Gestational diabetes mellitus (GDM) affects over 10% of all pregnancies, both in Korea and worldwide. GDM not only increases the risk of adverse pregnancy outcomes such as preeclampsia, preterm birth, macrosomia, neonatal hypoglycemia, and shoulder dystocia, but it also significantly increases the risk of developing postpartum type 2 diabetes mellitus and cardiovascular disease in the mother. Additionally, GDM is linked to a higher risk of childhood obesity and diabetes in offspring, as well as neurodevelopmental disorders, including autistic spectrum disorder. This review offers a comprehensive summary of clinical epidemiological studies concerning maternal and fetal complications and explores mechanistic investigations that reveal the underlying pathophysiology.

Gestational diabetes mellitus (GDM) affects over 10% of all pregnancies, both in Korea and worldwide. GDM not only increases the risk of adverse pregnancy outcomes such as preeclampsia, preterm birth, macrosomia, neonatal hypoglycemia, and shoulder dystocia, but it also significantly increases the risk of developing postpartum type 2 diabetes mellitus and cardiovascular disease in the mother. Additionally, GDM is linked to a higher risk of childhood obesity and diabetes in offspring, as well as neurodevelopmental disorders, including autistic spectrum disorder. This review offers a comprehensive summary of clinical epidemiological studies concerning maternal and fetal complications and explores mechanistic investigations that reveal the underlying pathophysiology.

Gestational diabetes mellitus (GDM) affects over 10% of all pregnancies, both in Korea and worldwide. GDM not only increases the risk of adverse pregnancy outcomes such as preeclampsia, preterm birth, macrosomia, neonatal hypoglycemia, and shoulder dystocia, but it also significantly increases the risk of developing postpartum type 2 diabetes mellitus and cardiovascular disease in the mother. Additionally, GDM is linked to a higher risk of childhood obesity and diabetes in offspring, as well as neurodevelopmental disorders, including autistic spectrum disorder. This review offers a comprehensive summary of clinical epidemiological studies concerning maternal and fetal complications and explores mechanistic investigations that reveal the underlying pathophysiology.

Gestational diabetes mellitus (GDM) affects over 10% of all pregnancies, both in Korea and worldwide. GDM not only increases the risk of adverse pregnancy outcomes such as preeclampsia, preterm birth, macrosomia, neonatal hypoglycemia, and shoulder dystocia, but it also significantly increases the risk of developing postpartum type 2 diabetes mellitus and cardiovascular disease in the mother. Additionally, GDM is linked to a higher risk of childhood obesity and diabetes in offspring, as well as neurodevelopmental disorders, including autistic spectrum disorder. This review offers a comprehensive summary of clinical epidemiological studies concerning maternal and fetal complications and explores mechanistic investigations that reveal the underlying pathophysiology.

Background: and Purpose: We developed a new digital cognitive assessment called Seoul Cognitive Status Test (SCST), formerly called Inbrain Cognitive Screening Test. The purpose of this study was to validate the clinical utility of the SCST by comparing its scores of those with subjective cognitive decline (SCD), amnestic mild cognitive impairment (aMCI), and dementia diagnosed by the Korean version of the Consortium to Establish a Registry for Alzheimer’s Disease Assessment Packet (CERAD-K). Methods: All participants (n=296) who completed the CERAD-K, SCST, and Instrumental Activities of Daily Living tests were included in this study. Total score, cognitive domain scores, and subtest scores of the SCST were compared among the 3 groups (SCD, aMCI, and dementia). Additionally, correlations between SCST and CERAD-K subtests were examined. Results: Cognitive domain scores and total score of the SCST showed significant differences among the three groups, with scores being the highest in the order of SCD, aMCI, and dementia (p<0.001). Most subtests of the SCST also showed higher scores in the order of SCD, aMCI, and dementia (p<0.001). However, SCD and aMCI groups showed no significant differences in scores of the Phonemic Word Fluency Test (p=0.083) or Korean Trail Making Test-Elderly version Part A (p=0.434). Additionally, there was no significant difference in the score of Place Recognition (p=0.274) of the Word-Place Association Test between aMCI and dementia groups. Conclusions In conclusion, differences in total score, cognitive domain scores, and subtest scores of the SCST among the 3 groups of participants diagnosed using CERAD-K confirm the clinical utility of the SCST for cognitive assessment.

Background: Inconsistent results have been reported regarding the association between the use of antidiabetic drugs and the clinical outcomes of coronavirus disease 2019 (COVID-19). This study aimed to investigate the effect of antidiabetic drugs on COVID-19 outcomes in patients with diabetes using data from the National Health Insurance Service (NHIS) in South Korea. Methods: We analyzed the NHIS data of patients aged ≥20 years who tested positive for COVID-19 and were taking antidiabetic drugs between December 2019 and June 2020. Multiple logistic regression analysis was performed to analyze the clinical outcomes of COVID-19 based on the use of antidiabetic drugs. Results: A total of 556 patients taking antidiabetic drugs tested positive for COVID-19, including 271 male (48.7%), most of whom were in their sixties. Of all patients, 433 (77.9%) were hospitalized, 119 (21.4%) received oxygen treatment, 87 (15.6%) were admitted to the intensive care unit, 31 (5.6%) required mechanical ventilation, and 61 (11.0%) died. Metformin was significantly associated with the lower risks of mechanical ventilation (odds ratio [OR], 0.281; 95% confidence interval [CI], 0.109 to 0.720; P=0.008), and death (OR, 0.395; 95% CI, 0.182 to 0.854; P=0.018). Dipeptidylpeptidase-4 inhibitor (DPP-4i) were significantly associated with the lower risks of oxygen treatment (OR, 0.565; 95% CI, 0.356 to 0.895; P=0.015) and death (OR, 0.454; 95% CI, 0.217 to 0.949; P=0.036). Sulfonylurea was significantly associated with the higher risk of mechanical ventilation (OR, 2.579; 95% CI, 1.004 to 6.626; P=0.049). Conclusion In patients with diabetes and COVID-19, metformin exhibited reduced risks of mechanical ventilation and death, DPP- 4i was linked with lower risks of oxygen treatment and death, while sulfonylurea was related to the increased risk of mechanical ventilation.

