Chronic kidney disease (CKD) is a major public health problem and a leading cause of cardiovascular disease and death. Early recognition and management of CKD risk factors are necessary to prevent its onset and progression. Neck circumference (NC) is a non-invasive and easily accessible anthropometric measure associated with central obesity and subcutaneous fat accumulation in the upper body. Our study aimed to explore the relationship between NC and the prevalence of CKD using data from the nationally representative Korea National Health and Nutrition Examination Survey (2019–2021). We analyzed data from 10,219 subjects (age > 19 years, no missing values). CKD was defined as an estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m². Logistic regression analysis was performed, which revealed a significant association between NC and CKD prevalence even after adjusting for confounding factors, both when NC was considered a continuous variable (OR [95% CI], 1.11 [1.03–1.19]) and in quartiles (Q1 as reference; Q2 OR [95% CI], 1.23 [0.91–1.67]; Q3 OR [95% CI], 1.59 [1.16–2.18]; Q4 OR [95% CI], 1.70 [1.16–2.50]). Our findings suggest that NC could be a simple and effective anthropometric measurement for identifying individuals at risk for CKD.

Background/Objectives: The association between chronic kidney disease (CKD) and body size phenotypes in metabolically diverse but apparently healthy adult populations remains inadequately understood. This study investigated the association between CKD and body size phenotypes in a nationally representative sample of healthy Korean adults. Methods: Data from 8227 participants in the 2019–2021 Korean National Health and Nutrition Examination Survey were analyzed. Participants were categorized into four body size phenotypes by combining BMI status (normal weight or obese) with metabolic health status (healthy or abnormal)—MHNW (Metabolically Healthy Normal Weight), MANW (Metabolically Abnormal Normal Weight), MHO (Metabolically Healthy Obese), or MAO (Metabolically Abnormal Obese). CKD was defined based on the urine albumin-to-creatinine ratio and estimated glomerular filtration rate (eGFR). To assess the association between CKD and body size phenotypes, multivariable logistic regression analyses were performed. Results: CKD prevalence was 4.4%. MANW and MAO made up 12.6% and 26.4% of the CKD group, compared to 5.0% and 13.2% of the non-CKD group. CKD prevalence by phenotype was observed as follows: MHNW, 3.2%; MANW, 10.5%; MHO, 4.0%; and MAO, 8.5%. CKD odds were highest in the MAO group (OR: 3.770, 95% CI: 2.648–5.367), followed by the MANW (OR: 2.492, 95% CI: 1.547–4.016) and MHO (OR: 1.974, 95% CI: 1.358–2.870) groups. MAO individuals carried a higher CKD risk than MHO individuals (OR: 1.897, 95% CI: 1.221–2.945). Conclusions: Among apparently healthy adults, body size phenotypes—particularly those with metabolic abnormalities—were significantly associated with the presence of CKD. These findings highlight the need to assess both metabolic health and body composition for effective CKD prevention and management.

We aimed to examine the correlation between periodontitis and body size phenotypes in 7301 participants without diagnosed chronic diseases in the Korean National Health and Nutrition Examination Survey 2013–2015. The participants were categorized into the following body size phenotype groups based on body mass index and the presence of metabolic syndrome: metabolically healthy normal weight (MHNW), metabolically abnormal normal weight (MANW), metabolically healthy obese (MHO), and metabolically abnormal obese (MAO). The prevalence rates of mild and severe periodontitis were 18.1% and 7.5%, respectively. Patients with periodontitis were older, current smokers, had a lower family income, were less likely to engage in regular tooth brushing or exercise, and had a higher body mass index and glucose levels. Periodontitis was more prevalent in the MANW and MAO groups than in the MHNW and MHO groups. Compared with the MHNW phenotype, the MAO and MANW phenotypes were significantly associated with mild and severe periodontitis, and the MHO phenotype was significantly associated with mild periodontitis. The MANW and MAO phenotypes are independent risk factors for periodontitis in adults without diagnosed chronic diseases. To enhance public health, a greater focus and effective approaches for identifying metabolic disease phenotypes among individuals with periodontal disease may be clinically relevant.

