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.

OBJECTIVEAlthough increasing evidence suggests the association between short-term variability of fasting plasma glucose (FPG) and diabetic complications or mortality, the impact of visit-to-visit variability of FPG on the development of type 2 diabetes (T2D) has not been evaluated. RESEARCH DESIGN AND METHODSOur analysis included 131,744 Korean men and women without diabetes using the Korean National Health Insurance System cohort with periodic health examination program. FPG variability was calculated using the coefficient of variation (FPG-CV), SD (FPG-SD), and variability independent of the mean (FPG-VIM). RESULTSDuring the median follow-up time of 8.3 years, Kaplan-Meier curves demonstrated lower disease-free probability in the higher FPG variability group compared with the lower FPG variability group. Multivariable Cox proportional hazards analysis exhibited that the hazard ratio for incident T2D was 1.67 (95% CI 1.58-1.77, P < 0.001) in the highest quartile of FPG-CV compared with the lowest quartile of FPG-CV after adjusting for confounding variables, including mean FPG. The association between FPG variability and the risk of T2D was consistent when modeling using FPG-SD and FPG-VIM in both normal and impaired fasting glucose groups. A 1 SD increase in the FPG-CV was associated with a 24% increased risk of T2D in the fully adjusted model. CONCLUSIONSIncreased variability of FPG is associated with the development of T2D independently of diverse risk factors.Glycemic variability has recently drawn attention as another aspect of glycemic control and may contribute to additional risk of diabetic complications independent of hemoglobin A 1c (HbA 1c ) (1). Several human studies (2,3) suggested an association between glycemic variability and all-cause/cardiovascular mortality in patients with type 2 diabetes. Interestingly, mean fasting plasma glucose (FPG) level was not significantly associated with mortality in a multivariate analysis after adjusting for the coefficient of variation (CV) of FPG (3). In addition, long-term FPG variability was

BackgroundSkeletal muscle mass was negatively associated with metabolic syndrome prevalence in previous cross-sectional studies. The aim of this study was to investigate the impact of baseline skeletal muscle mass and changes in skeletal muscle mass over time on the development of metabolic syndrome in a large population-based 7-year cohort study.MethodsA total of 14,830 and 11,639 individuals who underwent health examinations at the Health Promotion Center at Samsung Medical Center, Seoul, Korea were included in the analyses of baseline skeletal muscle mass and those changes from baseline over 1 year, respectively. Skeletal muscle mass was estimated by bioelectrical impedance analysis and was presented as a skeletal muscle mass index (SMI), a body weight-adjusted appendicular skeletal muscle mass value. Using Cox regression models, hazard ratio for developing metabolic syndrome associated with SMI values at baseline or changes of SMI over a year was analyzed.ResultsDuring 7 years of follow-up, 20.1% of subjects developed metabolic syndrome. Compared to the lowest sex-specific SMI tertile at baseline, the highest sex-specific SMI tertile showed a significant inverse association with metabolic syndrome risk (adjusted hazard ratio [AHR] = 0.61, 95% confidence interval [CI] 0.54–0.68). Furthermore, compared with SMI changes < 0% over a year, multivariate-AHRs for metabolic syndrome development were 0.87 (95% CI 0.78–0.97) for 0–1% changes and 0.67 (0.56–0.79) for > 1% changes in SMI over 1 year after additionally adjusting for baseline SMI and glycometabolic parameters.ConclusionsAn increase in relative skeletal muscle mass over time has a potential preventive effect on developing metabolic syndrome, independently of baseline skeletal muscle mass and glycometabolic parameters.Electronic supplementary materialThe online version of this article (10.1186/s12933-018-0659-2) contains supplementary material, which is available to authorized users.

