Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026
[Key determinants of global burden of non-alcoholic fatty liver disease: machine learning combined with Mendelian randomization analysis based on GBD data].
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Chen Hao, Li Zhenhan, Ji Mengjia, Wang Xincheng, Chen Bofeng, Guan Qian, Wu Man, Lu Linming
Аннотация
OBJECTIVES: To analyze the global trends, drivers, and health inequalities of non-alcoholic fatty liver disease (NAFLD) burden to identify key predictors of NAFLD-related mortality.
METHODS: Using data from the Global Burden of Disease (GBD) Study 2021, we extracted global measures of NAFLD from 1990 to 2021, and the trends were analyzed using joinpoint regression. Decomposition analysis was used to quantify the contributions of population growth, aging, and epidemiological changes. The health inequality was assessed using the concentration index. Using XGBoost-SHAP machine learning, the mortality predictors were identified, and two-sample Mendelian randomization was employed to test the causality for the key factors. All the analyses were conducted with data stratification by sex and the socio-demographic index (SDI).
RESULTS: The global age-standardized disability-adjusted life years (DALYs) rate showed an increasing trend in both males (average annual percentage change [AAPC]=+0.34%) and females (AAPC=+0.05%). Decomposition analysis revealed that population growth was the primary driver of the global increase in DALYs, while population aging contributed to 52.37% of male deaths in high-SDI regions. Health inequality analysis showed a concentration index of -0.05 for DALYs in 2021, indicating a concentration of burden among low-SDI populations. Machine learning identified smoking (relative importance=100%) and advanced age (70-74 years: 60%) as the most critical predictors of mortality, and the model demonstrated good fit on the test set (R2=0.98). SDI-stratified analysis showed smoking and aging are the top two predictors across all SDI regions. Mendelian randomization further confirmed positive causal associations of smoking initiation (OR=1.35, P<0.05) and aging (proxied by frailty index, OR=2.01, P<0.05) with NAFLD risk.
CONCLUSIONS: NAFLD burden is heavy globally with significant sex and socioeconomic inequalities. Smoking and advanced age are key risk factors for NAFLD, calling for integrated interventions for tobacco control, geriatric health management, and health equity promotion.
METHODS: Using data from the Global Burden of Disease (GBD) Study 2021, we extracted global measures of NAFLD from 1990 to 2021, and the trends were analyzed using joinpoint regression. Decomposition analysis was used to quantify the contributions of population growth, aging, and epidemiological changes. The health inequality was assessed using the concentration index. Using XGBoost-SHAP machine learning, the mortality predictors were identified, and two-sample Mendelian randomization was employed to test the causality for the key factors. All the analyses were conducted with data stratification by sex and the socio-demographic index (SDI).
RESULTS: The global age-standardized disability-adjusted life years (DALYs) rate showed an increasing trend in both males (average annual percentage change [AAPC]=+0.34%) and females (AAPC=+0.05%). Decomposition analysis revealed that population growth was the primary driver of the global increase in DALYs, while population aging contributed to 52.37% of male deaths in high-SDI regions. Health inequality analysis showed a concentration index of -0.05 for DALYs in 2021, indicating a concentration of burden among low-SDI populations. Machine learning identified smoking (relative importance=100%) and advanced age (70-74 years: 60%) as the most critical predictors of mortality, and the model demonstrated good fit on the test set (R2=0.98). SDI-stratified analysis showed smoking and aging are the top two predictors across all SDI regions. Mendelian randomization further confirmed positive causal associations of smoking initiation (OR=1.35, P<0.05) and aging (proxied by frailty index, OR=2.01, P<0.05) with NAFLD risk.
CONCLUSIONS: NAFLD burden is heavy globally with significant sex and socioeconomic inequalities. Smoking and advanced age are key risk factors for NAFLD, calling for integrated interventions for tobacco control, geriatric health management, and health equity promotion.