Can Precision Medicine Reduce the Burden of Diabetes?
Ontology highlight
ABSTRACT:
SUBMITTER: Burke W
PROVIDER: S-EPMC6919975 | biostudies-literature | 2019
REPOSITORIES: biostudies-literature
Publications
Can Precision Medicine Reduce the Burden of Diabetes?
Burke Wylie W Trinidad Susan Brown SB Schenck David D
Ethnicity & disease 20191212 Suppl 3
Precision medicine is a new health care concept intended to hasten progress toward individualized treatment and, in so doing, to improve everyone's opportunity to enjoy good health. Yet, this concept pays scant attention to opportunities for change in the social determinants that are the major drivers of health. Precision medicine research is likely to generate improvements in medical care but may have the unintended consequence of worsening existing disparities in health care access. For preven ...[more]
Similar Datasets
Project description:Type 1 diabetes is a complex, chronic disease in which the insulin-producing beta cells in the pancreas are sufficiently altered or impaired to result in requirement of exogenous insulin for survival. The development of type 1 diabetes is thought to be an autoimmune process, in which an environmental (unknown) trigger initiates a T cell-mediated immune response in genetically susceptible individuals. The presence of islet autoantibodies in the blood are signs of type 1 diabetes development, and risk of progressing to clinical type 1 diabetes is correlated with the presence of multiple islet autoantibodies. Currently, a "staging" model of type 1 diabetes proposes discrete components consisting of normal blood glucose but at least two islet autoantibodies (Stage 1), abnormal blood glucose with at least two islet autoantibodies (Stage 2), and clinical diagnosis (Stage 3). While these stages may, in fact, not be discrete and vary by individual, the format suggests important applications of precision medicine to diagnosis, prevention, prognosis, treatment and monitoring. In this paper, applications of precision medicine in type 1 diabetes are discussed, with both opportunities and barriers to global implementation highlighted. Several groups have implemented components of precision medicine, yet the integration of the necessary steps to achieve both short- and long-term solutions will need to involve researchers, patients, families, and healthcare providers to fully impact and reduce the burden of type 1 diabetes.
| S-EPMC10393845 | biostudies-literature
Project description:Monogenic diabetes refers to diabetes mellitus (DM) caused by a mutation in a single gene and accounts for approximately 1%-5% of diabetes. Correct diagnosis is clinically critical for certain types of monogenic diabetes, since the appropriate treatment is determined by the etiology of the disease (e.g., oral sulfonylurea treatment of HNF1A/HNF4A-diabetes vs. insulin injections in type 1 diabetes). However, achieving a correct diagnosis requires genetic testing, and the overlapping of the clinical features of monogenic diabetes with those of type 1 and type 2 diabetes has frequently led to misdiagnosis. Improvements in sequencing technology are increasing opportunities to diagnose monogenic diabetes, but challenges remain. In this Review, we describe the types of monogenic diabetes, including common and uncommon types of maturity-onset diabetes of the young, multiple causes of neonatal DM, and syndromic diabetes such as Wolfram syndrome and lipodystrophy. We also review methods of prioritizing patients undergoing genetic testing, and highlight existing challenges facing sequence data interpretation that can be addressed by forming collaborations of expertise and by pooling cases.
| S-EPMC7843214 | biostudies-literature
Project description:Diabetes has become a major burden of healthcare expenditure. Diabetes management following a uniform treatment algorithm is often associated with progressive treatment failure and development of diabetic complications. Recent advances in our understanding of the genomic architecture of diabetes and its complications have provided the framework for development of precision medicine to personalize diabetes prevention and management. In the present review, we summarized recent advances in the understanding of the genetic basis of diabetes and its complications. From a clinician's perspective, we attempted to provide a balanced perspective on the utility of genomic medicine in the field of diabetes. Using genetic information to guide management of monogenic forms of diabetes represents the best-known examples of genomic medicine for diabetes. Although major strides have been made in genetic research for diabetes, its complications and pharmacogenetics, ongoing efforts are required to translate these findings into practice by incorporating genetic information into a risk prediction model for prioritization of treatment strategies, as well as using multi-omic analyses to discover novel drug targets with companion diagnostics. Further research is also required to ensure the appropriate use of this information to empower individuals and healthcare professionals to make personalized decisions for achieving the optimal outcome.
| S-EPMC6123056 | biostudies-literature
Project description:The epidemic of type 2 diabetes (T2D) is a significant global public health challenge and a major cause of morbidity and mortality. Despite the recent proliferation of pharmacological agents for the treatment of T2D, current therapies simply treat the symptom, i.e. hyperglycemia, and do not directly address the underlying disease process or modify the disease course. This article summarizes how genomic discovery has contributed to unraveling the heterogeneity in T2D, reviews relevant discoveries in the pharmacogenetics of five commonly prescribed glucose-lowering agents, presents evidence supporting how pharmacogenetics can be leveraged to advance precision medicine, and calls attention to important research gaps to its implementation to guide treatment choices.
