Type 1 Diabetes Genetic Risk Score Research Articles

82-OR: A T1D Genetic Risk Score Combined with Clinical Features and Autoantibodies Enables Accurate Diabetes Classification in a Racial/Ethnically Diverse Population: The Search for Diabetes in Youth Study

Accurate classification of diabetes type guides correct treatment. We recently showed a type 1 diabetes (T1D) genetic risk score (GRS-1), combined with clinical features and biomarkers discriminates between T1D and type 2 (T2D) and MODY in European adults. We aimed to test the ability of an improved 67 SNP T1D score (GRS-2) to discriminate T1D in an ethnically diverse U.S. pediatric population. We included 1818 SEARCH Study participants who had a C-peptide at a median (IQR) of 8 (6-9) years post diagnosis. T1D was defined based on severe insulin deficiency (fasting C-peptide <0.25 ng/ml) at follow-up. We generated genetic risk scores from SNP data, and assessed discriminative power of the T1D GRS-2 using the area under the receiver operating curve (AUC) in the three largest ethnic groups in SEARCH: white (n=1101, 95% T1D), black (n=228, 59% T1D) and Hispanic (n=257, 77% T1D). We then assessed the discriminative power of GRSs combined with autoantibody and clinical data. The T1D GRS-2 was discriminative of T1D across all three ethnic groups AUC (95% CI) for GRS-1 vs. GRS-2 was 0.88 (0.84, 0.93) vs. 0.88 (0.83, 0.92) in white, 0.82 (0.77, 0.87) vs. 0.87 (0.83, 0.92) in blacks, and 0.87 (0.82, 0.92) vs. 0.9 (0.86, 0.95) in Hispanics). Combined risk scores were most accurate at classifying T1D. AUC for different scores were: 0.93 (0.91, 0.95) for type assigned by provider at diagnosis, 0.95 (0.93, 0.96) for age and BMI, 0.95 (0.94, 0.97) for GAD/IA-2/ZNT8 autoantibodies, and 0.99 (0.99, 1.00) for a combined approach using autoantibodies, GRS-2 and clinical features. These results were similar across all race/ethnic groups. A new T1D GRS-2 is discriminative of T1D in a racial/ethnically diverse pediatric population. Using GRS2 in a combined model of clinical features, autoantibodies and genetics offers near perfect classification of T1D and could be used to accurately classify diabetes type. Disclosure R.A. Oram: Other Relationship; Self; Randox Laboratories Ltd. S.A. Sharp: None. C. Pihoker: None. L.A. Ferrat: None. G. Imperatore: None. S. Saydah: None. A.H. Williams: None. L.E. Wagenknecht: None. J.M. Lawrence: None. M.N. Weedon: None. R. Dagostino: Consultant; Self; Acelity, Amgen Inc. W. Hagopian: Research Support; Self; Novo Nordisk A/S. J. Divers: None. D. Dabelea: None. Funding National Institutes of Health (UC4DK108173)

Monogenic diabetes: the impact of making the right diagnosis.

Monogenic forms of diabetes have received increased attention and genetic testing is more widely available; however, many patients are still misdiagnosed as having type 1 (T1D) or type 2 diabetes. This review will address updates to monogenic diabetes prevalence, identification, treatment, and genetic testing. The creation of a T1D genetic risk score and the use of noninvasive urinary C-peptide creatinine ratios have provided new tools to aid in the discrimination of possible monogenic diabetes from likely T1D. Early, high-dose sulfonylurea treatment in infants with a KCNJ11 or ABCC8 mutation continues to be well tolerated and effective. As the field moves towards more comprehensive genetic testing methods, there is an increased opportunity to identify novel genetic causes. Genetic testing results continue to allow for personalized treatment but should provide patient information at an appropriate health literacy level. Although there have been clinical and genetic advances in monogenic diabetes, patients are still misdiagnosed. Improved insurance coverage of genetic testing is needed. The majority of data on monogenic diabetes has been collected from Caucasian populations, therefore, research studies should endeavor to include broader ethnic and racial diversity to provide comprehensive information for all populations.

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

OBJECTIVEWe 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 METHODSWe 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.RESULTSHigher 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; P = 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; P = 0.0002).CONCLUSIONSThe T1D GRS independently predicts progression to T1D and improves prediction along T1D stages in autoantibody-positive relatives.

Clinical Utility of the T1D Genetic Risk Score—Examples from the U.S. Monogenic Diabetes Registry

