Type 1 diabetes genetic risk score is discriminative of diabetes in non-Europeans: evidence from a study in India

Islet autoantibody frequency in relatives of children with type 1 diabetes who have a type 2 diabetes diagnosis

AIM

This study aimed to evaluate characteristics of autoimmunity in individuals who have a type 2 diagnosis and are relatives of children with type 1 diabetes.

METHODS

Pre-diagnosis samples (median 17 months before onset) from relatives who were later diagnosed with type 2 diabetes were measured for autoantibodies to glutamate decarboxylase 65 (GADA), islet antigen-2 (IA-2A), zinc transporter 8 (ZnT8A) and insulin (IAA) as well as the type 1 diabetes genetic risk score (GRS2). Associations between islet autoantibodies, insulin treatment and GRS2 were analysed using Fisher's exact and t-tests.

RESULTS

Among 226 relatives (64% men; mean age at sampling 41 years; mean age 54 years at diagnosis), 32 (14%) were islet autoantibody-positive for at least one autoantibody more than a decade before diagnosis. Approximately half of these (n = 15) were treated with insulin. GADA-positivity was higher in insulin-treated relatives than in non-insulin-treated relatives (12/18 [67%] vs. 6/18 [33%], p < 0.001). IAA-positivity was observed in 13/32 (41%) of relatives with autoantibodies. GRS2 scores were increased in autoantibody-positive relatives (p = 0.032), but there was no clear evidence for a difference according to treatment (p = 0.072).

CONCLUSION

This study highlights the importance of measuring islet autoantibodies, including IAA, in relatives of people with type 1 diabetes to avoid misdiagnosis.

Untangling the genetics of beta cell dysfunction and death in type 1 diabetes

BACKGROUND

Type 1 diabetes (T1D) is a complex multi-system disease which arises from both environmental and genetic factors, resulting in the destruction of insulin-producing pancreatic beta cells. Over the past two decades, human genetic studies have provided new insight into the etiology of T1D, including an appreciation for the role of beta cells in their own demise.

SCOPE OF REVIEW

Here, we outline models supported by human genetic data for the role of beta cell dysfunction and death in T1D. We highlight the importance of strong evidence linking T1D genetic associations to bona fide candidate genes for mechanistic and therapeutic consideration. To guide rigorous interpretation of genetic associations, we describe molecular profiling approaches, genomic resources, and disease models that may be used to construct variant-to-gene links and to investigate candidate genes and their role in T1D.

MAJOR CONCLUSIONS

We profile advances in understanding the genetic causes of beta cell dysfunction and death at individual T1D risk loci. We discuss how genetic risk prediction models can be used to address disease heterogeneity. Further, we present areas where investment will be critical for the future use of genetics to address open questions in the development of new treatment and prevention strategies for T1D.

Diabetes mellitus-Progress and opportunities in the evolving epidemic

Diabetes, a complex multisystem metabolic disorder characterized by hyperglycemia, leads to complications that reduce quality of life and increase mortality. Diabetes pathophysiology includes dysfunction of beta cells, adipose tissue, skeletal muscle, and liver. Type 1 diabetes (T1D) results from immune-mediated beta cell destruction. The more prevalent type 2 diabetes (T2D) is a heterogeneous disorder characterized by varying degrees of beta cell dysfunction in concert with insulin resistance. The strong association between obesity and T2D involves pathways regulated by the central nervous system governing food intake and energy expenditure, integrating inputs from peripheral organs and the environment. The risk of developing diabetes or its complications represents interactions between genetic susceptibility and environmental factors, including the availability of nutritious food and other social determinants of health. This perspective reviews recent advances in understanding the pathophysiology and treatment of diabetes and its complications, which could alter the course of this prevalent disorder.

Genetic Associations with C-peptide Levels before Type 1 Diabetes Diagnosis in At-Risk Relatives

OBJECTIVE

We sought to determine whether the type 1 diabetes genetic risk score-2 (T1D-GRS2) and single nucleotide polymorphisms (SNPs) are associated with C-peptide preservation before type 1 diabetes diagnosis.

