Benign prostatic hyperplasia (BPH )is one the most common prostate diseases in middle aged and elderly men. This study was conducted to investigate diet and lifestyle risk factors for benign prostatic hyperplasia in a community-dwelling free-living population group. The dietary data were collected from the 601 male subjects aged 50 -79 years using the 24-hour recall method. The mean age of the BPH group (63.0 +/- 7.9 years )was significantly higher than that of the non-BPH (58.8 +/-7.4 years ). Among many nutrients, the amount of animal fat intake was increased while that of carbohydrate intake decreased in subjects with BPH compared to those with non-BPH. In BPH subjects, the proportion of energy from fat was also greater than in subjects with non-BPH. The logistic regression analysis on the food con-sumption data showed that the consumption of total animal food was increased while that of mushrooms was decreased in patients with BPH compared to the subjects with non-BPH. The age-adjusted odds ratios and 95% confidences limits for BPH incidence in subjects whose milk and milk products, beverages and alcoholic liquors intake was greater than the median were 1.796 (1.167 -2.782 )and 1.738 (1.129 -2.676 )respectively, compared to those in subjects whose intakes were below the median. These results may be applicable in the development of a nutrition intervention and education program toward a reduction in the risk for benign prostatic hyperplasia.
Agaricales ; Aged ; Alcoholics ; Animals ; Beverages ; Diet* ; Education ; Humans ; Incidence ; Life Style* ; Logistic Models ; Male ; Middle Aged ; Milk ; Odds Ratio ; Population Groups ; Prostate ; Prostatic Hyperplasia* ; Risk Factors*

OBJECTIVES: We conducted a comorbidity network analysis using data from the seventh Korea National Health and Nutrition Examination Survey to systematically quantify obesity-related comorbidities. METHODS: The study included 11,712 subjects aged 45 to 80 (5,075 male and 6,637 female). A prevalent disease was defined as a specific disease for which a subject had been diagnosed by a doctor and was being treated. Comorbidity network analysis was performed for diseases with a prevalence of 1% or more, including overweight and obesity. We estimated the observed-to-expected ratio of all possible disease pairs with comorbidity strength and visualized the network of obesity-related comorbidities. RESULTS: In subjects over 45 years old, 37.3% of people had a body mass index over 25.0 kg/m2. The most common prevalent disease was hypertension (42.3%), followed by dyslipidemia (17.4%) and diabetes (17.0%). Overweight and obese subjects were 2.1 times (95% confidence interval, 1.9 to 2.3) more likely to have a comorbidity (i.e., 2 or more diseases) than normal-weight subjects. Metabolic diseases such as hypertension, dyslipidemia, diabetes, and osteoarthritis were directly associated with overweight and obesity. The probability of coexistence for each of those 4 diseases was 1.3 times higher than expected. In addition, hypertension and dyslipidemia frequently coexisted in overweight and obese female along with other diseases. In obese male, dyslipidemia and diabetes were the major diseases in the comorbidity network. CONCLUSIONS Our results provide evidence justifying the management of metabolic components in obese individuals. In addition, our results will help prioritize interventions for comorbidity reduction as a public health goal.

OBJECTIVES: We conducted a comorbidity network analysis using data from the seventh Korea National Health and Nutrition Examination Survey to systematically quantify obesity-related comorbidities. METHODS: The study included 11,712 subjects aged 45 to 80 (5,075 male and 6,637 female). A prevalent disease was defined as a specific disease for which a subject had been diagnosed by a doctor and was being treated. Comorbidity network analysis was performed for diseases with a prevalence of 1% or more, including overweight and obesity. We estimated the observed-to-expected ratio of all possible disease pairs with comorbidity strength and visualized the network of obesity-related comorbidities. RESULTS: In subjects over 45 years old, 37.3% of people had a body mass index over 25.0 kg/m2. The most common prevalent disease was hypertension (42.3%), followed by dyslipidemia (17.4%) and diabetes (17.0%). Overweight and obese subjects were 2.1 times (95% confidence interval, 1.9 to 2.3) more likely to have a comorbidity (i.e., 2 or more diseases) than normal-weight subjects. Metabolic diseases such as hypertension, dyslipidemia, diabetes, and osteoarthritis were directly associated with overweight and obesity. The probability of coexistence for each of those 4 diseases was 1.3 times higher than expected. In addition, hypertension and dyslipidemia frequently coexisted in overweight and obese female along with other diseases. In obese male, dyslipidemia and diabetes were the major diseases in the comorbidity network. CONCLUSIONS Our results provide evidence justifying the management of metabolic components in obese individuals. In addition, our results will help prioritize interventions for comorbidity reduction as a public health goal.

