Publications by authors named "Richard A Oram"

45 Publications

Measurement of Peak C-Peptide at Diagnosis Informs Glycemic Control but not Hypoglycemia in Adults With Type 1 Diabetes.

J Endocr Soc 2021 Oct 17;5(10):bvab127. Epub 2021 Jul 17.

Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, Devon EX2 5DW, UK.

Context: High-residual C-peptide in longer-duration type 1 diabetes (T1D) is associated with fewer hypoglycemic events and reduced glycemic variability. Little is known about the impact of C-peptide close to diagnosis.

Objective: Using continuous glucose monitoring (CGM) data from a study of newly diagnosed adults with T1D, we aimed to explore if variation in C-peptide close to diagnosis influenced glycemic variability and risk of hypoglycemia.

Methods: We studied newly diagnosed adults with T1D who wore a Dexcom G4 CGM for 7 days as part of the Exercise in Type 1 Diabetes (EXTOD) study. We examined the relationship between peak stimulated C-peptide and glycemic metrics of variability and hypoglycemia for 36 CGM traces from 23 participants.

Results: For every 100 pmol/L-increase in peak C-peptide, the percentage of time spent in the range 3.9 to 10 mmol/L increased by 2.4% (95% CI, 0.5-4.3),  = .01) with a reduction in time spent at level 1 hyperglycemia (> 10 mmol/L) and level 2 hyperglycemia (> 13.9 mmol/L) by 2.6% (95% CI, -4.9 to -0.4,  = .02) and 1.3% (95% CI, -2.7 to -0.006,  = .04), respectively. Glucose levels were on average lower by 0.19 mmol/L (95% CI, -0.4 to 0.02,  = .06) and SD reduced by 0.14 (95% CI, -0.3 to -0.02,  = .02). Hypoglycemia was not common in this group and no association was observed between time spent in hypoglycemia ( = .97) or hypoglycemic risk ( = .72). There was no association between peak C-peptide and insulin dose-adjusted glycated hemoglobin A ( = .45).

Conclusion: C-peptide is associated with time spent in the normal glucose range and with less hyperglycemia, but not risk of hypoglycemia in newly diagnosed people with T1D.
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http://dx.doi.org/10.1210/jendso/bvab127DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8344843PMC
October 2021

Preventing type 1 diabetes in childhood.

Science 2021 07;373(6554):506-510

Wellcome Centre for Human Genetics, Nuffield Department of Medicine, National Institute for Health Research (NIHR) Biomedical Research Centre, University of Oxford, Oxford, UK.

Type 1 diabetes (T1D) is an autoimmune disease in which the insulin-producing β cells of the pancreas are destroyed by T lymphocytes. Recent studies have demonstrated that monitoring for pancreatic islet autoantibodies, combined with genetic risk assessment, can identify most children who will develop T1D when they still have sufficient β cell function to control glucose concentrations without the need for insulin. In addition, there has been recent success in secondary prevention using immunotherapy to delay the progression of preclinical disease, and primary prevention approaches to inhibiting the initiating autoimmune process have entered large-scale clinical trials. By changing the focus of T1D management from late diagnosis and insulin replacement to early diagnosis and β cell preservation, we can anticipate a future without the need for daily insulin injections for children with T1D.
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http://dx.doi.org/10.1126/science.abi4742DOI Listing
July 2021

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

Diabetologia 2021 Oct 16;64(10):2258-2265. Epub 2021 Jul 16.

Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK.

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.
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http://dx.doi.org/10.1007/s00125-021-05513-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8423681PMC
October 2021

100 years of insulin: celebrating the past, present and future of diabetes therapy.

Nat Med 2021 Jul 15;27(7):1154-1164. Epub 2021 Jul 15.

Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA.

The year 2021 marks the centennial of Banting and Best's landmark description of the discovery of insulin. This discovery and insulin's rapid clinical deployment effectively transformed type 1 diabetes from a fatal diagnosis into a medically manageable chronic condition. In this Review, we describe key accomplishments leading to and building on this momentous occasion in medical history, including advancements in our understanding of the role of insulin in diabetes pathophysiology, the molecular characterization of insulin and the clinical use of insulin. Achievements are also viewed through the lens of patients impacted by insulin therapy and the evolution of insulin pharmacokinetics and delivery over the past 100 years. Finally, we reflect on the future of insulin therapy and diabetes treatment, as well as challenges to be addressed moving forward, so that the full potential of this transformative discovery may be realized.
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http://dx.doi.org/10.1038/s41591-021-01418-2DOI Listing
July 2021

Reappearance of C-Peptide During the Third Trimester of Pregnancy in Type 1 Diabetes: Pancreatic Regeneration or Fetal Hyperinsulinism?

Diabetes Care 2021 Aug 26;44(8):1826-1834. Epub 2021 Jun 26.

Department of Diabetes Research, University of Exeter, Royal Devon and Exeter Hospital, Exeter, U.K.

Objective: We assessed longitudinal patterns of maternal C-peptide concentration to examine the hypothesis of β-cell regeneration in pregnancy with type 1 diabetes.

Research Design And Methods: C-peptide was measured on maternal serum samples from 127 participants (12, 24, and 34 weeks) and cord blood during the Continuous Glucose Monitoring in Women With Type 1 Diabetes in Pregnancy Trial (CONCEPTT). C-peptide was measured using a highly sensitive direct and solid-phase competitive electrochemiluminescent immunoassay.

Results: Three discrete patterns of maternal C-peptide trajectory were identified: pattern 1, undetectable throughout pregnancy, = 74 (58%; maternal C-peptide <3 pmol/L); pattern 2, detectable at baseline, = 22 (17%; maternal C-peptide 7-272 pmol/L at baseline); and pattern 3, undetectable maternal C-peptide at 12 and 24 weeks, which first became detectable at 34 weeks, = 31 (24%; maternal C-peptide 4-26 pmol/L at 34 weeks). Baseline characteristics and third trimester glucose profiles of women with pattern 1 and pattern 3 C-peptide trajectories were similar, but women in pattern 3 had suboptimal glycemia (50% time above range) at 24 weeks' gestation. Offspring of women with pattern 3 C-peptide trajectories had elevated cord blood C-peptide (geometric mean 1,319 vs. 718 pmol/L; = 0.007), increased rates of large for gestational age (90% vs. 60%; = 0.002), neonatal hypoglycemia (42% vs. 14%; = 0.001), and neonatal intensive care admission (45% vs. 23%; = 0.023) compared with pattern 1 offspring.

Conclusions: First maternal C-peptide appearance at 34 weeks was associated with midtrimester hyperglycemia, elevated cord blood C-peptide, and high rates of neonatal complications. This suggests transfer of C-peptide from fetal to maternal serum and is inconsistent with pregnancy-related β-cell regeneration.
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http://dx.doi.org/10.2337/dc21-0028DOI Listing
August 2021

Monogenic Diabetes and Integrated Stress Response Genes Display Altered Gene Expression in Type 1 Diabetes.

