Background: Cardiovascular disease is life-threatening yet preventable for patients with type 2 diabetes mellitus (T2DM). Because each patient with T2DM has a different risk of developing cardiovascular complications, the accurate stratification of cardiovascular risk is critical. In this study, we proposed cardiovascular risk engines based on machine-learning algorithms for newly diagnosed T2DM patients in Korea. Methods: To develop the machine-learning-based cardiovascular disease engines, we retrospectively analyzed 26,166 newly diagnosed T2DM patients who visited Seoul St. Mary’s Hospital between July 2009 and April 2019. To accurately measure diabetes-related cardiovascular events, we designed a buffer (1 year), an observation (1 year), and an outcome period (5 years). The entire dataset was split into training and testing sets in an 8:2 ratio, and this procedure was repeated 100 times. The area under the receiver operating characteristic curve (AUROC) was calculated by 10-fold cross-validation on the training dataset. Results: The machine-learning-based risk engines (AUROC XGBoost=0.781±0.014 and AUROC gated recurrent unit [GRU]-ordinary differential equation [ODE]-Bayes=0.812±0.016) outperformed the conventional regression-based model (AUROC=0.723± 0.036). Conclusion GRU-ODE-Bayes-based cardiovascular risk engine is highly accurate, easily applicable, and can provide valuable information for the individualized treatment of Korean patients with newly diagnosed T2DM.

Background: Human breast milk is essential and provides irreplaceable nutrients for early humans. However, breastfeeding is not easy for various reasons in medical institution environments. Therefore, in order to improve the breastfeeding environment, we investigated the difficult reality of breastfeeding through questionnaire responses from medical institution workers. Methods: A survey was conducted among 179 medical institution workers with experience in childbirth within the last five years. The survey results of 175 people were analyzed, with incoherent answers excluded. Results: Of the 175 people surveyed, a total of 108 people (61.7%) worked during the day, and 33 people (18.9%) worked in three shifts. Among 133 mothers who stayed with their babies in the same nursing room, 111 (93.3%) kept breastfeeding for more than a month, but among those who stayed apart, only 10 (71.4%) continued breastfeeding for more than a month (P = 0.024). Ninety-five (88.0%) of daytime workers, 32 (94.1%) two-shift workers, and 33 (100%) three-shift workers continued breastfeeding for more than a month (P = 0.026). Workers in general hospitals tended to breastfeed for significantly longer than those that worked in tertiary hospitals (P = 0.003). A difference was also noted between occupation categories (P = 0.019), but a more significant difference was found in the comparison between nurses and doctors (P = 0.012). Longer breastfeeding periods were noted when mothers worked three shifts (P = 0.037). Depending on the period planned for breastfeeding prior to childbirth, the actual breastfeeding maintenance period after birth showed a significant difference (P = 0.002). Of 112 mothers who responded to the question regarding difficulties in breastfeeding after returning to work, 87 (77.7%) mentioned a lack of time caused by being busy at work, 82 (73.2%) mentioned the need for places and appropriate circumstances. Conclusion In medical institutions, it is recommended that environmental improvements in medical institutions, the implementation of supporting policies, and the provision of specialized education on breastfeeding are necessary to promote breastfeeding.

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.

Hypoglycemia is one of the severe complications of diabetes. To prevent hypoglycemia, an emphasis is placed on maintaining an appropriate balance between nutrition, activity, and treatment, which can be achieved by the repetition of self-trials based on self-monitoring. Clinicians routinely focus on patients’ contribution, including timely intake of an adequate amount of carbohydrates, physical activity, antidiabetic medication, and abstinence from alcohol. Recently, many guidelines have highlighted the importance of clinicians’ factors and recommend individualized treatments according to lifestyle patterns and specific needs following the de-intensification of treatment. The optimal value of hemoglobin A1c (HbA1c) levels for blood glucose level regulation remains controversial among countries, but it generally does not exceed 8.0%. In populations that are at a risk of hypoglycemia, such as the older adults, it is advisable to adjust the target blood glucose level to less than 8.0%. Meanwhile, a blood glucose level of 7.0%–7.5% is generally recommended for healthy older adults. If the expected lifetime is shorter than 10 years or in patients with chronic kidney disease and severe cardiovascular disease, the HbA1c level target can be increased to 7.5%–8.0%. For even shorter lifetime expectancy, the target can be adjusted up to 8.0%–9.0%. To prevent hypoglycemia, the target blood glucose level needs to be adjusted, particularly in older adult patients. Ultimately, it is important to identify the maximum blood glucose levels that do not cause hypoglycemia and the minimum blood glucose levels that do not cause hyperglycemia-associated complications.

