Lipid Variability Induces Endothelial Dysfunction by Increasing Inflammation and Oxidative Stress.

Background: This study investigates the impact of fluctuating lipid levels on endothelial dysfunction. Methods: Human aortic and umbilical vein endothelial cells were cultured under varying palmitic acid (PA) concentrations: 0, 50, and 100 µM, and in a variability group alternating between 0 and 100 µM PA every 8 hours for 48 hours. In the lipid variability group, cells were exposed to 100 µM PA during the final 8 hours before analysis. We assessed inflammation using real-time polymerase chain reaction, Western blot, and cytokine enzyme-linked immunosorbent assay (ELISA); reactive oxygen species (ROS) levels with dichlorofluorescin diacetate assay; mitochondrial function through oxygen consumption rates via XF24 flux analyzer; and endothelial cell functionality via wound healing and cell adhesion assays. Cell viability was evaluated using the MTT assay. Results: Variable PA levels significantly upregulated inflammatory genes and adhesion molecules (Il6, Mcp1, Icam, Vcam, E-selectin, iNos) at both transcriptomic and protein levels in human endothelial cells. Oscillating lipid levels reduced basal respiration, adenosine triphosphate synthesis, and maximal respiration, indicating mitochondrial dysfunction. This lipid variability also elevated ROS levels, contributing to a chronic inflammatory state. Functionally, these changes impaired cell migration and increased monocyte adhesion, and induced endothelial apoptosis, evidenced by reduced cell viability, increased BAX, and decreased BCL2 expression. Conclusion: Lipid variability induce endothelial dysfunction by elevating inflammation and oxidative stress, providing mechanistic insights into how lipid variability increases cardiovascular risk.

Efficacy and Safety of Alogliptin-Pioglitazone Combination for Type 2 Diabetes Mellitus Poorly Controlled with Metformin: A Multicenter, Double-Blind Randomized Trial.

Background: Guidelines for switching to triple combination therapy directly after monotherapy failure are limited. This study investigated the efficacy, long-term sustainability, and safety of either mono or dual add-on therapy using alogliptin and pioglitazone for patients with type 2 diabetes mellitus (T2DM) who did not achieve their target glycemic range with metformin monotherapy. Methods: The Practical Evidence of Antidiabetic Combination Therapy in Korea (PEAK) was a multicenter, placebo-controlled, double-blind, randomized trial. A total of 214 participants were randomized to receive alogliptin+pioglitazone (Alo+Pio group, n=70), alogliptin (Alo group, n=75), or pioglitazone (Pio group, n=69). The primary outcome was the difference in glycosylated hemoglobin (HbA1c) levels between the three groups at baseline to 24 weeks. For durability, the achievement of HbA1c levels <7% and <6.5% was compared in each group. The number of adverse events was investigated for safety. Results: After 24 weeks of treatment, the change of HbA1c in the Alo+Pio, Alo, and Pio groups were -1.38%±0.08%, -1.03%±0.08%, and -0.84%±0.08%, respectively. The Alo+Pio group had significantly lower HbA1c levels than the other groups (P=0.0063, P<0.0001) and had a higher proportion of patients with target HbA1c achievement. In addition, insulin sensitivity and ß-cell function, lipid profiles, and other metabolic indicators were also improved. There were no significant safety issues in patients treated with triple combination therapy. Conclusion: Early combination triple therapy showed better efficacy and durability than the single add-on (dual) therapy. Therefore, combination therapy with metformin, alogliptin, and pioglitazone is a valuable early treatment option for T2DM poorly controlled with metformin monotherapy.

Prediction of Cardiovascular Complication in Patients with Newly Diagnosed Type 2 Diabetes Using an XGBoost/GRU-ODE-Bayes-Based Machine-Learning Algorithm.

BACKGRUOUND: 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.

Effectiveness of a Social Networking Site Based Automatic Mobile Message Providing System on Glycemic Control in Patients with Type 2 Diabetes Mellitus.

BACKGRUOUND: This study investigated the effectiveness of a social networking site (SNS)-based automatic mobile message providing system on glycemic control in patients with type 2 diabetes mellitus (T2DM). METHODS: A 3-month, randomized, open-label, controlled, parallel-group trial was conducted. One hundred and ten participants with T2DM were randomized to a mobile message system (MMS) (n=55) or control group (n=55). The MMS group received protocolbased automated messages two times per day for 10 weeks regarding diabetes self-management through KakaoTalk SNS messenger. The primary outcome was the difference in the change in glycated hemoglobin (HbA1c) levels (%) from baseline to week 12. RESULTS: HbA1c levels were more markedly decreased in the MMS group (8.4%±0.7% to 8.0%±1.1%) than in the control group (8.5%±0.8% to 8.4%±0.8%), resulting in a significant between-group difference (P=0.027). No differences were observed in changes in fasting glucose levels, lipid profiles, and the number of participants who experienced hypoglycemia, or in changes in lifestyle behavior between groups. However, the self-monitoring of blood glucose frequency was significantly increased in the MMS group compared to the control group (P=0.003). In addition, sleep duration was increased in the MMS group, but was not changed in the control group. CONCLUSION: An SNS-based automatic mobile message providing system was effective in improving glycemic control in patients in T2DM. Studies which based on a more individualized protocol, and investigate longer beneficial effect and sustainability will be required in the future.

