Clinical outcome of non-alcoholic fatty liver disease: an 11-year follow-up study

Global epidemiology of type 2 diabetes in patients with NAFLD or MAFLD: a systematic review and meta-analysis

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) and metabolic-associated fatty liver disease (MAFLD) shares common pathophysiological mechanisms with type 2 diabetes, making them significant risk factors for type 2 diabetes. The present study aimed to assess the epidemiological feature of type 2 diabetes in patients with NAFLD or MAFLD at global levels.

METHODS

Published studies were searched for terms that included type 2 diabetes, and NAFLD or MAFLD using PubMed, EMBASE, MEDLINE, and Web of Science databases from their inception to December 2022. The pooled global and regional prevalence and incidence density of type 2 diabetes in patients with NAFLD or MAFLD were evaluated using random-effects meta-analysis. Potential sources of heterogeneity were investigated using stratified meta-analysis and meta-regression.

RESULTS

A total of 395 studies (6,878,568 participants with NAFLD; 1,172,637 participants with MAFLD) from 40 countries or areas were included in the meta-analysis. The pooled prevalence of type 2 diabetes among NAFLD or MAFLD patients was 28.3% (95% confidence interval 25.2-31.6%) and 26.2% (23.9-28.6%) globally. The incidence density of type 2 diabetes in NAFLD or MAFLD patients was 24.6 per 1000-person year (20.7 to 29.2) and 26.9 per 1000-person year (7.3 to 44.4), respectively.

CONCLUSIONS

The present study describes the global prevalence and incidence of type 2 diabetes in patients with NAFLD or MAFLD. The study findings serve as a valuable resource to assess the global clinical and economic impact of type 2 diabetes in patients with NAFLD or MAFLD.

Severity of fatty liver is highly correlated with the risk of hypertension and diabetes: a cross-sectional and longitudinal cohort study

BACKGROUND AND AIMS

Fatty liver disease (FLD) is associated with several metabolic derangements. We conducted a retrospective cross-sectional and longitudinal study to evaluate the role of FL severity in the risk of new-onset and co-existing hypertension (HTN) and diabetes mellitus (DM).

METHODS

The cross-sectional cohort consisted of 41,888 adults who received health checkups in a tertiary hospital of Taiwan from 1999 to 2013. Of them, 34,865 without HTN and/or DM at baseline and within 1 year after enrollment were included as a longitudinal cohort (mean, 6.45 years for HTN; 6.75 years for DM). FL severity based on the degree of hepatic steatosis was assessed by ultrasound sonography.

RESULTS

In cross-sectional cohort, 22,852 (54.6%) subjects had FL (18,203 [43.46%] mild FL and 4,649 [11.10%] moderate/severe FL); 13.5% (n = 5668) had HTN; and 3.4% (n = 1411) had DM. Moderate/severe FL and mild FL had significantly higher risks of existing HTN (adjusted odds ratio/95% confidence interval [CI] 1.59/1.43-1.77 and 1.22/1.13-1.32, respectively). In longitudinal cohort, 3,209 and 822 subjects developed new-onset HTN and DM, respectively (annual incidence, 14.3 and 3.5 per 1000 person-years; 10-year cumulative incidence, 14.35% and 3.89%, respectively). Moderate/severe and mild FL had significantly higher risks of new-onset HTN (adjusted hazard ratio [aHR]/CI 1.54/1.34-1.77 and 1.26/1.16-1.37, respectively) and DM (aHR/CI 5.88/4.44-7.81 and 3.22/2.56-4.07, respectively). Resolved FL during follow-up decreased the risk of HTN and/or DM.

CONCLUSIONS

Patients with FL are at high risk of prevalent and incident HTN and/or DM. The risk increases with the severity of FL.

Increased NAFLD risk in newly diagnosed patients with RA during the first 4 years of follow-up: a nationwide, population-based cohort study

BACKGROUND

Although the non-alcoholic fatty liver disease (NAFLD) is prevalent in the general population, NAFLD risk in newly diagnosed rheumatoid arthritis (RA) has rarely been explored. In this population-based cohort, we examined NAFLD risk in patients with RA and identified the potential risk factors.

DESIGN

Retrospective study.

SETTING

Taiwan.

PARTICIPANTS

2281 newly diagnosed patients with RA and selected 91 240 individuals without RA to match with patients with RA (1:40) by age, gender, income status and urbanisation level of the residence.

OUTCOMES

In this retrospective study using the 2000-2018 claim data from two-million representative Taiwanese population, we identified and compared the incidence rates (IRs) of NAFLD and alcoholic fatty liver disease (AFLD) between RA and non-RA groups. Using multivariable regression analyses, we estimated adjusted HR (aHR) of NAFLD development in patients with RA compared with individuals without RA, with 95% CIs.

RESULTS

The incidences of NALFD and AFLD were not significantly different between individuals with RA and without RA during the 17-year follow-up period. However, patients with RA had significantly increased NAFLD risk during the first 4 years after RA diagnosis, with IR ratio of 1.66 fold (95% CI 1.18 to 2.33, p<0.005), but the risk was reduced after the first 4 years. Multivariable regression analyses revealed that aHR was 2.77-fold greater in patients not receiving disease-modifying anti-rheumatic drugs therapy than in non-RA subjects (p<0.05). Old age, women, low-income status and obesity could significantly predict NAFLD development.

CONCLUSIONS

We demonstrated elevated risk of NAFLD in patients with RA during the first 4 years after RA diagnosis, and old age, women, low-income status and obesity were significant predictors of NAFLD.

Association between hemoglobin glycation index and non-alcoholic fatty liver disease in the patients with type 2 diabetes mellitus

AIMS/INTRODUCTION

The hemoglobin glycation index (HGI) represent the disparity between actual glycated hemoglobin measurements and predicted HbA1c. It serves as a proxy for the degree of non-enzymatic glycation of hemoglobin, which has been found to be positively correlated with diabetic comorbidities. In this study, we investigated the relationship between HGI and non-alcoholic fatty liver disease (NAFLD), along with other relevant biological markers in patients with diabetes.

MATERIALS AND METHODS

This cross-sectional study consisted of 3,191 adults diagnosed with type 2 diabetes mellitus. We calculated the predicted glycated hemoglobin levels based on fasting blood glucose levels. Multivariate binary logistic regression analysis was conducted to examine the correlation between the HGI and NAFLD. Hepatic steatosis was diagnosed using ultrasonography.

RESULTS

Among all participants, 1,784 (55.91%) were diagnosed with NAFLD. Participants with confirmed NAFLD showed elevated body mass index, diastolic blood pressure, liver enzyme, total cholesterol, triglyceride, low-density lipoprotein and uric acid levels compared with those without NAFLD. In the unadjusted model, participants in the last tertile of HGI were 1.40-fold more likely to develop NAFLD than those in the first tertile (95% confidence interval 1.18-1.66; P < 0.001). In the fully adjusted model, those in the last tertile of HGI had a 39% increased risk of liver steatosis compared with confidence interval in the first tertile of HGI (95% confidence interval 1.12-1.74; P < 0.001).

CONCLUSIONS

A higher HGI suggests an elevated risk of developing NAFLD in patients with type 2 diabetes.

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

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.