized diagnoses and treatments for patients.