Multiparity increases the risk of diabetes by impairing the proliferative capacity of pancreatic ß cells.

Pregnancy imposes a substantial metabolic burden on women, but little is known about whether or how multiple pregnancies increase the risk of maternal postpartum diabetes. In this study, we assessed the metabolic impact of multiple pregnancies in humans and in a rodent model. Mice that underwent multiple pregnancies had increased adiposity, but their glucose tolerance was initially improved compared to those of age-matched virgin mice. Later, however, insulin resistance developed over time, but insulin secretory function and compensatory pancreatic ß cell proliferation were impaired in multiparous mice. The ß cells of multiparous mice exhibited aging features, including telomere shortening and increased expression of Cdkn2a. Single-cell RNA-seq analysis revealed that the ß cells of multiparous mice exhibited upregulation of stress-related pathways and downregulation of cellular respiration- and oxidative phosphorylation-related pathways. In humans, women who delivered more than three times were more obese, and their plasma glucose concentrations were elevated compared to women who had delivered three or fewer times, as assessed at 2 months postpartum. The disposition index, which is a measure of the insulin secretory function of ß cells, decreased when women with higher parity gained body weight after delivery. Taken together, our findings indicate that multiple pregnancies induce cellular stress and aging features in ß cells, which impair their proliferative capacity to compensate for insulin resistance.

Risk of Cause-Specific Mortality across Glucose Spectrum in Elderly People: A Nationwide Population-Based Cohort Study.

BACKGRUOUND: This study investigated the risk of cause-specific mortality according to glucose tolerance status in elderly South Koreans. METHODS: A total of 1,292,264 individuals aged ≥65 years who received health examinations in 2009 were identified from the National Health Information Database. Participants were classified as normal glucose tolerance, impaired fasting glucose, newly-diagnosed diabetes, early diabetes (oral hypoglycemic agents ≤2), or advanced diabetes (oral hypoglycemic agents ≥3 or insulin). The risk of system-specific and disease-specific deaths was estimated using multivariate Cox proportional hazards analysis. RESULTS: During a median follow-up of 8.41 years, 257,356 deaths were recorded. Diabetes was associated with significantly higher risk of all-cause mortality (hazard ratio [HR], 1.58; 95% confidence interval [CI], 1.57 to 1.60); death due to circulatory (HR, 1.49; 95% CI, 1.46 to 1.52), respiratory (HR, 1.51; 95% CI, 1.47 to 1.55), and genitourinary systems (HR, 2.22; 95% CI, 2.10 to 2.35); and neoplasms (HR, 1.30; 95% CI, 1.28 to 1.32). Diabetes was also associated with a significantly higher risk of death due to ischemic heart disease (HR, 1.70; 95% CI, 1.63 to 1.76), cerebrovascular disease (HR, 1.46; 95% CI, 1.41 to 1.50), pneumonia (HR, 1.69; 95% CI, 1.63 to 1.76), and acute or chronic kidney disease (HR, 2.23; 95% CI, 2.09 to 2.38). There was a stepwise increase in the risk of death across the glucose spectrum (P for trend <0.0001). Stroke, heart failure, or chronic kidney disease increased the risk of all-cause mortality at every stage of glucose intolerance. CONCLUSION: A dose-dependent association between the risk of mortality from various causes and severity of glucose tolerance was noted in the elderly population.

Risk of developing chronic kidney disease in young-onset Type 2 diabetes in Korea.

