Background: F-18-fluorodeoxyglucose positron emission tomography (FDG-PET)/computed tomography (CT) can be used to measure bone mineral density (BMD), cross-sectional muscle area (CSMA), Hounsfield units (HU) of liver and muscle, subcutaneous adipose tissue (SAT), abdominal visceral adipose tissue (VAT), and glucose metabolism. The present study aimed to identify age-related changes in body composition and glucose metabolism in Korean using opportunistic FDG-PET/CT imaging. Methods: We analyzed FDG-PET/CT, clinical history, and laboratory data abstracted from the medical records of patients who underwent health screening at a single institute between 2017 and 2022. Results: In total, 278 patients were included in the analysis (male:female=140:138). Age and body mass index were positively correlated in female, but negatively correlated in male. BMD decreased with age more in female, and CSMA decreased with age more in male. Muscle HU decreased with age for both sexes. In female, SAT and VAT increased with age; and in male, SAT decreased slightly while VAT remained stable. Muscle glucose metabolism showed no association with age in male but increased with age in female. CSMA correlated positively with BMD overall; and positively correlated with VAT and SAT in male only. In female only, both SAT and VAT showed negative correlations with glucose metabolism and correlated positively with muscle glucose metabolism. Liver HU values were inversely correlated with VAT, especially in female; and positively correlated with muscle glucose metabolism in female only. Conclusion FDG-PET/CT demonstrated distinct patterns of age-related changes in body composition and glucose metabolism, with significant differences between sexes.

Background: Given the association between nonalcoholic fatty liver disease and metabolic risks, a new term, metabolic dysfunction- associated steatotic liver disease (MASLD) has been proposed. We aimed to explore the association between MASLD and all-cause, cause-specific mortalities. Methods: We included individuals with steatotic liver disease (SLD) from the Korean National Health Insurance Service. Moreover, SLD was defined as a fatty liver index ≥30. Furthermore, MASLD, metabolic alcohol-associated liver disease (MetALD), and alcoholic liver disease (ALD) with metabolic dysfunction (MD) were categorized based on alcohol consumption and MD. We also analyzed all-cause, liver-, cancer-, hepatocellular carcinoma (HCC)- and cardiovascular (CV)-related mortalities. Results: This retrospective nationwide cohort study included 1,298,993 individuals aged 40 to 79 years for a mean follow-up duration of 9.04 years. The prevalence of MASLD, MetALD, and ALD with MD was 33.11%, 3.93%, and 1.00%, respectively. Relative to the “no SLD” group, multivariable analysis identified that MASLD (adjusted hazard ratio [aHR], 1.28; 95% confidence interval [CI], 1.26 to 1.31), MetALD (aHR, 1.38; 95% CI, 1.32 to 1.44), and ALD with MD group (aHR, 1.80; 95% CI, 1.68 to 1.93) have a significantly higher risk of all-cause mortality. Furthermore, MASLD, MetALD, ALD with MD groups showed higher liver-, cancer- and HCC-related mortality than “no SLD” group. While all-cause specific mortalities increase from MASLD to MetALD to ALD with MD, the MetALD group shows a lower risk of CV-related mortality compared to MASLD. However, ALD with MD group still have a higher risk of CV-related mortality compared to MASLD. Conclusion SLD is associated with an increased risk of all-cause, liver-, cancer-, HCC-, and CV-related mortalities.

