An empirical study on social network analysis for small residential communities in Gangwon State, South Korea.

Social Network Analysis (SNA) provides a dynamic framework for examining interactions and connections within networks, elucidating how these relationships impact behaviors and outcomes. This study targeted small residential communities in Gangwon State, South Korea, to explore network formation theories and derive strategies for enhancing health promotion services in rural communities. Conducted in 12 small residential areas, the survey led to a network categorization model distinguishing networks as formal, informal, or non-existent. Key findings demonstrated that demographic and socio-economic factors, specifically age, income, living environment, leisure activities, and education level, significantly influence network formation. Importantly, age, environmental conditions, satisfaction with public transportation, and walking frequency were closely associated with the evolution of formal networks. These results highlight the importance of early community network assessments, which must consider distinct network traits to develop effective health promotion models. Utilizing SNA early in the assessment process can improve understanding of network dynamics and optimize the effectiveness of health interventions.

Machine Learning Model for Mild Cognitive Impairment Stage Based on Gait and MRI Images.

In patients with mild cognitive impairment (MCI), a lower level of cognitive function is associated with a higher likelihood of progression to dementia. In addition, gait disturbances and structural changes on brain MRI scans reflect cognitive levels. Therefore, we aimed to classify MCI based on cognitive level using gait parameters and brain MRI data. Eighty patients diagnosed with MCI from three dementia centres in Gangwon-do, Korea, were recruited for this study. We defined MCI as a Clinical Dementia Rating global score of ≥0.5, with a memory domain score of ≥0.5. Patients were classified as early-stage or late-stage MCI based on their mini-mental status examination (MMSE) z-scores. We trained a machine learning model using gait and MRI data parameters. The convolutional neural network (CNN) resulted in the best classifier performance in separating late-stage MCI from early-stage MCI; its performance was maximised when feature patterns that included multimodal features (GAIT + white matter dataset) were used. The single support time was the strongest predictor. Machine learning that incorporated gait and white matter parameters achieved the highest accuracy in distinguishing between late-stage MCI and early-stage MCI.

Reference Standards for C-Peptide in Korean Population: A Korean Endocrine Hormone Reference Standard Data Center Study.

BACKGRUOUND: The Korean Endocrine Hormone Reference Standard Data Center (KEHRS DC) has created reference standards (RSs) for endocrine hormones since 2020. This study is the first of its kind, wherein the KEHRS DC established RSs for serum Cpeptide levels in a healthy Korean population. METHODS: Healthy Korean adults were recruited from May 2021 to September 2023. After excluding participants according to our criteria, serum samples were collected; each participant could then choose between fasting glucose only or fasting glucose plus an oral glucose tolerance test (OGTT). If their sample showed high glucose (≥100 mg/dL) or hemoglobin A1c (HbA1c) (≥5.70%), their C-peptide levels were excluded from analyzing the RSs. RESULTS: A total of 1,532 participants were recruited; however, only the data of 1,050 participants were analyzed after excluding those whose samples showed hyperglycemia or high HbA1c. Post-30-minute OGTT data from 342 subjects and post-120-minute OGTT data from 351 subjects were used. The means±2 standard deviations and expanded uncertainties of fasting, post-30-minute and 120-minute OGTT C-peptide levels were 1.26±0.82 and 0.34-3.18, 4.74±3.57 and 1.14-8.33, and 4.85±3.58 and 1.25-8.34 ng/mL, respectively. Serum C-peptide levels correlated with obesity, serum glucose levels, and HbA1c levels. CONCLUSION: The RSs for serum C-peptide levels established in this study are expected to be useful in both clinical and related fields.

Inverted U-Shaped Relationship between Obesity Parameters and Bone Mineral Density in Korean Adolescents.