Prior research has highlighted poor clinical outcomes in coronavirus disease 2019 (COVID-19)-infected patients with diabetes; however, susceptibility to COVID-19 infection in patients with diabetes has not been extensively studied. Participants aged ≥30 years who underwent COVID-19 testing from December 2019 to April 2020 were analyzed using the National Health Insurance Service data in South Korea. In a cohort comprising 29,433 1:1 propensity score-matched participants, COVID-19 positivity was significantly higher in participants with diabetes than in those without diabetes (512 [3.5%] vs. 395 [2.7%], P<0.001). Logistic regression analysis indicated that diabetes significantly increased the risk of COVID-19 test positivity (odds ratio, 1.307; 95% confidence interval, 1.144 to 1.493; P<0.001). Patients with diabetes exhibited heightened COVID-19 infection rates compared to individuals without diabetes, and diabetes increased the susceptibility to COVID-19, reinforcing the need for heightened preventive measures, particularly considering the poor clinical outcomes in this group.

Pediatric differentiated thyroid cancers (DTCs), mostly papillary thyroid cancer (PTC, 80-90%), are diagnosed at more advanced stages with larger tumor sizes and higher rates of locoregional and/or lung metastasis. Despite the higher recurrence rates of pediatric cancers than of adult thyroid cancers, pediatric patients demonstrate a lower mortality rate and more favorable prognosis. Considering the more advanced stage at diagnosis in pediatric patients, preoperative evaluation is crucial to determine the extent of surgery required. Furthermore, if hereditary tumor syndrome is suspected, genetic testing is required. Recommendations for pediatric DTCs focus on the surgical principles, radioiodine therapy according to the postoperative risk level, treatment and follow-up of recurrent or persistent diseases, and treatment of patients with radioiodine-refractory PTCs on the basis of genetic drivers that are unique to pediatric patients.

Differentiated thyroid cancer demonstrates a wide range of clinical presentations, from very indolent cases to those with an aggressive prognosis. Therefore, diagnosing and treating each cancer appropriately based on its risk status is important. The Korean Thyroid Association (KTA) has provided and amended the clinical guidelines for thyroid cancer management since 2007. The main changes in this revised 2024 guideline include 1) individualization of surgical extent according to pathological tests and clinical findings, 2) application of active surveillance in low-risk papillary thyroid microcarcinoma, 3) indications for minimally invasive surgery, 4) adoption of World Health Organization pathological diagnostic criteria and definition of terminology in Korean, 5) update on literature evidence of recurrence risk for initial risk stratification, 6) addition of the role of molecular testing, 7) addition of definition of initial risk stratification and targeting thyroid stimulating hormone (TSH) concentrations according to ongoing risk stratification (ORS), 8) addition of treatment of perioperative hypoparathyroidism, 9) update on systemic chemotherapy, and 10) addition of treatment for pediatric patients with thyroid cancer.

Background: Worldwide, sepsis is the leading cause of death in hospitals. If mortality rates in patients with sepsis can be predicted early, medical resources can be allocated efficiently. We constructed machine learning (ML) models to predict the mortality of patients with sepsis in a hospital emergency department. Methods: This study prospectively collected nationwide data from an ongoing multicenter cohort of patients with sepsis identified in the emergency department. Patients were enrolled from 19 hospitals between September 2019 and December 2020. For acquired data from 3,657 survivors and 1,455 deaths, six ML models (logistic regression, support vector machine, random forest, extreme gradient boosting [XGBoost], light gradient boosting machine, and categorical boosting [CatBoost]) were constructed using fivefold cross-validation to predict mortality. Through these models, 44 clinical variables measured on the day of admission were compared with six sequential organ failure assessment (SOFA) components (PaO 2 /FIO 2 [PF], platelets (PLT), bilirubin, cardiovascular, Glasgow Coma Scale score, and creatinine).The confidence interval (CI) was obtained by performing 10,000 repeated measurements via random sampling of the test dataset. All results were explained and interpreted using Shapley’s additive explanations (SHAP). Results: Of the 5,112 participants, CatBoost exhibited the highest area under the curve (AUC) of 0.800 (95% CI, 0.756–0.840) using clinical variables. Using the SOFA components for the same patient, XGBoost exhibited the highest AUC of 0.678 (95% CI, 0.626–0.730). As interpreted by SHAP, albumin, lactate, blood urea nitrogen, and international normalization ratio were determined to significantly affect the results. Additionally, PF and PLTs in the SOFA component significantly influenced the prediction results. Conclusion Newly established ML-based models achieved good prediction of mortality in patients with sepsis. Using several clinical variables acquired at the baseline can provide more accurate results for early predictions than using SOFA components. Additionally, the impact of each variable was identified.