Background: Diabetes in older adults is becoming a significant public burden to South Korea. However, a comprehensive understanding of epidemiologic trends and the detailed clinical characteristics of older adults with diabetes is lacking. Therefore, we evaluated epidemiologic trends and the metabolic and lifestyle characteristics of diabetes in Korean older adults. Methods: We analyzed data from the Korea National Health and Nutrition Examination Survey to assess diabetes prevalence according to diabetes duration and lifestyle behaviors. In addition, we drew upon the National Health Information Database of the National Health Insurance System to assess physical activity levels, antidiabetic medication use, polypharmacy, medication adherence, and major comorbidities. Results: The absolute number of newly diagnosed cases of diabetes among older adults doubled over the past decade. Management rates of metabolic indicators were higher in older adults with diabetes compared to those without diabetes. The proportion of older adults with diabetes meeting the minimum recommended physical activity increased over the years. Compared to 10 years before, the use of dipeptidyl peptidase-4 inhibitor or sodium-glucose cotransporter-2 inhibitor had increased, as had comorbidities such as dyslipidemia, dementia, cancer, heart failure, atrial fibrillation, and chronic kidney disease. Initial medication adherence was significantly lower in those with end-stage kidney disease or dementia, insulin use, high-risk alcohol use, and living alone. Continuing insulin use 1 year after diagnosis of diabetes was significantly higher in those who initiated insulin therapy at diagnosis, had retinopathy, were on triple antidiabetic medications, and had a history of cancer. Conclusion Comprehensive management of metabolic indicators and physical activity is essential for older adults with diabetes. Improvements in prescribing guidelines, personalized management of age-related comorbidities, and individualized approaches that consider the heterogeneous nature of older adults with diabetes are desirable. Further research, such as high-quality cohort and intervention studies specific to older adults, is needed to establish evidence-based management for older adults with diabetes.

Background: Insulin resistance (IR) is central to metabolic disorders and significantly influenced by diet. Studies on meal frequency (MF) and metabolic indicators have shown mixed results. This study explores the link between MF and IR in middle-aged and older adults. Methods: This prospective cohort study included 4,570 adults aged 40 to 69 years from the Korean Genome and Epidemiologic Study. MF were divided into two groups based on whether they consumed three or more, or fewer than three, meals daily. IR was evaluated using the homeostasis model assessment of insulin resistance (HOMA-IR); participants were classified as IR if their HOMA-IR value was ≥2.5. Multiple Cox proportional hazard regression analyses were conducted to examine the association between MF and the incidence of IR. Results: After adjusting for all variables, individuals in the MF ≥3 group showed a reduced incidence of IR compared to those in the MF <3 group (hazard ratio, 0.880; 95% confidence interval, 0.782 to 0.990). Additionally, subgroup analyses by sex, diabetes mellitus (DM), and body mass index (BMI) showed that this association persisted only in men, individuals without DM, and those with a BMI <25. Conclusion Our findings indicate that a higher MF among middle-aged and older adults is associated with a reduced incidence of IR. However, this association was maintained only in men, individuals without DM, and those without obesity.

Background: We aimed to investigate the predictive values of plasma C-peptide levels and the continuous glucose monitoring (CGM)-defined coefficient of variation (CV) in risk prediction for hypoglycemia in Korean people with diabetes with normal and impaired kidney function. Methods: We analyzed data from 1,185 participants diagnosed with type 1 and type 2 diabetes who underwent blinded professional CGM between January 2009 and May 2021 at outpatient clinics. We explored correlations among CGM-defined CV, plasma C-peptide levels, and time below range at <70 and 54 mg/dL across different kidney function categories. Results: In patients with chronic kidney disease (CKD) stages 1–2 (n=934), 89.3% who had a random plasma C-peptide level higher than 600 pmol/L exhibited a CV of ≤36%. Among those in CKD stage 3 (n=161) with a random plasma C-peptide level exceeding 600 pmol/L, 66.7% showed a CV of ≤36%. In stages 4–5 of CKD (n=90), the correlation between random C-peptide levels and CV was not significant (r=–0.05, P=0.640), including cases with a CV greater than 36% despite very high random plasma C-peptide levels. Random plasma C-peptide levels and CGM-assessed CV significantly predicted hypoglycemia in CKD stages 1–2 and 1–5, respectively. Conclusion The established C-peptide criteria in Western populations are applicable to Korean people with diabetes for hypoglycemic risk prediction, unless kidney function is impaired equivalent to CKD stage 3–5. The CGM-defined CV is informative for hypoglycemic risk prediction regardless of kidney function.

