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.

BACKGROUND: We evaluated the association of visceral-to-subcutaneous fat ratio (VSR) with nonalcoholic fatty liver disease (NAFLD) and advanced fibrosis degree based on noninvasive serum fibrosis markers in the general population with NAFLD. METHODS: This is a cross-sectional study, in 7,465 Korean adults who underwent health screening examinations. NAFLD was defined as fatty liver detected on ultrasonography, and visceral and subcutaneous abdominal fat was measured using computed tomography. We predicted fibrosis based on the fibrosis-4 (FIB-4) score and aspartate aminotransferase-to-platelet ratio index (APRI) and categorized the risk for advanced fibrosis as low, indeterminate, or high. RESULTS: The multivariable-adjusted prevalence ratios for indeterminate to high risk of advanced fibrosis based on FIB-4, determined by comparing the second, third, and fourth quartiles with the first quartile of VSR, were 3.38 (95% confidence interval [CI], 0.64 to 17.97), 9.41 (95% CI, 1.97 to 45.01), and 19.34 (95% CI, 4.06 to 92.18), respectively. The multivariable-adjusted prevalence ratios for intermediate to high degree of fibrosis according to APRI also increased across VSR quartiles (5.04 [95% CI, 2.65 to 9.59], 7.51 [95% CI, 3.91 to 14.42], and 19.55 [95% CI, 9.97 to 38.34], respectively). High VSR was more strongly associated with the prevalence of NAFLD in nonobese subjects than in obese subjects, and the associations between VSR and intermediate to high probability of advanced fibrosis in NAFLD were stronger in obese subjects than in nonobese subjects. CONCLUSION High VSR values predicted increased NAFLD risk and advanced fibrosis risk with NAFLD, and the predictive value of VSR for indeterminate to high risk of advanced fibrosis was higher in obese subjects than in nonobese subjects.

BACKGROUND: Being common, mild anemia is sometimes considered a mere consequence of aging; however, aging alone is unlikely to lead to anemia. Therefore, this study aimed to investigate the association between mild anemia and total mortality and cause-specific mortality in apparently healthy elderly subjects. METHODS: A retrospective cohort study was conducted on 10,114 apparently healthy elderly individuals who underwent cancer screening and routine medical check-ups at one Health Promotion Center between May 1995 and December 2007. We defined mild anemia as a hemoglobin concentration between 10.0 g/dL and 11.9 g/dL in women and between 10.0 g/dL and 12.9 g/dL in men. We assessed the relationship between the overall, cardiovascular (CV), and cancer mortality and mild anemia using Cox proportional hazard models. RESULTS: Mild anemia was present in 143 men (3.1%) and 246 women (6.1%). During an average follow-up of 7.6 years, 495 deaths occurred, including 121 CV and 225 cancer deaths. After adjustments, mild anemia was associated with a 128% increase in the risk of all-cause mortality (hazard ratio [HR], 2.28; 95% confidence interval [CI], 1.54–3.37) in men and cancer-related mortality (HR, 2.25; 95% CI, 1.22–4.13), particularly lung cancer (HR, 2.70; 95% CI, 1.03–7.08) in men, but not in women. In the subgroup analyses based on smoking status, obesity, and age, the associations were more prominent in never or former smoker groups and the older group. CONCLUSION: The present study shows that overall and cancer-related mortality was associated with mild anemia in elderly men. Future prospective studies are needed to consolidate our findings.
Aged ; Aging ; Anemia ; Cause of Death ; Cohort Studies ; Early Detection of Cancer ; Female ; Follow-Up Studies ; Health Promotion ; Humans ; Lung Neoplasms ; Male ; Mortality ; Obesity ; Proportional Hazards Models ; Prospective Studies ; Retrospective Studies ; Smoke ; Smoking