Precision medicine for managing diabetes

Omics technologies in personalized combination therapy for cardiovascular diseases: challenges and opportunities

The primary purpose of 'omics' technologies is to understand the intricacy of genomics, proteomics, metabolomics and other molecular mechanisms to reveal the complex traits of human diseases. The significant use of omics technologies and their applications in medicine gear up the study of the pathogenesis of several disorders. The detection of biomarkers in the early onset of diseases is challenging; still, omics can discover novel molecular mechanisms and biomarkers. In this review, the different types of omics and their technologies are explicated and aimed to provide their emerging applications in cardiovascular precision medicine. These technologies significantly impact optimizing medical treatment for individuals to reach a higher level in precision medicine.

Neural network fast-classifies biological images through features selecting to power automated microscopy

Artificial intelligence is nowadays used for cell detection and classification in optical microscopy during post-acquisition analysis. The microscopes are now fully automated and next expected to be smart by making acquisition decisions based on the images. It calls for analysing them on the fly. Biology further imposes training on a reduced data set due to cost and time to prepare the samples and have the data sets annotated by experts. We propose a real-time image processing compliant with these specifications by balancing accurate detection and execution performance. We characterised the images using a generic, high-dimensional feature extractor. We then classified the images using machine learning to understand the contribution of each feature in decision and execution time. We found that the non-linear-classifier random forests outperformed Fisher's linear discriminant. More importantly, the most discriminant and time-consuming features could be excluded without significant accuracy loss, offering a substantial gain in execution time. It suggests a feature-group redundancy likely related to the biology of the observed cells. We offer a method to select fast and discriminant features. In our assay, a 79.6 ± 2.4% accurate classification of a cell took 68.7 ± 3.5 ms (mean ± SD, 5-fold cross-validation nested in 10 bootstrap repeats), corresponding to 14 cells per second, dispatched into eight phases of the cell cycle, using 12 feature groups and operating a consumer market ARM-based embedded system. A simple neural network offered similar performances paving the way to faster training and classification, using parallel execution on a general-purpose graphic processing unit. Finally, this strategy is also usable for deep neural networks paving the way to optimizing these algorithms for smart microscopy.

Monogenic Diabetes: From Genetic Insights to Population-Based Precision in Care. Reflections From a Diabetes Care Editors' Expert Forum

Individualization of therapy based on a person's specific type of diabetes is one key element of a "precision medicine" approach to diabetes care. However, applying such an approach remains difficult because of barriers such as disease heterogeneity, difficulties in accurately diagnosing different types of diabetes, multiple genetic influences, incomplete understanding of pathophysiology, limitations of current therapies, and environmental, social, and psychological factors. Monogenic diabetes, for which single gene mutations are causal, is the category most suited to a precision approach. The pathophysiological mechanisms of monogenic diabetes are understood better than those of any other form of diabetes. Thus, this category offers the advantage of accurate diagnosis of nonoverlapping etiological subgroups for which specific interventions can be applied. Although representing a small proportion of all diabetes cases, monogenic forms present an opportunity to demonstrate the feasibility of precision medicine strategies. In June 2019, the editors of Diabetes Care convened a panel of experts to discuss this opportunity. This article summarizes the major themes that arose at that forum. It presents an overview of the common causes of monogenic diabetes, describes some challenges in identifying and treating these disorders, and reports experience with various approaches to screening, diagnosis, and management. This article complements a larger American Diabetes Association effort supporting implementation of precision medicine for monogenic diabetes, which could serve as a platform for a broader initiative to apply more precise tactics to treating the more common forms of diabetes.

The Need for Precision Medicine to be Applied to Diabetes

Precision medicine refers to the tailoring of medical treatment for an individual based on large amounts of biologic and extrinsic data. The fast advancing fields of molecular biology, gene sequencing, machine learning, and other technologies enable precision medicine to utilize this detailed information to enhance clinical management decision-making for an individual in the real time of the disease course. Traditional clinical decision making is based on reacting to a relatively limited number of phenotypes that are determined by history, physical examination, and conventional lab tests. Precision medicine depends on highly detailed profiling of the patient's genetic, morphologic, and metabolic makeup. The precision medicine approach can be applied to individuals with diabetes to select treatments most likely to offer benefit and least likely to cause side effects, offering prospects of improved clinical outcomes and economic costs savings over current empiric practices. As genetic, metabolomic, immunologic, and other sophisticated testing becomes less expensive and more widespread in the medical record, it is expected that precision medicine will become increasingly applied to diabetes care.