Background: Active surveillance (AS) has emerged as a viable management strategy for low-risk papillary thyroid microcarcinoma (PTMC), following pioneering trials at Kuma Hospital and the Cancer Institute Hospital in Japan. Numerous prospective cohort studies have since validated AS as a management option for low-risk PTMC, leading to its inclusion in thyroid cancer guidelines across various countries. From 2016 to 2020, the Multicenter Prospective Cohort Study of Active Surveillance on Papillary Thyroid Microcarcinoma (MAeSTro) enrolled 1,177 patients, providing comprehensive data on PTMC progression, sonographic predictors of progression, quality of life, surgical outcomes, and cost-effectiveness when comparing AS to immediate surgery. The second phase of MAeSTro (MAeSTro-EXP) expands AS to low-risk papillary thyroid carcinoma (PTC) tumors larger than 1 cm, driven by the hypothesis that overall risk assessment outweighs absolute tumor size in surgical decision-making. Methods: This protocol aims to address whether limiting AS to tumors smaller than 1 cm may result in unnecessary surgeries for low-risk PTCs detected during their rapid initial growth phase. By expanding the AS criteria to include tumors up to 1.5 cm, while simultaneously refining and standardizing the criteria for risk assessment and disease progression, we aim to minimize overtreatment and maintain rigorous monitoring to improve patient outcomes. Conclusion This study will contribute to optimizing AS guidelines and enhance our understanding of the natural course and appropriate management of low-risk PTCs. Additionally, MAeSTro-EXP involves a multinational collaboration between South Korea and Australia. This cross-country study aims to identify cultural and racial differences in the management of low-risk PTC, thereby enriching the global understanding of AS practices and their applicability across diverse populations.

Background: This study aimed to evaluate the applicability of deep learning technology to thyroid ultrasound images for classification of thyroid nodules. Methods: This retrospective analysis included ultrasound images of patients with thyroid nodules investigated by fine-needle aspiration at the thyroid clinic of a single center from April 2010 to September 2012. Thyroid nodules with cytopathologic results of Bethesda category V (suspicious for malignancy) or VI (malignant) were defined as thyroid cancer. Multiple deep learning algorithms based on convolutional neural networks (CNNs) —ResNet, DenseNet, and EfficientNet—were utilized, and Siamese neural networks facilitated multi-view analysis of paired transverse and longitudinal ultrasound images. Results: Among 1,048 analyzed thyroid nodules from 943 patients, 306 (29%) were identified as thyroid cancer. In a subgroup analysis of transverse and longitudinal images, longitudinal images showed superior prediction ability. Multi-view modeling, based on paired transverse and longitudinal images, significantly improved the model performance; with an accuracy of 0.82 (95% confidence intervals [CI], 0.80 to 0.86) with ResNet50, 0.83 (95% CI, 0.83 to 0.88) with DenseNet201, and 0.81 (95% CI, 0.79 to 0.84) with EfficientNetv2_ s. Training with high-resolution images obtained using the latest equipment tended to improve model performance in association with increased sensitivity. Conclusion CNN algorithms applied to ultrasound images demonstrated substantial accuracy in thyroid nodule classification, indicating their potential as valuable tools for diagnosing thyroid cancer. However, in real-world clinical settings, it is important to aware that model performance may vary depending on the quality of images acquired by different physicians and imaging devices.

