Mediating role of insulin resistance in the relationship between hypertension and NAFLD and construction of its risk prediction model.
10.11817/j.issn.1672-7347.2025.250055
- Author:
Yaxuan HE
1
;
Honghui HE
2
,
3
;
Yu CAO
4
;
Fang WANG
5
Author Information
1. Department of Endocrinology, Third Xiangya Hospital, Central South University, Changsha 410013, China. 248312117@csu.edu.cn.
2. Department of Endocrinology, Third Xiangya Hospital, Central South University, Changsha 410013, China. hehonghui@
3. com.
4. Department of Endocrinology, Third Xiangya Hospital, Central South University, Changsha 410013, China.
5. Department of Endocrinology, Third Xiangya Hospital, Central South University, Changsha 410013, China. wangfang1122@csu.edu.cn.
- Publication Type:Journal Article
- Keywords:
hypertension;
insulin resistance;
machine learning;
mediation effect;
non-alcoholic fatty liver disease
- MeSH:
Humans;
Non-alcoholic Fatty Liver Disease/complications*;
Insulin Resistance;
Hypertension/epidemiology*;
Male;
Female;
Middle Aged;
Risk Factors;
Adult;
Machine Learning;
Triglycerides/blood*
- From:
Journal of Central South University(Medical Sciences)
2025;50(7):1188-1201
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVES:Non-alcoholic fatty liver disease (NAFLD) and hypertension are common metabolic disorders, both closely associated with insulin resistance (IR), suggesting potential shared pathological mechanisms. This study aims to investigate the mediating role of IR in the relationship between hypertension and NAFLD, and to evaluate the applicability and modeling value of various IR surrogate indices in predicting NAFLD risk.
METHODS:A total of 280 976 individuals who underwent health examinations at the Health Management Center of the Third Xiangya Hospital of Central South University between August 2017 and December 2021 were included. NAFLD was diagnosed based on abdominal ultrasound findings, and hypertension was defined according to the criteria of the Chinese Guidelines for the Management of Hypertension. Demographic information, anthropometric indices, and biochemical parameters were collected, and multiple IR surrogate indices were constructed, including the triglyceride-glucose index (TyG) and its derivatives, as well as the metabolic score for insulin resistance (METS-IR). Group comparisons were performed between hypertensive and non-hypertensive participants, as well as between NAFLD and non-NAFLD participants. Pearson correlation analysis was applied to assess the associations of metabolic parameters and IR indices with NAFLD. Furthermore, mediation models were constructed to explore the mediating role of IR in the "hypertension-NAFLD" relationship. Finally, parametric models and machine learning algorithms were compared to evaluate their predictive performance and value in assessing NAFLD risk in this population.
RESULTS:The prevalence of NAFLD was significantly higher in hypertensive individuals than in non-hypertensive participants (63.61% vs 33.79%, P<0.001), accompanied by elevated IR levels and adverse metabolic features. Correlation analysis and variable importance rankings across multiple models consistently identified TyG-waist circumference (TyG-WC) and METS-IR as the IR indices most strongly associated with NAFLD. In mediation analysis, the TyG-WC pathway explained 32.03% of the total effect, and the METS-IR pathway explained 17.02%. Interaction analysis showed that hypertension status may attenuate the mediating effect of IR (all interaction estimates were negative). In prediction model comparisons, the simplified model incorporating sex, age, WC, TyG-WC, and METS-IR demonstrated good performance in the test set. Logistic regression and its regularized form (LASSO regression) achieved an accuracy of 0.83, receiver operating characteristic (ROC)-area under the curve (AUC) of 0.91, and a Brier score of 0.12, comparable to ensemble models (random forest and XGBoost), with consistently stable performance across different algorithms.
CONCLUSIONS:IR plays a significant mediating role in the association between hypertension and NAFLD, with TyG-WC identified as a key indicator showing strong mechanistic relevance and predictive value. Risk prediction models based on IR surrogate indices demonstrate advantages in simplicity and interpretability, providing empirical support for the early screening and individualized prevention of NAFLD in the general population.
