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Background/Aims@#Smoking is considered a risk factor for the development of nonalcoholic fatty liver disease (NAFLD). However, the association of a weight change after a change in smoking status and the risk of NAFLD remains undetermined. @*Methods@#This study used the Korean National Health Insurance Service-National Sample Cohort. Based on the first (2009 to 2010) and second (2011 to 2012) health examination periods, 139,180 adults aged at least 40 years were divided into nonsmoking, smoking cessation, smoking relapse, and sustained smoking groups. NAFLD was operationally defined using the fatty liver index. The adjusted odds ratio (aOR) and 95% confidence interval (CI) were calculated using multivariable-adjusted logistic regression. @*Results@#Compared to nonsmoking with no body mass index (BMI) change, the risk of NAFLD was significantly increased among subjects with BMI gain and nonsmoking (aOR, 4.07; 95% CI, 3.77 to 4.39), smoking cessation (aOR, 5.52; 95% CI, 4.12 to 7.40), smoking relapse (aOR, 7.51; 95% CI, 4.81 to 11.72), and sustained smoking (aOR, 6.65; 95% CI, 5.33 to 8.29), whereas the risk of NAFLD was reduced among participants with BMI loss in all smoking status groups. In addition, smoking cessation (aOR, 1.76; 95% CI, 1.35 to 2.29) and sustained smoking (aOR, 1.64; 95% CI, 1.39 to 1.94) were associated with higher risk of NAFLD among participants with no BMI change.The liver enzyme levels were higher among participants with smoking cessation and BMI gain. @*Conclusions@#Monitoring and management of weight change after a change in smoking status may be a promising approach to reducing NAFLD.
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Background/Aims@#Metabolic dysfunction (MD)-associated fatty liver disease is a new positive diagnostic criterion based on hepatic steatosis and MD. However, a comprehensive evaluation on the association of MD and hepatic steatosis with incident cardiovascular disease (CVD) has yet to be performed. @*Methods@#This retrospective cohort study included 333,389 participants from the Korean National Health Insurance Service database who received a health examination between 2009 and 2010. Hepatic steatosis was defined using the Korean National Health and Nutrition Examination Survey-derived nonalcoholic fatty liver disease scoring system. Cox proportional hazards regression was adopted to determine the adjusted hazard ratio (aHR) with 95% confidence interval (CI) for CVD according to the presence of hepatic steatosis and MD, as well as the composite term. @*Results@#This study included 179,437 men and 153,952 women with a median age of 57 years.Hepatic steatosis with MD (aHR, 2.00; 95% CI, 1.89 to 2.13) and without MD (aHR, 1.30; 95% CI, 1.10 to 1.54) significantly increased the risk of CVD compared to no steatosis without MD (reference). However, steatosis revealed no significant difference in the risk of CVD compared to no steatosis among participants with one MD (aHR, 1.09; 95% CI, 0.91 to 1.30). In participants with steatosis, the presence of one and ≥2 MDs had aHR values of 1.25 (95% CI, 0.87 to 1.79) and 1.71 (95% CI, 1.22 to 2.41), respectively, compared to no MD. @*Conclusions@#Combined consideration of hepatic steatosis and MD was significantly associated with increased CVD risk and showed better predictive performance for CVD than hepatic steatosis or MD alone.
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Background/Aims@#Accumulating evidence suggests a link between non-alcoholic fatty liver disease (NAFLD) and brain health. However, population-based evidence on the association between NAFLD and dementia remains unclear. This study was conducted to determine the association between NAFLD and incident dementia. @*Methods@#The study population included 608,994 adults aged ≥60 years who underwent health examinations between 2009 and 2010. Data were collected from the Korean National Health Insurance Service database. NAFLD was assessed using the fatty liver index (FLI). A Cox proportional hazards regression model was used to determine the association between NAFLD and dementia. @*Results@#During the 6,495,352 person-years of follow-up, 48,538 participants (8.0%) developed incident dementia. The participants were classified into low (FLI <30), intermediate (FLI ≥30 and <60), and high (FLI ≥60) groups. In the overall study population, the FLI groups were associated with a risk of dementia (P for trend <0.001). After propensity score matching, a low FLI was associated with a reduced risk of dementia (adjusted hazard ration [aHR], 0.96; 95% confidence interval [CI], 0.93–0.98; P=0.002), whereas a high FLI (NAFLD) was associated with an increased risk of dementia (aHR, 1.05; 95% CI, 1.02–1.08; P=0.001). A higher risk of dementia in the high FLI group than in the intermediate FLI group was attributed to Alzheimer’s disease (aHR, 1.04; 95% CI, 1.01–1.07; P=0.004) rather than vascular dementia (aHR, 0.94; 95% CI, 0.75–1.18; P=0.602). @*Conclusions@#NAFLD was associated with an increased risk of dementia, which was attributed to an increased risk of Alzheimer’s disease.
