Identification of a Risk-Prediction Model for Hypertension Patients Concomitant with Nonalcoholic Fatty Liver Disease

TL;DR

This study creates a model to predict which hypertension patients are at risk for nonalcoholic fatty liver disease, using factors like BMI and medication use.

Contribution

The study introduces a novel nomogram model for predicting NAFLD risk in HTN patients using clinical and biochemical factors.

Findings

The model includes predictors like BMI, medications, and blood markers with AUCs of 0.816 and 0.794 in training and validation sets.

The model showed good calibration and moderate predictive ability for identifying HTN patients at risk for NAFLD.

Decision curve analysis confirmed the model's clinical utility in identifying high-risk patients.

Abstract

Objective: Our study aims to develop a personalized nomogram model for predicting the risk of nonalcoholic fatty liver disease (NAFLD) in hypertension (HTN) patients and further validate its effectiveness. Methods: A total of 1250 hypertensive (HTN) patients from Guangxi, China, were divided into a training group (875 patients, 70%) and a validation set (375 patients, 30%). LASSO regression, in combination with univariate and multivariate logistic regression analyses, was used to identify predictive factors associated with nonalcoholic fatty liver disease (NAFLD) in HTN patients within the training set. Subsequently, the performance of an NAFLD nomogram prediction model was evaluated in the separate validation group, including assessments of differentiation ability, calibration performance, and clinical applicability. This was carried out using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results: The risk-prediction model for the HTN patients concomitant with NAFLD included oral antidiabetic drugs (OADs) (OR = 2.553, 95% CI: 1.368–4.763), antihypertensives (AHs) (OR = 7.303, 95% CI: 4.168–12.794), body mass index (BMI) (OR = 1.145, 95% CI: 1.084–1.209), blood urea nitrogen (BUN) (OR = 0.924, 95% CI: 0.860–0.992), triglycerides (TGs) (OR = 1.474, 95% CI: 1.201–1.809), aspartate aminotransferase (AST) (OR = 1.061, 95% CI: 1.018–1.105), and AST/ALT ratio (AAR) (OR = 0.249, 95% CI: 0.121–0.514) as significant predictors. The AUC of the NAFLD risk-prediction model in the training set and the validation set were 0.816 (95% CI: 0.785–0.847) and 0.794 (95% CI: 0.746–0.842), respectively. The Hosmer–Lemeshow test showed that the model has a good goodness-of-fit (p-values were 0.612 and 0.221). DCA suggested the net benefit of using a nomogram to predict the risk of HTN patients concomitant with NAFLD is higher. These results suggested that the model showed moderate predictive ability and good calibration. Conclusions: BMI, OADs, AHs, BUN, TGs, AST, and AAR were independent influencing factors of HTN combined with NAFLD, and the risk prediction model constructed based on this could help to identify the high-risk group of HTN combined with NAFLD at an early stage and guide the development of interventions. Larger cohorts with multiethnic populations are essential to verify our findings.

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