Metabolic dysfunction-associated fatty liver disease (MAFLD), introduced in 2020 by a multinational panel of experts through the International Consensus Statement, represents a paradigm shift from the previous diagnostic label of Non-alcoholic Fatty Liver Disease (NAFLD) (1). Similar to NAFLD, MAFLD is characterized by excessive hepatocellular lipid deposition (≥5%) as its pathognomonic histopathological feature (2). The disease spectrum encompasses simple steatosis, Metabolic Associated Steatohepatitis (MASH), progressive hepatic fibrosis, and hepatocellular carcinoma (HCC) (3). Emerging as the leading etiology of pediatric chronic liver disease globally, MAFLD demonstrates a multisystem metabolic perturbation profile that transcends hepatic pathology. Contemporary epidemiological data reveal its pathophysiological continuum with type 2 diabetes mellitus (T2DM), cardiovascular diseases (CVD), metabolic syndrome (MetS), and insulin resistance (4). Epidemiological surveillance data document a striking 2.7-fold surge in pediatric MAFLD prevalence rates worldwide, escalating from 3.9% in 2000 to 10.6% by 2020. Particularly alarming is the 34.2% prevalence observed in obese pediatric cohorts, establishing childhood obesity as the cardinal modifiable risk determinant. Geospatial analysis reveals accelerated incidence trajectories in Asian regions, with China and India demonstrating epidemiological transition patterns strongly correlated with Westernized dietary patterns and pervasive sedentarism (5–8). The predictive modeling framework developed by Li, J. et al. projects a staggering escalation in pediatric MAFLD prevalence, with global estimates reaching 30.7% by 2040, Asian populations demonstrate the most alarming trajectory, peaking at 49.7% (9). This epidemic acceleration correlates strongly with sustained obesogenic environments and epigenetic reprogramming induced by developmental overnutrition (9).

Compared with adults, MAFLD in children has unique clinical and histologic features due to its unique physiological stage. (1) Rapidly progressive: about 9.3% of children progressed to MASH within 5 years, significantly higher than adults (3.6%) (10). (2) Metabolic dysfunction is prominent, with more than 80% of children having comorbid insulin resistance (11). (3) Genetic predisposition is significant. The transmission rate of PNPLA3 rs738409 allele (GG type) was 1.5 times higher in children than in adults, and it was associated with early-onset severe fatty liver disease (12). It is more likely to cause serious intrahepatic and extrahepatic adverse outcomes, such as liver cirrhosis, HCC, and cardiovascular-related diseases (13, 14). However, typical symptoms are often absent in pediatric patients with NAFLD, but MAFLD-induced cognitive decline, low self-esteem, and social impairment have burdened children.

Early screening can detect MAFLD in children, and then through lifestyle intervention, 30% of liver fat can be reversed (15). However, mass screening may trigger unnecessary family anxiety or excessive medical behavior, and at the same time cause a waste of medical resources (16). Therefore, the identification of high-risk children can not only detect children with MAFLD in time, but also reduce the anxiety of patients' families and reduce the waste of medical resources.

In this study, we innovatively constructed a multidimensional health index scoring system, integrating demographic characteristics, anthropometric indicators, and behavioral parameters (17). Predictive models offer more comprehensive, accurate, and cost-effective disease prevention than single indicators (17–19). This study aimed to identify high-risk groups for MAFLD in children and provide a basis for accurate screening of MAFLD.

