Genetic Risk Score Modelling for Disease Progression in New-Onset Type 1 Diabetes Patients: Increased Genetic Load of Islet-Expressed and Cytokine-Regulated Candidate Genes Predicts Poorer Glycemic Control

Type 1 Diabetes Genetic Risk Scores: History, Application and Future Directions

PURPOSE OF REVIEW

To review the genetics of type 1 diabetes (T1D) and T1D genetic risk scores, focusing on their development, research and clinical applications, and future directions.

RECENT FINDINGS

More than 90 genetic loci have been linked to T1D risk, with approximately half of the genetic risk attributable to the human leukocyte antigen (HLA) locus, along with non-HLA loci that have smaller effects to disease risk. The practical use of T1D genetic risk scores simplifies the complex genetic information, within the HLA and non-HLA regions, by combining the additive effect and interactions of single nucleotide polymorphisms (SNPs) associated with risk. Genetic risk scores have proven to be useful in various aspects, including classifying diabetes (e.g., distinguishing between T1D vs. neonatal, type 2 or other diabetes types), predicting the risk of developing T1D, assessing the prognosis of the clinical course (e.g., determining the risk of developing insulin dependence and glycemic control), and research into the heterogeneity of diabetes (e.g., atypical diabetes). However, there are gaps in our current knowledge including the specific sets of genes that regulate transition between preclinical stages of T1D, response to disease modifying therapies, and other outcomes of interest such as persistence of beta cell function. Several T1D genetic risk scores have been developed and shown to be valuable in various contexts, from classification of diabetes to providing insights into its etiology and predicting T1D risk across different stages of T1D. Further research is needed to develop and validate T1D genetic risk scores that are effective across all populations and ancestries. Finally, barriers such as cost, and training of medical professionals have to be addressed before the use of genetic risk scores can be incorporated into routine clinical practice.

Heterogeneous endocrine cell composition defines human islet functional phenotypes

Phenotyping and genotyping initiatives within the Integrated Islet Distribution Program (IIDP), the largest source of human islets for research in the U.S., provide standardized assessment of islet preparations distributed to researchers, enabling the integration of multiple data types. Data from islets of the first 299 organ donors without diabetes, analyzed using this pipeline, highlights substantial heterogeneity in islet cell composition associated with hormone secretory traits, sex, reported race and ethnicity, genetically predicted ancestry, and genetic risk for type 2 diabetes (T2D). While α and β cell composition influenced insulin and glucagon secretory traits, the abundance of δ cells showed the strongest association with insulin secretion and was also associated with the genetic risk score (GRS) for T2D. These findings have important implications for understanding mechanisms underlying diabetes heterogeneity and islet dysfunction and may provide insight into strategies for personalized medicine and β cell replacement therapy.

Plasma proteomics in children with new-onset type 1 diabetes identifies new potential biomarkers of partial remission

Partial remission (PR) occurs in only half of people with new-onset type 1 diabetes (T1D) and corresponds to a transient period characterized by low daily insulin needs, low glycemic fluctuations and increased endogenous insulin secretion. While identification of people with newly-onset T1D and significant residual beta-cell function may foster patient-specific interventions, reliable predictive biomarkers of PR occurrence currently lack. We analyzed the plasma of children with new-onset T1D to identify biomarkers present at diagnosis that predicted PR at 3 months post-diagnosis. We first performed an extensive shotgun proteomic analysis using Liquid Chromatography-Tandem-Mass-Spectrometry (LCMS/MS) on the plasma of 16 children with new-onset T1D and quantified 98 proteins significantly correlating with Insulin-Dose Adjusted glycated hemoglobin A1c score (IDAA1C). We next applied a series of both qualitative and statistical filters and selected protein candidates that were associated to pathophysiological mechanisms related to T1D. Finally, we translationally verified several of the candidates using single-shot targeted proteomic (PRM method) on raw plasma. Taken together, we identified plasma biomarkers present at diagnosis that may predict the occurrence of PR in a single mass-spectrometry run. We believe that the identification of new predictive biomarkers of PR and β-cell function is key to stratify people with new-onset T1D for β-cell preservation therapies.