As the association between obesity and bone health remains controversial in children and adolescents, we investigate the effects of obesity parameters on bone mineral density (BMD) in 2060 Korean adolescents who participated in the 2008-2011 Korea National Health and Nutrition Examination Survey (KNHANES). Multiple regression analysis and analysis of covariance (ANCOVA) were conducted to examine both the linear and non-linear associations between total-body-less-head (TBLH) BMD and four obesity parameters: body mass index, waist circumference, waist-to-height ratio, and total-body fat mass (FM). In a multiple linear regression analysis adjusted for age, menarcheal status (in females only), and total-body lean mass, there was no significant linear association between obesity parameters and TBLH BMD, except for total-body FM in males. However, upon adding a second-order polynomial term for each obesity parameter, a significant quadratic relationship between all obesity parameters and TBLH BMD was observed, with the corresponding quadratic term being negative. The results of ANCOVA also revealed an inverted U-shaped relationship between each obesity parameter and TBLH BMD. Our findings suggest the existence of an optimal range of obesity parameters for developing or maintaining optimal bone health in Korean Adolescents. Deviation from this range, in either direction (being underweight or having obesity), may compromise bone health.

Relationship of Neural Correlates of Gait Characteristics and Cognitive Dysfunction in Patients with Mild Cognitive Impairment.

Background: Some patients with mild cognitive impairment (MCI) experience gait disturbances. However, there are few reports on the relationship between gait disturbance and cognitive function in patients with MCI. Therefore, we investigated the neural correlates of gait characteristics related to cognitive dysfunction. Methods: Eighty patients diagnosed with MCI from three dementia centers in Gangwon-do, Korea, were recruited for this study. We defined MCI as a Clinical Dementia Rating global score of 0.5 or higher, with a memory domain score of 0.5 or greater. The patients were classified as having either higher or lower MMSE and the groups were based on their Mini Mental Status Examination z-scores. Multiple logistic regression analysis was performed to examine the association between the gait characteristics and cognitive impairment. Analyses included variables such as age, sex, years of education, number of comorbidities, body mass index, and height. Results: Gait velocity, step count, step length, heel-to-heel base support, swing and stance phase duration, and support time were associated with cognitive function. A decrease in gray matter volume in the right pericalcarine area was associated with gait characteristics related to cognitive dysfunction. An increase in the curvature of gray matter in the right entorhinal, right lateral orbitofrontal, right cuneus, and right and left pars opercularis areas was also associated with gait characteristics related to cognitive dysfunction. Conclusion: Since gait impairment is an important factor in determining activities of daily living in patients with mild cognitive impairment, the evaluation of gait and cognitive functions in patients with mild cognitive impairment is important.

Metabolic Subtyping of Adrenal Tumors: Prospective Multi-Center Cohort Study in Korea.

BACKGROUND: Conventional diagnostic approaches for adrenal tumors require multi-step processes, including imaging studies and dynamic hormone tests. Therefore, this study aimed to discriminate adrenal tumors from a single blood sample based on the combination of liquid chromatography-mass spectrometry (LC-MS) and machine learning algorithms in serum profiling of adrenal steroids. METHODS: The LC-MS-based steroid profiling was applied to serum samples obtained from patients with nonfunctioning adenoma (NFA, n=73), Cushing's syndrome (CS, n=30), and primary aldosteronism (PA, n=40) in a prospective multicenter study of adrenal disease. The decision tree (DT), random forest (RF), and extreme gradient boost (XGBoost) were performed to categorize the subtypes of adrenal tumors. RESULTS: The CS group showed higher serum levels of 11-deoxycortisol than the NFA group, and increased levels of tetrahydrocortisone (THE), 20α-dihydrocortisol, and 6ß-hydroxycortisol were found in the PA group. However, the CS group showed lower levels of dehydroepiandrosterone (DHEA) and its sulfate derivative (DHEA-S) than both the NFA and PA groups. Patients with PA expressed higher serum 18-hydroxycortisol and DHEA but lower THE than NFA patients. The balanced accuracies of DT, RF, and XGBoost for classifying each type were 78%, 96%, and 97%, respectively. In receiver operating characteristics (ROC) analysis for CS, XGBoost, and RF showed a significantly greater diagnostic power than the DT. However, in ROC analysis for PA, only RF exhibited better diagnostic performance than DT. CONCLUSION: The combination of LC-MS-based steroid profiling with machine learning algorithms could be a promising one-step diagnostic approach for the classification of adrenal tumor subtypes.