Recent studies have shown an increased abundance of Sphingomonas paucimobilis, an aerobic, Gram-negative bacterium with a distinctive cell envelope rich in glycosphingolipids, within the gut microbiome of individuals with Alzheimer Disease (AD). However, the fact that S. paucimobilis is a well-known pathogen associated with nosocomial infections presents a significant challenge in investigating whether its presence in the gut microbiome is detrimental or beneficial, particularly in the context of AD. This study examines the impact of S. paucimobilis-derived extracellular vesicles (Spa-EV) on Aβ-induced pathology in cellular and animal models of AD. Microarray analysis reveals that Spa-EV treatment modulates Aβ42-induced alterations in gene expression in both HT22 neuronal cells and BV2 microglia cells. Among the genes significantly affected by SpaEV, notable examples include Bdnf, Nt3/4, and Trkb, which are key players of neurotrophic signaling; Pgc1α, an upstream regulator of mitochondrial biogenesis; Mecp2 and Sirt1, epigenetic factors that regulate numerous gene expressions; and Il1β, Tnfα, and Nfκb-p65, which are associated with neuroinflammation. Remarkably, Spa-EV effectively reverses Aβ42-induced alteration in the expression of these genes through the upregulation of Mecp2. Furthermore, administration of Spa-EV in Tg-APP/PS1 mice restores the reduced expression of neurotrophic factors, Pgc1α, MeCP2, and Sirt1, while suppressing the increased expression of proinflammatory genes in the brain. Our results indicate that Spa-EV has the potential to reverse Aβ-induced dysregulation of gene expression in neuronal and microglial cells. These alterations encompass those essential for neurotrophic signaling and neuronal plasticity, mitochondrial function, and the regulation of inflammatory processes.

Background/Aims: Inaccurate prediction of lymph node metastasis (LNM) may lead to unnecessary surgery following endoscopic resection of T1 colorectal cancer (CRC). We aimed to validate the usefulness of artificial intelligence (AI) models for predicting LNM in patients with T1 CRC. Methods: We analyzed the clinical data, laboratory results, pathological reports, and endoscopic findings of patients who underwent radical surgery for T1 CRC. We developed AI models to predict LNM using four algorithms: regularized logistic regression classifier (RLRC), random forest classifier (RFC), CatBoost classifier (CBC), and the voting classifier (VC). Four histological factors and four endoscopic findings were included to develop AI models. Areas under the receiver operating characteristics curves (AUROCs) were measured to distinguish AI model performance in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines. Results: Among 1,386 patients with T1 CRC, 173 patients (12.5%) had LNM. The AUROC values of the RLRC, RFC, CBC, and VC models for LNM prediction were significantly higher (0.673, 0.640, 0.679, and 0.677, respectively) than the 0.525 suggested in accordance with the Japanese Society for Cancer of the Colon and Rectum guidelines (vs RLRC, p<0.001; vs RFC, p=0.001; vs CBC, p<0.001; vs VC, p<0.001). The AUROC value was similar between T1 colon versus T1 rectal cancers (0.718 vs 0.615, p=0.700). The AUROC value was also similar between the initial endoscopic resection and initial surgery groups (0.581 vs 0.746, p=0.845). Conclusions AI models trained on the basis of endoscopic findings and pathological features performed well in predicting LNM in patients with T1 CRC regardless of tumor location and initial treatment method.

Background/Aims: Although gastrointestinal endoscopy (GIE) is a major contributor to the carbon footprint of national healthcare, the amount of medical waste generated by GIE procedures is not reported in South Korea. This study aimed to measure the amount of medical waste generated from GIE procedures in South Korea. Methods: We conducted a 5-day audit of medical waste generated during GIEs at seven hospitals. During the study period, medical waste in the endoscopy examination rooms was measured twice daily and documented as mass (kg). To calculate the mean mass of disposable waste generated during one esophagogastroduodenoscopy (EGD) and one colonoscopy, the mean mass of medical waste generated from seven examinations was calculated. The mean mass of medical waste generated during GIEs was calculated by dividing the total mass of medical waste generated by the number of GIE procedures. Results: Overall, 3,922 endoscopies were performed and 4,558 kg of waste was generated. The mean weight of medical waste generated per endoscopy was 1.34 kg. Each EGD and colonoscopy generated a mean of 0.24 kg and 0.43 kg of disposable waste, respectively. Applying the mean waste estimates from this study to annual GIE procedures performed in South Korea in 2022 showed that the total medical waste produced from GIE was 13,704,453 kg. In addition, the total masses of medical waste produced during EGD and colonoscopy procedures were 819,766 kg and 2,889,478 kg, respectively. Conclusions Our quantitative measurement showed that a large amount of medical waste is generated from GIE procedures. However, further research is warranted to reduce medical waste generated during GIE, which is an urgent unmet need.