From January 2024 to December 2024, a cross-sectional survey was conducted at a middle school in Shihezi City, Xinjiang Uyghur Autonomous Region, China. A total of 2,280 students aged 6–18 years were included in the systematic cluster sampling method for routine ultrasound screening. According to Guidelines for the prevention and treatment of metabolic dysfunction-associated (non-alcoholic) fatty liver disease (Version 2024) diagnostic criteria (20), combined with questionnaire survey and clinical data, 2,190 cases were finally included in the study cohort after strict screening criteria. Inclusion Criteria: (1) Ultrasonography meets diagnostic criteria for MAFLD; (2) Age 6–18 years old; (3) Written informed consent signed by study subjects and their legal guardians; (4) Complete the baseline questionnaire and clinical examination items. Exclusion Criteria: (1) Patients with viral hepatitis, drug-induced liver injury, hereditary metabolic liver disease, type I diabetes mellitus and other chronic liver and kidney diseases; (2) Those who have had more than 3 ultrasound examinations but still have poor image quality and cannot be diagnosed; (3) Key clinical data or questionnaire items are missing >10%.

In this study, a cross-sectional study design was adopted, and data collection was carried out based on the Assessment Scale of Risk Factors for Metabolism-related Fatty Liver Disease in Children made by our research group. After being reviewed by the Ethics Committee of the First Affiliated Hospital of Shihezi University (approval number: KJ2024-455-02), 2,280 school-age children aged 6–18 years were included by cluster sampling. The reliability and validity of the questionnaire were verified by pre-experiments, and the Cronbach's α coefficient was 0.82, and the content validity index was 0.90.

MAFLD has become a health problem that cannot be ignored in children, and it is gradually decreasing (27). This study analyzed the risk factors of MAFLD in children and confirmed the independent risk factors of anthropometric indicators (BMI, WHR, WHtR) and metabolic risk behaviors (lack of sleep, high-sugar diet) (17). Among them, the weight of WHtR (β = 1.39) was much higher than BMI (0.77). This finding is consistent with Li, C. et al., the abdominal obesity index can better predict NAFLD in the Chinese population, suggesting that in the child population, the abdominal obesity index can better reflect the metabolic harm of visceral fat ectopic deposition than BMI (28). This is related to the unique anatomical location and metabolic activity of visceral fat, which is usually directly diverted from visceral adipose tissue (such as mesentery and omentum fat) into the portal venous system, and the free fatty acids and adipokines released by it reach the liver directly without systemic dilution (29). In addition, the concentration of free fatty acids in the portal blood of visceral obese patients was much higher than that of systemic obese patients, which directly promoted the synthesis of fatty acids in hepatocytes and significantly increased the rate of liver TG synthesis (30). This study found that the detection rate of MAFLD gradually increased with age, which was consistent with the results of most investigators (31). This may be because as children age, sedentary time increases, and their diet deteriorates (high-sugar, high-fat foods), resulting in excess energy, visceral fat and liver fat deposits gradually increasing, and increased levels of sex hormones during puberty may affect fat distribution and metabolism, promoting visceral fat accumulation (32). Like adults, the detection rate of MAFLD in men is still higher than that in women, which is mainly related to the differences in sex hormones and fat distribution characteristics. At the same time, higher testosterone levels in men may promote visceral fat accumulation and increase liver fat accumulation (32, 33). This study found that the intake of high-frequency desserts will increase the detection rate of MAFLD, which may be because since common added sugars in desserts (such as sucrose and fructose syrup) contain a large amount of fructose, fructose, unlike glucose, its metabolism is not regulated by insulin, and it is mainly completed through the liver, and fructose is rapidly converted into triglycerides in the liver, resulting in fat accumulation in hepatocytes (34). At the same time, this study found that adequate sleep is a protective factor for MAFLD, and adequate sleep can reduce the risk of MAFLD by regulating hormones, inhibiting inflammation, and maintaining the balance of the biological clock (35). Research by Romero-Gómez, M. et al. demonstrated that physical exercise effectively reduces hepatic fat content (36). While this study also examined the relationship between exercise and fatty liver disease, no statistically significant association was observed. Subsequent telephone follow-up of 100 participants revealed that their reported exercise duration primarily reflected scheduled physical education (PE) class time. Further investigation, however, indicated that approximately 70% of students engaged in only minutes of actual active exercise during PE sessions—often exercising briefly followed by extended rest periods—resulting in significantly lower effective exercise duration than reported. Consequently, based on potentially overestimated exercise duration data, this study failed to establish a significant association between physical activity and fatty liver disease. Furthermore, research by Ioannou, G. N. et al. identified diabetes and insulin resistance as significant risk factors for MAFLD, mediated through disruption of lipid metabolism and induction of metabolic dysregulation (22). However, as the study cohort consisted exclusively of school-aged children and adolescents—a population with inherently low prevalence rates of diabetes and insulin resistance—no statistically significant association between these metabolic factors and fatty liver disease was observed in this cohort. Studies by Xuan, Y. et al. demonstrated that in patients younger than 20 years, the ROC for predicting MAFLD was 0.67 using triglyceride (TG), and 0.66 using aspartate aminotransferase (AST) (37). Research by Li, H. et al. confirmed an AUC of 0.61 for the triglyceride-glucose (TyG) index and 0.66 for the TyG index combined with TyG-BMI in predicting MAFLD (38). Compared to other predictive models, the health index developed in our study achieved AUC of 0.72 in the training cohort and 0.74 in the validation cohort for identifying individuals at high risk of MAFLD, surpassing the predictive performance of the aforementioned individual biomarkers as well as the TyG-BMI combination. Furthermore, this index is non-invasive and easily implementable.

