Personalized Management of Type 2 Diabetes

Intracellular calcium channels: Potential targets for type 2 diabetes mellitus?

Type 2 diabetes mellitus (T2DM) is a prevalent metabolic disorder. Despite the availability of numerous pharmacotherapies, a range of adverse reactions, including hypoglycemia, gastrointestinal discomfort, and lactic acidosis, limits their patient applicability and long-term application. Therefore, it is necessary to screen novel therapeutic drugs for T2DM treatment that have high efficacy but few adverse effects. AMP-activated protein kinase (AMPK) stands out as one of the most powerful targets for T2DM treatment. It can be activated through energy-sensing or calcium signaling. Medications that activate AMPK through the energy-sensing mechanism exhibit remarkable potency, but they are accompanied by lactic acidosis, carrying an alarmingly high mortality rate. Interestingly, medications that activate AMPK through calcium signaling, such as gliclazide, seldom induce lactic acidosis. However, the efficacy of gliclazide is much lower than metformin. Therefore, it is necessary to explore targets that activate AMPK via calcium signaling to avoid lactic acidosis while maintaining high potency. Ion channels are the main controller of intracellular calcium flow. Specific agonists and inhibitors targeting ion channels have been reported to activate AMPK. In this review, we will summarize the structure and function of calcium-permeable ion channels and discuss the potential of targeting these calcium channels for T2DM treatment.

Association between clinical and laboratory factors and response to sodium-glucose cotransporter 2 inhibitors in patients with type 2 diabetes: a retrospective observational study

BACKGROUND

This study aimed to investigate the association between clinical and laboratory parameters and response to therapy with sodium-glucose cotransporter 2 inhibitors (SGLT2i) in patients with type 2 diabetes mellitus (T2D).

RESEARCH DESIGN AND METHODS

We retrospectively analyzed the medical records of people with T2D in whom SGLT2i was started. Clinical and laboratory parameters were recorded before, 3 and 6 months after starting treatment. Specific criteria were applied to classify participants into good and poor responders in terms of weight loss (primary outcome) and glycemic control (secondary outcome), separately.

RESULTS

Fifty individuals (64% men) with a mean age of 65.8 ± 8.5 years were included in the analysis. 86% and 64% of the participants were classified into good response categories for glycemic control and weight loss, respectively. Good responders in terms of glycemic control had lower high-density lipoprotein cholesterol levels at baseline compared to poor responders (43.3 vs 57.4 mg/dl, p = 0.044). In the logistic regression analysis, a higher baseline weight was associated with a better response to therapy in terms of weight loss (p = 0.04).

CONCLUSIONS

Our findings suggest that specific clinical and laboratory parameters are associated with response to SGLT2i treatment and can contribute to a more personalized approach to T2D care.

Predicting three-month fasting blood glucose and glycated hemoglobin changes in patients with type 2 diabetes mellitus based on multiple machine learning algorithms

Fasting blood glucose (FBG) and glycosylated hemoglobin (HbA1c) are key indicators reflecting blood glucose control in type 2 diabetes mellitus (T2DM) patients. The purpose of this study is to establish a predictive model for blood glucose changes in T2DM patients after 3 months of treatment, achieving personalized treatment.A retrospective study was conducted on type 2 diabetes mellitus real-world medical data from 4 cities in Sichuan Province, China from January 2015 to December 2020. After data preprocessing, data inputting, data sampling, and feature screening, 16 kinds of machine learning methods were used to construct prediction models, and 5 prediction models with the best prediction performance were screened respectively. A total of 100,000 cases were included to establish the FBG model, and 2,169 cases were established to establish the HbA1c model. The best prediction model both of FBG and HbA1c finally obtained are realized by ensemble learning and modified random forest inputting, the AUC values are 0.819 and 0.970, respectively. The most important indicators of the FBG and HbA1c prediction model were FBG and HbA1c. Medication compliance, follow-up outcome, dietary habits, BMI, and waist circumference also had a greater impact on FBG levels. The prediction accuracy of the models of the two blood glucose control indicators is high and has certain clinical applicability.HbA1c and FBG are mutually important predictors, and there is a close relationship between them.