METHODS

We conducted a retrospective analysis of 713 autoantibody-positive participants who developed type 1 diabetes in the TrialNet Pathway to Prevention Study who had T1DExomeChip data. We evaluated the relationships of 16 known SNPs and T1D-GRS2 with area under the curve (AUC) C-peptide levels during oral glucose tolerance tests conducted in the 9 months before diagnosis.

RESULTS

Higher T1D-GRS2 was associated with lower C-peptide AUC in the 9 months before diagnosis in univariate (β=-0.06, P<0.0001) and multivariate (β=-0.03, P=0.005) analyses. Participants with the JAZF1 rs864745 T allele had lower C-peptide AUC in both univariate (β=-0.11, P=0.002) and multivariate (β=-0.06, P=0.018) analyses.

CONCLUSIONS

The type 2 diabetes-associated JAZF1 rs864745 T allele and higher T1D-GRS2 are associated with lower C-peptide AUC prior to diagnosis of type 1 diabetes, with implications for the design of prevention trials.

Use of a type 1 genetic risk score for classification of diabetes type in young Australian adults: the Fremantle Diabetes Study Phase II

The applicability of a UK-validated genetic risk score (GRS) was assessed in 158 participants in the Fremantle Diabetes Study Phase II diagnosed between 20 and <40 years of age with type 1 or type 2 diabetes or latent autoimmune diabetes of adults (LADA). For type 1 versus type 2/LADA, the area under the receiver operating characteristic curve (AUC) was highest for serum C-peptide (0.93) and lowest for the GRS (0.66). Adding age at diagnosis and body mass index to C-peptide increased the AUC minimally (0.96). The GRS appears of modest diabetes diagnostic value in young Australians.

Genetic and Epigenetic Aspects of Type 1 Diabetes Mellitus: Modern View on the Problem

Omics technologies accumulated an enormous amount of data that advanced knowledge about the molecular pathogenesis of type 1 diabetes mellitus and identified a number of fundamental problems focused on the transition to personalized diabetology in the future. Among them, the most significant are the following: (1) clinical and genetic heterogeneity of type 1 diabetes mellitus; (2) the prognostic significance of DNA markers beyond the HLA genes; (3) assessment of the contribution of a large number of DNA markers to the polygenic risk of disease progress; (4) the existence of ethnic population differences in the distribution of frequencies of risk alleles and genotypes; (5) the infancy of epigenetic research into type 1 diabetes mellitus. Disclosure of these issues is one of the priorities of fundamental diabetology and practical healthcare. The purpose of this review is the systemization of the results of modern molecular genetic, transcriptomic, and epigenetic investigations of type 1 diabetes mellitus in general, as well as its individual forms. The paper summarizes data on the role of risk HLA haplotypes and a number of other candidate genes and loci, identified through genome-wide association studies, in the development of this disease and in alterations in T cell signaling. In addition, this review assesses the contribution of differential DNA methylation and the role of microRNAs in the formation of the molecular pathogenesis of type 1 diabetes mellitus, as well as discusses the most currently central trends in the context of early diagnosis of type 1 diabetes mellitus.

Type 1 Diabetes Genetic Risk Score Differentiates Subgroups of Ketosis-Prone Diabetes

OBJECTIVE

To determine whether genetic risk for type 1 diabetes (T1D) differentiates the four Aβ subgroups of ketosis-prone diabetes (KPD), where A+ and A- define the presence or absence of islet autoantibodies and β+ and β- define the presence or absence of β-cell function.

RESEARCH DESIGN AND METHODS

We compared T1D genetic risk scores (GRS) of patients with KPD across subgroups, race/ethnicity, β-cell function, and glycemia.

RESULTS

Among 426 patients with KPD (54% Hispanic, 31% African American, 11% White), rank order of GRS was A+β- > A+β+ = A-β- > A-β+. GRS of A+β- KPD was lower than that of a T1D cohort, and GRS of A-β+ KPD was higher than that of a type 2 diabetes cohort. GRS was lowest among African American patients, with a similar distribution across KPD subgroups.

