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

BackgroundProgression time from islet autoimmunity to clinical type 1 diabetes is highly variable and the extent that genetic factors contribute is unknown.MethodsIn 341 islet autoantibody-positive children with the human leucocyte antigen (HLA) DR3/DR4-DQ8 or the HLA DR4-DQ8/DR4-DQ8 genotype from the prospective TEDDY (The Environmental Determinants of Diabetes in the Young) study, we investigated whether a genetic risk score that had previously been shown to predict islet autoimmunity is also associated with disease progression.ResultsIslet autoantibody-positive children with a genetic risk score in the lowest quartile had a slower progression from single to multiple autoantibodies (p=0.018), from single autoantibodies to diabetes (p=0.004), and by trend from multiple islet autoantibodies to diabetes (p=0.06). In a Cox proportional hazards analysis, faster progression was associated with an increased genetic risk score independently of HLA genotype (HR for progression from multiple autoantibodies to type 1 diabetes, 1.27, 95% CI 1.02 to 1.58 per unit increase), an earlier age of islet autoantibody development (HR, 0.68, 95% CI 0.58 to 0.81 per year increase in age) and female sex (HR, 1.94, 95% CI 1.28 to 2.93).ConclusionsGenetic risk scores may be used to identify islet autoantibody-positive children with high-risk HLA genotypes who have a slow rate of progression to subsequent stages of autoimmunity and type 1 diabetes.

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Autoimmunity plays a role in the onset of diabetes after 40 years of age

Diabetologia ◽

10.1007/s00125-019-05016-3 ◽

2019 ◽

Vol 63 (2) ◽

pp. 266-277 ◽

Cited By ~ 3

Author(s):

Olov Rolandsson ◽

Christiane S. Hampe ◽

Stephen J. Sharp ◽

Eva Ardanaz ◽

Heiner Boeing ◽

...

Keyword(s):

Type 2 Diabetes ◽

Type 1 Diabetes ◽

Genetic Risk ◽

Genetic Risk Score ◽

Incident Diabetes ◽

Risk Scores ◽

Antibody Positivity ◽

Gad65 Antibody

Abstract Aims/hypothesis Type 1 and type 2 diabetes differ with respect to pathophysiological factors such as beta cell function, insulin resistance and phenotypic appearance, but there may be overlap between the two forms of diabetes. However, there are relatively few prospective studies that have characterised the relationship between autoimmunity and incident diabetes. We investigated associations of antibodies against the 65 kDa isoform of GAD (GAD65) with type 1 diabetes and type 2 diabetes genetic risk scores and incident diabetes in adults in European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct, a case-cohort study nested in the EPIC cohort. Methods GAD65 antibodies were analysed in EPIC participants (over 40 years of age and free of known diabetes at baseline) by radioligand binding assay in a random subcohort (n = 15,802) and in incident diabetes cases (n = 11,981). Type 1 diabetes and type 2 diabetes genetic risk scores were calculated. Associations between GAD65 antibodies and incident diabetes were estimated using Prentice-weighted Cox regression. Results GAD65 antibody positivity at baseline was associated with development of diabetes during a median follow-up time of 10.9 years (HR for GAD65 antibody positive vs negative 1.78; 95% CI 1.43, 2.20) after adjustment for sex, centre, physical activity, smoking status and education. The genetic risk score for type 1 diabetes but not type 2 diabetes was associated with GAD65 antibody positivity in both the subcohort (OR per SD genetic risk 1.24; 95% CI 1.03, 1.50) and incident cases (OR 1.97; 95% CI 1.72, 2.26) after adjusting for age and sex. The risk of incident diabetes in those in the top tertile of the type 1 diabetes genetic risk score who were also GAD65 antibody positive was 3.23 (95% CI 2.10, 4.97) compared with all other individuals, suggesting that 1.8% of incident diabetes in adults was attributable to this combination of risk factors. Conclusions/interpretation Our study indicates that incident diabetes in adults has an element of autoimmune aetiology. Thus, there might be a reason to re-evaluate the present subclassification of diabetes in adulthood.

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Genetic architecture of type 1 diabetes with low genetic risk score informed by 41 unreported loci

Communications Biology ◽

10.1038/s42003-021-02368-8 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Hui-Qi Qu ◽

Jingchun Qu ◽

Jonathan Bradfield ◽

Luc Marchand ◽

Joseph Glessner ◽

...

