Background: With the aging population and advancements in medical care worldwide, the number of older patients with end-stage kidney disease continues to rise. This study aimed to identify factors associated with osteoporosis and osteopenia in older patients undergoing incident hemodialysis and assess their impact on mortality. Methods: We analyzed a large multicenter retrospective cohort of patients aged ≥70 years undergoing incident hemodialysis to identify factors associated with osteoporosis using logistic regression analysis and to assess the association of death with osteoporosis and osteopenia using Cox multivariable analysis. Results: Among 710 patients, 39.0% and 19.6% had osteoporosis and osteopenia, respectively. Osteoporosis was significantly associated with female sex, a history of fractures, and the absence of phosphate binder use. During a median follow-up of 36.8 months, 348 participants (58.8%) died. Mortality rates were the highest in the osteoporosis group (79.8%), followed by the osteopenia (77.2%) and normal bone mineral density (BMD) groups (35.2%). Cox regression analysis revealed that even after adjusting for covariates, the osteoporosis group was significantly associated with a higher mortality risk than the normal BMD group. Osteoporosis at the start of hemodialysis was significantly associated with higher mortality. Conclusion We should consider the importance of bone health in patients undergoing incident hemodialysis and pay attention to the use of phosphate binders and fracture prevention.

Objective: Suicide is a leading cause of death among adolescents, and despite the need to distinguish between suicidal consideration and suicide attempts, research focused on suicide attempts remains insufficient. Therefore, this study aims to investigate the influence of self-perceived weight on suicide attempts. Methods: This study utilized data from the Korea Youth Risk Behavior Web-based Survey for its analysis from 2005 to 2023, including a total of 1,156,728 participants. This study utilized various analytical methods to examine the influence of self-perceived weight on suicide attempts. We estimated weighted prevalence and used linear regression to assess temporal trend β coefficients and their differences (βdiff) with 95% confidence intervals (CIs), and survey-weighted logistic regression to estimate weighted odds ratios (wORs) and 95% CIs for the association between self-perceived weight and suicide attempts. Results: A comparison of suicide attempts based on self-perceived weight suggested that individuals who perceived themselves as overweight (weighted prevalence, 3.97% [95% CI, 3.89 to 4.04]) had the highest rate of suicide attempts, followed by those who perceived themselves as underweight (3.36% [95% CI, 3.28 to 3.44]), while those who perceived themselves as having a normal weight (3.20% [95% CI, 3.14 to 3.27]) had the lowest rate. Additionally, females (underweight: 4.47% [95% CI, 4.32 to 4.62]; normal weight: 3.91% [95% CI, 3.81 to 4.01]; overweight: 5.23% [5.11 to 5.35]) experienced more suicide attempts than males (underweight: 2.73% [95% CI, 2.65 to 2.82]; normal weight: 2.43% [95% CI, 2.35 to 2.51]; overweight: 2.60% [95% CI, 2.52 to 2.69]). Conclusion Findings from the present study suggest that self-perceived weight was associated with suicide attempts and interaction analyses indicated a potential sex-based difference in the impact of body image distortion. Therefore, this study suggests the introduction of programs and campaigns aimed at correcting distorted self-perceived weight.

Background: Although excessive physical activity (PA) does not always confer additional health benefits, there is a paucity of studies that have quantitatively examined the doseresponse relationship between PA and type 2 diabetes. Therefore, this study investigated the relationship between the type 2 diabetes prevalence and intensity, frequency, and metabolic equivalent of task (MET) score of PA in a large population sample. Methods: We conducted a nationwide cross-sectional analysis examining sociodemographic variables, PA habits, and type 2 diabetes prevalence in 2,428,448 participants included in the Korea Community Health Survey. The non-linear association between MET score and odds ratios (ORs) for type 2 diabetes prevalence was plotted using a weighted generalized additive model. Categorical analysis was used to examine the joint association of moderate-intensity PA (MPA) and vigorous-intensity PA (VPA), and the influence of PA frequency. Results: MET score and diabetes prevalence revealed a non-linear association with the nadir at 1,028 MET-min/week, beyond which ORs increased with additional PA. Joint analysis of MPA and VPA showed the lowest OR of 0.79 (95% confidence interval, 0.75–0.84) for those engaging in 300–600 MET-min/week of MPA and > 600 MET-min/week of VPA concurrently, corresponding with World Health Organization recommendations. Additionally, both “weekend warriors” and “regularly active” individuals showed lower ORs compared to the inactive, although no significant difference was noted between the active groups. Conclusion In a large South Korean sample, higher PA is not always associated with a lower prevalence of type 2 diabetes, as the association follows a non-linear pattern; differences existed across sociodemographic variables. Considering the joint association, an adequate combination of MPA and VPA is recommended. The frequency of PA does not significantly influence the type 2 diabetes prevalence.

