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  • 1
    In: Diabetes, American Diabetes Association, Vol. 71, No. Supplement_1 ( 2022-06-01)
    Abstract: Introduction: Screening for type 1 diabetes (T1D) genetic risk in early life can prevent life threatening complications such as diabetic ketoacidosis and allow cost-effective recruitment into intervention and immunotherapy trials. Celiac disease (CD) frequently presents as a comorbidity in those with T1D and shares a strong genetic basis with T1D. Single nucleotide polymorphism (SNP) based genetic risk scores (GRS) for both T1D and CD have shown to be very predictive of future disease but are difficult to generate and typically require SNP array genotyping. We aimed to develop a combined GRS screening panel that could be genotyped from a single dried blood spot at birth. Methods: We developed assays for proxy SNPs of common HLA-DQ haplotypes reported in previous GRS and additional loci from the most recent available genome-wide association studies (GWAS) . We used backwards stepwise regression to identify a subset of variants able to be genotyped with DNA eluted from 800 6mm dried blood spots. We developed and validated neural network models to quantify genetic risk of both T1D and CD using T1DGC and UK Celiac case-control SNP array data, validated in UK Biobank. Assays were developed with LGC Genomics and validated on 675 Seattle area samples. Results: The complete panel consisted of 71 validated SNP assays, including 11 backup variants for key loci. We generated neural nework models which demonstrate equivalent or greater predictive power (AUC: T1D=0.914, CD=0.893) to previously published GRS yet require much less expertise to apply. We have developed an algorithm for preparation of raw genotyping data and subsequent generation of GRS requiring little expertise to apply. Conclusion: A 71 SNP blood-spot screening panel is highly effective at screening genetic risk associated with T1D and CD at birth. Using a neural network model the panel enables widely available, easy to generate and inexpensive population screening of genetic risk for T1D and CD. Disclosure S. A. Sharp: None. J. M. Locke: None. Y. Xu: None. D. P. Fraser: None. L. A. Ferrat: None. M. N. Weedon: None. M. Inouye: None. R. A. Oram: Consultant; Janssen Research & Development, LLC, Research Support; Randox R & D. W. Hagopian: Research Support; Janssen Research & Development, LLC. Funding Diabetes UK (16/0005529) JDRF (3-SRA-2019-827-S-B)
    Type of Medium: Online Resource
    ISSN: 0012-1797
    Language: English
    Publisher: American Diabetes Association
    Publication Date: 2022
    detail.hit.zdb_id: 1501252-9
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  • 2
    In: Diabetes, American Diabetes Association, Vol. 68, No. Supplement_1 ( 2019-06-01)
    Abstract: 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 & lt;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)
    Type of Medium: Online Resource
    ISSN: 0012-1797 , 1939-327X
    Language: English
    Publisher: American Diabetes Association
    Publication Date: 2019
    detail.hit.zdb_id: 1501252-9
    Location Call Number Limitation Availability
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  • 3
    In: Diabetes, American Diabetes Association, Vol. 68, No. Supplement_1 ( 2019-06-01)
    Abstract: Background: There is an unmet need for accurate cost-effective estimation of future T1D risk. Methods: We derived a combined T1D prediction model using 7883 children followed closely from birth for a median of 9 yr, considering T1D genetic risk score (GRS), family T1D history (FH), standard islet autoantibodies (IA), race, birth circumstances, early growth and nutrient status. T1D developed in 326. Results: Machine learning and traditional methods (Cox models) performed equivalently as measured by time-dependent AUC ROC. Future T1D was accurately predicted by combining only 3 variables: GRS, FH and IA status. Accuracy of combined scores increased with age at scoring. By age 2, they were highly predictive for T1D in the next 5 years (AUC & gt;0.91, 95% CI 0.88-0.95) (see X on Figure). A 2-yr-old with 2 IA, positive FH and high GRS ( & gt;12) would have T1D risk over the next 1, 3 and 5 years of 14% (8-19%), 36% (25-45%) and 51% (39%-61%) respectively. A 2-yr-old with 1 IA, no FH and moderate GRS (10-11) has T1D risk of 0.8% (0.6-1.2%), 2.6% (1.9-3.2%) and 4.3% (3.4%-5.2%) respectively. After newborn genetic screening, only simple venous sampling in routine healthcare settings is required. Conclusion: This approach allows updated individual risk estimates by age, and in the future may enable release of low risk individuals from surveillance long after initial newborn screening for more cost-efficient population based pediatric T1D prediction. Disclosure L.A. Ferrat: None. K. Vehik: None. S.A. Sharp: None. Å. Lernmark: None. A. Ziegler: None. M. Rewers: None. J. She: None. J. Toppari: None. B. Akolkar: None. J. Krischer: None. M.N. Weedon: None. S.S. Rich: None. R.A. Oram: Other Relationship; Self; Randox Laboratories Ltd. W. Hagopian: Research Support; Self; Novo Nordisk A/S. Funding National Institute of Diabetes and Digestive and Kidney Diseases; National Institute of Allergy and Infectious Diseases; Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Environmental Health Sciences; JDRF; Centers for Disease Control and Prevention
    Type of Medium: Online Resource
    ISSN: 0012-1797 , 1939-327X
    Language: English
    Publisher: American Diabetes Association
    Publication Date: 2019
    detail.hit.zdb_id: 1501252-9
    Location Call Number Limitation Availability
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