CONCLUSIONS

T1D genetic risk delineates etiologic differences among KPD subgroups. Patients with A+β- KPD have the highest and those with A-β+ KPD the lowest GRS.

Utility of genetic risk scores in type 1 diabetes

Iterative advances in understanding of the genetics of type 1 diabetes have identified >70 genetic regions associated with risk of the disease, including strong associations across the HLA class II region that account for >50% of heritability. The increased availability of genetic data combined with the decreased costs of generating these data, have facilitated the development of polygenic scores that aggregate risk variants from associated loci into a single number: either a genetic risk score (GRS) or a polygenic risk score (PRS). PRSs incorporate the risk of many possibly correlated variants from across the genome, even if they do not reach genome-wide significance, whereas GRSs estimate the cumulative contribution of a smaller subset of genetic variants that reach genome-wide significance. Type 1 diabetes GRSs have utility in diabetes classification, aiding discrimination between type 1 diabetes, type 2 diabetes and MODY. Type 1 diabetes GRSs are also being used in newborn screening studies to identify infants at risk of future presentation of the disease. Most early studies of type 1 diabetes genetics have been conducted in European ancestry populations, but, to develop accurate GRSs across diverse ancestries, large case-control cohorts from non-European populations are still needed. The current barriers to GRS implementation within healthcare are mainly related to a lack of guidance and knowledge on integration with other biomarkers and clinical variables. Once these limitations are addressed, there is huge potential for 'test and treat' approaches to be used to tailor care for individuals with type 1 diabetes.

Polygenic scores of diabetes-related traits in subgroups of type 2 diabetes in India: a cohort study

Background

A machine-learning approach identified five subgroups of diabetes in Europeans which included severe autoimmune diabetes (SAID), severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD) and mild age-related diabetes (MARD) with partially distinct genetic aetiologies. We previously validated four of the non-autoimmune subgroups in people with young-onset type 2 diabetes (T2D) from the Indian WellGen study. Here, we aimed to apply European-derived centroids and genetic risk scores (GRSs) to the unselected (for age) WellGen to test their applicability and investigate the genetic aetiology of the Indian T2D subgroups.

Methods

We applied European derived centroids and GRSs to T2D participants of Indian ancestry (WellGen, n = 2217, 821 genotyped) and compared them with normal glucose tolerant controls (Pune Maternal Nutrition Study, n = 461).

Findings

SIDD was the predominant subgroup followed by MOD, whereas SIRD and MARD were less frequent. Weighted-GRS for T2D, obesity and lipid-related traits associated with T2D. We replicated some of the previous associations of GRS for T2D, insulin secretion, and BMI with SIDD and MOD. Unique to Indian subgroups was the association of GRS for (a) proinsulin with MOD and MARD, (b) liver-lipids with SIDD, SIRD and MOD, and (c) opposite effect of beta-cell GRS with SIDD and MARD, obesity GRS with MARD compared to Europeans. Genetic variants of fucosyltransferases were associated with T2D and MOD in Indians but not Europeans.

Interpretation

The similarities emphasise the applicability of some of the European-derived GRSs to T2D and its subgroups in India while the differences highlight the need for large-scale studies to identify aetiologies in diverse ancestries. The data provide robust evidence for genetically distinct aetiologies for the T2D subgroups and at least partly mirror those seen in Europeans.

Funding

Vetenskapsrådet, Diabetes Wellness, and Hjärt-Lungfonden (Sweden), DST (India), Wellcome Trust, Crafoord Foundation and Albert Påhlsson Foundation.