Management of Endocrine and Metabolic Toxicities of Immune-Checkpoint Inhibitors: From Clinical Studies to a Real-Life Scenario

Immune checkpoint inhibitors (ICIs) have revolutionized the therapeutic landscape of solid tumors. However, although ICIs are better tolerated than conventional chemotherapy, their use is associated with a peculiar toxicity profile, related to the enhancement of the immune response, affecting several organs. Among immune-related adverse events (irAEs), up to 10% involve the endocrine system. Most of them are represented by thyroid disorders (hypothyroidism and hyperthyroidism), mainly correlated to the use of anti-PD-1 and/or anti-PD-L1 agents. Less common endocrine irAEs include hypophysitis, adrenalitis, and metabolic irAEs. A deeper understanding of endocrine toxicities is a critical goal for both oncologists and endocrinologists. A strict collaboration between these specialists is mandatory for early recognition and proper treatment of these patients. In this review we will provide a comprehensive overview of endocrine and metabolic adverse events of ICIs, with particular interest in the pathogenesis, predisposing factors and clinical presentation of these irAEs, and their impact on clinical outcomes of patients. Furthermore, we will summarize the most recent studies and recommendations on the clinical approach to immune-related endocrinopathies with the purpose to optimize the diagnostic algorithm, and to help both oncologists and endocrinologists to improve the therapeutic management of these unique types of irAEs, in a real-life scenario.

A new ratio derived from inflammasome markers can serve as a marker of assessment of glycemic index in children with Type 1 diabetes

BACKGROUND

Mature inflammasome markers play a role in the development of Type 1 diabetes (T1D). This cross-sectional study aimed to derive ratios from the serum levels of interleukins (ILs): IL-1β and IL-18 and to relate their values with glycemic index and anti-inflammatory markers (IL-4 and IL-10) in children with T1D.

MATERIALS AND METHODS

This study was conducted at Hawler Medical University in Erbil-Iraq from April to July 2018. Healthy subjects (Group I, n = 40) and patients (Group II, n = 76) were recruited from primary schools and the Center of Diabetes in Erbil, respectively. Glycemic indices (including fasting serum glucose, insulin, glycosylated hemoglobin, and peptide C) and pro- and anti-inflammatory markers (including high-sensitivity C-reactive protein, IL-1β, IL-18, IL-4, and IL-10 and the ratio of neutrophil or platelet to lymphocyte) were determined.

RESULTS

Cutoff values of 105 pg/mL, 85 pg/mL, and 1.235 for serum IL-1β, IL-18, and IL-1β to IL-18 ratio, respectively, were found to be significant discriminators of glycemic index and anti-inflammatory markers with respect to the calculated area under the curve.

CONCLUSION

A ratio of IL-1β to IL-18 adjusted to 1.235 can serve as a useful marker of assessment of glycemic index. This ratio does not discriminate the status of anti-inflammatory markers (IL-4 and IL-10) in children with T1D.

Type 1 diabetes: genes associated with disease development

Type 1 diabetes (T1D) is the third most common autoimmune disease which develops due to genetic and environmental risk factors. Based on the World Health Organization (WHO) report from 2014 the number of people suffering from all types of diabetes ascended to 422 million, compared to 108 million in 1980. It was calculated that this number will double by the end of 2030. In 2015 American Diabetes Association (ADA) announced that 30.3 million Americans (that is 9.4% of the overall population) had diabetes of which only approximately 1.25 million had T1D. Nowadays, T1D represents roughly 10% of adult diabetes cases total. Multiple genetic abnormalities at different loci have been found to contribute to type 1 diabetes development. The analysis of genome-wide association studies (GWAS) of T1D has identified over 50 susceptible regions (and genes within these regions). Many of these regions are defined by single nucleotide polymorphisms (SNPs) but molecular mechanisms through which they increase or lower the risk of diabetes remain unknown. Genetic factors (in existence since birth) can be detected long before the emergence of immunological or clinical markers. Therefore, a comprehensive understanding of the multiple genetic factors underlying T1D is extremely important for further clinical trials and development of personalized medicine for diabetic patients. We present an overview of current studies and information about regions in the human genome associated with T1D. Moreover, we also put forward information about epigenetic modifications, non-coding RNAs and environmental factors involved in T1D development and onset.