Objectives: This study investigated the association between exacerbated economic hardship during the coronavirus disease 2019 (COVID-19) pandemic and changes in the health behaviors of Korean adolescents. Methods: We analyzed data from the 2021 Korea Youth Risk Behavior Survey and included 44 908 students (22 823 boys and 22 085 girls) as study subjects. The dependent variables included changes in health behaviors (breakfast habits, physical activity, and alcohol use) that occurred during the COVID-19 pandemic. The aggravation of economic hardship by COVID-19 and the subjective economic status of the family were used as exposure variables. Multiple logistic regression analysis was utilized to calculate the prevalence odds ratios (PORs). Results: Severe exacerbation of a family’s economic hardship due to COVID-19 was negatively associated with the health behaviors of adolescents, including increased breakfast skipping (POR, 1.85; 95% confidence interval [CI], 1.55 to 2.21 for boys and POR, 1.56; 95% CI, 1.27 to 1.92 for girls) and decreased physical activity (POR, 1.37; 95% CI, 1.19 to 1.57 for boys and POR, 1.38; 95% CI, 1.19 to 1.60 for girls). These negative changes in health behaviors were further amplified when combined with a low subjective family economic status. Conclusions The experience of worsening household hardship can lead to negative changes in health behavior among adolescents. It is crucial to implement measures that address the economic challenges that arise from stressful events such as COVID-19 and to strive to improve the lifestyles of adolescents under such circumstances.

PURPOSE: We conducted objective structured clinical examinations (OSCEs) in medical students both before entering clinical clerkship and after finishing clinical clerkship for the purpose of evaluating the usefulness of OSCEs before clinical clerkship. METHODS: The subjects of the study comprised 77 3rd-year medical students who participated in a 2-week course of physical diagnosis before clinical clerkship, and 98 4th-year medical students who had completed their clinical clerkship. The OSCE consisted of 17 cases and 17 stations for the 3rd-year medical students, and 20 cases and 28 stations for the 4th-year students. We assigned 4 minutes and 30 seconds to each station. OSCE stations were duplicated at two sites and were performed twice. After the OSCE, we used structured questionnaires to survey the subjects for their opinions of the current process and the need for an OSCE. RESULTS: At the psychiatric station, which applied an identical scenario and checklists to both the 3rd- and 4th-year medical students, the mean score of the 3rd-year medical students was significantly lower than that of the 4th-year students. The correlation coefficient between OSCE score and cumulative performance grade of 3rd-year medical students (r=0.29) also was lower than that of 4th-year medical students (r=0.53). Over 80% of the 3rd-year medical students and over 90% of the 4th-year responded that an OSCE is necessary. However, around 70% of students preferred an OSCE for each clinical class during clinical clerkship, and only 33~38% of students preferred an OSCE as a final examination. Almost all students wanted to receive feedback after an OSCE. CONCLUSIONS: Performing an OSCE on 3rd-year medical students before they enter clinical clerkship provides better preparation for the clinical clerkship than an evaluation alone. We suggest that an OSCE should be used as a formative assessment in addition to a summative evaluation such as a final examination.
Checklist ; Clinical Clerkship* ; Diagnosis ; Humans ; Students, Medical* ; Surveys and Questionnaires

