The Committee of Clinical Practice Guidelines of the Korean Diabetes Association (KDA) updated the previous clinical practice guidelines for Korean adults with diabetes and prediabetes and published the seventh edition in May 2021. We performed a comprehensive systematic review of recent clinical trials and evidence that could be applicable in real-world practice and suitable for the Korean population. The guideline is provided for all healthcare providers including physicians, diabetes experts, and certified diabetes educators across the country who manage patients with diabetes or the individuals at the risk of developing diabetes mellitus. The recommendations for screening diabetes and glucose-lowering agents have been revised and updated. New sections for continuous glucose monitoring, insulin pump use, and non-alcoholic fatty liver disease in patients with diabetes mellitus have been added. The KDA recommends active vaccination for coronavirus disease 2019 in patients with diabetes during the pandemic. An abridgement that contains practical information for patient education and systematic management in the clinic was published separately.

BackgroundFor an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set.MethodsWe use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance.ResultsIn predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models.ConclusionIn conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.

BackgroundBoth type 1 and type 2 diabetes are well-established risk factors for cardiovascular disease and early mortality. However, few studies have directly compared the hazards of cardiovascular outcomes and premature death among people with type 1 diabetes to those among people with type 2 diabetes and subjects without diabetes. Furthermore, information about the hazard of cardiovascular disease and early mortality among Asians with type 1 diabetes is sparse, although the clinical and epidemiological characteristics of Asians with type 1 diabetes are unlike those of Europeans. We estimated the hazard of myocardial infarction (MI), hospitalization for heart failure (HF), atrial fibrillation (AF), and mortality during follow-up in Korean adults with type 1 diabetes compared with those without diabetes and those with type 2 diabetes.MethodsWe used Korean National Health Insurance Service datasets of preventive health check-ups from 2009 to 2016 in this retrospective longitudinal study. The hazard ratios of MI, HF, AF, and mortality during follow-up were analyzed using the Cox regression analyses according to the presence and type of diabetes in ≥ 20-year-old individuals without baseline cardiovascular disease (N = 20,423,051). The presence and type of diabetes was determined based on the presence of type 1 or type 2 diabetes at baseline.ResultsDuring more than 93,300,000 person-years of follow-up, there were 116,649 MIs, 135,532 AF cases, 125,997 hospitalizations for HF, and 344,516 deaths. The fully-adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) for incident MI, hospitalized HF, AF, and all-cause death within the mean follow-up of 4.6 years were higher in the type 1 diabetes group than the type 2 diabetes [HR (95% CI) 1.679 (1.490–1.893) for MI; 2.105 (1.901–2.330) for HF; 1.608 (1.411–1.833) for AF; 1.884 (1.762–2.013) for death] and non-diabetes groups [HR (95% CI) 2.411 (2.138–2.718) for MI; 3.024 (2.730–3.350) for HF; 1.748 (1.534–1.993) for AF; 2.874 (2.689–3.073) for death].ConclusionsIn Korea, the presence of diabetes was associated with a higher hazard of cardiovascular disease and all-cause death. Specifically, people with type 1 diabetes had a higher hazard of cardiovascular disease and all-cause mortality compared to people with type 2 diabetes.