Personalized epigenetic management of diabetes

Clinical and genetic predictors of diabetes drug's response

Diabetes is a major health problem worldwide. Glycemic control is the main goal in the management of type 2 diabetes. While many anti-diabetic drugs and guidelines are available, almost half of diabetic patients do not reach their treatment goal and develop complications. The glucose-lowering response to anti-diabetic drug differs significantly between individuals. Relatively little is known about the factors that might underlie this response. The identification of predictors of response to anti-diabetic drugs is essential for treatment personalization. Unfortunately, the evidence on predictors of drugs response in type 2 diabetes is scarce. Only a few trials were designed for specific groups of patients (e.g. patients with renal impairment or older patients), while subgroup analyses of larger trials are frequently unreported. Physicians need help in picking the drug which provides the maximal benefit, with minimal side effects, in the right dose, for a specific patient, using an omics-based approach besides the phenotypic characteristics.

Collaborative platform development in nutrition as support for cardiovascular patients’ rehabilitation

Abstract Introduction. Enrollment of patients with cardiovascular disease in rehabilitation programs may contribute to implementation of a healthy lifestyle, including by promotion of a diet adequate for each patient’s profile. In this context, the current study is aimed at creating a traffic light system model allowing to obtain the development, innovation and diversification of menus and to improve the nutritional programs for this category of patients. Material and method. Based on the data provided by USDA Food Composition Databases, the composition in terms of different nutritive principles was determined for each ingredient and for each final menu. Comparison of menus depending on each nutritive principle, as well as comparison between menus and nutrient requirements according to indications for patients attending cardiovascular rehabilitation programs was made. Results. The traffic light system was developed, using color codes, comparing daily nutrient requirements with preparations’ content. Conclusions. The major benefit of the traffic light system would reside in the fact that starting from classic menus, an intervention on these can be achieved, and healthier, more nutritionally balanced models can be created, according to healthy nutrition principles. These new menus will be calorically and nutritionally adapted for patients attending cardiovascular rehabilitation programs. Key words: nutrition, rehabilitation, traffic light system,

Systems and precision medicine approaches to diabetes heterogeneity: a Big Data perspective

Big Data, and in particular Electronic Health Records, provide the medical community with a great opportunity to analyze multiple pathological conditions at an unprecedented depth for many complex diseases, including diabetes. How can we infer on diabetes from large heterogeneous datasets? A possible solution is provided by invoking next-generation computational methods and data analytics tools within systems medicine approaches. By deciphering the multi-faceted complexity of biological systems, the potential of emerging diagnostic tools and therapeutic functions can be ultimately revealed. In diabetes, a multidimensional approach to data analysis is needed to better understand the disease conditions, trajectories and the associated comorbidities. Elucidation of multidimensionality comes from the analysis of factors such as disease phenotypes, marker types, and biological motifs while seeking to make use of multiple levels of information including genetics, omics, clinical data, and environmental and lifestyle factors. Examining the synergy between multiple dimensions represents a challenge. In such regard, the role of Big Data fuels the rise of Precision Medicine by allowing an increasing number of descriptions to be captured from individuals. Thus, data curations and analyses should be designed to deliver highly accurate predicted risk profiles and treatment recommendations. It is important to establish linkages between systems and precision medicine in order to translate their principles into clinical practice. Equivalently, to realize their full potential, the involved multiple dimensions must be able to process information ensuring inter-exchange, reducing ambiguities and redundancies, and ultimately improving health care solutions by introducing clinical decision support systems focused on reclassified phenotypes (or digital biomarkers) and community-driven patient stratifications.

Personalized epigenetic management of diabetes

The novel genome-wide assays of epigenetic marks have resulted in a greater understanding of how genetics and the environment interact in the development and inheritance of diabetes. Chronic hyperglycemia induces epigenetic changes in multiple organs, contributing to diabetic complications. Specific epigenetic-modifying compounds have been developed to erase these modifications, possibly slowing down the onset of diabetes-related complications. The current review is an update of the previously published paper, describing the most recent advances in the epigenetics of diabetes.

Hyperglycemic memory in metabolism and cancer

Hyperglycemia is a hallmark of both type 1 diabetes mellitus (T1DM) and type 2 diabetes mellitus (T2DM). Recent evidence strongly suggests that prolonged exposure to hyperglycemia can epigenetically modify gene expression profiles in human cells and that this effect is sustained even after hyperglycemic control is therapeutically achieved; this phenomenon is called hyperglycemic memory. This metabolic memory effect contributes substantially to the pathology of various diabetic complications, such as diabetic retinopathy, hypertension, and diabetic nephropathy. Due to the metabolic memory in cells, diabetic patients suffer from various complications, even after hyperglycemia is controlled. With regard to this strong association between diabetes and cancer risk, cancer cells have emerged as key target cells of hyperglycemic memory in diabetic cancer patients. In this review, we will discuss the recent understandings of the molecular mechanisms underlying hyperglycemic memory in metabolism and cancer.

Genetic and epigenetic modifications in the pathogenesis of diabetic retinopathy: a molecular link to regulate gene expression

Intensification in the frequency of diabetes and the associated vascular complications has been a root cause of blindness and visual impairment worldwide. One such vascular complication which has been the prominent cause of blindness; retinal vasculature, neuronal and glial abnormalities is diabetic retinopathy (DR), a chronic complicated outcome of Type 1 and Type 2 diabetes. It has also become clear that "genetic" variations in population alone can't explain the development and progression of diabetes and its complications including DR. DR experiences engagement of foremost mediators of diabetes such as hyperglycemia, oxidant stress, and inflammatory factors that lead to the dysregulation of "epigenetic" mechanisms involving histone acetylation and histone and DNA methylation, chromatin remodeling and expression of a complex set of stress-regulated and disease-associated genes. In addition, both elevated glucose concentration and insulin resistance leave a robust effect on epigenetic reprogramming of the endothelial cells too, since endothelium associated with the eye aids in maintaining the vascular homeostasis. Furthermore, several studies conducted on the disease suggest that the modifications of the epigenome might be the fundamental mechanism(s) for the proposed metabolic memory' resulting into prolonged gene expression for inflammation and cellular dysfunction even after attaining the glycemic control in diabetics. Henceforth, the present review focuses on the aspects of genetic and epigenetic alterations in genes such as vascular endothelial growth factor and aldose reductase considered being associated with DR. In addition, we discuss briefly the role of the thioredoxin-interacting protein TXNIP, which is strongly induced by high glucose and diabetes, in cellular oxidative stress and mitochondrial dysfunction potentially leading to chromatin remodeling and ocular complications of diabetes. The identification of disease-associated genes and their epigenetic regulations will lead to potential new drugs and gene therapies as well as personalized medicine to prevent or slow down the progression of DR.

Challenges of implementing personalized (precision) medicine: a focus on diabetes

The concept of personalized (precision) medicine (PM) emphasizes the scientific and technological innovations that enable the physician to tailor disease prediction, diagnosis and treatment to the individual patient, based on a personalized data-driven approach. The major challenge for the medical systems is to translate the molecular and genomic advances into clinical available means. Patients and healthcare providers, the pharmaceutical and diagnostic industries manifest a growing interest in PM. Multiple stakeholders need adaptation and re-engineering for successful clinical implementation of PM. Drawing primarily from the field of ‘diabetes’, this article will summarize the main challenges to implementation of PM into current medical practice and some of the approaches currently being implemented to overcome these challenges.