Background/Aims: We aim to compare the remission of erosive esophagitis (EE) among individuals with different phenotypes based on their metabolic health and obesity status and investigate the impact of changes in metabolic health on the EE remission. Methods: Asymptomatic adults (n = 16 845) with EE at baseline, who underwent follow-up esophagogastroduodenoscopy (EGD) were categorized into 4 groups as follows: metabolically healthy (MH) nonobese, metabolically unhealthy (MU) nonobese, MH obese, and MU obese. EE was defined as grade A or higher mucosal breaks observed using esophagogastroduodenoscopy. Results: During a median follow-up of 2.2 years, the remission rates of EE were 286.4/10 3 , 260.1/10 3 , 201.5/10 3 , and 219.9/10 3 person-years in MH nonobese, MU nonobese, MH obese, and MU obese groups, respectively. Multivariate-adjusted hazard ratios (95% CI) for EE remission among the MH nonobese, MU nonobese, and MH obese groups versus that of the MU obese group were 1.30 (1.23-1.37), 1.17 (1.12-1.23), and 0.98 (0.90-1.06), respectively, whereas those of the persistent MH, progression of MH to MU, and remission of MU to MH compared with the persistent MU group were 1.37 (1.23-1.52), 1.15 (1.01-1.30), and 1.28 (1.12-1.46), respectively.Increased EE remission in the persistent MH group was consistently observed in individuals with and without obesity (or abdominal obesity). Conclusions Metabolic health and nonobesity independently and favorably impact EE remission. Maintaining normal weight and healthy metabolic status may contribute to EE remission.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: We aim to compare the remission of erosive esophagitis (EE) among individuals with different phenotypes based on their metabolic health and obesity status and investigate the impact of changes in metabolic health on the EE remission. Methods: Asymptomatic adults (n = 16 845) with EE at baseline, who underwent follow-up esophagogastroduodenoscopy (EGD) were categorized into 4 groups as follows: metabolically healthy (MH) nonobese, metabolically unhealthy (MU) nonobese, MH obese, and MU obese. EE was defined as grade A or higher mucosal breaks observed using esophagogastroduodenoscopy. Results: During a median follow-up of 2.2 years, the remission rates of EE were 286.4/10 3 , 260.1/10 3 , 201.5/10 3 , and 219.9/10 3 person-years in MH nonobese, MU nonobese, MH obese, and MU obese groups, respectively. Multivariate-adjusted hazard ratios (95% CI) for EE remission among the MH nonobese, MU nonobese, and MH obese groups versus that of the MU obese group were 1.30 (1.23-1.37), 1.17 (1.12-1.23), and 0.98 (0.90-1.06), respectively, whereas those of the persistent MH, progression of MH to MU, and remission of MU to MH compared with the persistent MU group were 1.37 (1.23-1.52), 1.15 (1.01-1.30), and 1.28 (1.12-1.46), respectively.Increased EE remission in the persistent MH group was consistently observed in individuals with and without obesity (or abdominal obesity). Conclusions Metabolic health and nonobesity independently and favorably impact EE remission. Maintaining normal weight and healthy metabolic status may contribute to EE remission.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: We aim to compare the remission of erosive esophagitis (EE) among individuals with different phenotypes based on their metabolic health and obesity status and investigate the impact of changes in metabolic health on the EE remission. Methods: Asymptomatic adults (n = 16 845) with EE at baseline, who underwent follow-up esophagogastroduodenoscopy (EGD) were categorized into 4 groups as follows: metabolically healthy (MH) nonobese, metabolically unhealthy (MU) nonobese, MH obese, and MU obese. EE was defined as grade A or higher mucosal breaks observed using esophagogastroduodenoscopy. Results: During a median follow-up of 2.2 years, the remission rates of EE were 286.4/10 3 , 260.1/10 3 , 201.5/10 3 , and 219.9/10 3 person-years in MH nonobese, MU nonobese, MH obese, and MU obese groups, respectively. Multivariate-adjusted hazard ratios (95% CI) for EE remission among the MH nonobese, MU nonobese, and MH obese groups versus that of the MU obese group were 1.30 (1.23-1.37), 1.17 (1.12-1.23), and 0.98 (0.90-1.06), respectively, whereas those of the persistent MH, progression of MH to MU, and remission of MU to MH compared with the persistent MU group were 1.37 (1.23-1.52), 1.15 (1.01-1.30), and 1.28 (1.12-1.46), respectively.Increased EE remission in the persistent MH group was consistently observed in individuals with and without obesity (or abdominal obesity). Conclusions Metabolic health and nonobesity independently and favorably impact EE remission. Maintaining normal weight and healthy metabolic status may contribute to EE remission.

