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

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.

PMID: 40569436
PMCID: PMC12361272
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

Jingyi Hu et al. Diabetologia. 2025 Sep.

. 2025 Sep;68(9):1969-1982.

doi: 10.1007/s00125-025-06455-x. Epub 2025 Jun 26.

Authors

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.

PMID: 40569436
PMCID: PMC12361272
DOI: 10.1007/s00125-025-06455-x

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.

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Conflict of interest statement

Acknowledgements: The authors thank all the participants and investigators of the HKDR and HKDB for their contributions. The full lists of team members of the Hong Kong Diabetes Biobank Study Group are included in ESM ‘Additional information’ section. 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. Funding: This work was supported by the Noncommunicable Chronic Diseases-National Science and Technology Major Project (2023ZD0507300, 2023ZD0508200, 2023ZD0508201, 2023ZD0509100), the National Key Research and Development Program of China (2018YFE0114500), the National Natural Science Foundation of China (82270891, 82200933) and the Science and Technology Innovation Program of Hunan Province (2024RC3054). The analysis of samples from Hong Kong was supported by the Research Grants Council Theme-based Research Scheme (T12-402/13 N), the Research Impact Fund (R4012-18) and the University Grants Committee Research Grants Matching Scheme (RGMS). Authors’ relationships and activities: RAO has funding from Randox to study GRS for autoimmune disease. The University of Exeter has a licensing and royalty agreement with Randox for a 10 SNP GRS. RAO reports consulting for Janssen, Provention Bio and Sanofi and receives research funding from Randox. RCWM is a member of the editorial board of Diabetologia. RCWM has received research funding from AstraZeneca, Bayer, Boehringer Ingelheim, Merck Sharp & Dohme, Novo Nordisk, Pfizer, Roche Diagnostics and Tricida Inc. for carrying out clinical trials or studies, and from AstraZeneca, Bayer, Boehringer Ingelheim, Eli Lilly and Merck for speaker honoraria or consultancy in advisory boards. All proceeds have been donated to the Chinese University of Hong Kong to support diabetes research. RCWM is a co-founder of GemVCare. CKPL holds a shareholding in GemVCare Limited. JCNC received consultancy fees or speaker honoraria from Applied Therapeutics, Astra Zeneca, Bayer, Boehringer Ingelheim, Celltrion, Eli Lilly, Hua Medicine, Lee Power Pharmaceuticals, Merck, MSD, Pfizer, Roche, Sanofi, Servier, Viatris Pharmaceutical and Zuelling Pharma. JCNC is the Chief Executive Officer (pro bono) of the Asian Diabetes Foundation. She holds patents for using biomarkers to predict diabetes and its complications and is the co-founder of GemVCare. The authors declare that there are no other relationships or activities that might bias, or be perceived to bias, their work. Contribution statement: YXiao and ZZ conceived the project and designed the experiments. RCWM and RAO supervised the study, interpreted the data, commented and edited the manuscript. JHu conducted the experiments, performed the statistical analysis and analysed the data. YXiao and JHu wrote the manuscript. WAH and MNW assisted in the design of the study, statistical analysis and result interpretation. GJ, JQ, ZW and GY contributed to data analysis and result interpretation. JHu, SL, BF, ZX, RW, XL, CHTT, JD, YXia, YY, JL, PJ, CKPL, AOYL, WYS, CW, JHuang and JCNC participated in the recruitment of participants and contributed to data acquisition. All authors contributed to the article and approved the submitted version. YXiao and ZZ are the guarantors of this work and had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Figures

**Fig. 1**
Flow chart of the study design. A two-stage GWAS (including a discovery cohort and an external replication cohort) in 1804 individuals with type 1 diabetes and 3089 control individuals was conducted. By combining HLA typing data, we generated SNP tags for HLA haplotypes in Chinese individuals and used them together with non-HLA loci to construct a C-GRS. We then identified the association between C-GRS and clinical indicators and validated the ability of C-GRS to differentiate between individuals with type 1 diabetes and control individuals, and between individuals with type 1 diabetes and those with type 2 diabetes, in an independent validation cohort

**Fig. 2**
GWAS results in the discovery cohort. (a) Principal component analysis revealed the population structure of the control group and the disease group. (b, d, f) Manhattan plots display genome-wide association results for type 1 diabetes in the HLA region (b), non-HLA region (d) and across all autosomes (f). (c, e, g) Quantile–quantile plots illustrate associations with type 1 diabetes risk in the HLA region (c), non-HLA region (e) and across all autosomes (g). Observed p values are plotted as a function of theoretical p values. The blue areas represent the 95% CI derived from a null distribution of p values. PC, principal component

**Fig. 3**
The diagnostic efficacy of C-GRS and its association with clinical indicators. (a) AUC results from ROC analysis for C-GRS in the discovery cohort. (b) The gradual improvement in the power of the C-GRS as additional SNPs were included. The blue shaded area represents the 95% CI. (c–g) A higher C-GRS was associated with an earlier age (c), lower BMI (d), lower fasting (e) and postprandial C-peptide levels (f) and higher proportion of multiple positive autoantibodies (g) at diagnosis. Box plots show median ± quartiles, and the whiskers extend from the hinge to the largest or smallest value no further than 1.5-fold of the IQR. *p<0.05, **p<0.01, ***p<0.001. Ab, autoantibody; FCP, fasting C-peptide; PCP, postprandial C-peptide

**Fig. 4**
GWAS After adjustment for C-GRS. (a, b) Manhattan plot showing the genome-wide association results for type 1 diabetes in the HLA region (a) and the non-HLA region (b). (c, d) Quantile–quantile plots were used to assess the association between type 1 diabetes risk and the HLA region (c) and the non-HLA region (d). The observed p values are plotted as a function of the expected p values. The blue areas represent the 95% CI derived from a null distribution of p values

**Fig. 5**
The validation of the discrimination performance of C-GRS in an independent cohort. (a) The C-GRS of type 1 diabetes was significantly higher than that of control individuals and those with type 2 diabetes in the validation cohort. (b) The type 1 diabetes C-GRS has high discriminatory power in distinguishing individuals with type 1 diabetes from both control individuals. (c) C-GRS demonstrated strong discriminatory ability between individuals with type 1 diabetes and those with type 2 diabetes. ***p<0.001. T1D, type 1 diabetes; T2D, type 2 diabetes

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References

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

Affiliations

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

Authors

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Abstract

Conflict of interest statement

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