Accurate diagnosis of type 1 diabetes (T1D) remains a major clinical challenge, with up to 40% of adults initially misclassified as having type 2 diabetes (T2D), leading to delayed insulin initiation and increased risk of complications.¹ Most existing genetic risk scores (GRS) were derived from European populations, with limited applicability in Asian populations due to differences in genomic architecture.¹ This highlights the need for ancestry-specific tools.^1-3 A study led by a joint research team from The Chinese University of Hong Kong (CUHK), the University of Exeter in the United Kingdom, and the Second Xiangya Hospital of Central South University in China developed and validated a Chinese-specific T1D genetic risk score (C-GRS) that improves differentiation between T1D and T2D compared to European-derived models.¹ 

Diabetes classification has traditionally relied on clinical features such as age of onset, body mass index (BMI), C-peptide levels, and islet autoantibodies.¹ However, these markers can be misleading in Asian populations, where T2D frequently presents at younger ages and lower BMI, and T1D may be diagnosed later in adulthood.¹ In addition, autoantibody testing is not widely available and may yield equivocal results, leaving physicians with limited tools for accurate classification.^1,4 To address the limited transferability of existing GRS models, this study aimed to identify T1D-associated genetic variants in the Chinese population, define single-nucleotide polymorphism (SNP) tags for human leukocyte antigen (HLA) DR-DQ haplotypes, and construct a C-GRS to distinguish T1D from both healthy controls and T2D patients.^1-3

The study employed a two-stage genome-wide association study (GWAS) design.¹ The discovery cohort included 1,303 T1D patients and 2,236 healthy controls from Hunan, China, with results replicated in 501 T1D cases and 853 controls from Hong Kong to ensure reproducibility across Chinese populations.¹ Genotyping was performed using a population-optimized microarray platform alongside HLA typing.¹

They identified genetic variations strongly associated with T1D, including both known and novel loci (e.g. rs10232170 in BMPER), and built a composite C-GRS that comprises 33 T1D-associated SNPs in Chinese individuals.¹ This includes 13 key HLA DR-DQ haplotypes, 12 additional HLA variants, and eight non-HLA loci.¹ The C-GRS was then evaluated for associations with clinical indicators, including age at onset, C-peptide concentrations, autoantibody status, and its ability to differentiate T1D from controls and T2D in a validation cohort of patients from Hong Kong and Mainland China (262 T1D patients, 1,080 T2D patients, and 208 controls).¹

The C-GRS demonstrated strong discriminatory performance in the discovery cohort, achieving a receiver operating characteristic (ROC) area under the curve (AUC) of 0.876.¹ When tested in the independent validation cohort, the C-GRS also effectively distinguished T1D from healthy controls (AUC=0.871) and T2D (AUC=0.869), significantly outperforming the European-derived GRS2 model (AUC=0.773 and 0.793 respectively; p<0.001), highlighting its clinical utility for accurate diabetes classification in Chinese populations.¹ Importantly, the C-GRS also demonstrated higher discrimination in youth-onset vs. adult-onset T1D (AUC=0.911 vs. 0.849; p=3.446x10-7).¹ Beyond diagnosis, the C-GRS demonstrated strong associations with clinically relevant features of T1D, with higher scores correlated with younger age at diagnosis, lower BMI, reduced fasting and postprandial C-peptide levels.¹ To guide clinical classification, the investigators proposed C-GRS cutoffs >1.211 for T1D and <-0.407 for T2D, each with 95% specificity.¹

Individuals in the highest tertile of the C-GRS also exhibited a greater prevalence of multiple autoantibody positivity.¹ The score captured not only T1D-susceptible HLA DR-DQ haplotypes but also other HLA haplotypes, their interactions, numerous class I alleles, and additional susceptibility variants, likely reflecting underlying immune dysregulation.¹ By capturing this broader spectrum of genetic risk, the C-GRS explains the higher autoantibody counts in high-score individuals and enhances its ability to accurately distinguish T1D from other forms of diabetes.¹

This study is the first to develop and validate a populationspecific GRS for Chinese patients with T1D.¹ By addressing the limitations of European-derived models, the study demonstrated that a tailored C-GRS comprising 33 SNPs can markedly improve differentiation between T1D and T2D.¹ Given the decreasing cost of GWAS and direct SNP genotyping, this approach represents a cost-effective advance for precision medicine in Asia.¹ Future efforts will expand validation across East Asian cohorts and develop optimized ancestry-specific or trans-ancestral GRS by integrating Chinese and European GWAS, while accounting for genetic and gene-environment interactions, population-specific diabetes incidence, and complementary biomarkers to further enhance diagnostic accuracy.¹