Diagnosis for Pheochromocytoma and Paraganglioma: A Joint Position Statement of the Korean Pheochromocytoma and Paraganglioma Task Force.

Pheochromocytoma and paraganglioma (PPGLs) are rare catecholamine-secreting neuroendocrine tumors but can be life-threatening. Although most PPGLs are benign, approximately 10% have metastatic potential. Approximately 40% cases are reported as harboring germline mutations. Therefore, timely and accurate diagnosis of PPGLs is crucial. For more than 130 years, clinical, molecular, biochemical, radiological, and pathological investigations have been rapidly advanced in the field of PPGLs. However, performing diagnostic studies to localize lesions and detect metastatic potential can be still challenging and complicated. Furthermore, great progress on genetics has shifted the paradigm of genetic testing of PPGLs. The Korean PPGL task force team consisting of the Korean Endocrine Society, the Korean Surgical Society, the Korean Society of Nuclear Medicine, the Korean Society of Pathologists, and the Korean Society of Laboratory Medicine has developed this position statement focusing on the comprehensive and updated diagnosis for PPGLs.

Associations between metabolic syndrome and type of dementia: analysis based on the National Health Insurance Service database of Gangwon province in South Korea.

BACKGROUND: Metabolic syndrome is a cluster of conditions that occur together, increasing the risk of cardiovascular disease. However, the relationship between metabolic syndrome and dementia has remained controversial. Using nationwide population cohort data, we investigated the association between metabolic syndrome and dementia, according to the dementia type. METHODS: We analyzed data of 84,144 individuals, in the aged group of more than 60 years, between January 1, 2009, to December 31, 2009, at Gangwon province by using the information of the (Korean) National Health Insurance Service. After eight years of gap, in 2017, we investigated the relationship between metabolic syndrome and dementia. We classified Dementia either as dementia of the Alzheimer type (AD) or vascular dementia (VD). AD and VD were defined as per the criteria of International Classification of Disease, Tenth Revision, Clinical Modification codes. Multiple logistic regression analyses examined the associations between metabolic syndrome or five metabolic syndrome components and dementia. Analyses included factors like age, sex, smoking, alcohol, physical inactivity, previous stroke, and previous cardiac disease. RESULTS: Metabolic syndrome was associated with AD (OR = 11.48, 95% CI 9.03-14.59), not with VD. Each of five components of metabolic syndrome were also associated with AD. (high serum triglycerides: OR = 1.87, 95% CI 1.60-2.19; high blood pressure: OR = 1.85, 95% CI 1.55-2.21; high glucose: OR = 1.77, 95% CI 1.52-2.06; abdominal obesity: OR = 1.88, 95% CI 1.57-2.25; low serum high-density lipoprotein cholesterol: OR = 1.91, 95% CI 1.63-2.24) However, among components of metabolic syndrome, only the high glucose level was associated with VD. (OR = 1.26, 95% CI 1.01-1.56) body mass index (BMI), fasting glucose, and smoking were also associated with AD. (BMI: OR = 0.951, 95% CI 0.927-0.975; fasting glucose: OR = 1.003, 95% CI 1.001-1.005; smoking: OR = 1.020, 95% CI 1.003-1.039) A history of the previous stroke was associated with both AD and VD. (AD: OR = 1.827, 95% CI 1.263-2.644; VD: OR 2.775, 95% CI 1.747-4.406) CONCLUSIONS: Metabolic syndrome was associated with AD but not with VD. Patients with metabolic syndrome had an 11.48 times more likeliness to develop AD compared to those without metabolic syndrome. VD was associated only with several risk factors that could affect the vascular state rather than a metabolic syndrome. We suggested that the associations between metabolic syndrome and dementia would vary depending on the type of dementia.

Effect of Sarcopenia and Body Shape on Cardiovascular Disease According to Obesity Phenotypes.