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BACKGROUND/OBJECTIVES@#The measurement of body composition, including muscle and fat mass, remains challenging in large epidemiological studies due to time constraint and cost when using accurate modalities. Therefore, this study aimed to develop and validate prediction equations according to sex to measure lean body mass (LBM), appendicular skeletal muscle mass (ASM), and body fat mass (BFM) using anthropometric measurement, serum creatinine level, and lifestyle factors as independent variables and dual-energy X-ray absorptiometry as the reference method. @*SUBJECTS/METHODS@#A sample of the Korean general adult population (men:7,599; women:10,009) from the Korean National Health and Nutrition Examination Survey 2008–2011 was included in this study. The participants were divided into the derivation and validation groups via a random number generator (with a ratio of 70:30). The prediction equations were developed using a series of multivariable linear regressions and validated using the Bland– Altman plot and intraclass correlation coefficient (ICC). @*RESULTS@#The initial and practical equations that included age, height, weight, and waist circumference had a different predictive ability for LBM (men: R2 = 0.85, standard error of estimate [SEE] = 2.7 kg; women: R2 = 0.78, SEE = 2.2 kg), ASM (men: R2= 0.81, SEE = 1.6 kg; women: R2 = 0.71, SEE = 1.2 kg), and BFM (men: R2 = 0.74, SEE = 2.7 kg; women: R2 = 0.83, SEE = 2.2 kg) according to sex. Compared with the first prediction equation, the addition of other factors, including serum creatinine level, physical activity, smoking status, and alcohol use, resulted in an R2 that is higher by 0.01 and SEE that is lower by 0.1. @*CONCLUSIONS@#All equations had low bias, moderate agreement based on the Bland–Altman plot, and high ICC, and this result showed that these equations can be further applied to other epidemiologic studies.
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Purpose@#Although smoking has a significant impact on mortality and morbidity of cancer patients, many patients continue to smoke post-diagnosis. The purpose of this study was to investigate prevalence and predictors of sustained smoking among male cancer survivors. @*Materials and Methods@#The Korean National Health Insurance Service-National Health Screening Cohort database was used for this population-based, retrospective study. Study subjects were 15,141 men who were diagnosed with their first incident cancer between 2004 and 2011. Changes in smoking status before and after a cancer diagnosis were investigated. For patients who were current smokers pre-diagnosis, association between post-diagnosis sustained smoking and demographic, socioeconomic, and clinical variables were examined. @*Results@#Of the 4,657 pre-diagnosis smokers, 2,255 (48%) had quit after cancer diagnosis, while 2,402 (51.6%) continued to smoke. In a multivariate logistic regression analysis, younger age at cancer diagnosis (adjusted odds ratio [aOR], 1.37; 95% confidence interval [CI], 1.21 to 1.55; p < 0.001), low socioeconomic status (aOR, 1.29; 95% CI, 1.15 to 1.45; p ≤ 0.001), pre-diagnosis heavy smoking (aOR, 1.24; 95% CI, 1.09 to 1.41; p=0.001), diagnosis of non-smoking– related cancer (aOR, 1.67; 95% CI, 1.42 to 1.96; p < 0.001), and high serum glucose level (aOR, 1.23; 95% CI, 1.03 to 1.46; p=0.019) were associated with sustained smoking after a cancer diagnosis. @*Conclusion@#Almost half of the male smokers continue to smoke after a cancer diagnosis. Targeted interventions for smoking cessation should be considered for patients with younger age, low socioeconomic status, heavy smoking history, non-smoking–related cancer, and high blood glucose levels.
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PURPOSE: The purpose of this study was to investigate the effect of smoking habit change on the risk of cancer. MATERIALS AND METHODS: From the Korean National Health Insurance Service database, we determined the change in smoking habit between the first (2002 and 2003) and second (2004 and 2005) health examination periods. A total of 143,071 men were categorized into baseline heavy (≥ 20 cigarettes per day), moderate (10-19 cigarettes per day), light (< 10 cigarettes per day) smokers, quitters, and never smokers, after which the change in smoking status was determined during the second health examination. The participants were then followed up from 2006 to 2013 for all cancer, smoking related cancer, and lung cancer. RESULTS: Compared to heavy continual smokers, heavy smokers who quit had reduced risk of smoking related cancer (hazard ratio [HR], 0.74; 95% confidence interval [CI], 0.56 to 0.97) and tended to have reduced risk of all cancer (HR, 0.87; 95% CI, 0.75 to 1.00). Moderate smokers who reduced the amount of smoking to light levels had decreased risk of all cancer (HR, 0.82; 95% CI, 0.72 to 0.94), smoking related cancer (HR, 0.74; 95% CI, 0.59 to 0.93), and lung cancer (HR, 0.55; 95% CI, 0.38 to 0.79) compared to heavy continual smokers. CONCLUSION: Smoking reduction decreases the risk of all cancer, smoking related cancer, and lung cancer. While smoking cessation should be the treatment of choice for smokers, smoking reduction may serve as an alternative strategy for those who cannot quit.