Role of fructosamine-3-kinase in protecting against the onset of microvascular and macrovascular complications in patients with T2DM

INTRODUCTION

Microangiopathic and macroangiopathic complications are the main cause of morbidity and mortality in the diabetic population. Numerous publications have highlighted the role of glycation in the onset of complications of diabetes. In this context, the detection of fructosamine-3-kinase (FN3K)-an enzyme capable of counteracting the effect of hyperglycemia by intervening in protein glycation-has attracted great interest. Several studies have linked FN3K genetic variability to its enzymatic activity and glycated hemoglobin (HbA1c) levels. Here, we investigated the role of FN3K polymorphisms in the development of microvascular and macrovascular complications of diabetes.

RESEARCH DESIGN AND METHODS

The anthropometric and biochemical parameters, and any medical history of microangiopathic and macroangiopathic complications, were documented in a sample of 80 subjects with type 2 diabetes. All subjects were screened for FN3K gene and analyzed for the combination of three polymorphisms known to be associated with its enzymatic activity (rs3859206 and rs2256339 in the promoter region and rs1056534 in exon 6).

RESULTS

The combination of allelic variants of FN3K polymorphisms resulted in 13 distinct genotypic variants within the cohort. Comparison between genotypes showed no significant differences in terms of demographic, anthropometric and biochemical parameters, risk markers and long-term complications, except for a higher age and vitamin E levels associated with the genotype presenting GG at position -385, TT at position -232, and CC at c.900 A. Evaluating the microangiopathic and macroangiopathic complications as a whole, we found that they appeared significantly less present in this genotype compared with all other genotypes (p=0.0306).

CONCLUSIONS

The group of patients carrying the favorable allele for the three polymorphisms of the FN3K gene revealed less severe microangiopathy and macroangiopathy, suggesting a protective role of this genotype against the onset of the complications of diabetes.

Characterizing the weight-glycemia phenotypes of type 1 diabetes in youth and young adulthood

INTRODUCTION

Individuals with type 1 diabetes (T1D) present with diverse body weight status and degrees of glycemic control, which may warrant different treatment approaches. We sought to identify subgroups sharing phenotypes based on both weight and glycemia and compare characteristics across subgroups.

RESEARCH DESIGN AND METHODS

Participants with T1D in the SEARCH study cohort (n=1817, 6.0-30.4 years) were seen at a follow-up visit >5 years after diagnosis. Hierarchical agglomerative clustering was used to group participants based on five measures summarizing the joint distribution of body mass index z-score (BMIz) and hemoglobin A1c (HbA1c) which were estimated by reinforcement learning tree predictions from 28 covariates. Interpretation of cluster weight status and glycemic control was based on mean BMIz and HbA1c, respectively.

RESULTS

The sample was 49.5% female and 55.5% non-Hispanic white (NHW); mean±SD age=17.6±4.5 years, T1D duration=7.8±1.9 years, BMIz=0.61±0.94, and HbA1c=76±21 mmol/mol (9.1±1.9)%. Six weight-glycemia clusters were identified, including four normal weight, one overweight, and one subgroup with obesity. No cluster had a mean HbA1c <58 mmol/mol (7.5%). Cluster 1 (34.0%) was normal weight with the lowest HbA1c and comprised 85% NHW participants with the highest socioeconomic position, insulin pump use, dietary quality, and physical activity. Subgroups with very poor glycemic control (ie, ≥108 mmol/mol (≥12.0%); cluster 4, 4.4%, and cluster 5, 7.5%) and obesity (cluster 6, 15.4%) had a lower proportion of NHW youth, lower socioeconomic position, and reported decreased pump use and poorer health behaviors (overall p<0.01). The overweight subgroup with very poor glycemic control (cluster 5) showed the highest lipids and blood pressure (p<0.01).