Evaluation of polygenic risk scores to differentiate between type 1 and type 2 diabetes

Polygenic risk scores (PRS) quantify the genetic liability to disease and are calculated using an individual's genotype profile and disease-specific genome-wide association study (GWAS) summary statistics. Type 1 (T1D) and type 2 (T2D) diabetes both are determined in part by genetic loci. Correctly differentiating between types of diabetes is crucial for accurate diagnosis and treatment. PRS have the potential to address possible misclassification of T1D and T2D. Here we evaluated PRS models for T1D and T2D in European genetic ancestry participants from the UK Biobank (UKB) and then in the Michigan Genomics Initiative (MGI). Specifically, we investigated the utility of T1D and T2D PRS to discriminate between T1D, T2D, and controls in unrelated UKB individuals of European ancestry. We derived PRS models using external non-UKB GWAS. The T1D PRS model with the best discrimination between T1D cases and controls (area under the receiver operator curve [AUC] = 0.805) also yielded the best discrimination of T1D from T2D cases in the UKB (AUC = 0.792) and separation in MGI (AUC = 0.686). In contrast, the best T2D model did not discriminate between T1D and T2D cases (AUC = 0.527). Our analysis suggests that a T1D PRS model based on independent single nucleotide polymorphisms may help differentiate between T1D, T2D, and controls in individuals of European genetic ancestry.

Lessons and Gaps in the Prediction and Prevention of Type 1 Diabetes

Type 1 diabetes (T1D) is a serious chronic autoimmune condition. Even though the root cause of T1D development has yet to be determined, enough is known about the natural history of T1D pathogenesis to allow study of interventions that may delay or even prevent the onset of hyperglycemia and clinical T1D. Primary prevention aims to prevent the onset of beta cell autoimmunity in asymptomatic people at high genetic risk for T1D. Secondary prevention strategies aim to preserve functional beta cells once autoimmunity is present, and tertiary prevention aims to initiate and extend partial remission of beta cell destruction after the clinical onset of T1D. The approval of teplizumab in the United States to delay the onset of clinical T1D marks an impressive milestone in diabetes care. This treatment opens the door to a paradigm shift in T1D care. People with T1D risk need to be identified early by measuring T1D related islet autoantibodies. Identifying people with T1D before they have symptoms will facilitate better understanding of pre-symptomatic T1D progression and T1D prevention strategies that may be effective.

Association between HLA Class II Alleles/Haplotypes and Genomic Ancestry in Brazilian Patients with Type 1 Diabetes: A Nationwide Exploratory Study

We aimed to identify HLA-DRB1, -DQA1, and -DQB1 alleles/haplotypes associated with European, African, or Native American genomic ancestry (GA) in admixed Brazilian patients with type 1 diabetes (T1D). This exploratory nationwide study enrolled 1599 participants. GA percentage was inferred using a panel of 46 ancestry informative marker-insertion/deletion. Receiver operating characteristic curve analysis (ROC) was applied to identify HLA class II alleles related to European, African, or Native American GA, and showed significant (p < 0.05) accuracy for identifying HLA risk alleles related to European GA: for DRB1*03:01, the area under the curve was (AUC) 0.533; for DRB1*04:01 AUC = 0.558, for DRB1*04:02 AUC = 0.545. A better accuracy for identifying African GA was observed for the risk allele DRB1*09:01AUC = 0.679 and for the protective alleles DRB1*03:02 AUC = 0.649, DRB1*11:02 AUC = 0.636, and DRB1*15:03 AUC = 0.690. Higher percentage of European GA was observed in patients with risk haplotypes (p < 0.05). African GA percentage was higher in patients with protective haplotypes (p < 0.05). Risk alleles and haplotypes were related to European GA and protective alleles/haplotypes to African GA. Future studies with other ancestry markers are warranted to fill the gap in knowledge regarding the genetic origin of T1D in highly admixed populations such as that found in Brazil.

Clinical and biochemical profile of childhood-adolescent-onset type 1 diabetes and adult-onset type 1 diabetes among Asian Indians

AIM

To compare the clinical and biochemical profile and prevalence of complications among childhood/adolescent-onset (CAO; onset of diabetes< 20 years of age) and adult-onset (AO; onset of diabetes- ≥ 20 years of age) type 1 diabetes (T1D), seen at a tertiary care diabetes center in south India.