SKAP2, a Candidate Gene for Type 1 Diabetes, Regulates β-Cell Apoptosis and Glycemic Control in Newly Diagnosed Patients

The single nucleotide polymorphism rs7804356 located in the Src kinase-associated phosphoprotein 2 (SKAP2) gene is associated with type 1 diabetes (T1D), suggesting SKAP2 as a causal candidate gene. The objective of the study was to investigate if SKAP2 has a functional role in the β-cells in relation to T1D. In a cohort of children with newly diagnosed T1D, rs7804356 predicted glycemic control and residual β-cell function during the 1st year after diagnosis. In INS-1E cells and rat and human islets, proinflammatory cytokines reduced the content of SKAP2. Functional studies revealed that knockdown of SKAP2 aggravated cytokine-induced apoptosis in INS-1E cells and primary rat β-cells, suggesting an antiapoptotic function of SKAP2. In support of this, overexpression of SKAP2 afforded protection against cytokine-induced apoptosis, which correlated with reduced nuclear content of S536-phosphorylated nuclear factor-κB (NF-κB) subunit p65, lower nitric oxide production, and diminished CHOP expression indicative of decreased endoplasmic reticulum stress. Knockdown of CHOP partially counteracted the increase in cytokine-induced apoptosis caused by SKAP2 knockdown. In conclusion, our results suggest that SKAP2 controls β-cell sensitivity to cytokines possibly by affecting the NF-κB-inducible nitric oxide synthase-endoplasmic reticulum stress pathway.

Diabetes and Genetics: A Relationship Between Genetic Risk Alleles, Clinical Phenotypes and Therapeutic Approaches

Unveiling human genome through successful completion of Human Genome Project and International HapMap Projects with the advent of state of art technologies has shed light on diseases associated genetic determinants. Identification of mutational landscapes such as copy number variation, single nucleotide polymorphisms or variants in different genes and loci have revealed not only genetic risk factors responsible for diseases but also region(s) playing protective roles. Diabetes is a global health concern with two major types - type 1 diabetes (T1D) and type 2 diabetes (T2D). Great progress in understanding the underlying genetic predisposition to T1D and T2D have been made by candidate gene studies, genetic linkage studies, genome wide association studies with substantial number of samples. Genetic information has importance in predicting clinical outcomes. In this review, we focus on recent advancement regarding candidate gene(s) associated with these two traits along with their clinical parameters as well as therapeutic approaches perceived. Understanding genetic architecture of these disease traits relating clinical phenotypes would certainly facilitate population stratification in diagnosing and treating T1D/T2D considering the doses and toxicity of specific drugs.

Endocrine-Related Adverse Events Related to Immune Checkpoint Inhibitors: Proposed Algorithms for Management

Immune checkpoint inhibitors have proven to be effective for various advanced neoplasia. Immune-related adverse events (irAEs) as a result of increased T cell activation are unique and potentially life-threating toxicities associated with the use of immune checkpoint inhibitors. Multiple endocrine irAEs, including primary hyperthyroidism and hypothyroidism, thyroiditis, primary adrenal insufficiency, type 1 diabetes mellitus, and hypophysitis, have been reported with the use of various immune checkpoint inhibitors. In some cases, these irAEs can lead to discontinuation of treatment. Here we propose for the general oncologist algorithms for managing endocrine irAEs to aid in the clinical care of patients receiving immunotherapy. KEY POINTS: There is a relative high risk of endocrine immune-related adverse events (irAEs) during therapy with checkpoint inhibitors, particularly when combination therapy is implemented. Patients treated with anti-CTLA-4 antibodies have an increased risk of hypophysitis, whereas patients treated with anti-PD-1/PD-L1 antibodies have a higher risk of primary thyroid dysfunction. Rarely, patients develop T1DM and central diabetes insipidus, and hypoparathyroidism is a rare occurrence. A growing clinical understanding of endocrine irAEs has led to effective treatment strategies with hormone replacement.