Background: This study aimed to assess the effects of sarcopenia and A Body Shape Index (ABSI) on cardiovascular disease (CVD) risk according to obesity phenotypes. Methods: We used data from the National Health and Nutrition Examination Survey 1999 to 2012. A total of 25,270 adults were included and classified into the following groups: metabolically healthy normal weight (MHNW), metabolically healthy overweight/obese (MHO), metabolically unhealthy normal weight (MUNW), and metabolically unhealthy overweight/obese (MUO). Sarcopenia was defined as the appendicular skeletal mass index <7 kg/m2 in men and <5.5kg/m2 in women. A multivariate logistic regression analysis was performed to evaluate the odds ratio (OR) of sarcopenia and ABSI for CVD events according to the obesity phenotype. Results: The MHNW participants with sarcopenia had higher risk for CVD than those without sarcopenia (OR, 2.69; 95% confidence interval [CI], 1.56 to 4.64). In the analysis with MHNW participants without sarcopenia as a reference, the participants with sarcopenia showed a higher OR for CVD than those without sarcopenia in both MHO (OR in participants without sarcopenia, 3.31; 95% CI, 1.94 to 5.64) (OR in participants with sarcopenia, 8.59; 95% CI, 2.63 to 28.04) and MUO participants (OR in participants without sarcopenia, 5.11; 95% CI, 3.21 to 8.15) (OR in participants with sarcopenia, 8.12; 95% CI, 4.04 to 16.32). Participants within the second and third tertiles of ABSI had higher ORs for CVDs than the counterpart of obesity phenotypes within the first tertile. Conclusion: These results suggest that clinical approaches that consider muscle and body shape are required.

Prediction of Coronary Artery Calcium Score Using Machine Learning in a Healthy Population.

BACKGROUND: Coronary artery calcium score (CACS) is a reliable predictor for future cardiovascular disease risk. Although deep learning studies using computed tomography (CT) images to predict CACS have been reported, no study has assessed the feasibility of machine learning (ML) algorithms to predict the CACS using clinical variables in a healthy general population. Therefore, we aimed to assess whether ML algorithms other than binary logistic regression (BLR) could predict high CACS in a healthy population with general health examination data. METHODS: This retrospective observational study included participants who had regular health screening including coronary CT angiography. High CACS was defined by the Agatston score ≥ 100. Univariable and multivariable BLR was performed to assess predictors for high CACS in the entire dataset. When performing ML prediction for high CACS, the dataset was randomly divided into a training and test dataset with a 7:3 ratio. BLR, catboost, and xgboost algorithms with 5-fold cross-validation and grid search technique were used to find the best performing classifier. Performance comparison of each ML algorithm was evaluated with the area under the receiver operating characteristic (AUROC) curve. RESULTS: A total of 2133 participants were included in the final analysis. Mean age and proportion of male sex were 55.4 ± 11.3 years and 1483 (69.5%), respectively. In multivariable BLR analysis, age (odds ratio [OR], 1.12; 95% confidence interval [CI], 1.10-1.15, p < 0.001), male sex (OR, 2.91; 95% CI, 1.57-5.38, p < 0.001), systolic blood pressure (OR, 1.02; 95% CI, 1.00-1.03, p = 0.019), and low-density lipoprotein cholesterol (OR, 1.00; 95% CI, 0.99-1.00, p = 0.047) were significant predictors for high CACS. Performance in predicting high CACS of xgboost was AUROC of 0.823, followed by catboost (0.750) and BLR (0.585). The comparison of AUROC between xgboost and BLR was significant (p for AUROC comparison < 0.001). CONCLUSIONS: Xgboost ML algorithm was found to be a more reliable predictor of CACS in healthy participants compared to the BLR algorithm. ML algorithms may be useful for predicting CACS with only laboratory data in healthy participants.

Validation of the Friedewald formula for estimating low density lipoprotein cholesterol: the Korea National Health and Nutrition Examination Survey, 2009 to 2011.