METHOD

Data of 5578 individuals with T1D, diagnosed based on a history of diabetic ketoacidosis or ketonuria, fasting C-peptide < 0.3 pmol/mL and stimulated C-peptide values < 0.6 pmol/mL, and requirement of insulin right from the time of diagnosis, presenting to our center between 1991 and 2021, were retrieved from our electronic medical records. Retinopathy was assessed by retinal photography, chronic kidney disease (CKD) by urinary albumin excretion ≥ 30 µg/mg of creatinine and/or eGFR < 60 mL/min, and neuropathy by vibration perception threshold >= 20v on biothesiometry.

RESULTS

Overall, 3559 (63.8%) of individuals with T1D, belonged to CAO group and 2019 (36.2%) to AO category. AO had higher prevalence of all microvascular complications compared to CAO at every diabetes duration interval, even after adjusting for A1c. Among the AO group, prevalence of retinopathy, CKD, and neuropathy was higher in the GAD negative group. Among CAO there were no differences between the GAD negative and GAD positive groups with respect to prevalence of complications of diabetes.

CONCLUSION

AO with T1D had higher prevalence of microvascular complications compared to CAO. Among AO, GAD negative individuals had higher percentage of retinopathy and CKD compared to GAD positive group.

Etiology, clinical characteristics and mortality among Indian patients with Addison's disease

The etiology, presentation and mortality of patients with primary adrenal insufficiency (PAI) in developing countries may differ from economically developed nations. However, information in this regard is scanty. The aim of this study was to determine the etiology and compare the clinical characteristics and mortality in infectious and autoimmune causes of PAI in Indian patients. All eligible (n = 89) patients (ages 15-83 years) diagnosed with PAI between 2006 and 2019 were studied. Patients were followed for a median duration of 5.9 (range 0.1-15.7) years. Eighty-six subjects underwent an abdominal computerized tomography scan or ultrasonography, and adrenal biopsy was performed in 60 patients. The most frequent etiologies of PAI were adrenal histoplasmosis (AH, 45%), adrenal tuberculosis (AT, 15%), autoimmunity (AI, 25%) and primary lymphoma (6%). Forty-two percent of patients presented with an acute adrenal crisis. AH and AT could not be differentiated on the basis of clinical features, except for a greater frequency of hepatomegaly-splenomegaly and type 2 diabetes mellitus (63% vs 15%, P < 0.01) in the former. Patients with an autoimmune etiology had a higher frequency of 21-hydroxylase antibodies (41% vs 3%) and autoimmune thyroid disease (46% vs 5%) vs those with infectious etiologies. Mortality was significantly higher in AH (45%) compared with AT (8%) or AI (5%) (P = 0.001). Causes of death included adrenal crises, progressive AH and unexplained acute events occurring at home. In conclusion, infections, especially AH, were the most frequent cause of PAI in north India. Despite appropriate therapy, AH had very high mortality as compared with AT and AI.

The phenotype of type 1 diabetes in sub-Saharan Africa

The phenotype of type 1 diabetes in Africa, especially sub-Saharan Africa, is poorly understood. Most previously conducted studies have suggested that type 1 diabetes may have a different phenotype from the classical form of the disease described in western literature. Making an accurate diagnosis of type 1 diabetes in Africa is challenging, given the predominance of atypical diabetes forms and limited resources. The peak age of onset of type 1 diabetes in sub-Saharan Africa seems to occur after 18-20 years. Multiple studies have reported lower rates of islet autoantibodies ranging from 20 to 60% amongst people with type 1 diabetes in African populations, lower than that reported in other populations. Some studies have reported much higher levels of retained endogenous insulin secretion than in type 1 diabetes elsewhere, with lower rates of type 1 diabetes genetic susceptibility and HLA haplotypes. The HLA DR3 appears to be the most predominant HLA haplotype amongst people with type 1 diabetes in sub-Saharan Africa than the HLA DR4 haplotype. Some type 1 diabetes studies in sub-Saharan Africa have been limited by small sample sizes and diverse methods employed. Robust studies close to diabetes onset are sparse. Large prospective studies with well-standardized methodologies in people at or close to diabetes diagnosis in different population groups will be paramount to provide further insight into the phenotype of type 1 diabetes in sub-Saharan Africa.

