scholarly journals Can We Prevent Mitochondrial Dysfunction and Diabetic Cardiomyopathy in Type 1 Diabetes Mellitus? Pathophysiology and Treatment Options

2020 ◽  
Vol 21 (8) ◽  
pp. 2852
Author(s):  
Aleksandra Cieluch ◽  
Aleksandra Uruska ◽  
Dorota Zozulinska-Ziolkiewicz
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Mitochondrial Dysfunction ◽  
Type 1 Diabetes Mellitus ◽  
Diabetic Cardiomyopathy ◽  
Treatment Options ◽  
Therapeutic Approaches ◽  
Stress Studies ◽  
Chronic Hyperglycemia

Type 1 diabetes mellitus is a disease involving changes to energy metabolism. Chronic hyperglycemia is a major cause of diabetes complications. Hyperglycemia induces mechanisms that generate the excessive production of reactive oxygen species, leading to the development of oxidative stress. Studies with animal models have indicated the involvement of mitochondrial dysfunction in the pathogenesis of diabetic cardiomyopathy. In the current review, we aimed to collect scientific reports linking disorders in mitochondrial functioning with the development of diabetic cardiomyopathy in type 1 diabetes mellitus. We also aimed to present therapeutic approaches counteracting the development of mitochondrial dysfunction and diabetic cardiomyopathy in type 1 diabetes mellitus.

scholarly journals A Comparison of Feature Selection and Forecasting Machine Learning Algorithms for Predicting Glycaemia in Type 1 Diabetes Mellitus

2021 ◽  
Vol 11 (4) ◽  
pp. 1742
Author(s):  
Ignacio Rodríguez-Rodríguez ◽  
José-Víctor Rodríguez ◽  
Wai Lok Woo ◽  
Bo Wei ◽  
Domingo-Javier Pardo-Quiles
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Type 1 Diabetes ◽  
Feature Selection ◽  
Blood Glucose ◽  
Type 1 Diabetes Mellitus ◽  
Support Vector ◽  
Chronic Hyperglycemia ◽  
Predictive Algorithms

Type 1 diabetes mellitus (DM1) is a metabolic disease derived from falls in pancreatic insulin production resulting in chronic hyperglycemia. DM1 subjects usually have to undertake a number of assessments of blood glucose levels every day, employing capillary glucometers for the monitoring of blood glucose dynamics. In recent years, advances in technology have allowed for the creation of revolutionary biosensors and continuous glucose monitoring (CGM) techniques. This has enabled the monitoring of a subject’s blood glucose level in real time. On the other hand, few attempts have been made to apply machine learning techniques to predicting glycaemia levels, but dealing with a database containing such a high level of variables is problematic. In this sense, to the best of the authors’ knowledge, the issues of proper feature selection (FS)—the stage before applying predictive algorithms—have not been subject to in-depth discussion and comparison in past research when it comes to forecasting glycaemia. Therefore, in order to assess how a proper FS stage could improve the accuracy of the glycaemia forecasted, this work has developed six FS techniques alongside four predictive algorithms, applying them to a full dataset of biomedical features related to glycaemia. These were harvested through a wide-ranging passive monitoring process involving 25 patients with DM1 in practical real-life scenarios. From the obtained results, we affirm that Random Forest (RF) as both predictive algorithm and FS strategy offers the best average performance (Root Median Square Error, RMSE = 18.54 mg/dL) throughout the 12 considered predictive horizons (up to 60 min in steps of 5 min), showing Support Vector Machines (SVM) to have the best accuracy as a forecasting algorithm when considering, in turn, the average of the six FS techniques applied (RMSE = 20.58 mg/dL).

Effect of the inflammation, chronic hyperglycemia, or malabsorption on the apolipoprotein A-IV concentration in type 1 diabetes mellitus and in diabetes secondary to chronic pancreatitis

Metabolism ◽  
2001 ◽  
Vol 50 (9) ◽  
pp. 1019-1024 ◽  
Author(s):  
Didier Quilliot ◽  
Evelyne Walters ◽  
Bruno Guerci ◽  
Jean-Charles Fruchart ◽  
Patrick Duriez ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Chronic Pancreatitis ◽  
Type 1 Diabetes Mellitus ◽  
Chronic Hyperglycemia ◽  
Apolipoprotein A

scholarly journals Analysis of circRNA regulatory network in myocardial tissue of type 1 diabetic mice

2021 ◽  
Vol 67 (3) ◽  
pp. 201-203
Author(s):  
Jianjun Li ◽  
Xiaoxiao Li ◽  
Xiaoming Qiao
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Type 1 Diabetes Mellitus ◽  
Diabetic Cardiomyopathy ◽  
Heart Function ◽  
Control Group ◽  
Diabetic Patients ◽  
Myocardial Tissue ◽  
Regulation Network