Background Nonalcoholic fatty liver disease (NAFLD) is a hepatic manifestation of metabolic disease and independently affects the development of cardiovascular (CV) disease. We investigated whether hepatic steatosis and/or fibrosis are associated with the development of incident heart failure (iHF), hospitalized HF (hHF), mortality, and CV death in both the general population and HF patients. Methods We analyzed 778,739 individuals without HF and 7445 patients with pre-existing HF aged 40 to 80 years who underwent a national health check-up from January 2009 to December 2012. The presence of hepatic steatosis and advanced hepatic fibrosis was determined using cutoff values for fatty liver index (FLI) and BARD score. We evaluated the association of FLI or BARD score with the development of iHF, hHF, mortality and CV death using multivariable-adjusted Cox regression models. Results A total of 28,524 (3.7%) individuals in the general population and 1422 (19.1%) pre-existing HF patients developed iHF and hHF respectively. In the multivariable-adjusted model, participants with an FLI ≥ 60 were at increased risk for iHF (hazard ratio [HR], 95% confidence interval [CI], 1.30, 1.24–1.36), hHF (HR 1.54, 95% CI 1.44–1.66), all-cause mortality (HR 1.62, 95% CI 1.54–1.70), and CV mortality (HR 1.41 95% CI 1.22–1.63) in the general population and hHF (HR 1.26, 95% CI 1.21–1.54) and all-cause mortality (HR 1.54 95% CI 1.24–1.92) in the HF patient group compared with an FLI < 20. Among participants with NAFLD, advanced liver fibrosis was associated with increased risk for iHF, hHF, and all-cause mortality in the general population and all-cause mortality and CV mortality in the HF patient group (all p < 0.05). Conclusion Hepatic steatosis and/or advanced fibrosis as assessed by FLI and BARD score was significantly associated with the risk of HF and mortality.

Background The purpose of this study was to establish the association between continuous glucose monitoring (CGM)‐defined glycaemic variability (GV) and cardiovascular autonomic neuropathy (CAN) in type 1 diabetes independent of mean glucose and to examine the relative contribution of each internationally standardized CGM parameter to this association. Materials and methods This study included 80 adults with type 1 diabetes who underwent 3‐day CGM and autonomic function tests within 3 months. The degree of association between internationally standardized CGM parameters and CAN, defined as at least two abnormal parasympathetic tests or the presence of orthostatic hypotension, were analysed by logistic regression, receiver operating characteristics (ROC), and dominance analysis. Results A total of 36 subjects (45.0%) were diagnosed with CAN. When adjusted with mean glucose and clinical risk factors of CAN, standard deviation, coefficient of variation, mean amplitude of glycaemic excursion, percent time in level 1 (glucose 54‐69 mg/dL) and level 2 (glucose < 54 mg/dL) hypoglycaemia, area under the curve in level 2 hypoglycaemia, low blood glucose index, high blood glucose index, and percent time in glucose 70 to 180 mg/dL were independently associated with CAN. Multivariable ROC analysis and dominance analysis revealed the highest relative contribution of percent time in level 2 hypoglycaemia to the independent associations between CGM parameters and presence of CAN. Conclusions CGM‐defined GV was associated with CAN independent of mean glucose in adults with type 1 diabetes. Among internationally standardized CGM parameters, those describing the degree of level 2 hypoglycaemia were the most significant contributors to this association.

Background: We aimed to investigate the hazard of hospitalization for heart failure (hHF) according to the transitions in metabolic health and obesity status. Methods: The Korean National Health Insurance Service datasets from 2002 to 2017 were used for this nationwide, longitudinal, population-based study. The hazard of hHF was analyzed according to the eight groups stratified by stability in metabolic health and transition in obesity status among initially metabolically healthy adults who underwent two cycles of health examinations in 2009-2010 and 2013-2014 (N = 7,148,763). Results: During two examinations, 48.43% of the initially metabolically healthy obese (MHO) individuals and 20.94% of the initially metabolically healthy non-obese (MHNO) individuals showed changes in their metabolic health and obesity status. During a mean follow-up of 3.70 years, 3151 individuals were hospitalized for HF. When stable MHNO individuals were set as the reference, transition to metabolically unhealthy phenotype was associated with an increased hazard of hHF; the hazard ratio (HR) and 95% confidence interval (CI) in the individuals who transformed from MHO to metabolically unhealthy non-obese was 2.033 (1.579-2.616). The constant MHO group had a 17.3% increased hazard of hHF compared with the stable MHNO group [HR (95% CI) 1.173 (1.039-1.325)]. Individuals who shifted from MHO to MHNO showed a 34.3% lower hazard of hHF compared with those who maintained the MHO category [HR (95% CI) 0.657 (0.508-0.849)]. Conclusion: Dynamic changes in metabolic health and obesity status were observed during a relatively short interval of 3-5 years. Loss of metabolic health was significantly associated with an increased hazard of hHF. Even if metabolic health was maintained, persistent obesity remained as a risk factor for hHF, and transition from MHO to MHNO had a protective effect against hHF. Therefore, the prevention and control of obesity while maintaining metabolic health would be crucial in preventing hHF.