Clinical variable-based cluster analysis identifies novel subgroups with a distinct genetic signature, lipidomic pattern and cardio-renal risks in Asian patients with recent-onset type 2 diabetes

AIMS/HYPOTHESIS

We sought to subtype South East Asian patients with type 2 diabetes by de novo cluster analysis on clinical variables, and to determine whether the novel subgroups carry distinct genetic and lipidomic features as well as differential cardio-renal risks.

METHODS

Analysis by k-means algorithm was performed in 687 participants with recent-onset diabetes in Singapore. Genetic risk for beta cell dysfunction was assessed by polygenic risk score. We used a discovery-validation approach for the lipidomics study. Risks for cardio-renal complications were studied by survival analysis.

RESULTS

Cluster analysis identified three novel diabetic subgroups, i.e. mild obesity-related diabetes (MOD, 45%), mild age-related diabetes with insulin insufficiency (MARD-II, 36%) and severe insulin-resistant diabetes with relative insulin insufficiency (SIRD-RII, 19%). Compared with the MOD subgroup, MARD-II had a higher polygenic risk score for beta cell dysfunction. The SIRD-RII subgroup had higher levels of sphingolipids (ceramides and sphingomyelins) and glycerophospholipids (phosphatidylethanolamine and phosphatidylcholine), whereas the MARD-II subgroup had lower levels of sphingolipids and glycerophospholipids but higher levels of lysophosphatidylcholines. Over a median of 7.3 years follow-up, the SIRD-RII subgroup had the highest risks for incident heart failure and progressive kidney disease, while the MARD-II subgroup had moderately elevated risk for kidney disease progression.

CONCLUSIONS/INTERPRETATION

Cluster analysis on clinical variables identified novel subgroups with distinct genetic, lipidomic signatures and varying cardio-renal risks in South East Asian participants with type 2 diabetes. Our study suggests that this easily actionable approach may be adapted in other ethnic populations to stratify the heterogeneous type 2 diabetes population for precision medicine.

Setting the Stage for Insulin Granule Dysfunction during Type-1-Diabetes: Is ER Stress the Culprit?

Type-1-diabetes (T1D) is a multifactorial disorder with a global incidence of about 8.4 million individuals in 2021. It is primarily classified as an autoimmune disorder, where the pancreatic β-cells are unable to secrete sufficient insulin. This leads to elevated blood glucose levels (hyperglycemia). The development of T1D is an intricate interplay between various risk factors, such as genetic, environmental, and cellular elements. In this review, we focus on the cellular elements, such as ER (endoplasmic reticulum) stress and its consequences for T1D pathogenesis. One of the major repercussions of ER stress is defective protein processing. A well-studied example is that of islet amyloid polypeptide (IAPP), which is known to form cytotoxic amyloid plaques when misfolded. This review discusses the possible association between ER stress, IAPP, and amyloid formation in β-cells and its consequences in T1D. Additionally, ER stress also leads to autoantigen generation. This is driven by the loss of Ca++ ion homeostasis. Imbalanced Ca++ levels lead to abnormal activation of enzymes, causing post-translational modification of β-cell proteins. These modified proteins act as autoantigens and trigger the autoimmune response seen in T1D islets. Several of these autoantigens are also crucial for insulin granule biogenesis, processing, and release. Here, we explore the possible associations between ER stress leading to defects in insulin secretion and ultimately β-cell destruction.

Type 1 diabetes in diverse ancestries and the use of genetic risk scores

Over 75 genetic loci within and outside of the HLA region influence type 1 diabetes risk. Genetic risk scores (GRS), which facilitate the integration of complex genetic information, have been developed in type 1 diabetes and incorporated into models and algorithms for classification, prognosis, and prediction of disease and response to preventive and therapeutic interventions. However, the development and validation of GRS across different ancestries is still emerging, as is knowledge on type 1 diabetes genetics in populations of diverse genetic ancestries. In this Review, we provide a summary of the current evidence on the evolutionary genetic variation in type 1 diabetes and the racial and ethnic differences in type 1 diabetes epidemiology, clinical characteristics, and preclinical course. We also discuss the influence of genetics on type 1 diabetes with differences across ancestries and the development and validation of GRS in various populations.

