A type 1 diabetes genetic risk score discriminates between type 1 diabetes and type 2 diabetes in a Chinese population

Jingyi Hu; Guozhi Jiang; Jiabi Qin; Shuoming Luo; Baoqi Fan; Zhiguo Xie; Raymond Wan; Xia Li; Claudia H T Tam; Zhenqian Wang; Jin Ding; Ying Xia; Yuanqin Yang; Jian Lin; Gechang Yu; Ping Jin; Cadmon K P Lim; Hong Kong Diabetes Biobank Study Group; Andrea O Y Luk; Wing Yee So; Juliana C N Chan; Congyi Wang; Jiaqi Huang; Michael N Weedon; William A Hagopian; Richard A Oram; Ronald C W Ma; Yang Xiao; Zhiguang Zhou

doi:10.1007/s00125-025-06455-x

A type 1 diabetes genetic risk score discriminates between type 1 diabetes and type 2 diabetes in a Chinese population

Diabetologia. 2025 Sep;68(9):1969-1982. doi: 10.1007/s00125-025-06455-x. Epub 2025 Jun 26.

Authors

Jingyi Hu¹, Guozhi Jiang², Jiabi Qin^{3

4}, Shuoming Luo¹, Baoqi Fan^{5

6

7

8}, Zhiguo Xie¹, Raymond Wan⁵, Xia Li¹, Claudia H T Tam^{5

6

7

8}, Zhenqian Wang², Jin Ding¹, Ying Xia¹, Yuanqin Yang¹, Jian Lin¹, Gechang Yu^{5

7

8}, Ping Jin⁹, Cadmon K P Lim^{5

6

7

8}; Hong Kong Diabetes Biobank Study Group; Andrea O Y Luk^{5

6

7

8

10}, Wing Yee So^{5

6

8}, Juliana C N Chan^{5

6

7

10}, Congyi Wang^{11

12}, Jiaqi Huang¹, Michael N Weedon¹³, William A Hagopian^{14

15}, Richard A Oram¹⁶, Ronald C W Ma^{17

18

19

20

21}, Yang Xiao²², Zhiguang Zhou²³

Affiliations

¹ National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, China.
² School of Public Health (Shenzhen), Sun Yat-sen University, Shenzhen, China.
³ Department of Epidemiology and Health Statistics, Xiangya School of Public Health, Central South University, Changsha, China.
⁴ National Health Committee Key Laboratory of Birth Defect for Research and Prevention, Hunan Provincial Maternal and Child Healthcare Hospital, Changsha, China.
⁵ Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China.
⁶ Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China.
⁷ CUHK-SJTU Joint Research Center in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China.
⁸ Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China.
⁹ Department of Endocrinology, The Third Xiangya Hospital of Central South University, Changsha, China.
¹⁰ Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China.
¹¹ Department of Respiratory and Critical Care Medicine, the Center for Biomedical Research, NHC Key Laboratory of Respiratory Diseases, Tongji Hospital, Tongji Medical College, Huazhong University of Sciences and Technology, Wuhan, China.
¹² Shanxi Bethune Hospital, Shanxi Academy of Medical Science, Tongji Shanxi Hospital, Third Hospital of Shanxi Medical University, the Key Laboratory of Endocrine and Metabolic Diseases of Shanxi Province, Taiyuan, China.
¹³ Department of Clinical and Biomedical Sciences, University of Exeter, Exeter, UK.
¹⁴ Department of Paediatrics, Indiana University School of Medicine, Indianapolis, IN, USA.
¹⁵ Department of Medicine, University of Washington, Seattle, WA, USA.
¹⁶ 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. r.oram@exeter.ac.uk.
¹⁷ Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China. rcwma@cuhk.edu.hk.
¹⁸ Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong, China. rcwma@cuhk.edu.hk.
¹⁹ CUHK-SJTU Joint Research Center in Diabetes Genomics and Precision Medicine, The Chinese University of Hong Kong, Hong Kong, China. rcwma@cuhk.edu.hk.
²⁰ Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China. rcwma@cuhk.edu.hk.
²¹ Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China. rcwma@cuhk.edu.hk.
²² National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, China. xiaoyang29@csu.edu.cn.
²³ National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology, Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, China. zhouzhiguang@csu.edu.cn.

Abstract

Aims/hypothesis: We aimed to generate a population-specific type 1 diabetes genetic risk score (GRS) and assess whether it could improve discrimination between type 1 diabetes and type 2 diabetes in a Chinese population.

Methods: We performed a genome-wide association analysis on 1303 individuals with type 1 diabetes and 2236 control individuals. An independent replication cohort of 501 individuals with type 1 diabetes and 853 control individuals was used to validate the top common variant associations. HLA typing data were used to identify tag SNPs for DQA1-DQB1 haplotypes. We integrated significant signals to construct a Chinese type 1 diabetes GRS (C-GRS). The accuracy of the C-GRS was tested in an independent validation cohort consisting of 262 individuals with type 1 diabetes, 1080 individuals with type 2 diabetes and 208 control individuals.

Results: We identified a variant, rs10232170, in BMPER as a possible novel type 1 diabetes locus (p=9.897×10^-9). We identified tag SNPs for 13 DQA1-DQB1 haplotypes and 12 non-DQA1-DQB1 loci. Integrating 33 significant SNPs from HLA and non-HLA regions, C-GRS demonstrated high discriminative power for type 1 diabetes (AUC=0.876). It was tested in an independent validation cohort and showed high discrimination (AUC 0.871 for type 1 diabetes vs control group, 0.869 for type 1 diabetes vs type 2 diabetes). The C-GRS outperformed a European-derived GRS (0.871 vs 0.773, and 0.869 vs 0.793, respectively).

Conclusions/interpretation: A type 1 diabetes C-GRS comprising 33 SNPs was highly discriminative of type 1 diabetes risk in the Chinese population and could aid in discriminating between type 1 diabetes and type 2 diabetes. This study highlights the potential of genetic information in improving prediction and precision diagnosis of type 1 diabetes in the Chinese population.

Data availability: The raw sequencing data and summary statistics of genomic DNA derived from human samples have been deposited at the China National Center for Bioinformation ( https://ngdc.cncb.ac.cn/omix ) under accession number PRJCA023730.

Keywords: Clinical diabetes; Clinical science; Genetics / Epidemiology (all); Genetics of type 1 diabetes; Prediction and prevention of type 1 diabetes.

MeSH terms

Adult
China
Diabetes Mellitus, Type 1* / genetics
Diabetes Mellitus, Type 2* / genetics
East Asian People / genetics
Female
Genetic Predisposition to Disease / genetics
Genetic Risk Score
Genome-Wide Association Study
Haplotypes
Humans
Male
Middle Aged
Polymorphism, Single Nucleotide / genetics
Risk Factors

Supplementary concepts

Chinese people

Abstract

MeSH terms

Supplementary concepts

Grants and funding