Understanding Type 2 Diabetes Mellitus Risk Parameters through Intermittent Fasting: A Machine Learning Approach

Type 2 diabetes mellitus (T2DM) is a chronic metabolic disorder characterized by elevated blood glucose levels. Despite the availability of pharmacological treatments, dietary plans, and exercise regimens, T2DM remains a significant global cause of mortality. As a result, there is an increasing interest in exploring lifestyle interventions, such as intermittent fasting (IF). This study aims to identify underlying patterns and principles for effectively improving T2DM risk parameters through IF. By analyzing data from multiple randomized clinical trials investigating various IF interventions in humans, a machine learning algorithm was employed to develop a personalized recommendation system. This system offers guidance tailored to pre-diabetic and diabetic individuals, suggesting the most suitable IF interventions to improve T2DM risk parameters. With a success rate of 95%, this recommendation system provides highly individualized advice, optimizing the benefits of IF for diverse population subgroups. The outcomes of this study lead us to conclude that weight is a crucial feature for females, while age plays a determining role for males in reducing glucose levels in blood. By revealing patterns in diabetes risk parameters among individuals, this study not only offers practical guidance but also sheds light on the underlying mechanisms of T2DM, contributing to a deeper understanding of this complex metabolic disorder.

Pharmacomicrobiomics and type 2 diabetes mellitus: A novel perspective towards possible treatment

Type 2 diabetes mellitus (T2DM), a major driver of mortality worldwide, is more likely to develop other cardiometabolic risk factors, ultimately leading to diabetes-related mortality. Although a set of measures including lifestyle intervention and antidiabetic drugs have been proposed to manage T2DM, problems associated with potential side-effects and drug resistance are still unresolved. Pharmacomicrobiomics is an emerging field that investigates the interactions between the gut microbiome and drug response variability or drug toxicity. In recent years, increasing evidence supports that the gut microbiome, as the second genome, can serve as an attractive target for improving drug efficacy and safety by manipulating its composition. In this review, we outline the different composition of gut microbiome in T2DM and highlight how these microbiomes actually play a vital role in its development. Furthermore, we also investigate current state-of-the-art knowledge on pharmacomicrobiomics and microbiome's role in modulating the response to antidiabetic drugs, as well as provide innovative potential personalized treatments, including approaches for predicting response to treatment and for modulating the microbiome to improve drug efficacy or reduce drug toxicity.

Drosophila melanogaster diabetes models and its usage in the research of anti-diabetes management with traditional Chinese medicines

The prevalence of diabetes mellitus (DM) is increasing rapidly worldwide, but the underlying molecular mechanisms of disease development have not been elucidated, and the current popular anti-diabetic approaches still have non-negligible limitations. In the last decades, several different DM models were established on the classic model animal, the fruit fly (Drosophila melanogaster), which provided a convenient way to study the mechanisms underlying diabetes and to discover and evaluate new anti-diabetic compounds. In this article, we introduce the Drosophila Diabetes model from three aspects, including signal pathways, established methods, and pharmacodynamic evaluations. As a highlight, the progress in the treatments and experimental studies of diabetes with Traditional Chinese Medicine (TCM) based on the Drosophila Diabetes model is reviewed. We believe that the values of TCMs are underrated in DM management, and the Drosophila Diabetes models can provide a much more efficient tool to explore its values of it.