BACKGROUND/AIMS: The aim of this study is to compare Friedewald-estimated and directly measured low density lipoprotein cholesterol (LDL-C) values and assess the concordance in guideline risk classification between the two methods. METHODS: The data were derived from the 2009 to 2011 Korea National Health and Nutrition Examination Survey. We included subjects with triglyceride (TG) levels < 400 mg/dL. Analysis was done for 6,454 subjects who had all lipid panels- total cholesterol, directly measured LDL-C, high density lipoprotein cholesterol (HDL-C), and TG. RESULTS: The subjects ranged in age from 10 to 87 years old. The mean age was 41.5 ± 17.3 years. For subjects with TG < 400 mg/dL, overall concordance in guideline risk classification was 79.1%. The Friedewald formula tended to underestimate LDL-C more at higher TG or lower HDL-C levels. Especially, the percent of subjects who were misclassified into a lower risk category was 31% when TG were 200 to 299 mg/dL; and 45.6% when TG were 300 to 399 mg/dL. A greater underestimation of LDL-C occurred at higher TG and lower Friedewald-estimated LDL-C levels. Of subjects with a Friedewald-estimated LDL-C < 70 mg/dL, 55.4% had a directly measured LDL-C ≥ 70 mg/dL when TG were 200 to 399 mg/dL. CONCLUSION: The Friedewald equation tends to underestimate LDL-C in highrisk subjects such as hypertriglyceridemia and hypo-HDL-cholesterolemia. For these individuals accurate assessment of LDL-C is crucial, and therefore additional evaluation is warranted.

The haemoglobin glycation index is associated with nonalcoholic fatty liver disease in healthy subjects.

OBJECTIVES: The hemoglobin glycation index (HGI) quantifies interindividual variations in glycated hemoglobin (HbA1c) and is associated with diabetic complications and metabolic diseases. However, information on the association between HGI and non-alcoholic fatty liver disease (NAFLD) in healthy subjects is limited, particularly in Asian populations. This study aimed to investigate the association between HGI and NAFLD in a healthy Korean cohort. DESIGN: Subjects were stratified in quartiles according to their HGI level. NAFLD was diagnosed by hepatic ultrasonography, hepatic steatosis index and fatty liver index. Multiple logistic regression analysis was performed to evaluate the association between HGI quartiles and the risk of NAFLD. PATIENTS: Data from subjects without diabetes who underwent liver ultrasonography during routine health examinations were retrospectively reviewed. RESULTS: Data from 14 465 subjects were included in the analysis. The prevalence of NAFLD increased significantly with each HGI quartile (24.8%, 29.7%, 32.6% and 40.6% in quartiles 1-4, respectively; P < 0.001). In comparison with the lowest HGI quartile group, the highest quartile exhibited worse metabolic parameters, including body weight, waist circumference, body mass index and lipid profiles. Multiple logistic regression analysis adjusted for multiple factors showed that the odds ratio of having NAFLD was 1.564 (95% CI: 1.350-1.813, P < 0.001) in the highest HGI quartile. CONCLUSIONS: Elevated HGI levels are independently associated with NAFLD in a healthy Asian population.

Effectiveness of Z-score of log-transformed A Body Shape Index (LBSIZ) in predicting cardiovascular disease in Korea: the Korean Genome and Epidemiology Study.

