Association between apolipoprotein B/A1 and the risk of metabolic dysfunction associated fatty liver disease according to different lipid profiles in a Chinese population: A cross-sectional study

Zhao, Ying

doi:10.1016/j.cca.2022.07.014

Cited by 7 publications

(6 citation statements)

References 36 publications

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“…Along with the obesity and diabetes epidemic, the disease burden of NAFLD is expected to increase 2- to 3-fold by 2030 in Western countries and Asia [ 1 ]. The association of triglyceride, HDL, and Apo-B with NAFLD is consistent with previous reports [ 42 - 44 ]. Further, we found a dose-dependent relationship between BMI, WC, Apo-B, triglyceride, and NAFLD; when they were greater than 22.65 kg/m 2 , 81.04 cm, 1.09 mmol/L, and 0.69 g/L, respectively, an increased risk of NAFLD was observed.…”

Section: Discussionsupporting

confidence: 92%

Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study

Huang,

Jin,

Mao

2023

J Med Internet Res

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Background Nonalcoholic fatty liver disease (NAFLD) has emerged as a worldwide public health issue. Identifying and targeting populations at a heightened risk of developing NAFLD over a 5-year period can help reduce and delay adverse hepatic prognostic events. Objective This study aimed to investigate the 5-year incidence of NAFLD in the Chinese population. It also aimed to establish and validate a machine learning model for predicting the 5-year NAFLD risk. Methods The study population was derived from a 5-year prospective cohort study. A total of 6196 individuals without NAFLD who underwent health checkups in 2010 at Zhenhai Lianhua Hospital in Ningbo, China, were enrolled in this study. Extreme gradient boosting (XGBoost)–recursive feature elimination, combined with the least absolute shrinkage and selection operator (LASSO), was used to screen for characteristic predictors. A total of 6 machine learning models, namely logistic regression, decision tree, support vector machine, random forest, categorical boosting, and XGBoost, were utilized in the construction of a 5-year risk model for NAFLD. Hyperparameter optimization of the predictive model was performed in the training set, and a further evaluation of the model performance was carried out in the internal and external validation sets. Results The 5-year incidence of NAFLD was 18.64% (n=1155) in the study population. We screened 11 predictors for risk prediction model construction. After the hyperparameter optimization, CatBoost demonstrated the best prediction performance in the training set, with an area under the receiver operating characteristic (AUROC) curve of 0.810 (95% CI 0.768-0.852). Logistic regression showed the best prediction performance in the internal and external validation sets, with AUROC curves of 0.778 (95% CI 0.759-0.794) and 0.806 (95% CI 0.788-0.821), respectively. The development of web-based calculators has enhanced the clinical feasibility of the risk prediction model. Conclusions Developing and validating machine learning models can aid in predicting which populations are at the highest risk of developing NAFLD over a 5-year period, thereby helping delay and reduce the occurrence of adverse liver prognostic events.

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Section: Discussionsupporting

confidence: 92%

Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study

Huang,

Jin,

Mao

2023

J Med Internet Res

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“…Along with the obesity and diabetes epidemic, the disease burden of NAFLD is expected to increase 2-to 3-fold by 2030 in Western countries and Asia [1]. The association of triglyceride, HDL, and Apo-B with NAFLD is consistent with previous reports [42][43][44]. Further, we found a dose-dependent relationship between BMI, WC, Apo-B, triglyceride, and NAFLD; when they were greater than 22.65 kg/m 2 , 81.04 cm, 1.09 mmol/L, and 0.69 g/L, respectively, an increased risk of NAFLD was observed.…”

Section: Principal Findingssupporting

confidence: 90%

Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study (Preprint)