In developing a predictive model for MAFLD risk, the dataset was randomly partitioned into training (70%) and validation (30%) sets. Notably, the model demonstrated marginally superior discriminative performance on the validation set, with an AUC of 0.74, compared to 0.72 on the training set. This observed discrepancy, where validation AUC exceeded training AUC, may be attributable to random sampling variability, potentially resulting in the validation cohort providing a slightly more representative characterization of underlying MAFLD risk factors (39, 40). Comparative analysis of key parameters revealed minimal differences between cohorts. Continuous variables (mean ± standard deviation) were comparable: Age (training: 12.78 ± 3.56 years vs. validation: 12.69 ± 3.56 years), BMI (19.94 ± 3.88 kg/m² vs. 19.91 ± 3.76 kg/m²), WHR (0.83 ± 0.07 vs. 0.83 ± 0.07), and WHtR (0.46 ± 0.06 vs. 0.46 ± 0.06). Similarly, categorical variables showed no significant differences in the proportion of individuals with sleep duration <6 h (11.8% vs. 12.2%) or dessert consumption frequency ≥6 times/week (2.6% vs. 3.2%), although male gender distribution reached statistical significance (45.3% vs. 50.2%, P < 0.05). Overall, the minor variations between sets fall within expected ranges of random fluctuation. These results support the robust generalization capability of the developed health index prediction model.

The health index scoring system constructed in this study achieves methodological breakthroughs in multiple dimensions (18, 19). (1) The introduction of WHtR instead of the traditional waist circumference index is more suitable for the body shape changes of children during the growth spurt period; (2) By quantifying the effect of dessert intake frequency and sleep deprivation, the explanatory dimension of the traditional metabolic model was expanded. The stable prediction performance of the model in the validation set (AUC = 0.74) suggests that it can be used as a community-based primary screening tool for children's MAFLD, and can detect high-risk groups of MAFLD in time.

In school, community, or household settings, utilizing only a weighing scale and a measuring tape following standardized protocols enables the efficient collection of children's height, weight, waist circumference, and hip circumference. These measurements are then used to calculate BMI, WHR, and WHtR using established formulas. Integrating this anthropometric data with an assessment of children's sleep patterns and dietary habits allows for the assignment of scores to each indicator according to the health index scoring system proposed in this study. Aggregating these scores yields a comprehensive health index. This health index, based on the optimal cut-off value of 11.5 points, serves as a practical tool for the timely identification of children at high risk for MAFLD. The health index can enable high-risk children to obtain early intervention opportunities before liver enzyme abnormalities occur. Compared with the general screening scheme, the risk stratification model can reduce ineffective screening and meet the evaluation criteria of health economics (41). Especially in areas with scarce medical resources, the tool can be implemented through the school medical system to realize the closed-loop management of the school-family-community three-level prevention and control network.