We investigated the risk of developing chronic kidney disease (CKD) in patients with young-onset Type 2 diabetes (YOD, diagnosed age < 40 years). We enrolled 84,384 patients aged 20-64 who started anti-diabetic medication between 2010 and 2011 from the Korea National Health Insurance Sharing Service; patients with Type 1 diabetes or a history of CKD were excluded. Multivariate logistic regression analyses were performed to adjust for YOD-distinct variables and compare the incidence of CKD between YOD and late-onset diabetes (LOD, diagnosed age ≥ 40 years). During the median observation period of 5.16 years (interquartile range: 4.58-5.77 years), 1480 out of 77,039 LOD patients and 34 out of 7345 YOD patients developed CKD. Patients with YOD had distinct baseline characteristics compared with the patients with LOD. The odds ratio of developing CKD in patients with YOD over LOD was 1.70 (95% CI 1.15-2.51) after adjusting clinically distinct variables. The increased CKD odds in YOD compared with LOD was greater in the non-smoking group (OR 2.03, 95% CI 1.26-3.26) than in the smoking group (OR 1.49, 95% CI 0.74-2.98, p = 0.0393 for interaction). Among YOD patients, hypertension (34.76% vs. 64.71%, p = 0.0003), dyslipidemia (46.87% vs. 73.53%, p = 0.0019), and sulfonylurea use (35.54% vs. 52.94%, p = 0.0345) were associated with CKD development. YOD patients have a greater risk of developing CKD than LOD patients after adjusting clinically distinct variables.

Opening the Precision Diabetes Care through Digital Healthcare.

The national healthcare systems of every country in the world cannot sustain the rise in healthcare expenditure caused by chronic diseases and their complications. To sustain the national healthcare system, a novel system should be developed to improve the quality of care and minimize healthcare costs. For 20 years, our team developed patient-communicating digital healthcare platforms and proved their efficacy. National scale randomized control trials are underway to systematically measure the efficacy and economic benefits of this digital health care system. Precision medicine aims to maximize effectiveness of disease management by considering individual variability. Digital health technologies enable precision medicine at a reasonable cost that was not available before. The government launched the "National Integrated Bio-big Data Project" which will collect diverse health data from the participants. Individuals will share their health information to physicians or researchers at their will by gateway named "My-Healthway.' Taken together, now we stand in front of the evolution of medical care, so-called "Precision medicine." led by various kinds of technologies and a huge amount of health information exchange. We should lead these new trends as pioneers, not as followers, to establish and implement the best care for our patients that can help them to withstand their devastating diseases.

Improving Machine Learning Diabetes Prediction Models for the Utmost Clinical Effectiveness.

The early prediction of diabetes can facilitate interventions to prevent or delay it. This study proposes a diabetes prediction model based on machine learning (ML) to encourage individuals at risk of diabetes to employ healthy interventions. A total of 38,379 subjects were included. We trained the model on 80% of the subjects and verified its predictive performance on the remaining 20%. Furthermore, the performances of several algorithms were compared, including logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), Cox regression, and XGBoost Survival Embedding (XGBSE). The area under the receiver operating characteristic curve (AUROC) of the XGBoost model was the largest, followed by those of the decision tree, logistic regression, and random forest models. For the survival analysis, XGBSE yielded an AUROC exceeding 0.9 for the 2- to 9-year predictions and a C-index of 0.934, while the Cox regression achieved a C-index of 0.921. After lowering the threshold from 0.5 to 0.25, the sensitivity increased from 0.011 to 0.236 for the 2-year prediction model and from 0.607 to 0.994 for the 9-year prediction model, while the specificity showed negligible changes. We developed a high-performance diabetes prediction model that applied the XGBSE algorithm with threshold adjustment. We plan to use this prediction model in real clinical practice for diabetes prevention after simplifying and validating it externally.

Characteristics of Glycemic Control and Long-Term Complications in Patients with Young-Onset Type 2 Diabetes.

BACKGRUOUND: The prevalence of young-onset diabetes (YOD) has been increasing worldwide. As the incidence of YOD increases, it is necessary to determine the characteristics of YOD and the factors that influence its development and associated complications. METHODS: In this retrospective study, we recruited patients who were diagnosed with type 2 diabetes mellitus between June 2001 and December 2021 at a tertiary hospital. The study population was categorized according to age: YOD (age <40 years), middle-age-onset diabetes (MOD, 40≤ age <65 years), and late-onset diabetes (LOD, age ≥65 years). We examined trends in glycemic control by analyzing fasting glucose levels during the first year in each age group. A Cox proportional-hazards model was used to determine the relative risk of developing complications according to glycemic control trends. RESULTS: The fasting glucose level at the time of diagnosis was highest in the YOD group (YOD 149±65 mg/dL; MOD 143±54 mg/dL; and LOD 140±55 mg/dL; p=0.009). In the YOD group, glucose levels decreased at 3 months, but increased by 12 months. YOD patients and those with poor glycemic control in the first year were at a higher risk of developing complications, whereas the risk in patients with LOD was not statistically significant. CONCLUSION: YOD patients had higher glucose levels at diagnosis, and their glycemic control was poorly maintained. As poor glycemic control can influence the development of complications, especially in young patients, intensive treatment is necessary for patients with YOD.

ß cell replacement therapy for the cure of diabetes.

