The Application of Genomics in Diabetes: Barriers to Discovery and Implementation

Systems Biology to Address Unmet Medical Needs in Neurological Disorders

Neurological diseases are highly prevalent and constitute a significant cause of mortality and disability. Neurological disorders encompass a heterogeneous group of neurodegenerative conditions, broadly characterized by injury to the peripheral and/or central nervous system. Although the etiology of neurological diseases varies greatly, they share several characteristics, such as heterogeneity of clinical presentation, non-cell autonomous nature, and diversity of cellular, subcellular, and molecular pathways. Systems biology has emerged as a valuable platform for addressing the challenges of studying heterogeneous neurological diseases. Systems biology has manifold applications to address unmet medical needs for neurological illness, including integrating and correlating different large datasets covering the transcriptome, epigenome, proteome, and metabolome associated with a specific condition. This is particularly useful for disentangling the heterogeneity and complexity of neurological conditions. Hence, systems biology can help in uncovering pathophysiology to develop novel therapeutic targets and assessing the impact of known treatments on disease progression. Additionally, systems biology can identify early diagnostic biomarkers, to help diagnose neurological disease preceded by a long subclinical phase, as well as define the exposome, the collection of environmental toxicants that increase risk of certain neurological diseases. In addition to these current applications, there are numerous potential emergent uses, such as precision medicine.

Current progress in pharmacogenomics of Type 2 diabetes: A systemic overview

INTRODUCTION

Type 2 diabetes mellitus (T2DM) is a prevalent disease with incidences increasing globally at a rapid rate. The goal of T2DM treatment is to control glucose levels and prevent the aggravation of glycemic symptoms.

TREATMENT OPTIONS

T2DM regimen include metformin as the first-line, with sulfonylurea, thiazolidinedione (TZD), GLP-1, DPP4I, and SGLT2 inhibitor as the second-line treatment options. However, even with a multitude of choices, patient-to-patient variability due to pharmacogenomic differences still prevail.

CONCLUSION

This review aims to discuss the responses of the major T2DM medications influenced by pharmacogenomics and investigate improved personalized therapy for T2DM patients.

Precision Health Care Elements, Definitions, and Strategies for Patients with Diabetes: A Literature Review

Diabetes is a prevalent disease with a high risk of complications. The number of people with diabetes worldwide was reported to increase every year. However, new integrated individualized health care related to diabetes is insufficiently developed. Purpose: The objective of this study was to conduct a literature review and discover precision health care elements, definitions, and strategies. Methods: This study involved a 2-stage process. The first stage comprised a systematic literature search, evidence evaluation, and article extraction. The second stage involved discovering precision health care elements and defining and developing strategies for the management of patients with diabetes. Results: Of 1337 articles, we selected 35 relevant articles for identifying elements and definitions of precision health care for diabetes, including personalized genetic or lifestyle factors, biodata- or evidence-based practice, glycemic target, patient preferences, glycemic control, interdisciplinary collaboration practice, self-management, and patient priority direct care. Moreover, strategies were developed to apply precision health care for diabetes treatment based on eight elements. Conclusions: We discovered precision health care elements and defined and developed strategies of precision health care for patients with diabetes. precision health care is based on team foundation, personalized glycemic target, and control as well as patient preferences and priority, thus providing references for future research and clinical practice.

Social and behavioral science at the forefront of genomics: Discovery, translation, and health equity

This special issue highlights the unique role that social and behavioral science has to play at the forefront of genomics. Through the introduction of papers comprising this special issue, we outline priority research areas at the nexus of genomics and the social and behavioral sciences. These include: Discovery science; clinical and community translation, and equity, including engagement and inclusion of diverse populations in genomic science. We advocate for genomic discovery that considers social context, neural, cognitive, and behavioral endophenotypes, and that is grounded in social and behavioral science research and theory. Further, the social and behavioral sciences should play a leadership role in identifying best practices for effective clinical and community translation of genomic discoveries. Finally, inclusive research that engages diverse populations is necessary for genomic discovery and translation to benefit all. We also highlight ways that genomics can be a fruitful testbed for the development and refinement of social and behavioral science theory. Indeed, an expanded ecological lens that runs from genomes to society will be required to fully understand human behavior.

Effects of a Lifestyle-Modification Program on Blood-Glucose Regulation and Health Promotion in Diabetic Patients: A Randomized Controlled Trial

Background

We aimed to investigate the efficacy of the lifestyle intervention (LSI) program in controlling blood glucose regulation and health promotion in type 2 diabetic (T2D) patients.

Methods

Thirty adults with a diagnosed with diabetes were randomly assigned to LSI and control groups. The LSI group maintained their daily routines after participating twice in the LSI program, while control group maintained 4 weeks of daily life without participating in an intervention.

