A research team led by Prof. Zhou Zhiguang and Xiao Yang from the Second Xiangya Hospital of Central South University, in collaboration with scientists from the University of Exeter (UK) and the Chinese University of Hong Kong, has developed a genetic risk scoring model (C-GRS) for type 1 diabetes diagnosis tailored to the Chinese population. The breakthrough, published in the international journal Diabetologia, offers a new tool for precise diabetes classification and personalized treatment, the hospital announced on July 10.
Diabetes, a chronic metabolic disorder characterized by high blood sugar, is primarily divided into type 1 and type 2. Type 1, caused by autoimmune reactions, leads to absolute insulin deficiency, requiring lifelong insulin injections. Type 2, linked to insulin resistance or insufficient secretion, can be effectively managed in nearly half of short-course, overweight patients.
Traditional typing relies on clinical features like age at onset and BMI, but this method is increasingly unreliable in China. Approximately two-thirds of Chinese type 1 cases occur in adults, often with atypical symptoms such as slow onset and overweight. Additionally, rising childhood obesity has blurred the line with type 2, while 25% of new type 1 patients lack detectable pancreatic islet autoantibodies early on, complicating diagnosis.
Built using genomic data from over 2,000 type 1 patients, 1,000 type 2 patients, and 3,000 healthy individuals, C-GRS has been validated in over 20,000 diabetic patients in Hong Kong. Preliminary data shows it identifies 20 to 40 more type 1 cases per 100 patients compared to traditional methods.
Further multi-center clinical trials involving 3,000 newly diagnosed patients across 80 top-tier hospitals in China are planned to evaluate its clinical value. The team aims to integrate C-GRS with environmental factors to build a more comprehensive prediction model, potentially enabling early identification of high-risk individuals for type 1 diabetes and risk assessment.
This advancement addresses critical challenges in diabetes diagnosis among Chinese populations, paving the way for more accurate typing and targeted therapies, which could significantly improve patient outcomes.
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