Hypoglycemia is one of the severe complications of diabetes. To prevent hypoglycemia, an emphasis is placed on maintaining an appropriate balance between nutrition, activity, and treatment, which can be achieved by the repetition of self-trials based on self-monitoring. Clinicians routinely focus on patients’ contribution, including timely intake of an adequate amount of carbohydrates, physical activity, antidiabetic medication, and abstinence from alcohol. Recently, many guidelines have highlighted the importance of clinicians’ factors and recommend individualized treatments according to lifestyle patterns and specific needs following the de-intensification of treatment. The optimal value of hemoglobin A1c (HbA1c) levels for blood glucose level regulation remains controversial among countries, but it generally does not exceed 8.0%. In populations that are at a risk of hypoglycemia, such as the older adults, it is advisable to adjust the target blood glucose level to less than 8.0%. Meanwhile, a blood glucose level of 7.0%–7.5% is generally recommended for healthy older adults. If the expected lifetime is shorter than 10 years or in patients with chronic kidney disease and severe cardiovascular disease, the HbA1c level target can be increased to 7.5%–8.0%. For even shorter lifetime expectancy, the target can be adjusted up to 8.0%–9.0%. To prevent hypoglycemia, the target blood glucose level needs to be adjusted, particularly in older adult patients. Ultimately, it is important to identify the maximum blood glucose levels that do not cause hypoglycemia and the minimum blood glucose levels that do not cause hyperglycemia-associated complications.

Background: We analyzed hemoglobin A1c (HbA1c) levels and various lung function test results in healthy individuals after a 6-year follow-up period to explore the influence of lung function changes on glycemic control. Methods: Subjects whose HbA1c levels did not qualify as diabetes mellitus (DM) and who had at least two consecutive lung function tests were selected among the people who visited a health promotion center. Lung function parameters, including forced expiratory volume in 1 second (FEV1), forced vital capacity (FVC), FEV/FVC ratio, and forced expiratory flow 25% to 75% (FEF25%−75%), were divided into four groups based on their baseline quantiles. To evaluate future DM onset risk in relation to lung function changes, the correlation between baseline HbA1c levels and changes in lung function parameters after a 6-year follow-up period was analyzed. Results: Overall, 17,568 individuals were included; 0.9% of the subjects were diagnosed with DM. The individuals included in the quartile with FEV1/FVC ratio values of 78% to 82% had lower risk of DM than those in the quartile with FEV1/FVC ratio values of ≥86% after adjusting for age, sex, and body mass index (P=0.04). Baseline percent predicted FEV1, FVC, FEV1/FVC ratio, and FEF25%−75%, and differences in the FEV1/FVC ratio or FEF25%−75%, showed negative linear correlations with baseline HbA1c levels. Conclusion Healthy subjects with FEV1/FVC ratio values between 78% and 82% had 40% lower risk for future DM. Smaller differences and lower baseline FEV1/FVC ratio or FEF25%−75% values were associated with higher baseline HbA1c levels. These findings suggest that airflow limitation affects systemic glucose control and that the FEV1/FVC ratio could be one of the factors predicting future DM risk in healthy individuals.