Genetic and Environmental Interaction in Type 1 Diabetes: a Relationship Between Genetic Risk Alleles and Molecular Traits of Enterovirus Infection?

PURPOSE OF REVIEW

We provide an overview of the current knowledge regarding the natural history of human type 1 diabetes (T1D) and the documented associations between virus infections (in particular the enteroviruses) and disease development. We review studies that examine whether T1D-specific risk alleles in genes involved in the function of the immune system can alter susceptibility to virus infections or affect the magnitude of the host antiviral response. We also highlight where the major gaps in our knowledge exist and consider possible implications that new insights gained from the discussed gene-environment interaction studies may bring.

RECENT FINDINGS

A commonality between several of the studied T1D risk variants studied is their role in modulating the host immune response to viral infection. Generally, little support exists indicating that the risk variants increase susceptibility to infection and moreover, they usually appear to predispose the immune system towards a hyper-reactive state, decrease the risk of infection, and/or favor the establishment of viral persistence. In conclusion, although the current number of studies is limited, this type of research can provide important insights into the mechanisms that are central to disease pathogenesis and further describe how genetic and environmental factors jointly influence the risk of T1D development. The latter may provide genetic markers that could be used for patient stratification and for the selection of method(s) for disease prevention.

A systematic review of non-genetic predictors and genetic factors of glycated haemoglobin in type 1 diabetes one year after diagnosis

Type 1 diabetes (T1D) results from autoimmune destruction of the pancreatic βcells. Although all T1D patients require daily administration of exogenous insulin, their insulin requirement to achieve good glycaemic control may vary significantly. Glycated haemoglobin (HbA1c) level represents a stable indicator of glycaemic control and is a reliable predictor of long-term complications of T1D. The purpose of this article is to systematically review the role of non-genetic predictors and genetic factors of HbA1c level in T1D patients after the first year of T1D, to exclude the honeymoon period. A total of 1974 articles published since January 2011 were identified and 78 were finally included in the analysis of non-genetic predictors. For genetic factors, a total of 277 articles were identified and 14 were included. The most significantly associated factors with HbA1c level are demographic (age, ethnicity, and socioeconomic status), personal (family characteristics, parental care, psychological traits...) and features related to T1D (duration of T1D, adherence to treatment …). Only a few studies have searched for genetic factors influencing HbA1c level, most of which focused on candidate genes using classical genetic statistical methods, with generally limited power and incomplete adjustment for confounding factors and multiple testing. Our review shows the complexity of explaining HbA1c level variations, which involves numerous correlated predictors. Overall, our review underlines the lack of studies investigating jointly genetic and non-genetic factors and their interactions to better understand factors influencing glycaemic control for T1D patients.

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.

Approaches and advances in the genetic causes of autoimmune disease and their implications

Genome-wide association studies are transformative in revealing the polygenetic basis of common diseases, with autoimmune diseases leading the charge. Although the field is just over 10 years old, advances in understanding the underlying mechanistic pathways of these conditions, which result from a dense multifactorial blend of genetic, developmental and environmental factors, have already been informative, including insights into therapeutic possibilities. Nevertheless, the challenge of identifying the actual causal genes and pathways and their biological effects on altering disease risk remains for many identified susceptibility regions. It is this fundamental knowledge that will underpin the revolution in patient stratification, the discovery of therapeutic targets and clinical trial design in the next 20 years. Here we outline recent advances in analytical and phenotyping approaches and the emergence of large cohorts with standardized gene-expression data and other phenotypic data that are fueling a bounty of discovery and improved understanding of human physiology.

Type 1 Diabetes: Disease Stratification

Type 1 diabetes, a disorder characterized by immune-mediated loss of functional pancreatic beta cells, is a disease continuum with specific presymptomatic stages with defined risk of progression to symptomatic disease. Prognostic biomarkers have been developed for disease staging and for stratification of subjects that address the heterogeneity in rate of disease progression. Using biomarkers for stratification of subjects at different stages of type 1 diabetes will enable smaller and shorter intervention clinical trials with greater effect size. Addressing the heterogeneity of the disease will allow precision medicine-based approaches to prevention and interception of presymptomatic stages of disease and treatment and cure of symptomatic disease.