HLA Genotypes and Type 1 Diabetes and Its Relationship to Reported Race/Skin Color in Their Relatives: A Brazilian Multicenter Study

We aimed to investigate the relationship between HLA alleles in patients with type 1 diabetes from an admixed population and the reported race/skin color of their relatives. This cross-sectional, multicenter study was conducted in public clinics in nine Brazilian cities and included 662 patients with type 1 diabetes and their relatives. Demographic data for patients and information on the race/skin color and birthplace of their relatives were obtained. Typing of the HLA-DRB1, -DQA1, and -DQB1 genes was performed. Most studied patients reported having a White relative (95.17%), and the most frequently observed allele among them was DRB1*03:01. Increased odds of presenting this allele were found only in those patients who reported having all White relatives. Considering that most of the patients reported having a White relative and that the most frequent observed allele was DRB1*03:01 (probably a European-derived allele), regardless of the race/skin color of their relatives, we conclude that the type 1 diabetes genotype comes probably from European, Caucasian ethnicity. However, future studies with other ancestry markers are needed to fill the knowledge gap regarding the genetic origin of the type 1 diabetes genotype in admixed populations such as the Brazilian.

Functional Impact of Risk Gene Variants on the Autoimmune Responses in Type 1 Diabetes

Type 1 diabetes (T1D) is an autoimmune disease that develops in the interplay between genetic and environmental factors. A majority of individuals who develop T1D have a HLA make up, that accounts for 50% of the genetic risk of disease. Besides these HLA haplotypes and the insulin region that importantly contribute to the heritable component, genome-wide association studies have identified many polymorphisms in over 60 non-HLA gene regions that also contribute to T1D susceptibility.

Combining the risk genes in a score (T1D-GRS), significantly improved the prediction of disease progression in autoantibody positive individuals. Many of these minor-risk SNPs are associated with immune genes but how they influence the gene and protein expression and whether they cause functional changes on a cellular level remains a subject of investigation. A positive correlation between the genetic risk and the intensity of the peripheral autoimmune response was demonstrated both for HLA and non-HLA genetic risk variants. We also observed epigenetic and genetic modulation of several of these T1D susceptibility genes in dendritic cells (DCs) treated with vitamin D3 and dexamethasone to acquire tolerogenic properties as compared to immune activating DCs (mDC) illustrating the interaction between genes and environment that collectively determines risk for T1D. A notion that targeting such genes for therapeutic modulation could be compatible with correction of the impaired immune response, inspired us to review the current knowledge on the immune-related minor risk genes, their expression and function in immune cells, and how they may contribute to activation of autoreactive T cells, Treg function or β-cell apoptosis, thus contributing to development of the autoimmune disease.

Subgroups of patients with young-onset type 2 diabetes in India reveal insulin deficiency as a major driver

AIM/HYPOTHESIS

Five subgroups were described in European diabetes patients using a data driven machine learning approach on commonly measured variables. We aimed to test the applicability of this phenotyping in Indian individuals with young-onset type 2 diabetes.

METHODS

We applied the European-derived centroids to Indian individuals with type 2 diabetes diagnosed before 45 years of age from the WellGen cohort (n = 1612). We also applied de novo k-means clustering to the WellGen cohort to validate the subgroups. We then compared clinical and metabolic-endocrine characteristics and the complication rates between the subgroups. We also compared characteristics of the WellGen subgroups with those of two young European cohorts, ANDIS (n = 962) and DIREVA (n = 420). Subgroups were also assessed in two other Indian cohorts, Ahmedabad (n = 187) and PHENOEINDY-2 (n = 205).