CONCLUSIONS

There are distinct subgroups of youth and young adults with T1D that share weight-glycemia phenotypes. Subgroups may benefit from tailored interventions addressing differences in clinical care, health behaviors, and underlying health inequity.

Creating a common language: defining individualized, personalized and precision prevention in public health

Background

Because of the limited success of population-based prevention methods and due to developments in genomic screening, public health professionals and health policy makers are increasingly interested in more individualized prevention strategies. However, the terminology applied in this field is still ambiguous and thus has the potential to create misunderstandings.

Methods

A narrative literature review was conducted to identify how individualized, personalized and precision prevention are used in research papers and documents. Based on the findings a set of definitions were created that distinguish between these activities in a meaningful way.

Results

Definitions were found only for precision prevention, not for individualized or personalized prevention. The definitions of individualized, personalized and precision medicine were therefore used to create the definitions for their prevention counterparts. By these definitions, individualized prevention consists of all types of prevention that are individual-based; personalized prevention also consists of at least one form of -omic screening; and precision prevention further includes psychological, behavioral and socioeconomic data for each patient.

Conclusions

By defining these three key terms for different types of individual-based prevention both researchers and health policy makers can differentiate and use them in their proper context.

Precision medicine for multiple sclerosis promotes preventative medicine

Multiple sclerosis (MS) is a chronic, lifelong disease, currently without a cure that is responsible for significant neurological injury in young adults. Precision medicine for MS aims to provide a more exacting and refined approach toward management by providing recommendations based on disease subtype, clinical status, existing radiological data, para-clinical data, and other biological markers. To achieve better outcomes, the three stages of care-diagnosis, treatment, and management-should be optimized. However, as the temporal profile of disease behavior is highly variable in MS, and unlike outcomes from other chronic conditions (i.e., hypertension, diabetes mellitus, etc.), should precision medicine for MS be one that focuses more on disease prevention and lifestyle modifications beyond recommendations for the use of disease-modifying therapies? As scientific advancements continue within the field of neuroimmunology, and until reliable biomarkers that predict disease outcomes are available, success may be better achieved by focusing on modifiable factors to reduce future disability.

Digital Health Interventions for Diabetes: Everything to Gain and Nothing to Lose

IN BRIEF The traditional approach to integrating new therapies involves long, expensive roadmaps with evidence generation required for multiple stakeholders, most notably regulators and clinicians. More recently, new technologies such as insulin delivery systems and continuous glucose monitoring devices have become mainstream without complete clinical evidence being available when they were first introduced. There is tremendous enthusiasm from investors, industry, and people with diabetes regarding the potential of digital health to add value to diabetes care, and this enthusiasm exists despite a paucity of high-quality clinical evidence from traditional randomized clinical trials. Moreover, the potential of diabetes digital health technologies has been recognized by the U.S. Food and Drug Administration and other regulators, who are changing their approaches to allow easier, earlier access to diabetes software and devices. This wager that digital health will add value makes sense.

Digital Diabetes Data and Artificial Intelligence: A Time for Humility Not Hubris

In the future artificial intelligence (AI) will have the potential to improve outcomes diabetes care. With the creation of new sensors for physiological monitoring sensors and the introduction of smart insulin pens, novel data relationships based on personal phenotypic and genotypic information will lead to selections of tailored, effective therapies that will transform health care. However, decision-making processes based exclusively on quantitative metrics that ignore qualitative factors could create a quantitative fallacy. Difficult to quantify inputs into AI-based therapeutic decision-making processes include empathy, compassion, experience, and unconscious bias. Failure to consider these "softer" variables could lead to important errors. In other words, that which is not quantified about human health and behavior is still part of the calculus for determining therapeutic interventions.

Precision Diabetes Is Slowly Becoming a Reality

The concept of precision medicine is becoming increasingly popular. The use of big data, genomics and other "omics" like metabolomics, proteomics and transcriptomics could make the dream of personalised medicine become a reality in the near future. As far as polygenic forms of diabetes like type 2 and type 1 diabetes are concerned, interesting leads are emerging, but precision diabetes is still in its infancy. However, with regard to monogenic forms of diabetes like maturity-onset diabetes of the young and neonatal diabetes mellitus, rapid strides have been made and precision diabetes has already become part of the clinical tools used at advanced diabetes centres. In patients with some monogenic form of diabetes, if the appropriate gene defects are identified, insulin injections can be stopped and be replaced by oral sulphonylurea drugs. In the coming years, rapid advances can be expected in the field of precision diabetes, thereby making the control of diabetes more effective and hopefully leading to prevention of its complications and improvement of the quality of life of people afflicted with diabetes.

