Association between triglyceride glucose-body mass index and new-onset metabolic dysfunction-associated fatty liver disease
- VernacularTitle:甘油三酯葡萄糖-体重指数与新发代谢相关脂肪性肝病的关联性分析
- Author:
Xiaohong XIANG
1
;
Yang LI
1
;
Bo LI
2
;
Mei WEI
2
;
Zhongfang ZHOU
1
;
Suqiong HUANG
1
Author Information
- Publication Type:Journal Article
- Keywords: Metabolic Dysfunction-Associated Fatty Liver Disease; Triglyceride Glucose-Body Mass Index; Risk Factors
- From: Journal of Clinical Hepatology 2026;42(4):840-847
- CountryChina
- Language:Chinese
- Abstract: ObjectiveTo investigate the association between serum fasting triglyceride glucose-body mass index （TyG-BMI） and new-onset metabolic dysfunction-associated fatty liver disease （MAFLD） within 10 years. MethodsA retrospective analysis was performed for the data of individuals who underwent physical examination in The Affiliated Traditional Chinese Medicine Hospital of Southwest Medical University in 2013， 2018， and 2023 and were not diagnosed with MAFLD in 2013， and a total of 1 340 valid subjects were enrolled according to the inclusion and exclusion criteria. The gbmt package in R 4.3.0 was used to construct the dynamic change trajectory model of TyG-BMI， and four different TyG-BMI trajectory groups were determined， i.e.， the low-level group （n=352）， the medium-level group （n=517）， the high-level group （n=314）， and the extremely high-level group （n=157）. The data on general information and blood biochemical parameters were collected from all subjects and were then compared between groups. The chi-square test was used for comparison of categorical data between groups， and the Kruskal-Wallis H test was used for comparison of non-normally distributed continuous data with heterogeneity of variance between multiple groups. The Cox regression analysis was used to investigate the association between different TyG-BMI trajectories and the risk of MAFLD， and the receiver operating characteristic （ROC） curve was used to assess the value of TyG-BMI in the diagnosis of MAFLD. ResultsThe cumulative incidence rate of MAFLD increased with the increase in the level of TyG-BMI trajectory， with a cumulative incidence rate of 4.83% in the low-level group， 29.98% in the medium-level group， 61.15% in the high-level group， and 83.44% in the extremely high-level group （P<0.001）， and the cumulative incidence rate of MAFLD in men was significantly higher than that in women （51.34% vs 20.67%， P<0.001）. The multivariate Cox regression analysis showed that increases in the levels of TyG-BMI trajectory， uric acid， diastolic blood pressure， hemoglobin， and alanine aminotransferase were independent risk factors for the onset of MAFLD （all P<0.05）， while the increase in high-density lipoprotein cholesterol was an independent protective factor against MAFLD （P<0.001）. After adjustment for confounding factors， the medium-， high-， and extremely high-level groups had a hazard ratio of 4.430 （95% confidence interval ［CI］： 2.660 — 7.377， P<0.001）， 6.937 （95%CI： 4.110 — 11.708， P<0.001）， and 7.989 （95%CI： 4.616 — 13.827， P<0.001）， respectively. The ROC curve analysis showed that TyG-BMI had the highest diagnostic value， with an area under the ROC curve of 0.859 （95%CI： 0.840 — 0.879）， a sensitivity of 79.8%， and a specificity of 76.3%. ConclusionThe risk of MAFLD increases with the increase in the level of TyG-BMI trajectory， and TyG-BMI can be used as a predictive indicator for MAFLD.