Islet transplantation is an important option in the treatment of type 1 diabetes. However, a donor shortage and immunosuppressant-related complications are the current major hurdles of islet transplantation. In this review, we discuss recent updates on islet transplantation to overcome these current obstacles and we share our perspectives on future ß cell replacement therapy.

PRMT1 Is Required for the Maintenance of Mature ß-Cell Identity.

Loss of functional ß-cell mass is an essential feature of type 2 diabetes, and maintaining mature ß-cell identity is important for preserving a functional ß-cell mass. However, it is unclear how ß-cells achieve and maintain their mature identity. Here we demonstrate a novel function of protein arginine methyltransferase 1 (PRMT1) in maintaining mature ß-cell identity. Prmt1 knockout in fetal and adult ß-cells induced diabetes, which was aggravated by high-fat diet-induced metabolic stress. Deletion of Prmt1 in adult ß-cells resulted in the immediate loss of histone H4 arginine 3 asymmetric dimethylation (H4R3me2a) and the subsequent loss of ß-cell identity. The expression levels of genes involved in mature ß-cell function and identity were robustly downregulated as soon as Prmt1 deletion was induced in adult ß-cells. Chromatin immunoprecipitation sequencing and assay for transposase-accessible chromatin sequencing analyses revealed that PRMT1-dependent H4R3me2a increases chromatin accessibility at the binding sites for CCCTC-binding factor (CTCF) and ß-cell transcription factors. In addition, PRMT1-dependent open chromatin regions may show an association with the risk of diabetes in humans. Together, our results indicate that PRMT1 plays an essential role in maintaining ß-cell identity by regulating chromatin accessibility.

Essential Role of Protein Arginine Methyltransferase 1 in Pancreas Development by Regulating Protein Stability of Neurogenin 3.

BACKGROUND: Protein arginine methyltransferase 1 (PRMT1) is a major enzyme responsible for the formation of methylarginine in mammalian cells. Recent studies have revealed that PRMT1 plays important roles in the development of various tissues. However, its role in pancreas development has not yet been elucidated. METHODS: Pancreatic progenitor cell-specific Prmt1 knock-out (Prmt1 PKO) mice were generated and characterized for their metabolic and histological phenotypes and their levels of Neurog3 gene expression and neurogenin 3 (NGN3) protein expression. Protein degradation assays were performed in mPAC cells. RESULTS: Prmt1 PKO mice showed growth retardation and a severely diabetic phenotype. The pancreatic size and ß-cell mass were significantly reduced in Prmt1 PKO mice. Proliferation of progenitor cells during the secondary transition was decreased and endocrine cell differentiation was impaired. These defects in pancreas development could be attributed to the sustained expression of NGN3 in progenitor cells. Protein degradation assays in mPAC cells revealed that PRMT1 was required for the rapid degradation of NGN3. CONCLUSION: PRMT1 critically contributes to pancreas development by destabilizing the NGN3 protein.

Triglyceride glucose index, a marker of insulin resistance, is associated with coronary artery stenosis in asymptomatic subjects with type 2 diabetes.

BACKGROUND: Insulin resistance is one of the most important contributing factors to cardiovascular disease. This study aimed to investigate the association between coronary artery stenosis (CAS) and triglyceride glucose index (TyG index), a simple insulin resistance marker, in asymptomatic subjects with type 2 diabetes. METHODS: We recruited asymptomatic adults with type 2 diabetes but without previous history of coronary heart disease (n = 888). Significant CAS was defined as maximum intraluminal stenosis ≥70 % by coronary CT angiography. TyG index was calculated as log [fasting triglycerides (mg/dl) x fasting glucose (mg/dl)/2]. RESULTS: Mean age was 63.8 ± 9.5 and 58.9 % of the subjects were men. We analyzed the participants according to the tertile of TyG index. The TyG index was correlated with HOMA-IR (r = 0.397, P < 0.001), and subjects with higher tertile of TyG index were younger but showed worse clinical and metabolic parameters. The prevalence of CAS was higher in subjects with higher tertile of TyG compared with those with lower tertile of TyG (14 % vs. 7.8 %, P = 0.022). On multiple regression analysis, the highest tertile of TyG index was an independent risk factor for CAS after adjustment for other confounders (odds ratio, 3.19 [95 % CI, 1.371-7.424]). Subgroup analysis showed that TyG index showed more significant association with CAS in patients with risk factors such as old age, longer duration of diabetes, poor glycemic control, no statin use, and male gender. CONCLUSION: Higher TyG index is associated with increased risk of CAS in asymptomatic subjects with type 2 diabetes, particularly when they have risk factors for cardiovascular disease. TRIAL REGISTRATION: This study was retrospectively registered in ClinicalTrials. gov with the registration number of NCT02070926 in Feb 23, 2014.