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

GLUT4-overexpressing engineered muscle constructs as a therapeutic platform to normalize glycemia in diabetic mice

Skeletal muscle insulin resistance is a main defect in type 2 diabetes (T2D), which is associated with impaired function and content of glucose transporter type 4 (GLUT4). GLUT4 overexpression in skeletal muscle tissue can improve glucose homeostasis. Therefore, we created an engineered muscle construct (EMC) composed of GLUT4-overexpressing (OEG4) cells. The ability of the engineered implants to reduce fasting glucose levels was tested in diet-induced obesity mice. Decrease and stabilization of basal glucose levels were apparent up to 4 months after implantation. Analysis of the retrieved constructs showed elevated expression of myokines and proteins related to metabolic processes. In addition, we validated the efficiency of OEG4-EMCs in insulin-resistant mice. Following high glucose load administration, mice showed improved glucose tolerance. Our data indicate that OEG4-EMC implant is an efficient mode for restoring insulin sensitivity and improving glucose homeostasis in diabetic mice. Such procedure is a potential innovative modality for T2D therapy.

THE FUTURE OF MEDICINE, healthcare innovation through precision medicine: policy case study of Qatar

In 2016, the World Innovation Summit for Health (WISH) published its Forum Report on precision medicine "PRECISION MEDICINE - A GLOBAL ACTION PLAN FOR IMPACT". Healthcare is undergoing a transformation, and it is imperative to leverage new technologies to generate new data and support the advent of precision medicine (PM). Recent scientific breakthroughs and technological advancements have improved our disease knowledge and altered diagnosis and treatment approaches resulting in a more precise, predictive, preventative and personalized health care that is customized for the individual patient. Consequently, the big data revolution has provided an opportunity to apply artificial intelligence and machine learning algorithms to mine such a vast data set. Additionally, personalized medicine promises to revolutionize healthcare, with its key goal of providing the right treatment to the right patient at the right time and dose, and thus the potential of improving quality of life and helping to bring down healthcare costs.This policy briefing will look in detail at the issues surrounding continued development, sustained investment, risk factors, testing and approval of innovations for better strategy and faster process. The paper will serve as a policy bridge that is required to enhance a conscious decision among the powers-that-be in Qatar in order to find a way to harmonize multiple strands of activity and responsibility in the health arena. The end goal will be for Qatar to enhance public awareness and engagement and to integrate effectively the incredible advances in research into healthcare systems, for the benefit of all patients.The PM policy briefing provides concrete recommendations on moving forward with PM initiatives in Qatar and internationally. Equally important, integration of PM within a primary care setting, building a coalition of community champions through awareness and advocacy, finally, communicating PM value, patient engagement/empowerment and education/continued professional development programs of the healthcare workforce.Key recommendations for implementation of precision medicine inside and outside Qatar: 1. Create Community Awareness and PM Education Programs 2. Engage and Empower Patients 3. Communicate PM Value 4. Develop appropriate Infrastructure and Information Management Systems 5. Integrate PM into standard Healthcare System and Ensure Access to Care PM is no longer futuristic. It is here. Implementing PM in routine clinical care does require some investment and infrastructure development. Invariably, cost and lack of expertise are cited as barriers to PM implementation. Equally consequential, are the curriculum and professional development of medical care experts.Policymakers need to lead and coordinate effort among stakeholders and consider cultural and faith perspectives to ensure success. It is essential that policymakers integrate PM approaches into national strategies to improve health and health care for all, and to drive towards the future of medicine precision health.

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.

Computational Methods to Investigate the Impact of miRNAs on Pathways

Pathway analysis is a wide class of methods allowing to determine the alteration of functional processes in complex diseases. However, biological pathways are still partial, and knowledge coming from posttranscriptional regulators has started to be considered in a systematic way only recently. Here we will give a global and updated view of the main pathway and subpathway analysis methodologies, focusing on the improvements obtained through the recent introduction of microRNAs as regulatory elements in these frameworks.

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.