Diabetes 2021 08 25;70(8):1885-1897. Epub 2021 May 25.

Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL

Type 1 diabetes (T1D) has a multifactorial autoimmune etiology, involving environmental prompts and polygenic predisposition. We hypothesized that pancreata from individuals with and at risk for T1D would exhibit dysregulated expression of genes associated with monogenic forms of diabetes caused by nonredundant single-gene mutations. Using a "monogenetic transcriptomic strategy," we measured the expression of these genes in human T1D, autoantibody-positive (autoantibody+), and control pancreas tissues with real-time quantitative PCR in accordance with the Minimum Information for Publication of Quantitative Real-Time PCR Experiments (MIQE) guidelines. Gene and protein expression was visualized in situ with use of immunofluorescence, RNAscope, and confocal microscopy. Two dozen monogenic diabetes genes showed altered expression in human pancreata from individuals with T1D versus unaffected control subjects. Six of these genes also saw dysregulation in pancreata from autoantibody+ individuals at increased risk for T1D. As a subset of these genes are related to cellular stress responses, we measured integrated stress response (ISR) genes and identified 20 with altered expression in T1D pancreata, including three of the four eIF2α-dependent kinases. Equally intriguing, we observed significant repression of the three arms of the ISR in autoantibody+ pancreata. Collectively, these efforts suggest monogenic diabetes and ISR genes are dysregulated early in the T1D disease process and likely contribute to the disorder's pathogenesis.
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http://dx.doi.org/10.2337/db21-0070DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8385619PMC
August 2021

IgA Nephropathy Genetic Risk Score to Estimate the Prevalence of IgA Nephropathy in UK Biobank.

Kidney Int Rep 2020 Oct 19;5(10):1643-1650. Epub 2020 Jul 19.

Institute of Biomedical and Clinical Sciences, University of Exeter Medical School, Exeter, UK.

Background: IgA nephropathy (IgAN) is the commonest glomerulonephritis worldwide. Its prevalence is difficult to estimate, as people with mild disease do not commonly receive a biopsy diagnosis. We aimed to generate an IgA nephropathy genetic risk score (IgAN-GRS) and estimate the proportion of people with hematuria who had IgAN in the UK Biobank (UKBB).

Methods: We calculated an IgAN-GRS using 14 single-nucleotide polymorphisms (SNPs) drawn from the largest European Genome-Wide Association Study (GWAS) and validated the IgAN-GRS in 464 biopsy-proven IgAN European cases from the UK Glomerulonephritis DNA Bank (UKGDB) and in 379,767 Europeans in the UKBB. We used the mean of IgAN-GRS to calculate the proportion of potential IgAN in 14,181 with hematuria and other nonspecific renal phenotypes from 379,767 Europeans in the UKBB.

Results: The IgAN-GRS was higher in the IgAN cohort (4.30; 95% confidence interval [95% CI: 4.23-4.38) than in controls (3.98; 3.97-3.98;  < 0.0001). The mean GRS in UKBB participants with hematuria ( = 12,858) was higher (4.04; 4.02-4.06) than UKBB controls (3.98; 3.97-3.98;  < 0.0001) and higher in those with hematuria, hypertension, and microalbuminuria ( = 1323) (4.07; 4.02-4.13) versus (3.98; 3.97-3.98;  = 0.0003). Using the difference in these means, we estimated that IgAN accounted for 19% of noncancer hematuria and 28% with hematuria, hypertension, and microalbuminuria in UKBB.

Conclusions: We used an IgAN-GRS to estimate the prevalence of IgAN contributing to common phenotypes that are not always biopsied. The noninvasive use of polygenic risk in this setting may have further utility to identify likely etiology of nonspecific renal phenotypes in large population cohorts.
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http://dx.doi.org/10.1016/j.ekir.2020.07.012DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7572308PMC
October 2020

Type 1 diabetes can present before the age of 6 months and is characterised by autoimmunity and rapid loss of beta cells.

Diabetologia 2020 12 8;63(12):2605-2615. Epub 2020 Oct 8.

Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK.

Aims/hypothesis: Diabetes diagnosed at <6 months of age is usually monogenic. However, 10-15% of affected infants do not have a pathogenic variant in one of the 26 known neonatal diabetes genes. We characterised infants diagnosed at <6 months of age without a pathogenic variant to assess whether polygenic type 1 diabetes could arise at early ages.

Methods: We studied 166 infants diagnosed with type 1 diabetes at <6 months of age in whom pathogenic variants in all 26 known genes had been excluded and compared them with infants with monogenic neonatal diabetes (n = 164) or children with type 1 diabetes diagnosed at 6-24 months of age (n = 152). We assessed the type 1 diabetes genetic risk score (T1D-GRS), islet autoantibodies, C-peptide and clinical features.

Results: We found an excess of infants with high T1D-GRS: 38% (63/166) had a T1D-GRS >95th centile of healthy individuals, whereas 5% (8/166) would be expected if all were monogenic (p < 0.0001). Individuals with a high T1D-GRS had a similar rate of autoantibody positivity to that seen in individuals with type 1 diabetes diagnosed at 6-24 months of age (41% vs 58%, p = 0.2), and had markedly reduced C-peptide levels (median <3 pmol/l within 1 year of diagnosis), reflecting rapid loss of insulin secretion. These individuals also had reduced birthweights (median z score -0.89), which were lowest in those diagnosed with type 1 diabetes at <3 months of age (median z score -1.98).

Conclusions/interpretation: We provide strong evidence that type 1 diabetes can present before the age of 6 months based on individuals with this extremely early-onset diabetes subtype having the classic features of childhood type 1 diabetes: high genetic risk, autoimmunity and rapid beta cell loss. The early-onset association with reduced birthweight raises the possibility that for some individuals there was reduced insulin secretion in utero. Comprehensive genetic testing for all neonatal diabetes genes remains essential for all individuals diagnosed with diabetes at <6 months of age. Graphical abstract.
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http://dx.doi.org/10.1007/s00125-020-05276-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7641942PMC
December 2020

A single nucleotide polymorphism genetic risk score to aid diagnosis of coeliac disease: a pilot study in clinical care.

Aliment Pharmacol Ther 2020 10 13;52(7):1165-1173. Epub 2020 Aug 13.

Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK.

Background: Single nucleotide polymorphism-based genetic risk scores (GRS) model genetic risk as a continuum and can discriminate coeliac disease but have not been validated in clinic. Human leukocyte antigen (HLA) DQ gene testing is available in clinic but does not include non-HLA attributed risk and is limited by discrete risk stratification.