Body mass index (BMI) and waist circumference (WC) have limitations in stratifying cardio-metabolic risks. Another obesity measure, A Body Shape Index (ABSI), has been introduced but its applicability remains limited. To address this, the z-score of the log-transformed ABSI (LBSIZ) was recently developed. This study aimed to examine the ability of LBSIZ, compared to that of WC and BMI, to predict cardiovascular disease (CVD) risk. The study included 8,485 participants aged 40-69 years (mean age = 52.1) who were followed for 10 years and recruited from the Korean Genome and Epidemiology Study, a population-based cohort study. The area under the curve was 0.635 (95% confidence interval [CI]: 0.614-0.657) for LBSIZ, 0.604 (95%CI: 0.580-0.627) for WC, and 0.538 (95%CI: 0.514-0.562) for BMI. The AUC of the Framingham risk score (FRS) was 0.680 (95%CI: 0.659-0.701) in comparison. When we added LBSIZ to the model, the integrated AUC significantly improved from 0.680 to 0.692 (95%CI: 0.672-0.713; p value, 0.033), whereas there were no changes with BMI (AUC, 0.678; 95%CI: 0.656-0.699) or WC (AUC, 0.679; 95%CI: 0.658-0.701). In the multivariate Cox regression analysis, LBSIZ but not BMI or WC showed a significant hazard ratio of CVD event compared to 1st decile of each parameter. In the restricted cubic spline regression, BMI and WC showed an overall J-shaped relationship with CVD events whereas LBSIZ showed a linear relationship. LBSIZ is strongly associated with CVD risk and should predict CVD risk better than BMI and WC in the general population.

Association of Z-Score of the Log-Transformed A Body Shape Index with Cardiovascular Disease in People Who Are Obese but Metabolically Healthy: The Korea National Health and Nutrition Examination Survey 2007-2010.

BACKGROUND: We aimed at evaluating the effect of the z-score of the log-transformed A Body Shape Index (LBSIZ) on cardiovascular disease (CVD) outcomes according to obesity phenotype. METHODS: Data were collected from the Korea National Health and Nutrition Examination Survey conducted from 2007 to 2010. Obesity was defined as a body mass index above 25 kg/m2 and metabolic abnormality was defined as the presence of two or more metabolic risk factors of the Adult Treatment Panel III definition. The participants were classified by obesity and metabolic healthy status: metabolically healthy non-obese (MHNO), metabolically healthy obese (MHO), metabolically unhealthy non-obese (MUNO), and metabolically unhealthy obese (MUO). Each group was further classified into three groups based on the tertile of LBSIZ. A multivariate logistic regression analysis with adjustment for age, sex, smoking status, income, education level, physical activities, alcohol, and energy intake was conducted to evaluate the odds ratio (OR) for CVD events. RESULTS: In the multivariate logistic regression model, MHO participants who are within the third tertile of LBSIZ had a significantly higher OR for CVD events, whereas those who are within the first and second tertile of LBSIZ were not at high risk of developing CVDs compared to MHNO participants who are within the first tertile of LBSIZ. In addition, a similar increase in the OR was observed in MUNO or MUO participants. CONCLUSION: LBSIZ had the lowest risk for CVDs in the first tertile of LBSIZ and a linear relationship with all its tertiles in MHO, MUNO, and MUO participants.

Characteristics of Korean Patients with Primary Adrenal Insufficiency: A Registry-Based Nationwide Survey in Korea.

BACKGROUND: Primary adrenal insufficiency (PAI) is a rare, potentially life-threatening condition. There are few Korean studies on PAI, and most have had small sample sizes. We aimed to examine the etiology, clinical characteristics, treatment, and mortality of PAI in Korean patients. METHODS: A nationwide, multicenter, registry-based survey was conducted to identify adults diagnosed with or treated for PAI at 30 secondary or tertiary care institutions in Korea between 2000 and 2014. RESULTS: A total of 269 patients with PAI were identified. The prevalence of PAI was 4.17 per million. The estimated incidence was 0.45 per million per year. The mean age at diagnosis was 49.0 years, and PAI was more prevalent in men. Adrenal tuberculosis was the most common cause of PAI in patients diagnosed before 2000; for those diagnosed thereafter, adrenal metastasis and tuberculosis were comparable leading causes. The etiology of PAI was not identified in 34.9% of cases. Of the patients receiving glucocorticoid replacement therapy, prednisolone was more frequently administered than hydrocortisone (69.4% vs. 26.5%, respectively), and only 27.1% of all patients received fludrocortisone. We observed an increased prevalence of metabolic disease and osteoporosis during the follow-up period (median, 60.2 months). The observed overall mortality and disease-specific mortality rates were 11.9% and 3.1%, respectively. CONCLUSION: The prevalence of PAI is significantly lower in Koreans than in reports from Western countries. The high frequency undetermined etiology in patients with PAI suggests the need to reveal accurate etiology of PAI in Korea.

