Background: Recent lifestyle changes have increased the prevalence of dyslipidemia in Korea. Young men are known to have a low awareness of dyslipidemia and a lack of motivation to maintain their weight. However, the association between weight change and dyslipidemia in young adults has not been thoroughly examined. Methods: Data from the Armed Forces Medical Command Defense Medical Information System database were used. In this study, 15,068 soldiers who underwent private and corporal health examinations between May 2020 and April 2022 were included. The difference in weights between the two different health examinations was used to quantify weight change. Four components of the lipid profile were used to assess dyslipidemia during the corporal health examination. Results: After adjusting for relevant covariates, weight gain was associated with increased risk of dyslipidemia (adjusted odds ratio [OR], 1.38 [95% confidence interval, CI, 1.15 to 1.64] for the 5% to 10% weight gain group;and OR, 2.02 [95% CI, 1.59 to 2.55] for the ≥10% weight gain group), whereas weight loss was associated with decreased risk (adjusted OR, 0.82 [95% CI, 0.68 to 0.98] for the 5% to 10% weight loss group; and OR, 0.38 [95% CI, 0.27 to 0.53] for the ≥10% weight loss group). In subgroup analysis based on the participants’ baseline body mass index, smoking status, regular exercise habits, and hypertension status, there were no significant differences between the subgroups. Conclusion Weight change was associated with dyslipidemia in Korean male soldiers. The findings suggest that limiting weight gain in young adults by encouraging a healthy lifestyle may help prevent dyslipidemia.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background: Recent lifestyle changes have increased the prevalence of dyslipidemia in Korea. Young men are known to have a low awareness of dyslipidemia and a lack of motivation to maintain their weight. However, the association between weight change and dyslipidemia in young adults has not been thoroughly examined. Methods: Data from the Armed Forces Medical Command Defense Medical Information System database were used. In this study, 15,068 soldiers who underwent private and corporal health examinations between May 2020 and April 2022 were included. The difference in weights between the two different health examinations was used to quantify weight change. Four components of the lipid profile were used to assess dyslipidemia during the corporal health examination. Results: After adjusting for relevant covariates, weight gain was associated with increased risk of dyslipidemia (adjusted odds ratio [OR], 1.38 [95% confidence interval, CI, 1.15 to 1.64] for the 5% to 10% weight gain group;and OR, 2.02 [95% CI, 1.59 to 2.55] for the ≥10% weight gain group), whereas weight loss was associated with decreased risk (adjusted OR, 0.82 [95% CI, 0.68 to 0.98] for the 5% to 10% weight loss group; and OR, 0.38 [95% CI, 0.27 to 0.53] for the ≥10% weight loss group). In subgroup analysis based on the participants’ baseline body mass index, smoking status, regular exercise habits, and hypertension status, there were no significant differences between the subgroups. Conclusion Weight change was associated with dyslipidemia in Korean male soldiers. The findings suggest that limiting weight gain in young adults by encouraging a healthy lifestyle may help prevent dyslipidemia.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Background and Objectives: Although various cardiac parameters on echocardiography have clinical importance, their measurement by conventional manual methods is time-consuming and subject to variability. We evaluated the feasibility, accuracy, and predictive value of an artificial intelligence (AI)-based automated system for echocardiographic analysis in patients with ST-segment elevation myocardial infarction (STEMI). Methods: The AI-based system was developed using a nationwide echocardiographic dataset from five tertiary hospitals, and automatically identified views, then segmented and tracked the left ventricle (LV) and left atrium (LA) to produce volume and strain values. Both conventional manual measurements and AI-based fully automated measurements of the LV ejection fraction and global longitudinal strain, and LA volume index and reservoir strain were performed in 632 patients with STEMI. Results: The AI-based system accurately identified necessary views (overall accuracy, 98.5%) and successfully measured LV and LA volumes and strains in all cases in which conventional methods were applicable. Inter-method analysis showed strong correlations between measurement methods, with Pearson coefficients ranging 0.81–0.92 and intraclass correlation coefficients ranging 0.74–0.90. For the prediction of clinical outcomes (composite of all-cause death, re-hospitalization due to heart failure, ventricular arrhythmia, and recurrent myocardial infarction), AI-derived measurements showed predictive value independent of clinical risk factors, comparable to those from conventional manual measurements. Conclusions Our fully automated AI-based approach for LV and LA analysis on echocardiography is feasible and provides accurate measurements, comparable to conventional methods, in patients with STEMI, offering a promising solution for comprehensive echocardiographic analysis, reduced workloads, and improved patient care.

Objective: This study aimed to identify serum biomarkers prospectively associated with remission at 12 weeks in outpatients with depressive disorders receiving stepwise psychopharmacotherapy, according to the main antidepressant used during the treatment period. Methods: This study included 1,024 depressive outpatients initially treated using antidepressant monotherapy, followed by alternating pharmacological strategies during the acute phase (3−12 weeks; 3-week interval). Fourteen serum biomarkers, sociodemographics, and clinical characteristics were evaluated at baseline. Based on the use frequency and mechanism of action, four main antidepressant types were distinguished: escitalopram, other selective serotonin reuptake inhibitors (SSRIs), serotonin norepinephrine reuptake inhibitors (SNRIs), and mirtazapine. A Hamilton Depression Rating Scale score ≤ 7 was take to indicate remission. Results: Lower high-sensitivity C-reactive protein levels were correlated with remission at 12 weeks for all antidepressant types. Lower interleukin (IL)-6 levels and tumor necrosis factor-alpha levels were associated with remission using escitalopram and other SSRIs respectively. Lower IL-1β and leptin levels, predicted remission in association with SSRIs including escitalopram. For SNRIs, remission at 12 weeks was predicted by lower IL-4 and IL-10 levels. For mirtazapine, remission at 12 weeks was associated with lower leptin levels, and higher serotonin and folate levels. Conclusion Baseline serum status, as estimated by nine serum markers, may help clinicians determine the most appropriate antidepressant to achieve remission in the acute phase of depression.

Background: Active surveillance (AS) has been introduced as a management strategy for low-risk papillary thyroid carcinoma (PTC) due to its typically indolent nature. Despite this, the widespread adoption of AS has encountered several challenges. The aim of this systematic review was to evaluate the safety of AS related to disease progression and its benefits compared with immediate surgery (IS). Methods: Studies related to AS in patients with low-risk PTC were searched through the Ovid MEDLINE, Embase, Cochrane Library, and KoreaMed databases. Studies on disease progression, surgical complication, quality of life (QoL), and cost-effectiveness were separately analyzed and narratively synthesized. Results: In the evaluation of disease progression, the proportions of cases with tumor growth ≥3 mm and a volume increase >50% were 2.2%–10.8% and 16.0%–25.5%, respectively. Newly detected lymph node metastasis was identified in 0.0%–1.4% of patients. No significant difference was found between IS and delayed surgery in surgical complications, including vocal cord paralysis and postoperative hypoparathyroidism. AS was associated with better QoL than IS. Studies on the cost-effectiveness of AS reported inconsistent data, but AS was more cost-effective when quality-adjusted life years were considered. Conclusion AS is an acceptable management option for patients with low-risk PTC based on the low rate of disease progression and the absence of an increased mortality risk. AS has additional benefits, including improved QoL and greater QoL-based cost-effectiveness.