Aims: To accurately characterise both HLA and non-HLA coeliac disease genetic risk as a single nucleotide polymorphism-based GRS and evaluate diagnostic utility.

Methods: We developed a 42 single nucleotide polymorphism coeliac disease GRS from a European case-control study (12 041 cases vs 12 228 controls) using HLA-DQ imputation and published genome-wide association studies. We validated the GRS in UK Biobank (1237 cases) and developed direct genotyping assays. We tested the coeliac disease GRS in a pilot clinical cohort of 128 children presenting with suspected coeliac disease.

Results: The GRS was more discriminative of coeliac disease than HLA-DQ stratification in UK Biobank (receiver operating characteristic area under the curve [ROC-AUC] = 0.88 [95% CIs: 0.87-0.89] vs 0.82 [95% CIs: 0.80-0.83]). We demonstrated similar discrimination in the pilot clinical cohort (114 cases vs 40 controls, ROC-AUC = 0.84 [95% CIs: 0.76-0.91]). As a rule-out test, no children with coeliac disease in the clinical cohort had a GRS below 38th population centile.

Conclusions: A single nucleotide polymorphism-based GRS may offer more effective and cost-efficient testing of coeliac disease genetic risk in comparison to HLA-DQ stratification. As a comparatively inexpensive test it could facilitate non-invasive coeliac disease diagnosis but needs detailed assessment in the context of other diagnostic tests and against current diagnostic algorithms.
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http://dx.doi.org/10.1111/apt.15826DOI Listing
October 2020

A combined risk score enhances prediction of type 1 diabetes among susceptible children.

Nat Med 2020 08 7;26(8):1247-1255. Epub 2020 Aug 7.

Pacific Northwest Research Institute, Seattle, WA, USA.

Type 1 diabetes (T1D)-an autoimmune disease that destroys the pancreatic islets, resulting in insulin deficiency-often begins early in life when islet autoantibody appearance signals high risk. However, clinical diabetes can follow in weeks or only after decades, and is very difficult to predict. Ketoacidosis at onset remains common and is most severe in the very young, in whom it can be life threatening and difficult to treat. Autoantibody surveillance programs effectively prevent most ketoacidosis but require frequent evaluations whose expense limits public health adoption. Prevention therapies applied before onset, when greater islet mass remains, have rarely been feasible because individuals at greatest risk of impending T1D are difficult to identify. To remedy this, we sought accurate, cost-effective estimation of future T1D risk by developing a combined risk score incorporating both fixed and variable factors (genetic, clinical and immunological) in 7,798 high-risk children followed closely from birth for 9.3 years. Compared with autoantibodies alone, the combined model dramatically improves T1D prediction at ≥2 years of age over horizons up to 8 years of age (area under the receiver operating characteristic curve ≥ 0.9), doubles the estimated efficiency of population-based newborn screening to prevent ketoacidosis, and enables individualized risk estimates for better prevention trial selection.
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http://dx.doi.org/10.1038/s41591-020-0930-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7556983PMC
August 2020

Postexercise Glycemic Control in Type 1 Diabetes Is Associated With Residual β-Cell Function.

Diabetes Care 2020 10 3;43(10):2362-2370. Epub 2020 Aug 3.

Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, U.K.

Objective: To investigate the impact of residual β-cell function on continuous glucose monitoring (CGM) outcomes following acute exercise in people with type 1 diabetes (T1D).

Research Design And Methods: Thirty participants with T1D for ≥3 years were recruited. First, participants wore a blinded CGM unit for 7 days of free-living data capture. Second, a 3-h mixed-meal test assessed stimulated C-peptide and glucagon. Peak C-peptide was used to allocate participants into undetectable (Cpep <3 pmol/L), low (Cpep 3-200 pmol/L), or high (Cpep >200 pmol/L) C-peptide groups. Finally, participants completed 45 min of incline treadmill walking at 60% VO followed by a further 48-h CGM capture.

Results: CGM parameters were comparable across groups during the free-living observation week. In the 12- and 24-h postexercise periods (12 h and 24 h), the Cpep group had a significantly greater amount of time spent with glucose 3.9-10 mmol/L (12 h, 73.5 ± 27.6%; 24 h, 76.3 ± 19.2%) compared with Cpep (12 h, 43.6 ± 26.1%, = 0.027; 24 h, 52.3 ± 25.0%, = 0.067) or Cpep (12 h, 40.6 ± 17.0%, = 0.010; 24 h, 51.3 ± 22.3%, = 0.041). Time spent in hyperglycemia (12 h and 24 h glucose >10 and >13.9 mmol/L, < 0.05) and glycemic variability (12 h and 24 h SD, < 0.01) were significantly lower in the Cpep group compared with Cpep and Cpep. Change in CGM outcomes from pre-exercise to 24-h postexercise was divergent: Cpep and Cpep experienced worsening (glucose 3.9-10 mmol/L: -9.1% and -16.2%, respectively), with Cpep experiencing improvement (+12.1%) ( = 0.017).

Conclusions: Residual β-cell function may partially explain the interindividual variation in the acute glycemic benefits of exercise in individuals with T1D. Quantifying C-peptide could aid in providing personalized and targeted support for exercising patients.
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http://dx.doi.org/10.2337/dc20-0300DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7510016PMC
October 2020

Logistic regression has similar performance to optimised machine learning algorithms in a clinical setting: application to the discrimination between type 1 and type 2 diabetes in young adults.

Diagn Progn Res 2020 4;4. Epub 2020 Jun 4.

Institute of Biomedical and Clinical Science, College of Medicine and Health, University of Exeter, Exeter, EX2 5DW UK.

Background: There is much interest in the use of prognostic and diagnostic prediction models in all areas of clinical medicine. The use of machine learning to improve prognostic and diagnostic accuracy in this area has been increasing at the expense of classic statistical models. Previous studies have compared performance between these two approaches but their findings are inconsistent and many have limitations. We aimed to compare the discrimination and calibration of seven models built using logistic regression and optimised machine learning algorithms in a clinical setting, where the number of potential predictors is often limited, and externally validate the models.

Methods: We trained models using logistic regression and six commonly used machine learning algorithms to predict if a patient diagnosed with diabetes has type 1 diabetes (versus type 2 diabetes). We used seven predictor variables (age, BMI, GADA islet-autoantibodies, sex, total cholesterol, HDL cholesterol and triglyceride) using a UK cohort of adult participants (aged 18-50 years) with clinically diagnosed diabetes recruited from primary and secondary care ( = 960, 14% with type 1 diabetes). Discrimination performance (ROC AUC), calibration and decision curve analysis of each approach was compared in a separate external validation dataset ( = 504, 21% with type 1 diabetes).