| S-EPMC9531144 | biostudies-literature
Project description:Diabetes mellitus is prevalent worldwide and affects 1 in 10 adults. Despite the successful development of glucose-lowering drugs, such as glucagon-like peptide-1 (GLP-1) receptor agonists and sodium-glucose cotransporter-2 inhibitors recently, the proportion of patients achieving satisfactory glucose control has not risen as expected. The heterogeneity of diabetes determines that a one-size-fits-all strategy is not suitable for people with diabetes. Diabetes is undoubtedly more heterogeneous than the conventional subclassification, such as type 1, type 2, monogenic and gestational diabetes. The recent progress in genetics and epigenetics of diabetes has gradually unveiled the mechanisms underlying the heterogeneity of diabetes, and cluster analysis has shown promising results in the substratification of type 2 diabetes, which accounts for 95% of diabetic patients. More recently, the rapid development of sophisticated glucose monitoring and artificial intelligence technologies further enabled comprehensive consideration of the complex individual genetic and clinical information and might ultimately realize a precision diagnosis and treatment in diabetics.
| S-EPMC10953763 | biostudies-literature
Project description:Diabetes has become a major burden of healthcare expenditure. Diabetes management following a uniform treatment algorithm is often associated with progressive treatment failure and development of diabetic complications. Recent advances in our understanding of the genomic architecture of diabetes and its complications have provided the framework for development of precision medicine to personalize diabetes prevention and management. In the present review, we summarized recent advances in the understanding of the genetic basis of diabetes and its complications. From a clinician's perspective, we attempted to provide a balanced perspective on the utility of genomic medicine in the field of diabetes. Using genetic information to guide management of monogenic forms of diabetes represents the best-known examples of genomic medicine for diabetes. Although major strides have been made in genetic research for diabetes, its complications and pharmacogenetics, ongoing efforts are required to translate these findings into practice by incorporating genetic information into a risk prediction model for prioritization of treatment strategies, as well as using multi-omic analyses to discover novel drug targets with companion diagnostics. Further research is also required to ensure the appropriate use of this information to empower individuals and healthcare professionals to make personalized decisions for achieving the optimal outcome.
| S-EPMC6123056 | biostudies-literature
Project description:Metabolic disorders present a public health challenge of staggering proportions. In diabetes, there is an urgent need to better understand disease heterogeneity, clinical trajectories, and related comorbidities. A pressing and timely question is whether we are ready for precision medicine in diabetes. Some biological insights that have emerged during the last decade have already been used to direct clinical decision making, especially in monogenic forms of diabetes. However, much work is necessary to integrate high-dimensional explorations into complex disease architectures, less penetrant biological alterations, and broader phenotypes, such as type 2 diabetes. In addition, for precision medicine to take hold in diabetes, reproducibility, interpretability, and actionability remain key guiding objectives. In this review, we examine how mounting data sets generated during the last decade to understand biological variability are now inspiring new venues to clarify diabetes nosology and ultimately translate findings into more effective prevention and treatment strategies.
| S-EPMC6686889 | biostudies-literature
Project description:Genomic medicine can enhance prevention and treatment. First, we propose that advances in genomics have the potential to enhance assessment of disease risk, improve prognostic predictions, and guide treatment development and application. Clinical implementation of polygenic risk scores (PRSs) has emerged as an area of active research. The pathway from genomic discovery to implementation is an iterative process. Second, we provide examples on how genomic medicine has the potential to solve problems in prevention and treatment using two examples: Lung cancer screening and evidence-based tobacco treatment are both under-utilized and great opportunities for genomic interventions. Third, we discuss the translational process for developing genomic interventions from evidence to implementation by presenting a model to evaluate genomic evidence for clinical implementation, mechanisms of genomic interventions, and patient desire for genomic interventions. Fourth, we present potential challenges in genomic interventions including a great need for evidence in all diverse populations, little evidence on treatment algorithms, challenges in accommodating a dynamic evidence base, and implementation challenges in real world clinical settings. Finally, we conclude that research to identify genomic markers that are associated with smoking cessation success and the efficacy of smoking cessation treatments is needed to empower people of all diverse ancestry. Importantly, genomic data can be used to help identify patients with elevated risk for nicotine addiction, difficulty quitting smoking, favorable response to specific pharmacotherapy, and tobacco-related health problems.
| S-EPMC10434839 | biostudies-literature
Project description:The driving force behind the current global type 2 diabetes epidemic is insulin resistance in overweight and obese individuals. Dietary factors, physical inactivity, and sedentary behaviors are the major modifiable risk factors for obesity. Nevertheless, many overweight/obese people do not develop diabetes and lifestyle interventions focused on weight loss and diabetes prevention are often ineffective. Traditionally, chronically elevated blood glucose concentrations have been the hallmark of diabetes; however, many individuals will either remain 'prediabetic' or regress to normoglycemia. Thus, there is a growing need for innovative strategies to tackle diabetes at scale. The emergence of biomarker technologies has allowed more targeted therapeutic strategies for diabetes prevention (precision medicine), though largely confined to pharmacotherapy. Unlike most drugs, lifestyle interventions often have systemic health-enhancing effects. Thus, the pursuance of lifestyle precision medicine in diabetes seems rational. Herein, we review the literature on lifestyle interventions and diabetes prevention, describing the biological systems that can be characterized at scale in human populations, linking them to lifestyle in diabetes, and consider some of the challenges impeding the clinical translation of lifestyle precision medicine.
| S-EPMC5609030 | biostudies-literature
Project description: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.
| S-EPMC5526830 | biostudies-other