Injury is a major public health concern because it is a major cause of death and may cause lifelong disabilities. New environmental risk factors, such as extreme climates, are now emerging, and the vulnerable elderly population is rapidly growing. Therefore, understanding the epidemiological characteristics and trends of injury is necessary to establish preventive policies and actions.Current Concepts: Injury accounts for 13.3% of the disease burden in Korea, which is higher than the global proportion (9.8%). In addition, in 2019, the life years lost due to injury in Korea was 973,030, which is also higher than in the other 37 countries of the Organisation for Economic Co-operation and Development (OECD). Mortality due to injury has shown a downward trend, while mortality due to falls has shown an upward trend since 2010. Mortality due to injury in Korea is higher than the OECD average, and mortality due to intentional self-harm was the highest. Intentional self-harm accounts for 50.8% of deaths due to injury. In hospitalization due to injury, falls account for the largest proportion (38.5%) and frequently occur in older adults.Discussion and Conclusion: Although the mortality rate of injury is decreasing, the magnitude of injury in Korea is still higher than the OECD average. We hope these findings are used as basic data to find a targeted approach for injury prevention.

Background: Cumulative evidence consistently shows that anthropometric and body composition measurements are strongly linked to the risk of incident diabetes, typically based on baseline measurements. This study aims to assess whether repeated measurements enhance the prediction of diabetes risk beyond baseline assessments alone. Methods: We utilized data from a 16-year population-based follow-up cohort within the Korean Genome and Epidemiology Study, comprising 6,030 individuals aged 40 to 69 years at baseline. We included eight indices: a body shape index (ABSI), body adiposity index (BAI), waist circumference (WC), body mass index (BMI), waist-to-hip ratio (WHR), weight-adjusted skeletal muscle index (SMI), percent body fat, and fat-to-muscle ratio. The effect of these measurements for incident diabetes was estimated using Harrell’s C-indexes and hazard ratios with 95% confidence intervals, employing time-dependent Cox proportional hazard models. Results: Over the 16-year follow-up, 939 new diabetes cases were identified (cumulative incidence, 15.6%). The median number of indicator measurements per participant was eight. The basic model, including 10 features (sex, age, education levels, physical activity, alcohol intake, current smoking, total energy intake, dietary diversity score, and log-transformed C-reactive protein levels, and quartiles of unweighted genetic risk score at baseline), yielded a Harrell’s C-index of 0.610. The highest C-index in repeated measurements was for WC (0.668) across the general population, weight-adjusted SMI in men, and WHR in women. However, except for ABSI and BAI, the diabetes predictive power of the other indicators was comparable. Additionally, repeated measurements of WC, BMI, and WHR in women were found to contribute to improved discrimination compared to baseline measurements, but not in men. Conclusion Utilizing repeated measurements of general and central adiposity to predict diabetes may be helpful in predicting hidden risks, especially in women.

Background: Cumulative evidence consistently shows that anthropometric and body composition measurements are strongly linked to the risk of incident diabetes, typically based on baseline measurements. This study aims to assess whether repeated measurements enhance the prediction of diabetes risk beyond baseline assessments alone. Methods: We utilized data from a 16-year population-based follow-up cohort within the Korean Genome and Epidemiology Study, comprising 6,030 individuals aged 40 to 69 years at baseline. We included eight indices: a body shape index (ABSI), body adiposity index (BAI), waist circumference (WC), body mass index (BMI), waist-to-hip ratio (WHR), weight-adjusted skeletal muscle index (SMI), percent body fat, and fat-to-muscle ratio. The effect of these measurements for incident diabetes was estimated using Harrell’s C-indexes and hazard ratios with 95% confidence intervals, employing time-dependent Cox proportional hazard models. Results: Over the 16-year follow-up, 939 new diabetes cases were identified (cumulative incidence, 15.6%). The median number of indicator measurements per participant was eight. The basic model, including 10 features (sex, age, education levels, physical activity, alcohol intake, current smoking, total energy intake, dietary diversity score, and log-transformed C-reactive protein levels, and quartiles of unweighted genetic risk score at baseline), yielded a Harrell’s C-index of 0.610. The highest C-index in repeated measurements was for WC (0.668) across the general population, weight-adjusted SMI in men, and WHR in women. However, except for ABSI and BAI, the diabetes predictive power of the other indicators was comparable. Additionally, repeated measurements of WC, BMI, and WHR in women were found to contribute to improved discrimination compared to baseline measurements, but not in men. Conclusion Utilizing repeated measurements of general and central adiposity to predict diabetes may be helpful in predicting hidden risks, especially in women.