Keyword(s):

Type 1 Diabetes ◽

Genetic Risk ◽

Genetic Architecture ◽

Genome Wide Association Study ◽

Rare Variants ◽

Genetic Risk Score ◽

Autism Spectrum ◽

Risk Scores ◽

Interferon Α

AbstractType 1 diabetes (T1D) patients with low genetic risk scores (GRS) may be non-autoimmune or autoimmune mediated by other genetic loci. The T1D-GRS2 provides us an opportunity to look into the genetic architecture of these patients. A total of 18,949 European individuals were included in this study, including 6599 T1D cases and 12,323 controls. 957 (14.5%) T1D patients were identified with low GRS (GRS < 8.43). The genome-wide association study on these patients identified 41 unreported loci. Two loci with common variants and 39 loci with rare variants were identified in this study. This study identified common SNPs associated with both low GRS T1D and expression levels of the interferon-α-induced MNDA gene, indicating the role of viral infection in T1D. Interestingly, 16 of the 41 unreported loci have been linked to autism spectrum disorder (ASD) by previous studies, suggesting that genes residing at these loci may underlie both T1D and autism.

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Genetic Risk for Type 1 Diabetes Profoundly Influences the Core Gut Microbiome in Children

Diabetes ◽

10.2337/db18-209-lb ◽

2018 ◽

Vol 67 (Supplement 1) ◽

pp. 209-LB ◽

Cited By ~ 2

Author(s):

JORDAN RUSSELL ◽

LUIZ ROESCH ◽

MARK A. ATKINSON ◽

DESMOND SCHATZ ◽

ERIC W. TRIPLETT ◽

...

Keyword(s):

Type 1 Diabetes ◽

Gut Microbiome ◽

Genetic Risk ◽

The Core

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1535-P: An Insulin Resistance Genetic Risk Score (IR_GRS) Is Associated with Mortality in Type 1 Diabetes (T1D)

Diabetes ◽

10.2337/db19-1535-p ◽

2019 ◽

Vol 68 (Supplement 1) ◽

pp. 1535-P

Author(s):

RACHEL G. MILLER ◽

TINA COSTACOU ◽

SUNA ONENGUT-GUMUSCU ◽

WEI-MIN CHEN ◽

STEPHEN S. RICH ◽

...

Keyword(s):

Insulin Resistance ◽

Type 1 Diabetes ◽

Risk Score ◽

Genetic Risk ◽

Genetic Risk Score

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De‐ coding genetic risk variants in type 1 diabetes

Immunology and Cell Biology ◽

10.1111/imcb.12438 ◽

2021 ◽

Author(s):

Melanie R Shapiro ◽

Puchong Thirawatananond ◽

Leeana Peters ◽

Robert C Sharp ◽

Similoluwa Ogundare ◽

...

Keyword(s):

Type 1 Diabetes ◽

Genetic Risk ◽

Risk Variants ◽

Genetic Risk Variants

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Assessing the clinical utility of genetic risk scores for targeted cancer screening

Journal of Translational Medicine ◽

10.1186/s12967-020-02699-w ◽

2021 ◽

Vol 19 (1) ◽

Author(s):

Carly A. Conran ◽

Zhuqing Shi ◽

William Kyle Resurreccion ◽

Rong Na ◽

Brian T. Helfand ◽

...

Keyword(s):

Colorectal Cancer ◽

Primary Care ◽

High Risk ◽

Risk Score ◽

Genetic Risk ◽

Clinical Utility ◽

Low Risk ◽

Risk Scores ◽

Average Risk ◽

Genetic Risk Scores

Abstract Background Genome-wide association studies have identified thousands of disease-associated single nucleotide polymorphisms (SNPs). A subset of these SNPs may be additively combined to generate genetic risk scores (GRSs) that confer risk for a specific disease. Although the clinical validity of GRSs to predict risk of specific diseases has been well established, there is still a great need to determine their clinical utility by applying GRSs in primary care for cancer risk assessment and targeted intervention. Methods This clinical study involved 281 primary care patients without a personal history of breast, prostate or colorectal cancer who were 40–70 years old. DNA was obtained from a pre-existing biobank at NorthShore University HealthSystem. GRSs for colorectal cancer and breast or prostate cancer were calculated and shared with participants through their primary care provider. Additional data was gathered using questionnaires as well as electronic medical record information. A t-test or Chi-square test was applied for comparison of demographic and key clinical variables among different groups. Results The median age of the 281 participants was 58 years and the majority were female (66.6%). One hundred one (36.9%) participants received 2 low risk scores, 99 (35.2%) received 1 low risk and 1 average risk score, 37 (13.2%) received 1 low risk and 1 high risk score, 23 (8.2%) received 2 average risk scores, 21 (7.5%) received 1 average risk and 1 high risk score, and no one received 2 high risk scores. Before receiving GRSs, younger patients and women reported significantly more worry about risk of developing cancer. After receiving GRSs, those who received at least one high GRS reported significantly more worry about developing cancer. There were no significant differences found between gender, age, or GRS with regards to participants’ reported optimism about their future health neither before nor after receiving GRS results. Conclusions Genetic risk scores that quantify an individual’s risk of developing breast, prostate and colorectal cancers as compared with a race-defined population average risk have potential clinical utility as a tool for risk stratification and to guide cancer screening in a primary care setting.

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