Results

HbA1c levels in the LSI group decreased significantly after participation (p = 0.025) compared with levels before the study, but there was no significant difference between the groups. The weight and body mass index (BMI) of the LSI group tended to decrease significantly compared with the control group (p = 0.054 and p = 0.055, respectively), and the waist circumference (WC) of the LSI group decreased significantly compared with that of the control group (p = 0.048). In the effects of the LSI program according to the polymorphism of GCKR genes, changes in glycated albumin (GA) (%), HbA1c, WC, BMI, and weight showed a significant decrease in the non-risk (TT genotype) GCKR group compared with the risk group (CC and TC genotype).

Conclusion

Application of the four-week LSI program to diabetics revealed positive effects on blood-glucose control and improvement in obesity indicators. In particular, the risk group with variations in the GCKR gene was associated with more genetic effects on indicators such as blood glucose and obesity than was the non-risk group.

A theoretical framework to guide a study for exploring the impact of established and potential risk factors for type 2 diabetes mellitus

AIM

The purpose of this paper is to present the conceptual framework that was used to guide a study on the development of type 2 diabetes mellitus (T2DM).

BACKGROUND

The Stress and Coping Framework for Type 2 Diabetes Mellitus was developed in response to a review of the literature that suggests psychological stress, or simply distress, can contribute toward the development of T2DM with the established risk factors of genetic risk for diabetes (GRD), obesity, and advancing age. The literature shows that distress can influence insulin sensitivity and contribute to the development of T2DM; however, much of the literature fails to acknowledge the influence of distress in collaboration with GRD, obesity, and advancing age.

METHOD

As part of creating the current framework, an earlier version of the conceptual framework was used for a pilot study. An integrative review was conducted to examine the relationships among GRD, obesity (as a response to weight management), advancing age, distress, and coping (as a response to distress) to further refine the conceptual framework. Theoretical and empirical studies were examined to define distress, describe the nature and impact of the stress response, and determine how distress interacts with GRD, obesity, and advancing age. The literature was used to create a conceptual framework and model consisting of the interactions among the variables.

CONCLUSION

The Stress and Coping Framework for Type 2 Diabetes Mellitus shows how distress can contribute to the development of T2DM by interacting directly with established risk factors and in promoting insulin resistance.

Recent advances and perspectives in next generation sequencing application to the genetic research of type 2 diabetes

Type 2 diabetes (T2D) mellitus is a common complex disease that currently affects more than 400 million people worldwide and has become a global health problem. High-throughput sequencing technologies such as whole-genome and whole-exome sequencing approaches have provided numerous new insights into the molecular bases of T2D. Recent advances in the application of sequencing technologies to T2D research include, but are not limited to: (1) Fine mapping of causal rare and common genetic variants; (2) Identification of confident gene-level associations; (3) Identification of novel candidate genes by specific scoring approaches; (4) Interrogation of disease-relevant genes and pathways by transcriptional profiling and epigenome mapping techniques; and (5) Investigation of microbial community alterations in patients with T2D. In this work we review these advances in application of next-generation sequencing methods for elucidation of T2D pathogenesis, as well as progress and challenges in implementation of this new knowledge about T2D genetics in diagnosis, prevention, and treatment of the disease.

Progress in the discovery of naturally occurring anti-diabetic drugs and in the identification of their molecular targets

Diabetes mellitus (DM), a chronic metabolic disease, severely affects patients' life and intensively increases risks of developing other diseases. It is estimated that 0.4 billion individuals worldwide are subjected to diabetes, especially type 2 diabetes mellitus. At present, although various synthetic drugs for diabetes such as Alogliptin and Rosiglitazone, etc. have been used to manage diabetes, some of them showed severe side effects. Given that the pathogenesis of type 2 diabetes mellitus, natural occurring drugs are beneficial alternatives for diabetes therapy with low adverse effects or toxicity. Recently, more and more plant-derived extracts or compounds were evaluated to have anti-diabetic activities. Their anti-diabetic mechanisms involve certain key targets like α-glucosidase, α-amylase, DPP-4, PPAR γ, PTP1B, and GLUT4, etc. Here, we summarize the newly found anti-diabetic (type 2 diabetes mellitus) natural compounds and extracts from 2011-2017, and give the identification of their molecular targets. This review could provide references for the research of natural agents curing type 2 diabetes mellitus (T2DM).

The Continuing Evolution of Precision Health in Type 2 Diabetes: Achievements and Challenges

PURPOSE OF REVIEW

The purpose of this review was to summarize recent advances in the genomics of type 2 diabetes (T2D) and to highlight current initiatives to advance precision health.

RECENT FINDINGS

Generation of multi-omic data to measure each of the "biologic layers," developments in describing genomic function and annotation in T2D relevant tissue, along with the increasing recognition that T2D is a heterogeneous disease, and large-scale collaborations have all contributed to advancing our understanding of the molecular basis of T2D. Substantial advances have been made in understanding the molecular basis of T2D pathogenesis, such that precision health diabetes is increasingly becoming a reality. For precision diabetes to become a routine in clinical and public health, additional large-scale multi-omic initiatives are needed along with better assessment of our environment to delineate an individual's diabetes subtype for improved detection and management.

Precision medicine in diabetes prevention, classification and management

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.

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.