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Lei Gao Department of Geriatrics, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China

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Wenxia Cui Department of Geriatrics, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China

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Dinghuang Mu Department of Geriatrics, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing, China

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Shaoping Li Department of Health Management Center, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China

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Nan Li Department of Geriatrics, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China

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Weihong Zhou Department of Health Management Center, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China

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Yun Hu Department of Geriatrics, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, China

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Objective

To create a nomogram-based model to estimate the Chinese population's 5-year risk of metabolic dysfunction-associated steatotic liver disease (MASLD).

Methods

We randomly divided 7582 participants into two groups in a 7:3 ratio: one group was assigned to work with the training set, which consisted of 5307 cases, and the other group was assigned to validate the model using 2275 cases. The least absolute shrinkage and selection operator model was employed to ascertain the variables with the highest correlation among all potential variables. A logistic model was constructed by incorporating these selected variables, which were subsequently visualized using a nomogram. The discriminatory ability, calibration, and clinical utility of the model were assessed using the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

Results

During the 5-year follow-up, 1034 (13.64%) total participants were newly diagnosed with MASLD. Using eight variables (gender, body mass index, waist, hemoglobin, alanine aminotransferase, uric acid, triglycerides, and high-density lipoprotein), we built a 5-year MASLD risk prediction model. The nomogram showed an area under the ROC of 0.795 (95% CI: 0.779–0.811) in the training set and 0.785 (95% CI: 0.760–0.810) in the validation set. The calibration curves revealed a 5-year period of agreement between the observed and predicted MASLD risks. DCA curves illustrated the practicality of this nomogram over threshold probability profiles ranging from 5% to 50%.

Conclusion

We created and tested a nomogram to forecast the risk of MASLD prevalence over the next 5 years.

Open access