Development and validation of screening scores of non-alcoholic fatty liver disease in middle-aged and elderly Chinese

Non-alcoholic fatty liver disease risk prediction model and health management strategies for older Chinese adults: a cross-sectional study

BACKGROUND

Non-alcoholic fatty liver disease (NAFLD) is a common chronic liver condition that affects a quarter of the global adult population. To date, only a few NAFLD risk prediction models have been developed for Chinese older adults aged ≥ 60 years. This study presented the development of a risk prediction model for NAFLD in Chinese individuals aged ≥ 60 years and proposed personalised health interventions based on key risk factors to reduce NAFLD incidence among the population.

METHODS

A cross-sectional survey was carried out among 9,041 community residents in Shanghai. Three NAFLD risk prediction models (I, II, and III) were constructed using multivariate logistic regression analysis based on the least absolute shrinkage and selection operator regression analysis, and random forest model to select individual characteristics, respectively. To determine the optimal model, the three models' discrimination, calibration, clinical application, and prediction capability were evaluated using the receiver operating characteristic (ROC) curve, calibration plot, decision curve analysis, and net reclassification index (NRI), respectively. To evaluate the optimal model's effectiveness, the previously published NAFLD risk prediction models (Hepatic steatosis index [HSI] and ZJU index) were evaluated using the following five indicators: accuracy, precision, recall, F1-score, and balanced accuracy. A dynamic nomogram was constructed for the optimal model, and a Bayesian network model for predicting NAFLD risk in older adults was visually displayed using Netica software.

RESULTS

The area under the ROC curve of Models I, II, and III in the training dataset was 0.810, 0.826, and 0.825, respectively, and that of the testing data was 0.777, 0.797, and 0.790, respectively. No significant difference was found in the accuracy or NRI between the models; therefore, Model III with the fewest variables was determined as the optimal model. Compared with the HSI and ZJU index, Model III had the highest accuracy (0.716), precision (0.808), recall (0.605), F1 score (0.692), and balanced accuracy (0.723). The risk threshold for Model III was 20%-80%. Model III included body mass index, alanine aminotransferase level, triglyceride level, and lymphocyte count.

CONCLUSIONS

A dynamic nomogram and Bayesian network model were developed to identify NAFLD risk in older Chinese adults, providing personalized health management strategies and reducing NAFLD incidence.

Impact of diabetes mellitus on in-hospital mortality in adult patients with COVID-19: a systematic review and meta-analysis

BACKGROUND

The novel coronavirus disease 2019 (COVID-19) has spread worldwide since the beginning of 2020, placing the heavy burden on the health systems all over the world. The population that particularly has been affected by the pandemic is the group of patients suffering from diabetes mellitus. Having taken the public health in considerations, we have decided to perform a systematic review and meta-analysis of diabetes mellitus on in-hospital mortality in patients with COVID-19.

METHODS

A systematic literature review (MEDLINE, EMBASE, Web of Science, Scopus, Cochrane) including all published clinical trials or observational studies published till December 10, 2020, was performed using following terms "diabetes mellitus" OR "diabetes" OR "DM" AND "survival" OR "mortality" AND "SARS-CoV-2" OR "COVID-19".

RESULTS

Nineteen studies were included out of the 7327 initially identified studies. Mortality of DM patients vs non-DM patients was 21.3 versus 6.1%, respectively (OR = 2.39; 95%CI: 1.65, 3.64; P < 0.001), while severe disease in DM and non-DM group varied and amounted to 34.8% versus 22.8% (OR = 1.43; 95%CI: 0.82, 2.50; P = 0.20). In the DM group, the complications were observed far more often when compared with non-DM group, both in acute respiratory distress (31.4 vs. 17.2%; OR = 2.38; 95%CI:1.80, 3.13; P < 0.001), acute cardiac injury (22.0% vs. 12.8%; OR = 2.59; 95%CI: 1.81, 3.73; P < 0.001), and acute kidney injury (19.1 vs. 10.2%; OR = 1.97; 95%CI: 1.36, 2.85; P < 0.001).

CONCLUSIONS

Based on the findings, we shall conclude that diabetes is an independent risk factor of the severity of COVID-19 in-hospital settings; therefore, patients with diabetes shall aim to reduce the exposure to the potential infection of COVID-19.

Nonalcoholic fatty liver disease and the risk of atrial fibrillation stratified by body mass index: a nationwide population-based study

We evaluated the association between nonalcoholic fatty liver disease (NAFLD) and incident atrial fibrillation (AF) and analyzed the impact of NAFLD on AF risk in relation to body mass index (BMI). A total of 8,048,055 subjects without significant liver disease who were available fatty liver index (FLI) values were included. Subjects were categorized into 3 groups based on FLI: < 30, 30 to < 60, and ≥ 60. During a median 8-year of follow-up, 534,442 subjects were newly diagnosed as AF (8.27 per 1000 person-years). Higher FLI was associated with an increased risk of AF (hazard ratio [HR] 1.053, 95% confidence interval [CI] 1.046–1.060 in 30 ≤ FLI < 60, and HR 1.115, 95% CI 1.106–1.125 in FLI ≥ 60). In underweight subjects (BMI < 18.5 kg/m²), higher FLI raised the risk of AF (by 1.6-fold in 30 ≤ FLI < 60 and by twofold in FLI ≥ 60). In normal- and overweight subjects, higher FLI was associated with an increased risk of AF, but the HRs were attenuated. In obese subjects, higher FLI was not associated with higher risk of AF. NAFLD as assessed by FLI was independently associated with an increased risk of AF in nonobese subjects with BMI < 25 kg/m². The impact of NAFLD on AF risk was accentuated in lean subjects with underweight.