Background: Cumulative evidence consistently shows that anthropometric and body composition measurements are strongly linked to the risk of incident diabetes, typically based on baseline measurements. This study aims to assess whether repeated measurements enhance the prediction of diabetes risk beyond baseline assessments alone. Methods: We utilized data from a 16-year population-based follow-up cohort within the Korean Genome and Epidemiology Study, comprising 6,030 individuals aged 40 to 69 years at baseline. We included eight indices: a body shape index (ABSI), body adiposity index (BAI), waist circumference (WC), body mass index (BMI), waist-to-hip ratio (WHR), weight-adjusted skeletal muscle index (SMI), percent body fat, and fat-to-muscle ratio. The effect of these measurements for incident diabetes was estimated using Harrell’s C-indexes and hazard ratios with 95% confidence intervals, employing time-dependent Cox proportional hazard models. Results: Over the 16-year follow-up, 939 new diabetes cases were identified (cumulative incidence, 15.6%). The median number of indicator measurements per participant was eight. The basic model, including 10 features (sex, age, education levels, physical activity, alcohol intake, current smoking, total energy intake, dietary diversity score, and log-transformed C-reactive protein levels, and quartiles of unweighted genetic risk score at baseline), yielded a Harrell’s C-index of 0.610. The highest C-index in repeated measurements was for WC (0.668) across the general population, weight-adjusted SMI in men, and WHR in women. However, except for ABSI and BAI, the diabetes predictive power of the other indicators was comparable. Additionally, repeated measurements of WC, BMI, and WHR in women were found to contribute to improved discrimination compared to baseline measurements, but not in men. Conclusion Utilizing repeated measurements of general and central adiposity to predict diabetes may be helpful in predicting hidden risks, especially in women.

Background: Cumulative evidence consistently shows that anthropometric and body composition measurements are strongly linked to the risk of incident diabetes, typically based on baseline measurements. This study aims to assess whether repeated measurements enhance the prediction of diabetes risk beyond baseline assessments alone. Methods: We utilized data from a 16-year population-based follow-up cohort within the Korean Genome and Epidemiology Study, comprising 6,030 individuals aged 40 to 69 years at baseline. We included eight indices: a body shape index (ABSI), body adiposity index (BAI), waist circumference (WC), body mass index (BMI), waist-to-hip ratio (WHR), weight-adjusted skeletal muscle index (SMI), percent body fat, and fat-to-muscle ratio. The effect of these measurements for incident diabetes was estimated using Harrell’s C-indexes and hazard ratios with 95% confidence intervals, employing time-dependent Cox proportional hazard models. Results: Over the 16-year follow-up, 939 new diabetes cases were identified (cumulative incidence, 15.6%). The median number of indicator measurements per participant was eight. The basic model, including 10 features (sex, age, education levels, physical activity, alcohol intake, current smoking, total energy intake, dietary diversity score, and log-transformed C-reactive protein levels, and quartiles of unweighted genetic risk score at baseline), yielded a Harrell’s C-index of 0.610. The highest C-index in repeated measurements was for WC (0.668) across the general population, weight-adjusted SMI in men, and WHR in women. However, except for ABSI and BAI, the diabetes predictive power of the other indicators was comparable. Additionally, repeated measurements of WC, BMI, and WHR in women were found to contribute to improved discrimination compared to baseline measurements, but not in men. Conclusion Utilizing repeated measurements of general and central adiposity to predict diabetes may be helpful in predicting hidden risks, especially in women.