Background: Although excessive physical activity (PA) does not always confer additional health benefits, there is a paucity of studies that have quantitatively examined the doseresponse relationship between PA and type 2 diabetes. Therefore, this study investigated the relationship between the type 2 diabetes prevalence and intensity, frequency, and metabolic equivalent of task (MET) score of PA in a large population sample. Methods: We conducted a nationwide cross-sectional analysis examining sociodemographic variables, PA habits, and type 2 diabetes prevalence in 2,428,448 participants included in the Korea Community Health Survey. The non-linear association between MET score and odds ratios (ORs) for type 2 diabetes prevalence was plotted using a weighted generalized additive model. Categorical analysis was used to examine the joint association of moderate-intensity PA (MPA) and vigorous-intensity PA (VPA), and the influence of PA frequency. Results: MET score and diabetes prevalence revealed a non-linear association with the nadir at 1,028 MET-min/week, beyond which ORs increased with additional PA. Joint analysis of MPA and VPA showed the lowest OR of 0.79 (95% confidence interval, 0.75–0.84) for those engaging in 300–600 MET-min/week of MPA and > 600 MET-min/week of VPA concurrently, corresponding with World Health Organization recommendations. Additionally, both “weekend warriors” and “regularly active” individuals showed lower ORs compared to the inactive, although no significant difference was noted between the active groups. Conclusion In a large South Korean sample, higher PA is not always associated with a lower prevalence of type 2 diabetes, as the association follows a non-linear pattern; differences existed across sociodemographic variables. Considering the joint association, an adequate combination of MPA and VPA is recommended. The frequency of PA does not significantly influence the type 2 diabetes prevalence.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.

Background: Although excessive physical activity (PA) does not always confer additional health benefits, there is a paucity of studies that have quantitatively examined the doseresponse relationship between PA and type 2 diabetes. Therefore, this study investigated the relationship between the type 2 diabetes prevalence and intensity, frequency, and metabolic equivalent of task (MET) score of PA in a large population sample. Methods: We conducted a nationwide cross-sectional analysis examining sociodemographic variables, PA habits, and type 2 diabetes prevalence in 2,428,448 participants included in the Korea Community Health Survey. The non-linear association between MET score and odds ratios (ORs) for type 2 diabetes prevalence was plotted using a weighted generalized additive model. Categorical analysis was used to examine the joint association of moderate-intensity PA (MPA) and vigorous-intensity PA (VPA), and the influence of PA frequency. Results: MET score and diabetes prevalence revealed a non-linear association with the nadir at 1,028 MET-min/week, beyond which ORs increased with additional PA. Joint analysis of MPA and VPA showed the lowest OR of 0.79 (95% confidence interval, 0.75–0.84) for those engaging in 300–600 MET-min/week of MPA and > 600 MET-min/week of VPA concurrently, corresponding with World Health Organization recommendations. Additionally, both “weekend warriors” and “regularly active” individuals showed lower ORs compared to the inactive, although no significant difference was noted between the active groups. Conclusion In a large South Korean sample, higher PA is not always associated with a lower prevalence of type 2 diabetes, as the association follows a non-linear pattern; differences existed across sociodemographic variables. Considering the joint association, an adequate combination of MPA and VPA is recommended. The frequency of PA does not significantly influence the type 2 diabetes prevalence.