Evaluation of the Clinical Efficacy of the Treatment of Overweight and Obesity in Type 2 Diabetes Mellitus by the Telemedicine Management System Based on the Internet of Things Technology

Objective

To explore the application value of medical intelligent electronic system under the background of Internet of Things in the clinical study of the treatment of overweight/obesity in type 2 diabetes mellitus (T2DM) with empagliflozin combined with liraglutide; 50 overweight and obese adult T2DM patients in our hospital were randomly divided into the combined group and the control group, 25 cases in each group. The control group was treated with liraglutide alone, while the combined group was treated with empagliflozin on the basis of liraglutide. Based on the Internet of Things technology, with diabetes management as the core, the functions of information collection, transmission, and storage of T2DM patients are realized. Doctors pass the diabetes management plan to T2DM patients through the platform, supervise the implementation, and finally compare the clinical efficacy of the two groups.

Results

Compared with before treatment, the body mass index (BMI), fasting blood glucose (FPG), postprandial blood glucose (2hPG), glycosylated hemoglobin (HbAlc), islet beta cell secretion function index (HOMA-β), islet resistance index (HOMA-IR), total cholesterol (TC), and triglyceride (TG) in both groups decreased significantly after treatment. After combined treatment, systolic blood pressure (SBP), diastolic blood pressure (DBP), FPG, 2hPG, HbA1c, and HOMA-IR in the combined group were significantly lower than those in the control group (P < 0.05). Hypoglycemia occurred in both groups, with 2 cases in the control group and 4 cases in the combined group.

Conclusion

The telemedicine management system based on Internet of Things technology can improve patients' self-management ability and provide a new choice for individualized treatment of overweight/obesity T2DM patients. The combination therapy of empagliflozin and liraglutide can effectively reduce blood sugar, weight, blood pressure, blood lipid, and hypoglycemia and effectively improve insulin resistance and secretion function of islet β cells in T2DM patients.

[Chronic kidney injury in patients with liver diseases - Reappraising pathophysiology and treatment options]

Acute and chronic kidney disease concurs commonly with liver disease and is associated with a wide array of complications including dialysis dependency and increased mortality. Patients with liver disease or liver cirrhosis show a higher prevalence of chronic kidney disease. This is attributed to concomitant comorbidities, such as metabolic syndrome, chronic inflammation, hypercoagulability, hyperfibrinolysis, diabetes mellitus and dyslipidaemias. But chronic progressive kidney disease is not always due to hepatorenal syndrome. Beyond that, other diseases or disease entities should be considered. Among them are diabetic nephropathy, secondary IgA nephropathy, hepatitis C -associated membranoproliferative Glomerulonephritis (MPGN) and hepatitis B-associated membranous nephropathy.Coexisting diseases, similar underlying pathophysiologic mechanisms, or simultaneously concurring pathophysiological processes and overlapping clinical manifestations, impede the etiologic diagnosis and corresponding treatment of chronic kidney disease in the setting of chronic liver disease. In this review, we focus on common and rare pathologies, which can lead to chronic kidney disease in this particular patient group and try to summarize the most recent therapeutic modalities.

Genetic predisposition in type 2 diabetes: A promising approach toward a personalized management of diabetes

Diabetes mellitus, also known simply as diabetes, has been described as a chronic and complex endocrine metabolic disorder that is a leading cause of death across the globe. It is considered a key public health problem worldwide and one of four important non-communicable diseases prioritized for intervention through world health campaigns by various international foundations. Among its four categories, Type 2 diabetes (T2D) is the commonest form of diabetes accounting for over 90% of worldwide cases. Unlike monogenic inherited disorders that are passed on in a simple pattern, T2D is a multifactorial disease with a complex etiology, where a mixture of genetic and environmental factors are strong candidates for the development of the clinical condition and pathology. The genetic factors are believed to be key predisposing determinants in individual susceptibility to T2D. Therefore, identifying the predisposing genetic variants could be a crucial step in T2D management as it may ameliorate the clinical condition and preclude complications. Through an understanding the unique genetic and environmental factors that influence the development of this chronic disease individuals can benefit from personalized approaches to treatment. We searched the literature published in three electronic databases: PubMed, Scopus and ISI Web of Science for the current status of T2D and its associated genetic risk variants and discus promising approaches toward a personalized management of this chronic, non-communicable disorder.