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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.

BackgroundHyperuricemia is common during tuberculosis (TB) treatment, especially in association with pyrazinamide (PZA). This study investigated the relationship between major adverse cardiovascular events (MACEs) and hyperuricemia during TB treatment.MethodsWe conducted a single-center retrospective cohort study. From January 2010 through June 2017, we assessed all consecutive TB patients at Chonnam National University Hospital in South Korea. Hyperuricemia was defined as serum uric acid levels exceeding 7.0 mg/dL (men) and 6.0 mg/dL (women).ResultsOf the 1,143 patients included, PZA was administered to 1,081 (94.6%), and hyperuricemia was detected in 941 (82.3%). Eight patients experienced MACEs. Multivariate analysis using logistic regression indicated that prior ischemic heart disease was associated with MACE development (OR,14.087; 95% CI,3.304-60.061; P < 0.000), while hyperuricemia was not (OR, 1.505; 95% CI, 0.184-12.299; P = 0.703). For patients without drug-resistant TB, the absence of hyperuricemia was associated with higher mortality (OR, 2.609; 95% CI, 1.066-6.389; P = 0.036), whereas hyperuricemia was associated with less worse outcomes (OR,0.316; 95% CI,0.173-0.576; P < 0.000).ConclusionsAlthough most patients treated with PZA developed hyperuricemia, it was not associated with MACE development. Hyperuricemia during TB treatment was associated with better outcomes, possibly due to consistent adherence to TB treatment.

Background We determined the clinical characteristics and predictive factors of long‐term response to pemetrexed maintenance therapy as first‐line treatment for non‐small cell lung cancer (NSCLC). Methods A total of 950 advanced NSCLC patients received pemetrexed (500 mg/m2) plus cisplatin (60 mg/m2) (Pem‐Cis) induction chemotherapy every three weeks as first‐line treatment between January 2010 and August 2018. Patients who did not show progression after four cycles of Pem‐Cis and received at least one cycle of pemetrexed maintenance were recruited (n = 199). Results Patients were divided into subgroups according to total cycles of pemetrexed: ≤ 10 (F10, n = 134) and > 10 (M10, n = 65). The M10 group had a higher proportion of patients with stage M1a (intrathoracic metastasis alone) and exhibited lower levels of thymidylate synthase (TS) than the F10 group (median H‐score 10.0% vs. 60.0%; P = 0.031). Further subgrouping identified extreme responders: ≤ 7 (F7, n = 101) and ≥ 20 (M20, n = 26) cycles. The M20 group showed lower mean serum CEA levels before (17.5 vs. 147.0; P = 0.099) and after (6.9 vs. 53.2; P = 0.001) Pem‐Cis treatment, and a higher incidence of normalization after Pem‐Cis (abnormal 41.7% vs. 68.5%; P = 0.015). M1a stage, normalization of CEA levels after Pem‐Cis, and lower TS H‐score were predictors of progression‐free survival in patients administered pemetrexed maintenance. Conclusion M1a stage and lower TS expression were predictors of long‐term response to pemetrexed maintenance. CEA normalization after Pem‐Cis could be an additional surrogate marker of positive response to long‐term treatment.

Background There are many models for predicting diabetes mellitus (DM), but their clinical implication remains vague. Therefore, we aimed to create various DM prediction models using easily accessible health screening test parameters. Methods Two sets of variables were used to develop eight DM prediction models. One set comprised 62 easily accessible examination results of commonly used variables from a tertiary university hospital. The second set comprised 27 of the 62 variables included in the national routine health checkups. Gradient boosting and random forest algorithms were used to develop the models. Internal validation was performed using the stratified 10-fold cross-validation method. Results The area under the receiver operating characteristic curve (ROC-AUC) for the 62-variable DM model making 12-month predictions for subjects without diabetes was the largest (0.928) among those of the eight DM prediction models. The ROC-AUC dropped by more than 0.04 when training with the simplified 27-variable set but still showed fairly good performance with ROC-AUCs between 0.842 and 0.880. The accuracy was up to 11.5% higher (from 0.807 to 0.714) when fasting glucose was included. Conclusion We created easily applicable diabetes prediction models that deliver good performance using parameters commonly assessed during tertiary university hospital and national routine health checkups. We plan to perform prospective external validation, hoping that the developed DM prediction models will be widely used in clinical practice.

A well-balanced naval ship design to enhance the survivability from the initial design stage in parallel with other designs focusing on the major function and its inherent mission is considered as a desirable design trend in ROK navy since Cheonan sinking accident. Because it is difficult to estimate a hit location of a given threat for survivability evaluation, a statistical approach based on multiple-hit scenarios is frequently addressed, and it is necessary to accomplish rapid analyzes considering the tight design schedule. In addition, accurately estimating the extent of damage caused by the given threat is also an important matter to secure the reliability of evaluation. In particular, the empirical formula-based damage extent estimation method, which is frequently used for initial rapid vulnerability analysis, has limitations in not faithfully reflecting the latest technology advances such as hull design changes and diversification of threats, so researches on this have been continuously accomplished. In this study, a method for analyzing the extent of damage was developed considering the structural response of a ship to damage under a blast load. The proposed method quickly and easily calculates the extent of damage using physical design parameters together with the accurate analysis results and is also very effective at the initial design stage of the naval ship (e.g., in evaluating various design candidates for structural configurations). To show the effectiveness of suggested method, FLACS, a well-known commercial program for explosion analysis, is used for the analysis of nine representative scenarios together with the stepwise validations of the suggested procedure; the analysis results are observed the same in most cases with the developed program based on the proposed procedure, with a difference of approximately 15% for one scenario.