The management of MAFLD necessitates multidisciplinary collaboration, focusing on reducing body weight and waist circumference, improving insulin resistance, preventing/treating T2DM, alleviating MASH, and reversing fibrosis (20, 25). All patients require health education for lifestyle modification, with pharmacological intervention indicated for those with metabolic cardiovascular risks or liver injury. Combined diet-exercise therapy is fundamental: Weight loss magnitude correlates positively with metabolic benefits in overweight/obese patients (3%–5% reduction reverses steatosis within 1 year; 7%–10% alleviates MASH; ≥ 10% reverses fibrosis; ≥15% may ameliorate T2DM) (25). A daily caloric deficit of 500–1,000 kcal achieves progressive weight loss and hepatic fat reduction, with efficacy demonstrated for low-carbohydrate, low-fat, intermittent fasting, and Mediterranean diets. For exercise, ≥150 min/week of moderate aerobic activity (e.g., brisk walking) or 3–5 HIIT sessions weekly reduces hepatic fat, improves cardiorespiratory fitness, and decreases waist circumference (showing a dose-response relationship) (25). Resistance training alone is reserved for patients with poor cardiorespiratory function. Crucially, combined diet-exercise therapy outperforms either modality in isolation and requires sustained personalized implementation (20, 25).

There are the following limitations in this study. (1) As the study cohort excluded children with diabetes and insulin resistance, the established relationship between these conditions and MAFLD could not be confirmed. Consequently, the predictive health index model developed herein does not incorporate diabetes or insulin resistance metrics. Therefore, the application of the 11.5 diagnostic threshold to children with insulin resistance or diabetes may fail to accurately identify their risk of developing MAFLD. (2) Dietary habits, primarily reported by guardians, may underestimate the frequency of sugary food intake, as guardians are often unaware of their children's dietary choices during school hours. (3) MAFLD diagnosis in this study relied on conventional ultrasonography performed with portable devices. The diagnostic accuracy of this method is inherently lower than that achieved with MRI-PDFF quantification. (4) The health index assessment system was developed and validated exclusively in a pediatric population. Thus, its applicability and performance in adult populations require further investigation.

Subsequent research will focus on: (1) developing a novel health index model incorporating insulin resistance and metabolic syndrome indicators for children with diabetes and insulin resistance, based on the current study, to establish diagnostic thresholds for identifying high-risk MAFLD individuals within this population; (2) constructing and validating a health index diagnostic model tailored for high-risk MAFLD adults and determining its optimal diagnostic threshold; (3) evaluating the effectiveness of implementing dietary control, lifestyle modification, and increased physical activity interventions for improving MAFLD in pediatric patients with a confirmed diagnosis.

The comprehensive health index scoring system provides a quantifiable decision-making tool for the precise prevention and control of MAFLD in children, and its standardized and low-cost characteristics meet the needs of the hierarchical diagnosis and treatment system. In the future, it is necessary to improve the adaptability of the model through multi-center verification and explore a dynamic risk early warning system based on artificial intelligence, to finally realize the paradigm shift from disease diagnosis to health risk management.

This study showed that the occurrence of MAFLD was significantly independently associated with age, gender, ethnicity, BMI, WHR, WHtR, sleep duration, and high-frequency dessert intake (P < 0.05). The health index scoring system based on multi-dimensional risk factors showed good risk stratification ability, with the optimal prediction threshold of 12.25 (Youden index = 0.68), and the AUC of the training set and the validation set were 0.72 (95% CI 0.68–0.76) and 0.74 (95% CI 0.70–0.78), respectively. The model was well calibrated (Hosmer-Lemeshow χ² = 7.21, P = 0.12), suggesting that it could be used as an early screening tool for high-risk groups of MAFLD in children and provide a quantitative basis for targeted intervention.