Activation of Nrf2 signaling by natural products-can it alleviate diabetes?

Type 2 diabetes mellitus (DM) has reached pandemic proportions and effective prevention strategies are wanted. Its onset is accompanied by cellular distress, the nuclear factor erythroid 2-related factor 2 (Nrf2) is a transcription factor boosting cytoprotective responses, and many phytochemicals activate Nrf2 signaling. Thus, Nrf2 activation by natural products could presumably alleviate DM. We summarize function, regulation and exogenous activation of Nrf2, as well as diabetes-linked and Nrf2-susceptible forms of cellular stress. The reported amelioration of insulin resistance, β-cell dysfunction and diabetic complications by activated Nrf2 as well as the status quo of Nrf2 in precision medicine for DM are reviewed.

Continuous Glucose Monitoring: Current Use in Diabetes Management and Possible Future Applications

The recent announcement of the production of new low-cost continuous glucose monitoring (CGM) sensors, the approval of marketed CGM sensors for making treatment decisions, and new reimbursement criteria have the potential to revolutionize CGM use. After briefly summarizing current CGM applications, we discuss how, in our opinion, these changes are expected to extend CGM utilization beyond diabetes patients, for example, to subjects with prediabetes or even healthy individuals. We also elaborate on how the integration of CGM data with other relevant information, for example, health records and other medical device/wearable sensor data, will contribute to creating a digital data ecosystem that will improve our understanding of the etiology and complications of diabetes and will facilitate the development of data analytics for personalized diabetes management and prevention.

Demonstration of disinfection procedure for the development of accurate blood glucose meters in accordance with ISO 15197:2013

Despite measures to reduce disease transmission, a risk can occur when blood glucose meters (BGMs) are used on multiple individuals or by caregivers assisting a patient. The laboratory and in-clinic performance of a BGM system before and after disinfection should be demonstrated to guarantee accurate readings and reliable control of blood glucose (BG) for patients. In this study, an effective disinfection procedure, conducting wiping 10 times to assure a one minute contact time of the disinfectant on contaminated surface, was first demonstrated using test samples of the meter housing materials, including acrylonitrile butadiene styrene (ABS), polymethyl methacrylate (PMMA), and polycarbonate (PC), in accordance with ISO 15197:2013. After bench studies comprising 10,000 disinfection cycles, the elemental compositions of the disinfected ABS, PMMA, and PC samples were almost the same as in the original samples, as indicated by electron spectroscopy for chemical analysis. Subsequently, the validated disinfection procedure was then directly applied to disinfect 5 commercial BGM systems composed of ABS, PMMA, or PC to observe the effect of the validated disinfection procedure on meter accuracy. The results of HBsAg values after treatment with HBV sera and disinfectant wipes for each material were less than the LoD of each material of 0.020 IU/mL. Before and after the multiple disinfection cycles, 900 of 900 samples (100%) were within the system accuracy requirements of ISO 15197:2013. All of the systems showed high performance before and after the series of disinfection cycles and met the ISO 15197:2013 requirements. In addition, our results demonstrated multiple cleaning and disinfection cycles that represented normal use over the lifetime of a meter of 3-5 years. Our validated cleaning and disinfection procedure can be directly applied to other registered disinfectants for cleaning commercial BGM products in the future.

Equity and Value in 'Precision Medicine'

Precision medicine carries huge potential in the treatment of many diseases, particularly those with high-penetrance monogenic underpinnings. However, precision medicine through genomic technologies also has ethical implications. We will define allocative, personal, and technical value ('triple value') in healthcare and how this relates to equity. Equity is here taken to be implicit in the concept of triple value in countries that have publicly funded healthcare systems. It will be argued that precision medicine risks concentrating resources to those that already experience greater access to healthcare and power in society, nationally as well as globally. Healthcare payers, clinicians, and patients must all be involved in optimising the potential of precision medicine, without reducing equity. Throughout, the discussion will refer to the NHS RightCare Programme, which is a national initiative aiming to improve value and equity in the context of NHS England.