Background: Although excessive physical activity (PA) does not always confer additional health benefits, there is a paucity of studies that have quantitatively examined the doseresponse relationship between PA and type 2 diabetes. Therefore, this study investigated the relationship between the type 2 diabetes prevalence and intensity, frequency, and metabolic equivalent of task (MET) score of PA in a large population sample. Methods: We conducted a nationwide cross-sectional analysis examining sociodemographic variables, PA habits, and type 2 diabetes prevalence in 2,428,448 participants included in the Korea Community Health Survey. The non-linear association between MET score and odds ratios (ORs) for type 2 diabetes prevalence was plotted using a weighted generalized additive model. Categorical analysis was used to examine the joint association of moderate-intensity PA (MPA) and vigorous-intensity PA (VPA), and the influence of PA frequency. Results: MET score and diabetes prevalence revealed a non-linear association with the nadir at 1,028 MET-min/week, beyond which ORs increased with additional PA. Joint analysis of MPA and VPA showed the lowest OR of 0.79 (95% confidence interval, 0.75–0.84) for those engaging in 300–600 MET-min/week of MPA and > 600 MET-min/week of VPA concurrently, corresponding with World Health Organization recommendations. Additionally, both “weekend warriors” and “regularly active” individuals showed lower ORs compared to the inactive, although no significant difference was noted between the active groups. Conclusion In a large South Korean sample, higher PA is not always associated with a lower prevalence of type 2 diabetes, as the association follows a non-linear pattern; differences existed across sociodemographic variables. Considering the joint association, an adequate combination of MPA and VPA is recommended. The frequency of PA does not significantly influence the type 2 diabetes prevalence.

With a rapidly aging population and increasing use of antiresorptive agents, medication-related osteonecrosis of the jaw (MRONJ) represents a growing clinical challenge worldwide. To address the need for tailored clinical guidance, a multidisciplinary task force was convened. Five Korean academic societies—the Korean Society for Bone and Mineral Research, the Korean Association of Oral and Maxillofacial Surgeons, the Korean Society of Maxillofacial Plastic and Reconstructive Surgeons, the Korean Osteoporosis Society, and the Korean Endocrine Society—collaborated to develop this position statement. The consensus was formulated through comprehensive reviews of literature, combined with three rounds of formal surveys to consolidate expert opinion on controversial topics. This position paper provides evidence-based clinical guidelines for the prevention, diagnosis, and management of MRONJ tailored to the Korean healthcare environment. The diagnostic criteria affirm the standard definition but add a provision for diagnosis based on clinical or radiographic evidence of necrotic bone, even if the traditional 8-week timeframe has not been met. The committee advocates for retaining stage 0 in the staging system to emphasize early detection and preventive intervention. Key recommendations include prescriptive, drug-specific guidelines for prophylactic drug holidays (e.g., a 2-month pause for oral bisphosphonates; timing surgery 3 to 4 months after the last denosumab injection) to minimize MRONJ risk from dental procedures. This statement also provides a clear framework for therapeutic drug holidays in established MRONJ, carefully balancing the need for jaw healing against systemic fracture risk. For treatment, this statement advocates for early and active surgical intervention across all MRONJ stages, supported by evidence of superior long-term outcomes compared to conservative management. This position statement offers a unique, evidence-based Korean clinical practice guideline for managing MRONJ. It is intended to standardize care, reduce clinical confusion, and ultimately improve patient outcomes by providing a clear framework for decision-making.

Synthetic data, generated using advanced artificial intelligence (AI) techniques, replicates the statistical properties of real-world datasets while excluding identifiable information.Although synthetic data does not consist of actual data points, it is derived from original datasets, thereby enabling analyses that yield results comparable to those obtained with real data. Synthetic datasets are evaluated based on their utility—a measure of how effectively they mirror real data for analytical purposes. This paper presents the generation of synthetic datasets through the Healthcare Big Data Showcase Project (2019–2023). The original dataset comprises comprehensive multi-omics data from 400 individuals, including cancer survivors, chronic disease patients, and healthy participants. Synthetic data facilitates efficient access and robust analyses, serving as a practical tool for research and education. It addresses privacy concerns, supports AI research, and provides a foundation for innovative applications across diverse fields, such as public health and precision medicine.