Genetic Risk Scores for Type 1 Diabetes Prediction and Diagnosis

PURPOSE OF REVIEW

About 50% of the heritability of type 1 diabetes (T1D) is attributed to human leukocyte antigen (HLA) alleles and the remainder to several (close to 50) non-HLA loci. A current challenge in the field of the genetics of T1D is to apply the knowledge accumulated in the last 40 years towards differential diagnosis and risk assessment.

RECENT FINDINGS

T1D genetic risk scores seek to combine the information from HLA and non-HLA alleles to improve the accuracy of diagnosis, prediction, and prognosis. Here, we describe genetic risk scores that have been developed and validated in various settings and populations. Several genetic scores have been proposed that merge disease risk information from multiple genetic factors to optimize the use of genetic information and ultimately improve prediction and diagnosis of T1D.

Proteomic analysis on roots of Oenothera glazioviana under copper-stress conditions

Proteomic studies were performed to identify proteins involved in the response of Oenothera glazioviana seedlings under Cu stress. Exposure of 28-d-old seedlings to 50 μM CuSO4 for 3 d led to inhibition of shoot and root growth as well as a considerable increase in the level of lipid peroxidation in the roots. Cu absorbed by O. glazioviana accumulated more easily in the root than in the shoot. Label-free proteomic analysis indicated 58 differentially abundant proteins (DAPs) of the total 3,149 proteins in the roots of O. glazioviana seedlings, of which 36 were upregulated and 22 were downregulated under Cu stress conditions. Gene Ontology analysis showed that most of the identified proteins could be annotated to signal transduction, detoxification, stress defence, carbohydrate, energy, and protein metabolism, development, and oxidoreduction. We also retrieved 13 proteins from the enriched Kyoto Encyclopaedia of Genes and Genomes and the protein-protein interaction databases related to various pathways, including the citric acid (CA) cycle. Application of exogenous CA to O. glazioviana seedlings exposed to Cu alleviated the stress symptoms. Overall, this study provided new insights into the molecular mechanisms of plant response to Cu at the protein level in relation to soil properties.

The recurrence risk of genetic complex diseases

Complex inherited diseases affected by an interaction between collective effects of the genotype at one or multiple loci either to increase or to lower susceptibility to disease, combined with a variety of environmental exposures that may trigger, accelerate, exacerbate, or protect against the disease process. The new aspects of genetic techniques have been opened for diagnosis and analysis of inherited disorders. While appropriate Mendelian laws is applied to estimate the recurrence risk of single gene diseases, using empirical recurrence risks are the most important and available method to evaluate pedigree of complex (multifactorial), chromosomal, and unknown etiology disorders. Although, generally, empirical recurrent risks are not accurate, either because of the difference of gene frequencies and environmental factors among populations or heterogeneity of disease; using results of plenty family population studies, computerized estimating programs, genotyping technologies, and Genome-wide association studies (GWASs) of single nucleotide polymorphisms (SNPs), can make it possible nowadays to estimate these risks. The specific family situation and importance recurrence risks of some common complex genetic diseases will be presented in this review and some important multifactorial disorders’ recurrence risks will be summarized to help genetic counselors for supporting families and representing better view of genetic disorders.

Type 1 Diabetes Candidate Genes Linked to Pancreatic Islet Cell Inflammation and Beta-Cell Apoptosis

Type 1 diabetes (T1D) is a chronic immune-mediated disease resulting from the selective destruction of the insulin-producing pancreatic islet β-cells. Susceptibility to the disease is the result of complex interactions between environmental and genetic risk factors. Genome-wide association studies (GWAS) have identified more than 50 genetic regions that affect the risk of developing T1D. Most of these susceptibility loci, however, harbor several genes, and the causal variant(s) and gene(s) for most of the loci remain to be established. A significant part of the genes located in the T1D susceptibility loci are expressed in human islets and β cells and mounting evidence suggests that some of these genes modulate the β-cell response to the immune system and viral infection and regulate apoptotic β-cell death. Here, we discuss the current status of T1D susceptibility loci and candidate genes with focus on pancreatic islet cell inflammation and β-cell apoptosis.