The Need for a Privacy Standard for Medical Devices That Transmit Protected Health Information Used in the Precision Medicine Initiative for Diabetes and Other Diseases

Privacy is an important concern for the Precision Medicine Initiative (PMI) because success of this initiative will require the public to be willing to participate by contributing large amounts of genetic/genomic information and sensor data. This sensitive personal information is intended to be used only for specified research purposes. Public willingness to participate will depend on the public's level of trust that their information will be protected and kept private. Medical devices may constantly provide information. Therefore, assuring privacy for device-generated information may be essential for broad participation in the PMI. Privacy standards for devices should be an important early step in the development of the PMI.

Innovative measures to combat rare diseases in China: The national rare diseases registry system, larger-scale clinical cohort studies, and studies in combination with precision medicine research

China is facing the great challenge of treating the world's largest rare disease population, an estimated 16 million patients with rare diseases. One effort offering promise has been a pilot national project that was launched in 2013 and that focused on 20 representative rare diseases. Another government-supported special research program on rare diseases - the "Rare Diseases Clinical Cohort Study" - was launched in December 2016. According to the plan for this research project, the unified National Rare Diseases Registry System of China will be established as of 2020, and a large-scale cohort study will be conducted from 2016 to 2020. The project plans to develop 109 technical standards, to establish and improve 2 national databases of rare diseases - a multi-center clinical database and a biological sample library, and to conduct studies on more than 50,000 registered cases of 50 different rare diseases. More importantly, this study will be combined with the concept of precision medicine. Chinese population-specific basic information on rare diseases, clinical information, and genomic information will be integrated to create a comprehensive predictive model with a follow-up database system and a model to evaluate prognosis. This will provide the evidence for accurate classification, diagnosis, treatment, and estimation of prognosis for rare diseases in China. Numerous challenges including data standardization, protecting patient privacy, big data processing, and interpretation of genetic information still need to be overcome, but research prospects offer great promise.

Quantitative haplotyping of PCR products by nonsynchronous pyrosequencing with di-base addition

Molecular haplotyping is becoming increasingly important for studying the disease association of a specific allele because of its ability of providing more information than any single nucleotide polymorphism (SNP). Computational analysis and experimental techniques are usually performed for haplotypic determination. However, established methods are not suitable for analyzing haplotypes of massive natural DNA samples. Here we present a simple molecular approach to analyze haplotypes of conventional polymerase chain reaction (PCR) products quantitatively in a single sequencing run. In this approach, specific types and proportions of haplotypes in both individual and pooled samples could be determined by solving equations constructed from nonsynchronous pyrosequencing with di-base addition. Two SNPs (rs11176013 and rs11564148) in the gene for leucine-rich repeat kinase 2 (LRRK2) related to Parkinson's disease were selected as experimental sites. A series of DNA samples, including these two heterozygous loci, were investigated. This approach could accurately identify multiple DNA samples indicating that the approach is likely to be applied for haplotyping of unrestricted conventional PCR products from natural samples, and be especially applicable for analyzing short sequences in clinical diagnosis. Graphical Abstract One DNA sample consisting of 4 different DNA templates with different proportion are sequenced by nonsynchronous pyrosequencing with di-base addition. The number of incorporated nucleotides produced by a single sequencing reaction equals to the total of incorporated nucleotides. Four independent equations are constructed from the pyrograms of nonsynchronous pyrosequencing data. Molecular haplotypes of two adjacent SNPs can be quantitatively identified by solving these equations.

Proteomics for prediction of disease progression and response to therapy in diabetic kidney disease

The past decade has resulted in multiple new findings of potential proteomic biomarkers of diabetic kidney disease (DKD). Many of these biomarkers reflect an important role in the (patho)physiology and biological processes of DKD. Situations in which proteomics could be applied in clinical practice include the identification of individuals at risk of progressive kidney disease and those who would respond well to treatment, in order to tailor therapy for those at highest risk. However, while many proteomic biomarkers have been discovered, and even found to be predictive, most lack rigorous external validation in sufficiently powered studies with renal endpoints. Moreover, studies assessing short-term changes in the proteome for therapy-monitoring purposes are lacking. Collaborations between academia and industry and enhanced interactions with regulatory agencies are needed to design new, sufficiently powered studies to implement proteomics in clinical practice.