Type 1 diabetes mellitus, also called insulin-dependent diabetes is associated with elevated blood glucose concentration arising from the inability of the pancreas to produce insulin. Diabetic cardiomyopathy is a major cause of death in diabetic patients. CircRNAs have been reported to participate in various human diseases, including diabetic cardiomyopathy. In this study, the regulation network of circRNA in type 1 diabetes mellitus was investigated. Streptozotocin treatment was implemented to induce type 1 diabetes mellitus in the mouse model, and echocardiography was implemented to detect the heart function of the type 1 diabetes mellitus mouse. Also, the qRT-PCR assay was used to identify the circRNA expression in type 1 diabetes mellitus mouse myocardial tissue. Findings showed that heart function of type 1 diabetes mellitus mouse was significantly damaged than control group mouse and cardiac hypertrophy in type 1 diabetes mellitus mouse, circRNAs were aberrantly regulated in type 1 diabetes mellitus mouse myocardial tissue. The following circRNAs were mmu_circ_0001560, mmu_circ_0001800, mmu_circ_0001801, mmu_circ_0002281 and mmu_circ_0000614 were expressed low in type 1 diabetes mellitus mouse myocardial tissue. In conclusion, type 1 diabetes mellitus caused alterations in the regulation network of circRNAs.

scholarly journals A Comparison of Different Models of Glycemia Dynamics for Improved Type 1 Diabetes Mellitus Management with Advanced Intelligent Analysis in an Internet of Things Context

2020 ◽  
Vol 10 (12) ◽  
pp. 4381 ◽  
Author(s):  
Ignacio Rodríguez-Rodríguez ◽  
José-Víctor Rodríguez ◽  
José-María Molina-García-Pardo ◽  
Miguel-Ángel Zamora-Izquierdo ◽  
María-Teresa Martínez-Inglés
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Blood Glucose ◽  
Internet Of Things ◽  
Type 1 Diabetes Mellitus ◽  
High Volume ◽  
Daily Basis ◽  
Data Set ◽  
Chronic Hyperglycemia

The metabolic disease Type 1 Diabetes Mellitus (DM1) is caused by a reduction in the production of pancreatic insulin, which causes chronic hyperglycemia. Patients with DM1 are required to perform multiple blood glucose measurements on a daily basis to monitor their blood glucose dynamics through the use of capillary glucometers. In more recent times, technological developments have led to the development of cutting-edge biosensors and Continuous Glucose Monitoring (CGM) systems that can monitor patients’ blood glucose levels on a real-time basis. This offers medical providers access to glucose oscillations modeling interventions that can enhance DM1 treatment and management approaches through the use of novel disruptive technologies, such as Cloud Computing (CC), big data, Intelligent Data Analysis (IDA) and the Internet of Things (IoT). This work applies some advanced modeling techniques to a complete data set of glycemia-related biomedical features—obtained through an extensive, passive monitoring campaign undertaken with 25 DM1 patients under real-world conditions—in order to model glucose level dynamics through the proper identification of patterns. Hereby, four methods, which are run through CC due to the high volume of data collected, are applied and compared within an IoT context. The results show that Bayesian Regularized Neural Networks (BRNN) offer the best performance (0.83 R2) with a reduced Root Median Squared Error (RMSE) of 14.03 mg/dL.

scholarly journals Cardiovascular Disease in Type 1 Diabetes Mellitus: Epidemiology and Management of Cardiovascular Risk

2021 ◽  
Vol 10 (8) ◽  
pp. 1798
Author(s):  
Cristina Colom ◽  
Anna Rull ◽  
José Luis Sanchez-Quesada ◽  
Antonio Pérez
Keyword(s):  
Diabetes Mellitus ◽  
Risk Factors ◽  
Cardiovascular Disease ◽  
Type 1 Diabetes ◽  
Cardiovascular Risk ◽  
Type 1 Diabetes Mellitus ◽  
Current Knowledge ◽  
Chronic Hyperglycemia ◽  
Potential Risk Factors

Cardiovascular disease (CVD) is a major cause of mortality in type 1 diabetes mellitus (T1DM) patients, and cardiovascular risk (CVR) remains high even in T1DM patients with good metabolic control. The underlying mechanisms remain poorly understood and known risk factors seem to operate differently in T1DM and type 2 diabetes mellitus (T2DM) patients. However, evidence of cardiovascular risk assessment and management in T1DM patients often is extrapolated from studies on T2DM patients or the general population. In this review, we examine the existing literature about the prevalence of clinical and subclinical CVD, as well as current knowledge about potential risk factors involved in the development and progression of atherosclerosis in T1DM patients. We also discuss current approaches to the stratification and therapeutic management of CVR in T1DM patients. Chronic hyperglycemia plays an important role, but it is likely that other potential factors are involved in increased atherosclerosis and CVD in T1DM patients. Evidence on the estimation of 10-year and lifetime risk of CVD, as well as the efficiency and age at which current cardiovascular medications should be initiated in young T1DM patients, is very limited and clearly insufficient to establish evidence-based therapeutic approaches to CVD management.

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