Diabetes mellitus (DM) is a major global health concern, associated with both microvascular and macrovascular complications. Early identification of individuals at risk of hyperglycemia and diabetes progression is crucial for preventing long-term complications and improving patient outcomes. We investigated the association between neck circumference (NC) and hyperglycemia in non-diabetic individuals and in patients with uncontrolled DM, using data from the nationally representative Korea National Health and Nutrition Examination Survey (KNHANES) 2019–2021. Uncontrolled DM was defined as hemoglobin A1c (HbA1c) ≥ 7.0%, while hyperglycemia in non-diabetic individuals was defined as fasting blood glucose ≥ 126 mg/dL or HbA1c ≥ 6.5%. Logistic regression analyses were conducted to evaluate the association between NC and glycemic outcomes. NC was independently associated with hyperglycemia in non-diabetic individuals (Model 1: odds ratio (OR): 1.09; 95% confidence interval (CI): 1.05–1.13; Model 2: OR: 1.09; 95% CI: 1.05–1.13) and patients with uncontrolled DM (Model 1: OR: 1.10; 95% CI: 1.03–1.17; Model 2: OR: 1.11; 95% CI: 1.04–1.18) after adjusting for potential confounders. This study demonstrates that NC is a significant risk factor for hyperglycemia in the general population and for individuals with uncontrolled DM. NC may serve as a simple, non-invasive anthropometric marker to help identify individuals at elevated risk for poor glycemic control.

Background and Objectives: Hepatic steatosis is associated with an increased risk of liver-related morbidity and mortality. Although numerous studies have reported associations between depression, obesity, metabolic syndrome, and cardiovascular disease, the relationship between depression and hepatic steatosis has not yet been fully elucidated. Moreover, obesity is a shared risk factor for hepatic steatosis and depression; however, few studies have adequately adjusted for obesity as a potential confounder. In this study, we investigated the association between depression and hepatic steatosis stratified by obese and non-obese status. Materials and Methods: This study used data from the Korean National Health and Nutrition Examination Survey, conducted by the Korean Ministry of Health and Welfare between 2010 and 2019, which was a cross-sectional and nationally representative study of non-institutionalized civilians using a stratified, multistage, clustered probability sampling design. Multivariate logistic regression analyses were conducted to evaluate the association between depression and hepatic steatosis in the groups stratified by obese status. Results: Of 80,861 participants, data from 45,307 were included in the analysis. The prevalence of non-obese and obese hepatic steatosis was 3.1% and 19.3%, respectively, and the prevalence of diagnosed depression was 4.6%. Individuals with hepatic steatosis showed less favorable metabolic profiles, including higher rates of diabetes and elevated liver enzyme levels. Those with depression were older, predominantly female, and had lower socioeconomic status. After fully adjusting for confounding factors, multivariate logistic regression analysis showed that non-obese hepatic steatosis was significantly associated with an increased risk of depression, and obese hepatic steatosis was significantly associated with suicidal ideation and attempts. Conclusions: This study suggests a significant association between depression and hepatic steatosis with and without obese status. Given the significant impact of hepatic steatosis on depression outcomes, healthcare providers should screen patients with hepatic steatosis for depression and provide appropriate treatment as needed.

Background/Objectives: This study analyzes the relationship between obesity phenotypes and sleep apnea risk in the Korean population. Methods: This study utilized data from the Korean National Health and Nutrition Examination Survey (KNHANES) collected between 2019 and 2021 (n = 10,970 adults; age ≥ 40 years). Obesity phenotypes were classified into four groups based on body mass index (BMI) and the presence of metabolic syndrome: metabolically healthy normal weight (MHNW), metabolically abnormal normal weight (MANW), metabolically healthy obese (MHO), and metabolically abnormal obese (MAO). Sleep apnea risk was assessed using the STOP-Bang questionnaire, and multivariate logistic regression analyses were performed to evaluate the association between obesity phenotypes and sleep apnea. Results: Among the 10,970 participants, the phenotypes were as follows: MHNW, 51.1%; MANW, 10.3%; MHO, 15.8%; and MAO, 21.8%. Baseline characteristics differed significantly across phenotypes, with the metabolically unhealthy groups (MANW and MAO) being older and exhibiting more cardiometabolic risk factors than the metabolically healthy groups. The prevalence of STOP-Bang questionnaire components differed significantly across phenotypes (all p < 0.001), and the mean STOP-Bang score increased from MHNW to MAO. In multivariate logistic regression analyses, the odds (adjusted odds ratio [95% CI]) of high sleep apnea risk were significantly elevated in all non-MHNW phenotypes: MAO (10.27 [7.71–13.68]), MHO (6.17 [4.35–8.75]), and MANW (1.91 [1.22–2.98]). Notably, MAO conferred a significantly higher risk than MHO (OR 1.69 [1.34–2.13]), highlighting the synergy of obesity and metabolic dysfunction. Obesity phenotypes, defined by BMI and metabolic health status, were differentially associated with sleep apnea risk in Korean adults. The highest risk was observed in individuals with both obesity and metabolic syndrome, while metabolically abnormal normal-weight adults also showed a significantly increased risk. Conclusions: Metabolic dysfunction may contribute to sleep apnea risk beyond body size alone and may be considered in risk stratification strategies.