The effects of electronic-based lifestyle interventions on nonalcoholic fatty liver disease: A systematic review.

Objective: Lifestyle interventions are increasingly becoming an integrated part of nonalcoholic fatty liver disease (NAFLD) management. Electronic lifestyle interventions may be able to expand the access and utility of this approach. This study aimed to synthesize the evidence for the effects of electronic-based lifestyle interventions on weight, anthropometric, and liver enzyme measurements in patients with NAFLD. Methods: Medline, Scopus, and Web of Science were searched up to February 2023. Clinical trials investigating the effects of electronic lifestyle interventions on weight, body mass index (BMI), waist circumference (WC), and liver enzymes in NAFLD patients were reviewed. After reviewing full-text articles, seven clinical trials were included in the systematic review. Results: Two articles included telephone calls, one was based on text messaging, two studies were based on web-based lifestyle modifications, and two used mobile apps. Except for one, all other six studies indicated a significant impact on weight loss. BMI was reported in six of seven studies. Except for one, BMI was significantly reduced in the group receiving e-health. WC was reported in four studies, which indicated a significant reduction in the e-health intervention group. Alanine transaminase (ALT) was reported in all the included studies. Except for two, others demonstrated a significant improvement in ALT in the e-health intervention groups. As reported in four studies, Aspartate transaminase (AST) significantly decreased in the group receiving e-health interventions, except in one study. Conclusions: The results support applying electronic lifestyle interventions in NAFLD patients to reduce weight, BMI, WC, AST, and ALT.

Prediction of serum anti-HSP27 antibody titers changes using a light gradient boosting machine (LightGBM) technique.

Previous studies have proposed that heat shock proteins 27 (HSP27) and its anti-HSP27 antibody titers may play a crucial role in several diseases including cardiovascular disease. However, available studies has been used simple analytical methods. This study aimed to determine the factors that associate serum anti-HSP27 antibody titers using ensemble machine learning methods and to demonstrate the magnitude and direction of the predictors using PFI and SHAP methods. The study employed Python 3 to apply various machine learning models, including LightGBM, CatBoost, XGBoost, AdaBoost, SVR, MLP, and MLR. The best models were selected using model evaluation metrics during the K-Fold cross-validation strategy. The LightGBM model (with RMSE: 0.1900 ± 0.0124; MAE: 0.1471 ± 0.0044; MAPE: 0.8027 ± 0.064 as the mean ± sd) and the SHAP method revealed that several factors, including pro-oxidant-antioxidant balance (PAB), physical activity level (PAL), platelet distribution width, mid-upper arm circumference, systolic blood pressure, age, red cell distribution width, waist-to-hip ratio, neutrophils to lymphocytes ratio, platelet count, serum glucose, serum cholesterol, red blood cells were associated with anti-HSP27, respectively. The study found that PAB and PAL were strongly associated with serum anti-HSP27 antibody titers, indicating a direct and indirect relationship, respectively. These findings can help improve our understanding of the factors that determine anti-HSP27 antibody titers and their potential role in disease development.

Comparing food consumption during the COVID-19 pandemic: analysis of household income and expenditure survey data in Iran.

BACKGROUND: On March 11, 2020, the WHO declared the outbreak of the infectious disease COVID-19 as a pandemic. The health strategies of nations lead to possible changes in lifestyle and increase poor eating habits. Hence, the purpose of this study is to compare food consumption during COVID-19 pandemic in Iran. METHODS: This cross-sectional study used secondary data from the Households Income and Expenditure Survey (HIES) conducted annually by the Statistical Centre of Iran. Food cost data of HIES included the amount of all food items in household food baskets during the last month. Then, they were classified into six food groups to evaluate their energy intake. The consequence of food consumption was analyzed as a function of socioeconomic status (SES) variables and residence pre- and post-COVID-19 pandemic. RESULTS: In total, 75,885 households (83.5% male) were included in the study. Among the population of urban and rural areas as well as in different SES categories, people tended to increase the consumption of meat (P < 0.05) and fresh foods, especially vegetable groups (P < 0.001) and decrease the consumption of fruit (P < 0.001), fat and sweets groups (P < 0.05) and also in energy intake (P < 0.05). Macronutrient changes were different in the category of SES, urban and rural. CONCLUSION: Our study indicated that the COVID-19 pandemic had different effects on food groups, energy and macronutrients consumption, which could be due to possible changes in food patterns as a result of the pandemic.