Huang,

Jin,

Mao

2023

Preprint

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BACKGROUND Nonalcoholic fatty liver disease (NAFLD) has emerged as a worldwide public health issue. Identifying and targeting populations at a heightened risk of developing NAFLD over a 5-year period can help reduce and delay adverse hepatic prognostic events. OBJECTIVE This study aimed to investigate the 5-year incidence of NAFLD in the Chinese population. It also aimed to establish and validate a machine learning model for predicting the 5-year NAFLD risk. METHODS The study population was derived from a 5-year prospective cohort study. A total of 6196 individuals without NAFLD who underwent health checkups in 2010 at Zhenhai Lianhua Hospital in Ningbo, China, were enrolled in this study. Extreme gradient boosting (XGBoost)–recursive feature elimination, combined with the least absolute shrinkage and selection operator (LASSO), was used to screen for characteristic predictors. A total of 6 machine learning models, namely logistic regression, decision tree, support vector machine, random forest, categorical boosting, and XGBoost, were utilized in the construction of a 5-year risk model for NAFLD. Hyperparameter optimization of the predictive model was performed in the training set, and a further evaluation of the model performance was carried out in the internal and external validation sets. RESULTS The 5-year incidence of NAFLD was 18.64% (n=1155) in the study population. We screened 11 predictors for risk prediction model construction. After the hyperparameter optimization, CatBoost demonstrated the best prediction performance in the training set, with an area under the receiver operating characteristic (AUROC) curve of 0.810 (95% CI 0.768-0.852). Logistic regression showed the best prediction performance in the internal and external validation sets, with AUROC curves of 0.778 (95% CI 0.759-0.794) and 0.806 (95% CI 0.788-0.821), respectively. The development of web-based calculators has enhanced the clinical feasibility of the risk prediction model. CONCLUSIONS Developing and validating machine learning models can aid in predicting which populations are at the highest risk of developing NAFLD over a 5-year period, thereby helping delay and reduce the occurrence of adverse liver prognostic events.

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“…For instance, of the top 20 significant up- and down-regulated DEGs in EF(Veh. vs. PCB126) comparison, up-regulation of solute carrier family 46 member 3 ( Slc46a3 ) 34 and down-regulation of progestin and adipoQ receptor family member 9 ( Paqr9 ), 41 apolipoprotein A-1 ( Apoa1 ), 42 and elongation of very long chain fatty acids protein 2 ( Elovl2 ) 43 have demonstrated to be involved in the development of steatosis or altered lipid metabolism.…”

Section: Discussionmentioning

confidence: 99%

Multiomics Analysis of PCB126’s Effect on a Mouse Chronic-Binge Alcohol Feeding Model

Gripshover,

Wahlang,

Head

et al. 2024

Environ Health Perspect

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Background: Environmental pollutants, including polychlorinated biphenyls (PCBs) have been implicated in the pathogenesis of liver disease. Our group recently demonstrated that PCB126 promoted steatosis, hepatomegaly, and modulated intermediary metabolism in a rodent model of alcohol-associated liver disease (ALD). Objective: To better understand how PCB126 promoted ALD in our previous model, the current study adopts multiple omics approaches to elucidate potential mechanistic hypotheses. Methods: Briefly, male C57BL/6J mice were exposed to polychlorinated biphenyl (PCB) 126 or corn oil vehicle prior to ethanol (EtOH) or control diet feeding in the chronic-binge alcohol feeding model. Liver tissues were collected and prepared for mRNA sequencing, phosphoproteomics, and inductively coupled plasma mass spectrometry for metals quantification. Results: Principal component analysis showed that PCB126 uniquely modified the transcriptome in EtOH-fed mice. EtOH feeding alone resulted in differentially expressed genes (DEGs), and PCB126 exposure resulted in more DEGs in the EtOH-fed group (907 DEGs) in comparison with the pair-fed group (503 DEGs). Top 20 significant gene ontology (GO) biological processes included “peptidyl tyrosine modifications,” whereas top 25 significantly decreasing GO molecular functions included “metal/ion/zinc binding.” Quantitative, label-free phosphoproteomics and western blot analysis revealed no major significant PCB126 effects on total phosphorylated tyrosine residues in EtOH-fed mice. Quantified hepatic essential metal levels were primarily significantly lower in EtOH-fed mice. PCB126-exposed mice had significantly lower magnesium, cobalt, and zinc levels in EtOH-fed mice. Discussion: Previous work has demonstrated that PCB126 is a modifying factor in metabolic dysfunction-associated steatotic liver disease (MASLD), and our current work suggests that pollutants also modify ALD. PCB126 may, in part, be contributing to the malnutrition aspect of ALD, where metal deficiency is known to contribute and worsen prognosis. https://doi.org/10.1289/EHP14132

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Association between apolipoprotein B/A1 and the risk of metabolic dysfunction associated fatty liver disease according to different lipid profiles in a Chinese population: A cross-sectional study

Cited by 7 publications

References 36 publications

Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study

Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study

Predicting the 5-Year Risk of Nonalcoholic Fatty Liver Disease Using Machine Learning Models: Prospective Cohort Study (Preprint)

Multiomics Analysis of PCB126’s Effect on a Mouse Chronic-Binge Alcohol Feeding Model

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