Results: Average performance obtained in internal validation was similar in all models (ROC AUC ≥ 0.94). In external validation, there were very modest reductions in discrimination with AUC ROC remaining ≥ 0.93 for all methods. Logistic regression had the numerically highest value in external validation (ROC AUC 0.95). Logistic regression had good performance in terms of calibration and decision curve analysis. Neural network and gradient boosting machine had the best calibration performance. Both logistic regression and support vector machine had good decision curve analysis for clinical useful threshold probabilities.

Conclusion: Logistic regression performed as well as optimised machine algorithms to classify patients with type 1 and type 2 diabetes. This study highlights the utility of comparing traditional regression modelling to machine learning, particularly when using a small number of well understood, strong predictor variables.
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http://dx.doi.org/10.1186/s41512-020-00075-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7318367PMC
June 2020

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

Sci Rep 2020 06 11;10(1):9450. Epub 2020 Jun 11.

Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, Devon, UK.

Type 1 diabetes (T1D) is a significant problem in Indians and misclassification of T1D and type 2 diabetes (T2D) is a particular problem in young adults in this population due to the high prevalence of early onset T2D at lower BMI. We have previously shown a genetic risk score (GRS) can be used to discriminate T1D from T2D in Europeans. We aimed to test the ability of a T1D GRS to discriminate T1D from T2D and controls in Indians. We studied subjects from Pune, India of Indo-European ancestry; T1D (n = 262 clinically defined, 200 autoantibody positive), T2D (n = 345) and controls (n = 324). We used the 9 SNP T1D GRS generated in Europeans and assessed its ability to discriminate T1D from T2D and controls in Indians. We compared Indians with Europeans from the Wellcome Trust Case Control Consortium study; T1D (n = 1963), T2D (n = 1924) and controls (n = 2938). The T1D GRS was discriminative of T1D from T2D in Indians but slightly less than in Europeans (ROC AUC 0.84 v 0.87, p < 0.0001). HLA SNPs contributed the majority of the discriminative power in Indians. A T1D GRS using SNPs defined in Europeans is discriminative of T1D from T2D and controls in Indians. As with Europeans, the T1D GRS may be useful for classifying diabetes in Indians.
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http://dx.doi.org/10.1038/s41598-020-65317-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7289794PMC
June 2020

Studies of insulin and proinsulin in pancreas and serum support the existence of aetiopathological endotypes of type 1 diabetes associated with age at diagnosis.

Diabetologia 2020 06 15;63(6):1258-1267. Epub 2020 Mar 15.

Institute of Biomedical and Clinical Science, University of Exeter Medical School, RILD Building, Barrack Road, Exeter, EX2 5DW, UK.

Aims/hypothesis: It is unclear whether type 1 diabetes is a single disease or if endotypes exist. Our aim was to use a unique collection of pancreas samples recovered soon after disease onset to resolve this issue.

Methods: Immunohistological analysis was used to determine the distribution of proinsulin and insulin in the islets of pancreas samples recovered soon after type 1 diabetes onset (<2 years) from young people diagnosed at age <7 years, 7-12 years and ≥13 years. The patterns were correlated with the insulitis profiles in the inflamed islets of the same groups of individuals. C-peptide levels and the proinsulin:C-peptide ratio were measured in the circulation of a cohort of living patients with longer duration of disease but who were diagnosed in these same age ranges.

Results: Distinct patterns of proinsulin localisation were seen in the islets of people with recent-onset type 1 diabetes, which differed markedly between children diagnosed at <7 years and those diagnosed at ≥13 years. Proinsulin processing was aberrant in most residual insulin-containing islets of the younger group but this was much less evident in the group ≥13 years (p < 0.0001). Among all individuals (including children in the middle [7-12 years] range) aberrant proinsulin processing correlated with the assigned immune cell profiles defined by analysis of the lymphocyte composition of islet infiltrates. C-peptide levels were much lower in individuals diagnosed at <7 years than in those diagnosed at ≥13 years (median <3 pmol/l, IQR <3 to <3 vs 34.5 pmol/l, IQR <3-151; p < 0.0001), while the median proinsulin:C-peptide ratio was increased in those with age of onset <7 years compared with people diagnosed aged ≥13 years (0.18, IQR 0.10-0.31) vs 0.01, IQR 0.009-0.10 pmol/l; p < 0.0001).

Conclusions/interpretation: Among those with type 1 diabetes diagnosed under the age of 30 years, there are histologically distinct endotypes that correlate with age at diagnosis. Recognition of such differences should inform the design of future immunotherapeutic interventions designed to arrest disease progression.
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http://dx.doi.org/10.1007/s00125-020-05115-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7228905PMC
June 2020

Introducing the Endotype Concept to Address the Challenge of Disease Heterogeneity in Type 1 Diabetes.

Diabetes Care 2020 01 21;43(1):5-12. Epub 2019 Nov 21.

Department of Pediatrics, University of Florida, Gainesville, FL.

The clinical diagnosis of new-onset type 1 diabetes has, for many years, been considered relatively straightforward. Recently, however, there is increasing awareness that within this single clinical phenotype exists considerable heterogeneity: disease onset spans the complete age range; genetic susceptibility is complex; rates of progression differ markedly, as does insulin secretory capacity; and complication rates, glycemic control, and therapeutic intervention efficacy vary widely. Mechanistic and immunopathological studies typically show considerable patchiness across subjects, undermining conclusions regarding disease pathways. Without better understanding, type 1 diabetes heterogeneity represents a major barrier both to deciphering pathogenesis and to the translational effort of designing, conducting, and interpreting clinical trials of disease-modifying agents. This realization comes during a period of unprecedented change in clinical medicine, with increasing emphasis on greater individualization and precision. For complex disorders such as type 1 diabetes, the option of maintaining the "single disease" approach appears untenable, as does the notion of individualizing each single patient's care, obliging us to conceptualize type 1 diabetes less in terms of phenotypes (observable characteristics) and more in terms of disease endotypes (underlying biological mechanisms). Here, we provide our view on an approach to dissect heterogeneity in type 1 diabetes. Using lessons from other diseases and the data gathered to date, we aim to delineate a roadmap through which the field can incorporate the endotype concept into laboratory and clinical practice. We predict that such an effort will accelerate the implementation of precision medicine and has the potential for impact on our approach to translational research, trial design, and clinical management.
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http://dx.doi.org/10.2337/dc19-0880DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6925574PMC
January 2020

Methods for quick, accurate and cost-effective determination of the type 1 diabetes genetic risk score (T1D-GRS).

Clin Chem Lab Med 2020 03;58(4):e102-e104

Institute of Biomedical and Clinical Science, University of Exeter Medical School, Level 3, RILD Building, Barrack Road, Exeter, Devon, EX2 5DW, UK, Phone: 01392 408538, Fax: 01392 408388.

