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Development and validation of an online nomogram for screening metabolic-associated fatty liver disease in obese
Jiaqian Hu1,2,3, Mengqin Wang1,2,3, Xi Wang1,2,3
1Tianjian Laboratory of Advanced Biomedical Sciences, Academy of Medical Sciences, Zhengzhou University, Zhengzhou, Henan, China.
Insights
This study developed an online nomogram to screen for metabolic-associated fatty liver disease (MAFLD) in obese children. The tool shows high accuracy and clinical usefulness for early MAFLD risk assessment.
Area of Science:
- Pediatric Endocrinology
- Hepatology
- Medical Informatics
Background:
- Metabolic-associated fatty liver disease (MAFLD) is a growing concern in obese children, presenting diagnostic challenges.
- Non-invasive screening methods for pediatric MAFLD are crucial but limited.
- This study aimed to create an accessible online tool for MAFLD screening in this population.
Purpose of the Study:
- To develop and validate a web-based nomogram for the early detection of MAFLD in obese children.
- To compare the nomogram's diagnostic performance against existing indices.
Main Methods:
- A retrospective cross-sectional study of 2,512 obese children.
- Logistic regression analysis identified key risk factors (age, gender, BMI Z-score, WC, HOMA-IR, ALT).
- A nomogram was constructed and validated, with performance evaluated using ROC curves, calibration plots, and DCA.
Main Results:
- The nomogram incorporated six independent risk factors and demonstrated superior diagnostic performance (AUC 0.874 training, 0.870 validation) compared to other models.
- Ultrasound confirmed MAFLD in 53.50% of participants.
- The nomogram showed good calibration and strong clinical applicability.
Conclusions:
- An online nomogram was successfully developed for screening MAFLD in obese children.
- The nomogram offers high diagnostic accuracy and clinical utility, aiding in early risk assessment.
Background:
Metabolic-associated fatty liver disease (MAFLD) has emerged as a critical pediatric health concern, particularly among children with obesity. However, its diagnosis poses substantial challenges, especially in the use of non-invasive methods. Our goal was to construct an online nomogram for screening MAFLD in obese children.
Methods:
We designed a retrospective cross-sectional study involving 2,512 obese children. Detailed anthropometric data and laboratory parameters were collected. The study dataset was randomly allocated into training (n = 1758) and validation (n = 754) sets at a 7:3 ratio. To identify MAFLD risk factors, we conducted logistic regression analyses, from which a web-based predictive nomogram was constructed. Using receiver operating characteristic (ROC) curves and area under the curve (AUC), the nomogram's performance was assessed and contrasted with the triglyceride glucose (TyG) index, Zhejiang University (ZJU) index, and Korean NAFLD (K-NAFLD) score. The goodness-of-fit of the nomogram was evaluated using calibration plots, and the nomogram's clinical value was assessed using decision curve analysis (DCA).
Results:
A total of 1,344 participants (53.50%) were diagnosed with MAFLD by ultrasound. Age, gender, BMI Z-score, waist circumference (WC), homeostatic model assessment of insulin resistance (HOMA-IR), and alanine aminotransferase (ALT) were identified as independent factors influencing MAFLD in obese children. These six variables were selected for the construction of the nomogram. ROC analysis revealed that the nomogram had superior diagnostic performance for MAFLD detection compared to the other three models, with AUC values of 0.874 (95% confidence interval [CI]: 0.858-0.890) in the training set and 0.870 (95% CI: 0.845-0.895) in the validation set. Calibration plots indicated a good fit of the nomogram in both datasets. Furthermore, DCA demonstrated its strong clinical applicability.
Conclusions:
This study developed an online nomogram that demonstrates robust diagnostic accuracy and clinical utility for assessing obese children's MAFLD risk.
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