RESULTS

Both Indian and European young-onset type 2 diabetes patients were predominantly classified into severe insulin-deficient (SIDD) and mild obesity-related (MOD) subgroups, while the severe insulin-resistant (SIRD) and mild age-related (MARD) subgroups were rare. In WellGen, SIDD (53%) was more common than MOD (38%), contrary to findings in Europeans (Swedish 26% vs 68%, Finnish 24% vs 71%, respectively). A higher proportion of SIDD compared with MOD was also seen in Ahmedabad (57% vs 33%) and in PHENOEINDY-2 (67% vs 23%). Both in Indians and Europeans, the SIDD subgroup was characterised by insulin deficiency and hyperglycaemia, MOD by obesity, SIRD by severe insulin resistance and MARD by mild metabolic-endocrine disturbances. In WellGen, nephropathy and retinopathy were more prevalent in SIDD compared with MOD while the latter had higher prevalence of neuropathy.

CONCLUSIONS /INTERPRETATION

Our data identified insulin deficiency as the major driver of type 2 diabetes in young Indians, unlike in young European individuals in whom obesity and insulin resistance predominate. Our results provide useful clues to pathophysiological mechanisms and susceptibility to complications in type 2 diabetes in the young Indian population and suggest a need to review management strategies.

The importance of increasing population diversity in genetic studies of type 2 diabetes and related glycaemic traits

Type 2 diabetes has a global prevalence, with epidemiological data suggesting that some populations have a higher risk of developing this disease. However, to date, most genetic studies of type 2 diabetes and related glycaemic traits have been performed in individuals of European ancestry. The same is true for most other complex diseases, largely due to use of 'convenience samples'. Rapid genotyping of large population cohorts and case-control studies from existing collections was performed when the genome-wide association study (GWAS) 'revolution' began, back in 2005. Although global representation has increased in the intervening 15 years, further expansion and inclusion of diverse populations in genetic and genomic studies is still needed. In this review, I discuss the progress made in incorporating multi-ancestry participants in genetic analyses of type 2 diabetes and related glycaemic traits, and associated opportunities and challenges. I also discuss how increased representation of global diversity in genetic and genomic studies is required to fulfil the promise of precision medicine for all.

DR15-DQ6 remains dominantly protective against type 1 diabetes throughout the first five decades of life

AIMS/HYPOTHESIS

Among white European children developing type 1 diabetes, the otherwise common HLA haplotype DR15-DQ6 is rare, and highly protective. Adult-onset type 1 diabetes is now known to represent more overall cases than childhood onset, but it is not known whether DR15-DQ6 is protective in older-adult-onset type 1 diabetes. We sought to quantify DR15-DQ6 protection against type 1 diabetes as age of onset increased.

METHODS

In two independent cohorts we assessed the proportion of type 1 diabetes cases presenting through the first 50 years of life with DR15-DQ6, compared with population controls. In the After Diabetes Diagnosis Research Support System-2 (ADDRESS-2) cohort (n = 1458) clinician-diagnosed type 1 diabetes was confirmed by positivity for one or more islet-specific autoantibodies. In UK Biobank (n = 2502), we estimated type 1 diabetes incidence rates relative to baseline HLA risk for each HLA group using Poisson regression. Analyses were restricted to white Europeans and were performed in three groups according to age at type 1 diabetes onset: 0-18 years, 19-30 years and 31-50 years.

RESULTS

DR15-DQ6 was protective against type 1 diabetes through to age 50 years (OR < 1 for each age group, all p < 0.001). The following ORs for type 1 diabetes, relative to a neutral HLA genotype, were observed in ADDRESS-2: age 5-18 years OR 0.16 (95% CI 0.08, 0.31); age 19-30 years OR 0.10 (0.04, 0.23); and age 31-50 years OR 0.37 (0.21, 0.68). DR15-DQ6 also remained highly protective at all ages in UK Biobank. Without DR15-DQ6, the presence of major type 1 diabetes high-risk haplotype (either DR3-DQ2 or DR4-DQ8) was associated with increased risk of type 1 diabetes.

CONCLUSIONS/INTERPRETATION

HLA DR15-DQ6 confers dominant protection from type 1 diabetes across the first five decades of life.