Background The incidence density of metabolic dysfunction-associated fatty liver disease (MAFLD) and the effect of a healthy lifestyle on the risk of MAFLD remain unknown. We evaluated the prevalence and incidence density of MAFLD and investigated the association between healthy lifestyle and the risk of MAFLD.
Methods A cross-sectional analysis was conducted on 37,422 participants to explore the prevalence of MAFLD. A cohort analysis of 18,964 individuals was conducted to identify the incidence of MAFLD, as well as the association between healthy lifestyle and MAFLD. Cox proportional hazards regression was used to calculate the hazard ratio (HR) and 95% confidence interval (CI) with adjustments for confounding factors.
Results The prevalence of MAFLD, non-alcoholic fatty liver disease, and their comorbidities were 30.38%, 28.09%, and 26.13%, respectively. After approximately 70 thousand person-years of follow-up, the incidence densities of the three conditions were 61.03, 55.49, and 51.64 per 1,000 person-years, respectively. Adherence to an overall healthy lifestyle was associated with a 19% decreased risk of MAFLD (HR, 0.81; 95% CI, 0.72 to 0.92), and the effects were modified by baseline age, sex, and body mass index (BMI). Subgroup analyses revealed that younger participants, men, and those with a lower BMI experienced more significant beneficial effects from healthy lifestyle.
Conclusion Our results highlight the beneficial effect of adherence to a healthy lifestyle on the prevention of MAFLD. Health management for improving dietary intake, physical activity, and smoking and drinking habits are critical to improving MAFLD.
Citations
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Background We explored the association between continuous glucose monitoring (CGM) use and glycemia among adults with type 1 diabetes mellitus (T1DM) and determined the status of CGM metrics among adults with T1DM using CGM in the real-world.
Methods For this propensity-matched cross-sectional study, individuals with T1DM who visited the outpatient clinic of the Endocrinology Department of Samsung Medical Center between March 2018 and February 2020 were screened. Among them, 111 CGM users (for ≥9 months) were matched based on propensity score considering age, sex, and diabetes duration in a 1:2 ratio with 203 CGM never-users. The association between CGM use and glycemic measures was explored. In a subpopulation of CGM users who had been using official applications (not “do-it-yourself” software) such that Ambulatory Glucose Profile data for ≥1 month were available (n=87), standardized CGM metrics were summarized.
Results Linear regression analyses identified CGM use as a determining factor for log-transformed glycosylated hemoglobin. The fully-adjusted odds ratio (OR) and 95% confidence interval (CI) for uncontrolled glycosylated hemoglobin (>8%) were 0.365 (95% CI, 0.190 to 0.703) in CGM users compared to never-users. The fully-adjusted OR for controlled glycosylated hemoglobin (<7%) was 1.861 (95% CI, 1.119 to 3.096) in CGM users compared to never-users. Among individuals who had been using official applications for CGM, time in range (TIR) values within recent 30- and 90-day periods were 62.45%±16.63% and 63.08%±15.32%, respectively.
Conclusion CGM use was associated with glycemic control status among Korean adults with T1DM in the real-world, although CGM metrics including TIR might require further improvement among CGM users.
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