Genome-wide Association Study of Different Types of Diabetes and Construction of Genetic Risk Score

NCT ID: NCT06791330

Last Updated: 2025-01-24

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

7000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2010-01-31

Study Completion Date

2024-04-30

Brief Summary

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To enhance the genetic profile of T1D patients in China and construct a Chinese-specific GRS and evaluated its ability to differentiate between T1D and controls, as well as between T1D and T2D.

Detailed Description

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Type 1 diabetes (T1D) is caused by autoimmune destruction of pancreatic beta cells, leading to severe insulin deficiency and requires life-long insulin therapy. Although traditionally viewed as a disease primarily affecting children and adolescents, recent epidemiological studies have demonstrated that over half of all new-onset T1D cases worldwide occur in adults. At present, methods to differentiate adult-onset T1D from T2D rely on the clinical phenotypes such as onset age and pattern, body mass index (BMI), C-peptide levels, and islet autoantibodies. However, the increasing rates of obesity in T1D, the presence of ketosis-prone T2D and idiopathic T1D, as well as unavailable autoantibody detection in some districts make it an increasing difficult challenge to accurately classify between adult-onset T1D and T2D. The discrimination of diabetes types is more challenging in the Chinese population due to the higher prevalence of early-onset T2D at a lower BMI than in European populations. Incorrectly identifying T1D as T2D can result in inadequate control of blood glucose levels, an elevated risk of ketoacidosis, and potentially severe and life-endangering complications. Therefore, it is crucial to search for new discriminative methods for different types of diabetes.

The genetic pathogenesis of T1D and T2D differ significantly, allowing for the differentiation of diabetes types based on genetic information. A T1D genetic risk score (GRS), comprising 31 HLA and non-HLA T1D-associated single nucleotide polymorphisms (SNPs), can be a useful tool to aid the discrimination between T1D and T2D. GRS2, which improved SNP capture of HLA DR-DQ risk including haplotype interactions and added non-DR-DQ loci, showed better discriminative power for the classification of diabetes type among multiethnic youth.

In this study, we employed GWAS to enhance the genetic profile of T1D patients in China. Additionally, our aim was to identify SNP tags for the HLA DR-DQ risk in the Chinese population, which could capture HLA DR-DQ risk and the interactions between these haplotypes. Using this information, we constructed a Chinese-specific GRS and evaluated its ability to differentiate between T1D and controls, as well as between T1D and T2D.

Conditions

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Type 1 Diabetes

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

CROSS_SECTIONAL

Study Groups

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Controls

Controls were defined as individuals without diabetes or a family history of diabetes within the same geographic area who had normal oral glucose tolerance test results

No interventions assigned to this group

Type 1 diabetes

All diagnoses for diabetes were required to adhere to the 1999 World Health Organization diabetes diagnosis criteria. Type 1 diabetes mellitus patients needed to meet specific criteria, including insulin dependence in the first 6 months and the presence of at least one designated islet autoantibody: glutamic acid decarboxylase antibody (GADA), protein tyrosine phosphatase antibody (IA-2A) and zinc transporter 8 antibody (ZnT8A).

No interventions assigned to this group

Type 2 diabetes

All diagnoses for diabetes were required to adhere to the 1999 World Health Organization diabetes diagnosis criteria. Type 2 diabetes mellitus patients also needed to show negative results for all three islet autoantibodies at onset, including glutamic acid decarboxylase antibody (GADA), protein tyrosine phosphatase antibody (IA-2A) and zinc transporter 8 antibody (ZnT8A).

No interventions assigned to this group

Eligibility Criteria

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Inclusion Criteria

* All diagnoses for diabetes were required to adhere to the 1999 World Health Organization diabetes diagnosis criteria. Type 1 diabetes mellitus patients needed to meet specific criteria, including insulin dependence in the first 6 months and the presence of at least one designated islet autoantibody: glutamic acid decarboxylase antibody (GADA), protein tyrosine phosphatase antibody (IA-2A) and zinc transporter 8 antibody (ZnT8A). Type 2 diabetes mellitus patients also needed to show negative results for all three islet autoantibodies at onset. Controls were defined as individuals without diabetes or a family history of diabetes within the same geographic area who had normal oral glucose tolerance test results.

Exclusion Criteria

* Subjects with the following conditions were excluded: severe gastrointestinal, cardiovascular, cerebrovascular, liver or kidney diseases; other autoimmune diseases; a history of gastrointestinal surgery; tumours; pregnancy; treatments with oral hypoglycaemic agents or immunomodulators; and use of probiotics, prebiotics or antibiotics within one month.
Minimum Eligible Age

3 Years

Maximum Eligible Age

80 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Second Xiangya Hospital of Central South University

OTHER

Sponsor Role lead

Responsible Party

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Yang Xiao

Professor, Department of Metabolism and Endocrinology, Institute of Metabolism and Endocrinology, National Clinical Research Center for Metabolic Diseases, Second Xiangya Hospital, Central South University

Responsibility Role PRINCIPAL_INVESTIGATOR

Other Identifiers

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2019GRS

Identifier Type: -

Identifier Source: org_study_id

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