Migraine in the era of precision medicine

Migraine is a common neurovascular disorder in the neurologic clinics whose mechanisms have been explored for several years. The aura has been considered to be attributed to cortical spreading depression (CSD) and dysfunction of the trigeminovascular system is the key factor that has been considered in the pathogenesis of migraine pain. Moreover, three genes (CACNA1A, ATP1A2, and SCN1A) have come from studies performed in individuals with familial hemiplegic migraine (FHM), a monogenic form of migraine with aura. Therapies targeting on the neuropeptids and genes may be helpful in the precision medicine of migraineurs. 5-hydroxytryptamine (5-HT) receptor agonists and calcitonin gene-related peptide (CGRP) receptor antagonists have demonstrated efficacy in the acute specific treatment of migraine attacks. Therefore, ongoing and future efforts to find new vulnerabilities of migraine, unravel the complexity of drug therapy, and perform biomarker-driven clinical trials are necessary to improve outcomes for patients with migraine.

Drugs meeting the molecular basis of diabetic kidney disease: bridging from molecular mechanism to personalized medicine

Diabetic kidney disease (DKD) is a complex, multifactorial disease and is associated with a high risk of renal and cardiovascular morbidity and mortality. Clinical practice guidelines for diabetes recommend essentially identical treatments for all patients without taking into account how the individual responds to the instituted therapy. Yet, individuals vary widely in how they respond to medications and therefore optimal therapy differs between individuals. Understanding the underlying molecular mechanisms of variability in drug response will help tailor optimal therapy. Polymorphisms in genes related to drug pharmacokinetics have been used to explore mechanisms of response variability in DKD, but with limited success. The complex interaction between genetic make-up and environmental factors on the abundance of proteins and metabolites renders pharmacogenomics alone insufficient to fully capture response variability. A complementary approach is to attribute drug response variability to individual variability in underlying molecular mechanisms involved in the progression of disease. The interplay of different processes (e.g. inflammation, fibrosis, angiogenesis, oxidative stress) appears to drive disease progression, but the individual contribution of each process varies. Drugs at the other hand address specific targets and thereby interfere in certain disease-associated processes. At this level, biomarkers may help to gain insight into which specific pathophysiological processes are involved in an individual followed by a rational assessment whether a specific drug's mode of action indeed targets the relevant process at hand. This article describes the conceptual background and data-driven workflow developed by the SysKid consortium aimed at improving characterization of the molecular mechanisms underlying DKD at the interference of the molecular impact of individual drugs in order to tailor optimal therapy to individual patients.

Toward precision medicine in Parkinson's disease

Precision medicine refers to an innovative approach selected for disease prevention and health promotion according to the individual characteristics of each patient. The goal of precision medicine is to formulate prevention and treatment strategies based on each individual with novel physiological and pathological insights into a certain disease. A multidimensional data-driven approach is about to upgrade "precision medicine" to a higher level of greater individualization in healthcare, a shift towards the treatment of individual patients rather than treating a certain disease including Parkinson's disease (PD). As one of the most common neurodegenerative diseases, PD is a lifelong chronic disease with clinical and pathophysiologic complexity, currently it is treatable but neither preventable nor curable. At its advanced stage, PD is associated with devastating chronic complications including both motor dysfunction and non-motor symptoms which impose an immense burden on the life quality of patients. Advances in computational approaches provide opportunity to establish the patient's personalized disease data at the multidimensional levels, which finally meeting the need for the current concept of precision medicine via achieving the minimal side effects and maximal benefits individually. Hence, in this review, we focus on highlighting the perspectives of precision medicine in PD based on multi-dimensional information about OMICS, molecular imaging, deep brain stimulation (DBS) and wearable sensors. Precision medicine in PD is expected to integrate the best evidence-based knowledge to individualize optimal management in future health care for those with PD.