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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.

The incidence of obesity is increasing throughout the world, including Korea. Liraglutide, the main purpose of which is glucose control, has recently gained significant attention due to its additional effect on weight loss. Liraglutide injections have been widely used as an important treatment for obese patients in Korea. In addition to weight loss, liraglutide has various other effects, such as prevention of cardiovascular disease. Despite its excellent effect on weight loss, notable side effects, such as nausea and vomiting, have also been associated with liraglutide. Despite these side effects, liraglutide has not been discontinued due to its beneficial effects on weight loss. Nonetheless, there are reports wherein patients did not experience weight loss upon taking the drug. As such, there is a possibility of liraglutide misuse and abuse. Therefore, physicians need to have a broad understanding of liraglutide and understand the advantages and disadvantages of liraglutide prescription.

Background: 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 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.

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 β cell replacement therapy.

We investigated the risk of developing chronic kidney disease (CKD) in patients with young-onset Type 2 diabetes (YOD, diagnosed age 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), 1,480 out of 77,039 LOD patients and 34 out of 7,345 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.

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.

Type 2 diabetes develops due to functional β cell loss after insulin resistance. Sustained increased insulin demand compels β cells into apoptosis and dedifferentiation resulting in decreased β cell mass. In adult humans, β cell regeneration (proliferation, neogenesis or transdifferentiation) rarely occurs in a physiologic condition. Because of this stagnant nature of β cells, β cell mass are not sufficiently recovered once their numbers are severely decreased. Constant efforts have been made to replenish the decreased β cell mass in diabetic patients by islet or pancreas transplantation1,2 .

Loss of functional β-cell mass is an essential feature of type 2 diabetes, and maintaining mature β-cell identity is important for preserving a functional β-cell mass. However, it is unclear how β-cells achieve and maintain their mature identity. Here we demonstrate a novel function of protein arginine methyltransferase 1 (PRMT1) in maintaining mature β-cell identity. Prmt1 knockout in fetal and adult β-cells induced diabetes, which was aggravated by high-fat diet–induced metabolic stress. Deletion of Prmt1 in adult β-cells resulted in the immediate loss of histone H4 arginine 3 asymmetric dimethylation (H4R3me2a) and the subsequent loss of β-cell identity. The expression levels of genes involved in mature β-cell function and identity were robustly downregulated as soon as Prmt1 deletion was induced in adult β-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 β-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 β-cell identity by regulating chromatin accessibility.

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 β-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.

Purpose: Brainstem gliomas are usually inoperable and have a dismal prognosis. Based on the robust tropisms of neural stem cells (NSC) and mesenchymal stem cells (MSC) to brain tumors, we compared the tumor-tropic migratory capacities of these stem cells and evaluated the therapeutic potential of genetically engineered human NSCs encoding cytosine deaminase (CD) and IFNβ against brainstem gliomas. Experimental Design: The directed migratory capacities of NSCs and MSCs to brainstem glioma (F98) were evaluated both in vitro and in vivo. The human NSCs (HB1.F3) and various human MSCs, such as bone marrow–derived MSCs (HM3.B10), adipose tissue–derived MSCs, and umbilical cord blood–derived MSCs, were tested. Human fibroblast cells (HFF-1) were used as the negative control. As a proof of concept, the bioactivity of HB1.F3-CD-IFNβ was analyzed with a cell viability assay, and animals with brainstem gliomas were injected with HB1.F3-CD-IFNβ cells followed by systemic 5-fluorocytosine treatment. Results: In an in vitro modified Transwell migration assay and in vivo stem cell injection into established brainstem gliomas in rats, all the stem cells showed a significant migratory capacity compared with that of the control (P < 0.01). Histologic analysis showed a 59% reduction in tumor volume in the HB1.F3-CD-IFNβ–treated group (P < 0.05). Apoptotic cells were increased 2.33-fold in animals treated with HB1.F3-CD-IFNβ compared with the respective control groups (P < 0.01). Conclusion: The brainstem glioma-tropic migratory capacities of MSCs from various sources were similar to those of NSCs. Genetically engineered NSCs show therapeutic efficacy against brainstem gliomas.