Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study

Nomogram for predicting 5-year metabolic dysfunction-associated steatotic liver disease risk: retrospective cohort study

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.

The burden of non-alcoholic fatty liver disease among working-age people in the Western Pacific Region, 1990-2019: an age-period-cohort analysis of the Global Burden of Disease study

BACKGROUND

The growing prevalence of non-alcoholic fatty liver disease (NAFLD) in younger populations, particularly those of working age (15-64 years), has become a public health concern. Being diagnosed at a younger age implies a greater likelihood of accruing disability-adjusted life years (DALYs) later in life due to potential progression to conditions such as cirrhosis or hepatocellular carcinoma. This study aims to analyze NAFLD prevalence trends over three decades globally, regionally, and nationally, with a focus on age, period, and birth cohort associations.

METHODS

Global, regional, and country time trends in the prevalence of NAFLD among working-age people from 1990 to 2019: Age-period-cohort analysis based on Global Burden of Disease Study 2019 estimates and 95% uncertainty interval (UI) of NAFLD prevalence in the working age population was extracted from the Global Burden of Diseases, Injuries and Risk Factors Study 2019. Age-period-cohort models were used to estimate the prevalence within each age group from 1990 to 2019 (local drift, % per year), fitted longitudinal age-specific rates adjusted for period bias (age effect), and period/cohort relative risk (period/cohort effect).

RESULTS

The global age-standardized prevalence (ASPR) of NAFLD increased significantly from 1990 (14,477.6 per 100 000) to 2019 (19,837.6 per 100 000). In the Western Pacific, there were 42,903.8 NAFLD cases in 2019, 54.15% higher than in 1990. The ASPR also increased significantly in the region over the past three decades. At the national level, Palau had the highest ASPR while Brunei Darussalam had the lowest. Age-period-cohort analysis showed that in the Western Pacific, unlike globally, the risk of NAFLD declined after age 60-64 years. Relative to 1980-1989, incidence and DALY risks decreased but prevalence increased in subsequent birth cohorts. Future predictions indicate an upward trend in NAFLD burden, especially among women and medium (SDI) regions like China.

CONCLUSION

Non-alcoholic fatty liver disease imparts an immense health burden that continues to grow globally and in the Asia Pacific region. Our work highlights working age adults as an at-risk group and calls attention to socioeconomic gradients within Western Pacific countries. Upward future projections demonstrate that NAFLD prevention is an urgent priority.

Identifying metabolic dysfunction-associated steatotic liver disease in patients with hypertension and pre-hypertension: An interpretable machine learning approach

Objective

Metabolic dysfunction-associated steatotic liver disease (MASLD) is one of the most prevalent liver diseases and is associated with pre-hypertension and hypertension. Our research aims to develop interpretable machine learning (ML) models to accurately identify MASLD in hypertensive and pre-hypertensive populations.

Methods

The dataset for 4722 hypertensive and pre-hypertensive patients is from subjects in the NAGALA study. Six ML models, including the decision tree, K-nearest neighbor, gradient boosting, naive Bayes, support vector machine, and random forest (RF) models, were used in this study. The optimal model was constructed according to the performances of models evaluated by K-fold cross-validation (k = 5), the area under the receiver operating characteristic curve (AUC), average precision (AP), accuracy, sensitivity, specificity, and F1. Shapley additive explanation (SHAP) values were employed for both global and local interpretation of the model results.

Results

The prevalence of MASLD in hypertensive and pre-hypertensive patients was 44.3% (362 cases) and 28.3% (1107 cases), respectively. The RF model outperformed the other five models with an AUC of 0.889, AP of 0.800, accuracy of 0.819, sensitivity of 0.816, specificity of 0.821, and F1 of 0.729. According to the SHAP analysis, the top five important features were alanine aminotransferase, body mass index, waist circumference, high-density lipoprotein cholesterol, and total cholesterol. Further analysis of the feature selection in the RF model revealed that incorporating all features leads to optimal model performance.

Conclusions

ML algorithms, especially RF algorithm, improve the accuracy of MASLD identification, and the global and local interpretation of the RF model results enables us to intuitively understand how various features affect the chances of MASLD in patients with hypertension and pre-hypertension.