BACKGROUND: We evaluated the efficacy and safety of acarbose add-on therapy in Korean patients with type 2 diabetes mellitus (T2DM) who are inadequately controlled with metformin and sitagliptin. METHODS: A total of 165 subjects were randomized to metformin and sitagliptin (Met+Sita, n=65), metformin, sitagliptin, and acarbose (Met+Sita+Acarb, n=66) and sitagliptin and acarbose (Sita+Acarb, exploratory assessment, n=34) therapy in five institutions in Korea. After 16 weeks of acarbose add-on or metformin-switch therapy, a triple combination therapy was maintained from week 16 to 24. RESULTS: The add-on of acarbose (Met+Sita+Acarb group) demonstrated a 0.44%±0.08% (P<0.001 vs. baseline) decrease in glycosylated hemoglobin (HbA1c) at week 16, while changes in HbA1c were insignificant in the Met+Sita group (−0.09%±0.10%, P=0.113). After 8 weeks of triple combination therapy, HbA1c levels were comparable between Met+Sita and Met+Sita+Acarb group (7.66%±0.13% vs. 7.47%±0.12%, P=0.321). Acarbose add-on therapy demonstrated suppressed glucagon secretion (area under the curve of glucagon, 4,726.17±415.80 ng·min/L vs. 3,314.38±191.63 ng·min/L, P=0.004) in the absence of excess insulin secretion during the meal tolerance tests at week 16 versus baseline. The incidence of adverse or serious adverse events was similar between two groups. CONCLUSION: In conclusion, a 16-week acarbose add-on therapy to metformin and sitagliptin, effectively lowered HbA1c without significant adverse events. Acarbose might be a good choice as a third-line therapy in addition to metformin and sitagliptin in Korean subjects with T2DM who have predominant postprandial hyperglycemia and a high carbohydrate intake.
Acarbose ; Diabetes Mellitus, Type 2 ; Drug Therapy, Combination ; Glucagon ; Hemoglobin A, Glycosylated ; Humans ; Hyperglycemia ; Incidence ; Insulin ; Korea ; Meals ; Metformin ; Sitagliptin Phosphate

PURPOSE: Routine screening for toxoplasmosis, rubella, cytomegalovirus (CMV), and herpes simplex virus (TORCH) in intrauterine growth restriction (IUGR) and small for gestational age (SGA) neonates has become a common practice. However, the incidence of TORCH varies across countries, and the cost of TORCH testing may be disadvantageous compared to disease-specific screening. To evaluate the efficacy of TORCH screening, the medical charts of IUGR or SGA neonates born in a single institution in Bucheon, Korea from 2011 to 2015 were reviewed. METHODS: The clinical data of the 126 IUGR or SGA neonates were gathered, including gestational age, Apgar scores, neonatal sonographic findings, chromosome study, morbidities, developmental follow-up, and growth catch-up. Maternal factors including underlying maternal disease and fetal sonography were collected, and placental findings were recorded when available. TORCH screening was done using serum IgM, CMV urine culture, quantification of CMV DNA with real-time polymerase chain reaction, and rapid plasma reagin qualitative test for syphilis. Tests were repeated only for those with positive results. RESULTS: Of the 119 TORCH screenings, only one was positive for toxoplasmosis IgM. This result was deemed false positive due to negative IgM on repeated testing and the absence of clinical symptoms. CONCLUSION: Considering the incidence and risk of TORCH in Korea, the financial burden of TORCH screening, and the single positive TORCH finding in our study, we suggest disease-specific screening based on maternal history and the clinical symptoms of the neonate. Regarding CMV, which may present asymptomatically, universal screening may be appropriate upon cost-benefit analysis.
Cost-Benefit Analysis ; Cytomegalovirus* ; DNA ; Fetal Growth Retardation ; Follow-Up Studies ; Gestational Age* ; Gyeonggi-do ; Herpes Simplex* ; Humans ; Immunoglobulin M ; Incidence ; Infant, Newborn* ; Korea* ; Mass Screening* ; Plasma ; Real-Time Polymerase Chain Reaction ; Rubella* ; Simplexvirus ; Syphilis ; Toxoplasmosis ; Ultrasonography