Purpose: The incidence of early-onset colorectal cancer (CRC) and associated mortality have been increasing. However, the potential benefits of CRC screening are largely unknown in young individuals. We aimed to evaluate the effect of CRC screening with colonoscopy on all-cause and CRC mortality among young (aged < 45 years) and older (aged ≥ 45 years) individuals. Materials and Methods: This cohort study included 528,046 Korean adults free of cancer at baseline who underwent a comprehensive health examination. The colonoscopic screening group was defined as those who reported undergoing colonoscopy for CRC screening. Mortality follow-up until December 31, 2019 was ascertained based on nationwide death certificate data from the Korea National Statistical Office. Results: Colonoscopic screening was associated with a lower risk of all-cause mortality in both young and older individuals. Multivariable-adjusted time-dependent hazard ratios (95% confidence intervals) for all-cause mortality comparing ever- to never-screening were 0.86 (0.75-0.99) for young individuals and 0.71 (0.65-0.78) for older individuals. Colonoscopic screenings were also associated with a reduced risk of CRC mortality without significant interaction by age, although this association was significant only among participants aged ≥ 45 years, with corresponding time-dependent hazard ratios of 0.47 (0.15-1.44) for young individuals and 0.52 (0.31-0.87) for those aged ≥ 45 years. Conclusion Colonoscopic CRC screening decreased all-cause mortality among both young and older individuals, while significantly decreased CRC mortality was observed only in those aged ≥ 45 years. Screening initiation at an earlier age warrants more rigorous confirmatory studies.

Objectives: We introduced the cohort studies included in the Korean Cohort Consortium (KCC), focusing on large-scale cohort studies established in Korea with a prolonged follow-up period. Moreover, we also provided projections of the follow-up and estimates of the sample size that would be necessary for big-data analyses based on pooling established cohort studies, including population-based genomic studies. Methods: We mainly focused on the characteristics of individual cohort studies from the KCC. We developed “PROFAN”, a Shiny application for projecting the follow-up period to achieve a certain number of cases when pooling established cohort studies. As examples, we projected the follow-up periods for 5000 cases of gastric cancer, 2500 cases of prostate and breast cancer, and 500 cases of non-Hodgkin lymphoma. The sample sizes for sequencing-based analyses based on a 1:1 case-control study were also calculated. Results: The KCC consisted of 8 individual cohort studies, of which 3 were community-based and 5 were health screening-based cohorts. The population-based cohort studies were mainly organized by Korean government agencies and research institutes. The projected follow-up period was at least 10 years to achieve 5000 cases based on a cohort of 0.5 million participants. The mean of the minimum to maximum sample sizes for performing sequencing analyses was 5917-72 102. Conclusions We propose an approach to establish a large-scale consortium based on the standardization and harmonization of existing cohort studies to obtain adequate statistical power with a sufficient sample size to analyze high-risk groups or rare cancer subtypes.

Objectives: . Sensitization to specific inhalant allergens is a major risk factor for the development of atopic diseases, which impose a major socioeconomic burden and significantly diminish quality of life. However, patterns of inhalant allergic sensitization have yet to be precisely described. Therefore, to enhance the understanding of aeroallergens, we performed a cluster analysis of inhalant allergic sensitization using a computational model. Methods: . Skin prick data were collected from 7,504 individuals. A positive skin prick response was defined as an allergen-to-histamine wheal ratio ≥1. To identify the clustering of inhalant allergic sensitization, we performed computational analysis using the four-parameter unified-Richards model. Results: . Hierarchical cluster analysis grouped inhalant allergens into three clusters based on the Davies-Bouldin index (0.528): cluster 1 (Dermatophagoides pteronyssinus and Dermatophagoides farinae), cluster 2 (mugwort, cockroach, oak, birch, cat, and dog), and cluster 3 (Alternaria tenus, ragweed, Candida albicans, Kentucky grass, and meadow grass). Computational modeling revealed that each allergen cluster had a different trajectory over the lifespan. Cluster 1 showed a high level (>50%) of sensitization at an early age (before 19 years), followed by a sharp decrease in sensitization. Cluster 2 showed a moderate level (10%–20%) of sensitization before 29 years of age, followed by a steady decrease in sensitization. However, cluster 3 revealed a low level (<10%) of sensitization at all ages. Conclusion . Computational modeling suggests that allergic sensitization consists of three clusters with distinct patterns at different ages. The results of this study will be helpful to allergists in managing patients with atopic diseases.