Monogenic forms of diabetes are often difficult to distinguish from type 1 (T1D) and type 2 diabetes due to overlapping clinical features. As a result, a growing majority of patients remain misdiagnosed and often inappropriately treated. We used a T1D genetic risk score (T1D-GRS), generated from 10 T1D-associated common genetic variants, to help identify patients with a low T1D-GRS, and thus a potential monogenic etiology. We genotyped 798 probands from the U.S. Monogenic Diabetes Registry: 37% (n=293) had a known monogenic cause and 63% (n=505) had an unknown cause or were not exhaustively sequenced. To improve the discriminatory power of the T1D-GRS, participants were categorized using a T1D-GRS equivalent to the 5th, 25th, 50th, 75th, and 95th centile of the UK T1D cohort. 45% (132) of the registry participants with a confirmed monogenic cause had a T1D-GRS below the 5th percentile, 35% (103) had a T1D-GRS between the 5-25th percentile, 12% (35) between the 25-50th percentile, 5% (15) between the 50-75th percentile, and 2% (7) between the 75-95th percentile. Of the participants with an unknown cause, 24% (n=108) had a T1D-GRS below the 5th percentile, 30% (n=137) had a T1D-GRS between the 5-25th percentile, 16% (72) between the 25-50th percentile, 15% (67) between the 50-75th percentile, and 15% (69) between the 75-95th percentile. We sequenced a total of 124 participants with a T1D-GRS below the 25th percentile using a targeted next-generation sequencing library that includes more than 140 genes known to be associated with diabetes. 21% (n=24) were found to have a pathogenic variant in a known diabetes gene. Further tests are ongoing. Our results show that the T1D-GRS is a useful discriminatory tool between diabetes types and a strong predictor of whether a monogenic cause is likely. More importantly, patients with a low T1D-GRS and negative for all known monogenic subtypes should be prioritized for exome or whole genome sequencing to maximize the chance of novel gene discoveries. Disclosure M. Sanyoura: None. L.R. Letourneau: Other Relationship; Self; Novo Nordisk Inc.. R.N. Naylor: None. L.H. Philipson: Research Support; Self; Novo Nordisk Inc.. Speaker's Bureau; Self; Merck & Co., Inc.. Research Support; Self; JAEB Center For Health Research, JDRF, Janssen Pharmaceuticals, Inc., Medtronic MiniMed, Inc.. M.N. Weedon: None. R.A. Oram: Other Relationship; Self; Randox. S.W. Greeley: Research Support; Self; Novo Nordisk Inc..

A Comprehensive Type 1 Diabetes Genetic Risk Score Is Associated with Type 2 Diabetes in the Framingham Heart Study

It is often difficult to distinguish between type 1 (T1D) and type 2 diabetes (T2D), particularly in young adulthood. Both T1D and T2D have significant genetic contributions to risk and understanding their genetic overlap may offer clinical insight. We examined whether a T1D genetic risk score (GRS) is associated with clinical features and diagnosis of T2D in the Framingham Heart Study (FHS). We constructed a T1D GRS based on 79 SNPs from fine mapping results, with SNPs weighted by published effect sizes. The GRS included both HLA (27 SNPs) and non-HLA (52 SNPs) regions, and was applied to 1503 participants with T2D. Multivariable regression models assessed the association of the GRS with age of T2D onset, BMI, HbA1C, HOMA-β and HOMA-IR at the exam of diagnosis. Case-control analysis of 1503 cases and 5649 controls was done using multivariate generalized estimating equations (GEE) logistic models to determine the association between the T1D GRS and T2D risk with age and sex as covariates. In FHS participants, the mean age was 57.2 y, BMI was 30 kg/m2, and HbA1c 6.05%. The T1D GRS was not associated with T2D age of onset, or BMI, HbA1c, HOMA-β and HOMA-IR after excluding participants on medications for T2D. There was a trend towards lower HOMA-β and HOMA-IR values in the top quintile of the GRS (n=244, mean=2.47, SD= 0.7 for ln(HOMA-β) and mean=2.88, SD=0.8 for ln(HOMA-IR)) when compared to the lowest quintile of the GRS (n=260, mean= 2.53, SD=0.8 for ln(HOMA-β) and mean=2.92, SD=0.8 for ln(HOMA-IR)). The T1D GRS was associated with T2D (β = 0.per SD increase in GRS, SE= 0.03, OR =1.1, P = 0.04). When the non-HLA and HLA components of the GRS were tested separately, the results remained significant only for the non-HLA T1D GRS (β = 0.per SD increase in GRS, SE= 0.03, OR=1.1, P= 0.02). A T1D GRS of non-HLA SNPs is associated with T2D in the FHS participants. Our further analysis with increased sample size in multi-ethnic populations may refine the impact of the T1D GRS to characterize individuals with T2D. Disclosure S. Srinivasan: None. A. Leong: None. M. Udler: None. B.C. Porneala: None. J.B. Meigs: None. S.S. Rich: None. J. Dupuis: None.

Type 1 Diabetes Genetic Risk Score: A Novel Tool to Discriminate Monogenic and Type 1 Diabetes.

Distinguishing patients with monogenic diabetes from those with type 1 diabetes (T1D) is important for correct diagnosis, treatment, and selection of patients for gene discovery studies. We assessed whether a T1D genetic risk score (T1D-GRS) generated from T1D-associated common genetic variants provides a novel way to discriminate monogenic diabetes from T1D. The T1D-GRS was highly discriminative of proven maturity-onset diabetes of young (MODY) (n = 805) and T1D (n = 1,963) (receiver operating characteristic area under the curve 0.87). A T1D-GRS of >0.280 (>50th T1D centile) was indicative of T1D (94% specificity, 50% sensitivity). We then analyzed the T1D-GRS of 242 white European patients with neonatal diabetes (NDM) who had been tested for all known NDM genes. Monogenic NDM was confirmed in 90, 59, and 8% of patients with GRS <5th T1D centile, 50-75th T1D centile, and >75th T1D centile, respectively. Applying a GRS 50th T1D centile cutoff in 48 NDM patients with no known genetic cause identified those most likely to have a novel monogenic etiology by highlighting patients with probable early-onset T1D (GRS >50th T1D centile) who were diagnosed later and had less syndromic presentation but additional autoimmune features compared with those with proven monogenic NDM. The T1D-GRS is a novel tool to improve the use of biomarkers in the discrimination of monogenic diabetes from T1D.