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http://dx.doi.org/10.1515/cclm-2019-0787DOI Listing
March 2020

New insights on the genetics of type 1 diabetes.

Curr Opin Endocrinol Diabetes Obes 2019 08;26(4):181-187

Pediatric Diabetes and Endocrinology, Texas Children's Hospital, Baylor College of Medicine, Houston, Texas, USA.

Purpose Of Review: The genetic risk for type 1 diabetes has been studied for over half a century, with the strong genetic associations of type 1 diabetes forming critical evidence for the role of the immune system in pathogenesis. In this review, we discuss some of the original research leading to recent developments in type 1 diabetes genetics.

Recent Findings: We examine the translation of polygenic scores for type 1 diabetes into tools for prediction and diagnosis of type 1 diabetes, in particular, when used in combination with other biomarkers and clinical features, such as age and islet-specific autoantibodies. Furthermore, we review the description of age associations with type 1 diabetes genetic risk, and the investigation of loci linked to type 2 diabetes in progression of type 1 diabetes. Finally, we consider current limitations, including the scarcity of data from racial and ethnic minorities, and future directions.

Summary: The development of polygenic risk scores has allowed the integration of type 1 diabetes genetics into diagnosis and prediction. Emerging information on the role of specific genes in subgroups of individuals with the disease, for example, early-onset, mild autoimmunity, and so forth, is facilitating our understanding of the heterogeneity of type 1 diabetes, with the ultimate goal of using genetic information in research and clinical practice.
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http://dx.doi.org/10.1097/MED.0000000000000489DOI Listing
August 2019

Type 1 diabetes defined by severe insulin deficiency occurs after 30 years of age and is commonly treated as type 2 diabetes.

Diabetologia 2019 07 10;62(7):1167-1172. Epub 2019 Apr 10.

Institute of Biomedical and Clinical Science, University of Exeter Medical School, Barrack Road, Exeter, EX25DW, UK.

Aims/hypothesis: Late-onset type 1 diabetes can be difficult to identify. Measurement of endogenous insulin secretion using C-peptide provides a gold standard classification of diabetes type in longstanding diabetes that closely relates to treatment requirements. We aimed to determine the prevalence and characteristics of type 1 diabetes defined by severe endogenous insulin deficiency after age 30 and assess whether these individuals are identified and managed as having type 1 diabetes in clinical practice.

Methods: We assessed the characteristics of type 1 diabetes defined by rapid insulin requirement (within 3 years of diagnosis) and severe endogenous insulin deficiency (non-fasting C-peptide <200 pmol/l) in 583 participants with insulin-treated diabetes, diagnosed after age 30, from the Diabetes Alliance for Research in England (DARE) population cohort. We compared characteristics with participants with retained endogenous insulin secretion (>600 pmol/l) and 220 participants with severe insulin deficiency who were diagnosed under age 30.

Results: Twenty-one per cent of participants with insulin-treated diabetes who were diagnosed after age 30 met the study criteria for type 1 diabetes. Of these participants, 38% did not receive insulin at diagnosis, of whom 47% self-reported type 2 diabetes. Rapid insulin requirement was highly predictive of severe endogenous insulin deficiency: 85% required insulin within 1 year of diagnosis, and 47% of all those initially treated without insulin who progressed to insulin treatment within 3 years of diagnosis had severe endogenous insulin deficiency. Participants with late-onset type 1 diabetes defined by development of severe insulin deficiency had similar clinical characteristics to those with young-onset type 1 diabetes. However, those with later onset type 1 diabetes had a modestly lower type 1 diabetes genetic risk score (0.268 vs 0.279; p < 0.001 [expected type 2 diabetes population median, 0.231]), a higher islet autoantibody prevalence (GAD-, islet antigen 2 [IA2]- or zinc transporter protein 8 [ZnT8]-positive) of 78% at 13 years vs 62% at 26 years of diabetes duration; (p = 0.02), and were less likely to identify as having type 1 diabetes (79% vs 100%; p < 0.001) vs those with young-onset disease.

Conclusions/interpretation: Type 1 diabetes diagnosed over 30 years of age, defined by severe insulin deficiency, has similar clinical and biological characteristics to that occurring at younger ages, but is frequently not identified. Clinicians should be aware that patients progressing to insulin within 3 years of diagnosis have a high likelihood of type 1 diabetes, regardless of initial diagnosis.
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http://dx.doi.org/10.1007/s00125-019-4863-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6559997PMC
July 2019

Beta cells in type 1 diabetes: mass and function; sleeping or dead?

Diabetologia 2019 04 14;62(4):567-577. Epub 2019 Feb 14.

Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA.

Histological analysis of donor pancreases coupled with measurement of serum C-peptide in clinical cohorts has challenged the idea that all beta cells are eventually destroyed in type 1 diabetes. These findings have raised a number of questions regarding how the remaining beta cells have escaped immune destruction, whether pools of 'sleeping' or dysfunctional beta cells could be rejuvenated and whether there is potential for new growth of beta cells. In this Review, we describe histological and in vivo evidence of persistent beta cells in type 1 diabetes and discuss the limitations of current methods to distinguish underlying beta cell mass in comparison with beta cell function. We highlight that evidence for new beta cell growth in humans many years from diagnosis is limited, and that this growth may be very minimal if at all present. We review recent contributions to the debate around beta cell abnormalities contributing to the pathogenesis of type 1 diabetes. We also discuss evidence for restoration of beta cell function, as opposed to mass, in recent-onset type 1 diabetes, but highlight the absence of data supporting functional recovery in the setting of long-duration diabetes. Finally, future areas of research are suggested to help resolve the source and phenotype of residual beta cells that persist in some, but not all, people with type 1 diabetes.
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http://dx.doi.org/10.1007/s00125-019-4822-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6688846PMC
April 2019

Development and Standardization of an Improved Type 1 Diabetes Genetic Risk Score for Use in Newborn Screening and Incident Diagnosis.

Diabetes Care 2019 02;42(2):200-207

Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, U.K.

Objective: Previously generated genetic risk scores (GRSs) for type 1 diabetes (T1D) have not captured all known information at non-HLA loci or, particularly, at HLA risk loci. We aimed to more completely incorporate HLA alleles, their interactions, and recently discovered non-HLA loci into an improved T1D GRS (termed the "T1D GRS2") to better discriminate diabetes subtypes and to predict T1D in newborn screening studies.

Research Design And Methods: In 6,481 case and 9,247 control subjects from the Type 1 Diabetes Genetics Consortium, we analyzed variants associated with T1D both in the HLA region and across the genome. We modeled interactions between variants marking strongly associated HLA haplotypes and generated odds ratios to create the improved GRS, the T1D GRS2. We validated our findings in UK Biobank. We assessed the impact of the T1D GRS2 in newborn screening and diabetes classification and sought to provide a framework for comparison with previous scores.