BACKGROUND/AIMS: This study evaluated the relationship between thyroid nodules and metabolic syndrome (MS) and its components in apparently healthy Koreans. METHODS: We reviewed the records of 3,298 subjects with no noticeable symptoms who underwent thyroid ultrasound imaging as part of a routine check-up between July 2009 and June 2010; of these, 1,308 were excluded based upon predefined criteria. Among the remaining 1,990 patients, we examined the association between MS and its components and the incidence of thyroid nodules. RESULTS: Of the 1,990 subjects included in this study, 38.4% (n = 764) had thyroid nodules and 12.7% (n = 253) had MS. Female sex, older age, higher body mass index, larger waist circumference, higher glycated hemoglobin level, lower thyroid stimulating hormone level, and presence of MS were all closely related with the presence of thyroid nodules (all p < 0.05). Furthermore, the relevant number of MS components showed a positive linear correlation with the occurrence of thyroid nodules (p < 0.001). Evidence of MS alone was not independently associated with thyroid nodules after adjusting for sex and age in a multivariate binary logistic regression analysis; however, glycated hemoglobin for females and waist circumference for males, as well as both age and thyroid stimulating hormone for all patients, were identified as independent predictors for the existence of thyroid nodules (all p < 0.05). CONCLUSIONS: This study suggests a positive relationship between the components of MS and thyroid nodules in an ostensibly healthy Korean population. Our data support the idea that the recent increase in thyroid nodules is partly due to increases in both MS and obesity.
Adult ; Biomarkers/blood ; Body Mass Index ; Chi-Square Distribution ; Cross-Sectional Studies ; Diabetes Mellitus/diagnosis/epidemiology ; Female ; Hemoglobin A, Glycosylated/analysis ; Humans ; Incidence ; Linear Models ; Logistic Models ; Male ; Metabolic Syndrome X/blood/diagnosis/*epidemiology ; Middle Aged ; Multivariate Analysis ; Obesity/diagnosis/epidemiology ; Odds Ratio ; Republic of Korea/epidemiology ; Retrospective Studies ; Risk Factors ; Thyroid Nodule/blood/diagnostic imaging/*epidemiology ; Thyrotropin/blood ; Waist Circumference

Chemerin is a recently identified adipokine suggested to play a role in obesity and its metabolic complications. The relationship between visceral obesity and serum chemerin levels in type 2 diabetes (T2DM) is unknown and may differ from that of subjects without diabetes. Therefore, we evaluated whether serum chemerin was associated with visceral abdominal obesity in patients with T2DM. A total of 218 Korean patients with T2DM were enrolled and metabolic parameters, abdominal visceral and subcutaneous fat areas, and serum chemerin levels were measured. Serum chemerin level showed positive correlation with fasting insulin, HOMA-IR, serum triglyceride, serum creatinine, urine albumin/creatinine ratio, high-sensitivity C-reactive protein (hsCRP), fibrinogen, abdominal visceral fat area, visceral to subcutaneous fat area ratio, and negatively correlation with high density lipoprotein cholesterol and creatinine clearance (CCr) after adjusting for age, gender and body mass index. Multiple linear stepwise regression analysis showed that abdominal visceral fat area (β = 0.001, P < 0.001), serum triglyceride (β = 0.001, P < 0.001), CCr (β = -0.003, P = 0.001), hsCRP (β = 0.157, P = 0.001), fibrinogen (β = 0.001, P < 0.001) and BMI (β = 0.02, P = 0.008) independently affected log transformed serum chemerin levels. Higher serum chemerin level was associated with higher level of abdominal visceral fat area, serum triglyceride, hsCRP and fibrinogen and lower level of CCr in patients with T2DM. Serum chemerin may be used as a biomarker of visceral adiposity and chemerin may play a role in inflammation, decreased renal function, and increased cardiovascular risk in T2DM.
Adult ; Biomarkers/blood ; Body Mass Index ; C-Reactive Protein/analysis ; Chemokines/*blood ; Creatinine/blood/urine ; Diabetes Mellitus, Type 2/*blood/diagnosis ; Female ; Humans ; Insulin/blood ; Intercellular Signaling Peptides and Proteins/*blood ; Intra-Abdominal Fat/*pathology ; Linear Models ; Lipocalins/blood ; Male ; Middle Aged ; Obesity/complications ; Triglycerides/blood