Relationships between cognitive function and body composition among community-dwelling older adults: a cross-sectional study.

BACKGROUND: Previous studies reported mixed results regarding the association between cognition and body weight in late life. We evaluated the relationships between cognitive function and body composition among community-dwelling older adults. METHODS: Three hundred twenty subjects (≥65 years, women 53%) with available data of cognitive function and body composition from 2010 Hallym Aging Study. Cognitive function was assessed using Korean Mini-Mental State Examination (K-MMSE). Dual-energy X-ray absorptiometry (DEXA) was used for measuring body composition including body fat and lean body mass. Anthropometric measurements and laboratory data were collected in clinical examination. Body composition variables were divided into sex-specific tertiles, and examined by multivariable logistic regression. RESULTS: Among female, the highest tertile group of fat mass and second tertile group of total lean body mass were associated with lower risk for cognitive impairment compared to the respective first tertile groups (odds ratios, 0.23 and 0.09, respectively; 95% confidence intervals, 0.04-0.88 and 0.01-0.44, respectively) after adjusting for confounding factors. In male, higher arm bone mineral content was associated with lower risk for cognitive impairment, but significance was lost after adjusting for adiponectin, age, and education. CONCLUSIONS: Higher fat mass and lean body mass were associated with lower risk of cognitive impairment in older women. These observations suggest that body fat and lean mass later in life might be beneficial for cognition.

Clinical Guidelines for the Management of Adrenal Incidentaloma.

An adrenal incidentaloma is an adrenal mass found in an imaging study performed for other reasons unrelated to adrenal disease and often accompanied by obesity, diabetes, or hypertension. The prevalence and incidence of adrenal incidentaloma increase with age and are also expected to rise due to the rapid development of imaging technology and frequent imaging studies. The Korean Endocrine Society is promoting an appropriate practice guideline to meet the rising incidence of adrenal incidentaloma, in cooperation with the Korean Adrenal Gland and Endocrine Hypertension Study Group. In this paper, we discuss important core issues in managing the patients with adrenal incidentaloma. After evaluating core proposition, we propose the most critical 20 recommendations from the initially organized 47 recommendations by Delphi technique.

Management Status of Cardiovascular Disease Risk Factors for Dyslipidemia among Korean Adults.

PURPOSE: Dyslipidemia, hypertension, and diabetes are well-established risk factors for cardiovascular disease (CVD). This study investigated the prevalence and management status of these factors for dyslipidemia among Korean adults aged 30 years old and older. MATERIALS AND METHODS: The prevalence and management status of dyslipidemia, hypertension, and diabetes were analyzed among 12229 subjects (≥30 years) participating in the Korea National Health and Nutrition Survey 2010-2012. Dyslipidemia was defined according to treatment criteria rather than diagnostic criteria in Korea. Therefore, hyper-low-density lipoprotein (LDL) cholesterolemia was defined if LDL cholesterol levels exceeded the appropriate risk-based threshold established by the National Cholesterol Education Program Adult Treatment Panel III. RESULTS: The age-standardized prevalence was highest for dyslipidemia (39.6%), followed by hypertension (32.8%) and diabetes (9.8%). The lowest patient awareness was found for dyslipidemia (27.9%). The treatment rate was 66.5% for diabetes and 57.3% for hypertension, but only 15.7% for dyslipidemia. The control rate among those undergoing treatment was highest for hypertension (64.2%), followed by dyslipidemia (59.2%) and diabetes (22.1%). The higher the risk levels of CVD were, the lower the control rate of dyslipidemia. CONCLUSION: While the prevalence of dyslipidemia was higher than hypertension and diabetes, awareness and treatment rates thereof were lower. Higher CVD-risk categories showed lower control rates of dyslipidemia. In order to improve awareness and control rates of dyslipidemia, diagnostic criteria should be reconciled with treatment targets based on cardiovascular risk in Korean populations.