Results: The T1D GRS2 used 67 single nucleotide polymorphisms (SNPs) and accounted for interactions between 18 HLA DR-DQ haplotype combinations. The T1D GRS2 was highly discriminative for all T1D (area under the curve [AUC] 0.92; < 0.0001 vs. older scores) and even more discriminative for early-onset T1D (AUC 0.96). In simulated newborn screening, the T1D GRS2 was nearly twice as efficient as HLA genotyping alone and 50% better than current genetic scores in general population T1D prediction.

Conclusions: An improved T1D GRS, the T1D GRS2, is highly useful for classifying adult incident diabetes type and improving newborn screening. Given the cost-effectiveness of SNP genotyping, this approach has great clinical and research potential in T1D.
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http://dx.doi.org/10.2337/dc18-1785DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6341291PMC
February 2019

Analysis of serum Hsp90 as a potential biomarker of β cell autoimmunity in type 1 diabetes.

PLoS One 2019 10;14(1):e0208456. Epub 2019 Jan 10.

Department of Pediatrics and the Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, United States of America.

Heat shock protein 90 (Hsp90) is a protein chaperone that is upregulated and released from pancreatic β cells under pro-inflammatory conditions. We hypothesized that serum Hsp90 may have utility as a biomarker of type 1 diabetes risk and exhibit elevations before the onset of clinically significant hyperglycemia. To this end, total levels of the alpha cytoplasmic isoform of Hsp90 were assayed in autoantibody-positive progressors to type 1 diabetes using banked serum samples from the TrialNet Pathway to Prevention Cohort that had been collected 12 months prior to diabetes onset, with comparison to age, sex, and BMI-category matched autoantibody-positive nonprogressors and healthy controls. Hsp90 levels were higher in autoantibody-positive progressors and nonprogressors ≤ 18 years of age compared to matched healthy controls. However, Hsp90 levels were not different between progressors and nonprogressors in any age group. Hsp90 was positively correlated with age in control subjects, but this correlation was absent in autoantibody positive individuals. In aggregate these data indicate that elevated Hsp90 levels are present in youth with β cell autoimmunity, but are not able to distinguish youth or adult type 1 diabetes progressors from nonprogressors in samples collected 12 months prior to diabetes development.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0208456PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6328179PMC
September 2019

A Type 1 Diabetes Genetic Risk Score Can Identify Patients With GAD65 Autoantibody-Positive Type 2 Diabetes Who Rapidly Progress to Insulin Therapy.

Diabetes Care 2019 02 23;42(2):208-214. Epub 2018 Oct 23.

National Institute for Health Research Exeter Clinical Research Facility, University of Exeter Medical School, Exeter, U.K.

Objective: Progression to insulin therapy in clinically diagnosed type 2 diabetes is highly variable. GAD65 autoantibodies (GADA) are associated with faster progression, but their predictive value is limited. We aimed to determine if a type 1 diabetes genetic risk score (T1D GRS) could predict rapid progression to insulin treatment over and above GADA testing.

Research Design And Methods: We examined the relationship between T1D GRS, GADA (negative or positive), and rapid insulin requirement (within 5 years) using Kaplan-Meier survival analysis and Cox regression in 8,608 participants with clinical type 2 diabetes (onset >35 years and treated without insulin for ≥6 months). T1D GRS was both analyzed continuously (as standardized scores) and categorized based on previously reported centiles of a population with type 1 diabetes (<5th [low], 5th-50th [medium], and >50th [high]).

Results: In GADA-positive participants (3.3%), those with higher T1D GRS progressed to insulin more quickly: probability of insulin requirement at 5 years (95% CI): 47.9% (35.0%, 62.78%) (high T1D GRS) vs. 27.6% (20.5%, 36.5%) (medium T1D GRS) vs. 17.6% (11.2%, 27.2%) (low T1D GRS); = 0.001. In contrast, T1D GRS did not predict rapid insulin requirement in GADA-negative participants ( = 0.4). In Cox regression analysis with adjustment for age of diagnosis, BMI, and cohort, T1D GRS was independently associated with time to insulin only in the presence of GADA: hazard ratio per SD increase was 1.48 (1.15, 1.90); = 0.002.

Conclusions: A T1D GRS alters the clinical implications of a positive GADA test in patients with clinical type 2 diabetes and is independent of and additive to clinical features.
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http://dx.doi.org/10.2337/dc18-0431DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6828553PMC
February 2019

A Type 1 Diabetes Genetic Risk Score Predicts Progression of Islet Autoimmunity and Development of Type 1 Diabetes in Individuals at Risk.

Diabetes Care 2018 09 12;41(9):1887-1894. Epub 2018 Jul 12.

Objective: We tested the ability of a type 1 diabetes (T1D) genetic risk score (GRS) to predict progression of islet autoimmunity and T1D in at-risk individuals.

Research Design And Methods: We studied the 1,244 TrialNet Pathway to Prevention study participants (T1D patients' relatives without diabetes and with one or more positive autoantibodies) who were genotyped with Illumina ImmunoChip (median [range] age at initial autoantibody determination 11.1 years [1.2-51.8], 48% male, 80.5% non-Hispanic white, median follow-up 5.4 years). Of 291 participants with a single positive autoantibody at screening, 157 converted to multiple autoantibody positivity and 55 developed diabetes. Of 953 participants with multiple positive autoantibodies at screening, 419 developed diabetes. We calculated the T1D GRS from 30 T1D-associated single nucleotide polymorphisms. We used multivariable Cox regression models, time-dependent receiver operating characteristic curves, and area under the curve (AUC) measures to evaluate prognostic utility of T1D GRS, age, sex, Diabetes Prevention Trial-Type 1 (DPT-1) Risk Score, positive autoantibody number or type, HLA DR3/DR4-DQ8 status, and race/ethnicity. We used recursive partitioning analyses to identify cut points in continuous variables.

Results: Higher T1D GRS significantly increased the rate of progression to T1D adjusting for DPT-1 Risk Score, age, number of positive autoantibodies, sex, and ethnicity (hazard ratio [HR] 1.29 for a 0.05 increase, 95% CI 1.06-1.6; = 0.011). Progression to T1D was best predicted by a combined model with GRS, number of positive autoantibodies, DPT-1 Risk Score, and age (7-year time-integrated AUC = 0.79, 5-year AUC = 0.73). Higher GRS was significantly associated with increased progression rate from single to multiple positive autoantibodies after adjusting for age, autoantibody type, ethnicity, and sex (HR 2.27 for GRS >0.295, 95% CI 1.47-3.51; = 0.0002).

Conclusions: The T1D GRS independently predicts progression to T1D and improves prediction along T1D stages in autoantibody-positive relatives.
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http://dx.doi.org/10.2337/dc18-0087DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6105323PMC
September 2018

Type 1 diabetes.

