Which Comes First, Nonalcoholic Fatty Liver Disease or Arterial Hypertension?

Metabolic dysfunction-associated steatotic liver disease and cardiovascular risk: a comprehensive review

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously termed nonalcoholic fatty liver disease (NAFLD), poses a significant global health challenge due to its increasing prevalence and strong association with cardiovascular disease (CVD). This comprehensive review summarizes the current knowledge on the MASLD-CVD relationship, compares analysis of how different terminologies for fatty liver disease affect cardiovascular (CV) risk assessment using different diagnostic criteria, explores the pathophysiological mechanisms connecting MASLD to CVD, the influence of MASLD on traditional CV risk factors, the role of noninvasive imaging techniques and biomarkers in the assessment of CV risk in patients with MASLD, and the implications for clinical management and prevention strategies. By incorporating current research and clinical guidelines, this review provides a comprehensive overview of the complex interplay between MASLD and cardiovascular health.

Evaluating the relative importance of different blood pressure indices in screening for NAFLD: a survey report based on a health examination population

Objective

While hypertension is a well-recognized risk factor for non-alcoholic fatty liver disease (NAFLD), the specific roles of various common blood pressure measurements [diastolic blood pressure (DBP), systolic blood pressure (SBP), pulse pressure (PP), mean arterial pressure (MAP)] in detecting NAFLD and evaluating the associated risk in adults remain unclear.

Methods

A retrospective analysis was conducted on 14,251 adult participants undergoing health screenings in the NAfld in the Gifu Area, Longitudinal Analysis project (NAGALA). Following the Z-transformation of the independent variables, we evaluated the relationships between the four blood pressure indices and NAFLD through multivariable logistic regression models. This analysis documented the odds ratio (OR) and 95% confidence interval (CI) for each standard deviation (SD) increase. Additionally, the effectiveness of these indices in identifying NAFLD was comparatively analyzed using receiver operating characteristic (ROC) curves.

Results

After adequately adjusting for confounders, all blood pressure indices except PP showed a positive correlation with NAFLD. For each SD increment, MAP had the strongest association with NAFLD compared to SBP and DBP. This finding was confirmed in populations without exercise habits, under 60 years of age, with normal blood pressure, and in non-obese groups. Furthermore, based on ROC analysis, MAP was found to have the highest accuracy in identifying NAFLD compared to the other three blood pressure indices.

Conclusion

Among the four blood pressure indices evaluated, MAP demonstrates the greatest efficacy in identifying NAFLD and assessing its associated risk. These findings underscore the potential of MAP as the most promising blood pressure index for screening NAFLD.

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.