Lancet 2018 06;391(10138):2449-2462

Institute of Biomedical and Clinical Science, University of Exeter Medical School, and The Academic Kidney Unit, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK.

Type 1 diabetes is a chronic autoimmune disease characterised by insulin deficiency and resultant hyperglycaemia. Knowledge of type 1 diabetes has rapidly increased over the past 25 years, resulting in a broad understanding about many aspects of the disease, including its genetics, epidemiology, immune and β-cell phenotypes, and disease burden. Interventions to preserve β cells have been tested, and several methods to improve clinical disease management have been assessed. However, wide gaps still exist in our understanding of type 1 diabetes and our ability to standardise clinical care and decrease disease-associated complications and burden. This Seminar gives an overview of the current understanding of the disease and potential future directions for research and care.
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http://dx.doi.org/10.1016/S0140-6736(18)31320-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6661119PMC
June 2018

Clinical and research uses of genetic risk scores in type 1 diabetes.

Curr Opin Genet Dev 2018 06 24;50:96-102. Epub 2018 Apr 24.

Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK; The Renal Unit, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK. Electronic address:

Type 1 diabetes (T1D) is a chronic disease of high blood glucose caused by autoimmune destruction of pancreatic beta cells eventually resulting in severe insulin deficiency. T1D has a significant heritable risk. Genetic associations found are particularly strong in the HLA class II region but T1D is a polygenic disease associated with over 60 loci across the genome. Polygenic risk scores are one method of summing these genetic risk elements as a single continuous variable. This review discusses the clinical and research utility of genetic risk scores in T1D particularly in disease prediction and progression. We also explore creative uses of genetic risk scores in big data and the limitations of using a genetic risk score. The increase in publically available genetic data and rapid fall in costs of genotyping mean that a T1D genetic risk score (T1D GRS) is likely to prove useful for disease prediction, discrimination, investigation of unusual cohorts, and investigation of biology in large datasets where genetic data are available.
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http://dx.doi.org/10.1016/j.gde.2018.03.009DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6089633PMC
June 2018

Genetic scores to stratify risk of developing multiple islet autoantibodies and type 1 diabetes: A prospective study in children.

PLoS Med 2018 04 3;15(4):e1002548. Epub 2018 Apr 3.

Institute of Diabetes Research, Helmholtz Zentrum München, Munich, Germany.

Background: Around 0.3% of newborns will develop autoimmunity to pancreatic beta cells in childhood and subsequently develop type 1 diabetes before adulthood. Primary prevention of type 1 diabetes will require early intervention in genetically at-risk infants. The objective of this study was to determine to what extent genetic scores (two previous genetic scores and a merged genetic score) can improve the prediction of type 1 diabetes.

Methods And Findings: The Environmental Determinants of Diabetes in the Young (TEDDY) study followed genetically at-risk children at 3- to 6-monthly intervals from birth for the development of islet autoantibodies and type 1 diabetes. Infants were enrolled between 1 September 2004 and 28 February 2010 and monitored until 31 May 2016. The risk (positive predictive value) for developing multiple islet autoantibodies (pre-symptomatic type 1 diabetes) and type 1 diabetes was determined in 4,543 children who had no first-degree relatives with type 1 diabetes and either a heterozygous HLA DR3 and DR4-DQ8 risk genotype or a homozygous DR4-DQ8 genotype, and in 3,498 of these children in whom genetic scores were calculated from 41 single nucleotide polymorphisms. In the children with the HLA risk genotypes, risk for developing multiple islet autoantibodies was 5.8% (95% CI 5.0%-6.6%) by age 6 years, and risk for diabetes by age 10 years was 3.7% (95% CI 3.0%-4.4%). Risk for developing multiple islet autoantibodies was 11.0% (95% CI 8.7%-13.3%) in children with a merged genetic score of >14.4 (upper quartile; n = 907) compared to 4.1% (95% CI 3.3%-4.9%, P < 0.001) in children with a genetic score of ≤14.4 (n = 2,591). Risk for developing diabetes by age 10 years was 7.6% (95% CI 5.3%-9.9%) in children with a merged score of >14.4 compared with 2.7% (95% CI 1.9%-3.6%) in children with a score of ≤14.4 (P < 0.001). Of 173 children with multiple islet autoantibodies by age 6 years and 107 children with diabetes by age 10 years, 82 (sensitivity, 47.4%; 95% CI 40.1%-54.8%) and 52 (sensitivity, 48.6%, 95% CI 39.3%-60.0%), respectively, had a score >14.4. Scores were higher in European versus US children (P = 0.003). In children with a merged score of >14.4, risk for multiple islet autoantibodies was similar and consistently >10% in Europe and in the US; risk was greater in males than in females (P = 0.01). Limitations of the study include that the genetic scores were originally developed from case-control studies of clinical diabetes in individuals of mainly European decent. It is, therefore, possible that it may not be suitable to all populations.

Conclusions: A type 1 diabetes genetic score identified infants without family history of type 1 diabetes who had a greater than 10% risk for pre-symptomatic type 1 diabetes, and a nearly 2-fold higher risk than children identified by high-risk HLA genotypes alone. This finding extends the possibilities for enrolling children into type 1 diabetes primary prevention trials.
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http://dx.doi.org/10.1371/journal.pmed.1002548DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5882115PMC
April 2018

Application of a Genetic Risk Score to Racially Diverse Type 1 Diabetes Populations Demonstrates the Need for Diversity in Risk-Modeling.

Sci Rep 2018 03 14;8(1):4529. Epub 2018 Mar 14.

Departments of Pathology, Immunology, and Laboratory Medicine, College of Medicine, Gainesville, Florida, USA.

Prior studies identified HLA class-II and 57 additional loci as contributors to genetic susceptibility for type 1 diabetes (T1D). We hypothesized that race and/or ethnicity would be contextually important for evaluating genetic risk markers previously identified from Caucasian/European cohorts. We determined the capacity for a combined genetic risk score (GRS) to discriminate disease-risk subgroups in a racially and ethnically diverse cohort from the southeastern U.S. including 637 T1D patients, 46 at-risk relatives having two or more T1D-related autoantibodies (≥2AAb), 790 first-degree relatives (≤1AAb), 68 second-degree relatives (≤1 AAb), and 405 controls. GRS was higher among Caucasian T1D and at-risk subjects versus ≤ 1AAb relatives or controls (P < 0.001). GRS receiver operating characteristic AUC (AUROC) for T1D versus controls was 0.86 (P < 0.001, specificity = 73.9%, sensitivity = 83.3%) among all Caucasian subjects and 0.90 for Hispanic Caucasians (P < 0.001, specificity = 86.5%, sensitivity = 84.4%). Age-at-diagnosis negatively correlated with GRS (P < 0.001) and associated with HLA-DR3/DR4 diplotype. Conversely, GRS was less robust (AUROC = 0.75) and did not correlate with age-of-diagnosis for African Americans. Our findings confirm GRS should be further used in Caucasian populations to assign T1D risk for clinical trials designed for biomarker identification and development of personalized treatment strategies. We also highlight the need to develop a GRS model that accommodates racial diversity.
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http://dx.doi.org/10.1038/s41598-018-22574-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5852207PMC
March 2018

Genetic risk scores in adult-onset type 1 diabetes - Authors' reply.

Lancet Diabetes Endocrinol 2018 03;6(3):169

Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter EX2 5DW, UK. Electronic address:

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http://dx.doi.org/10.1016/S2213-8587(18)30046-9DOI Listing
March 2018

Meta-genome-wide association studies identify a locus on chromosome 1 and multiple variants in the MHC region for serum C-peptide in type 1 diabetes.

Diabetologia 2018 05 5;61(5):1098-1111. Epub 2018 Feb 5.

Genetics and Genome Biology Program, Peter Gilgan Centre for Research and Learning (PGCRL), The Hospital for Sick Children, 686 Bay Street, Toronto, ON, M5G 1H3, Canada.

Aims/hypothesis: The aim of this study was to identify genetic variants associated with beta cell function in type 1 diabetes, as measured by serum C-peptide levels, through meta-genome-wide association studies (meta-GWAS).

Methods: We performed a meta-GWAS to combine the results from five studies in type 1 diabetes with cross-sectionally measured stimulated, fasting or random C-peptide levels, including 3479 European participants. The p values across studies were combined, taking into account sample size and direction of effect. We also performed separate meta-GWAS for stimulated (n = 1303), fasting (n = 2019) and random (n = 1497) C-peptide levels.

Results: In the meta-GWAS for stimulated/fasting/random C-peptide levels, a SNP on chromosome 1, rs559047 (Chr1:238753916, T>A, minor allele frequency [MAF] 0.24-0.26), was associated with C-peptide (p = 4.13 × 10), meeting the genome-wide significance threshold (p < 5 × 10). In the same meta-GWAS, a locus in the MHC region (rs9260151) was close to the genome-wide significance threshold (Chr6:29911030, C>T, MAF 0.07-0.10, p = 8.43 × 10). In the stimulated C-peptide meta-GWAS, rs61211515 (Chr6:30100975, T/-, MAF 0.17-0.19) in the MHC region was associated with stimulated C-peptide (β [SE] = - 0.39 [0.07], p = 9.72 × 10). rs61211515 was also associated with the rate of stimulated C-peptide decline over time in a subset of individuals (n = 258) with annual repeated measures for up to 6 years (p = 0.02). In the meta-GWAS of random C-peptide, another MHC region, SNP rs3135002 (Chr6:32668439, C>A, MAF 0.02-0.06), was associated with C-peptide (p = 3.49 × 10). Conditional analyses suggested that the three identified variants in the MHC region were independent of each other. rs9260151 and rs3135002 have been associated with type 1 diabetes, whereas rs559047 and rs61211515 have not been associated with a risk of developing type 1 diabetes.

Conclusions/interpretation: We identified a locus on chromosome 1 and multiple variants in the MHC region, at least some of which were distinct from type 1 diabetes risk loci, that were associated with C-peptide, suggesting partly non-overlapping mechanisms for the development and progression of type 1 diabetes. These associations need to be validated in independent populations. Further investigations could provide insights into mechanisms of beta cell loss and opportunities to preserve beta cell function.
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http://dx.doi.org/10.1007/s00125-018-4555-9DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5876265PMC
May 2018

Frequency and phenotype of type 1 diabetes in the first six decades of life: a cross-sectional, genetically stratified survival analysis from UK Biobank.

Lancet Diabetes Endocrinol 2018 02 30;6(2):122-129. Epub 2017 Nov 30.

Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK. Electronic address:

Background: Type 1 diabetes is typically considered a disease of children and young adults. Genetic susceptibility to young-onset type 1 diabetes is well defined and does not predispose to type 2 diabetes. It is not known how frequently genetic susceptibility to type 1 diabetes leads to a diagnosis of diabetes after age 30 years. We aimed to investigate the frequency and phenotype of type 1 diabetes resulting from high genetic susceptibility in the first six decades of life.

Methods: In this cross-sectional analysis, we used a type 1 diabetes genetic risk score based on 29 common variants to identify individuals of white European descent in UK Biobank in the half of the population with high or low genetic susceptibility to type 1 diabetes. We used Kaplan-Meier analysis to evaluate the number of cases of diabetes in both groups in the first six decades of life. We genetically defined type 1 diabetes as the additional cases of diabetes that occurred in the high genetic susceptibility group compared with the low genetic susceptibility group. All remaining cases were defined as type 2 diabetes. We assessed the clinical characteristics of the groups with genetically defined type 1 or type 2 diabetes.

Findings: 13 250 (3·5%) of 379 511 white European individuals in UK Biobank had developed diabetes in the first six decades of life. 1286 more cases of diabetes were in the half of the population with high genetic susceptibility to type 1 diabetes than in the half of the population with low genetic susceptibility. These genetically defined cases of type 1 diabetes were distributed across all ages of diagnosis; 537 (42%) were in individuals diagnosed when aged 31-60 years, representing 4% (537/12 233) of all diabetes cases diagnosed after age 30 years. The clinical characteristics of the group diagnosed with type 1 diabetes when aged 31-60 years were similar to the clinical characteristics of the group diagnosed with type 1 diabetes when aged 30 years or younger. For individuals diagnosed with diabetes when aged 31-60 years, the clinical characteristics of type 1 diabetes differed from those of type 2 diabetes: they had a lower BMI (27·4 kg/m [95% CI 26·7-28·0] vs 32·4 kg/m [32·2-32·5]; p<0·0001), were more likely to use insulin in the first year after diagnosis (89% [476/537] vs 6% [648/11 696]; p<0·0001), and were more likely to have diabetic ketoacidosis (11% [61/537] vs 0·3% [30/11 696]; p<0·0001).

Interpretation: Genetic susceptibility to type 1 diabetes results in non-obesity-related, insulin-dependent diabetes, which presents throughout the first six decades of life. Our results highlight the difficulty of identifying type 1 diabetes after age 30 years because of the increasing background prevalence of type 2 diabetes. Failure to diagnose late-onset type 1 diabetes can have serious consequences because these patients rapidly develop insulin dependency.

Funding: Wellcome Trust and Diabetes UK.
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http://dx.doi.org/10.1016/S2213-8587(17)30362-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5805861PMC
February 2018
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