Joglekar Mugdha V, Wong Wilson K M, Kunte Pooja S, Hardikar Hrishikesh P, Kulkarni Reshmi A, Ahmed Ikhlak, Farr Ryan J, Pham Nhan Ho Trong, Coles Madilyn, Kaur Simranjeet, Maynard Cody L, Hayward Riley, Thorat Vinod, Pant Aniruddha, Akil Ammira A, Donaghue Kim C, Jenkins Alicia J, Piya Milan K, Craig Maria E, Hague William M, Yajnik Chittaranjan S, Chan Juliana C N, Shapiro A M James, Davis Elizabeth A, Jones Timothy W, Gitelman Stephen E, Ma Ronald C W, Pociot Flemming, Hardikar Anandwardhan A
Diabetes & Islet Biology Group, Western Sydney University, School of Medicine, Sydney, New South Wales, Australia.
Sidra Medical Research Centre, Doha, Qatar.
Nat Med. 2025 Jun 5. doi: 10.1038/s41591-025-03730-7.
Identifying individuals at high risk of type 1 diabetes (T1D) is crucial as disease-delaying medications are available. Here we report a microRNA (miRNA)-based dynamic (responsive to the environment) risk score developed using multicenter, multiethnic and multicountry ('multicontext') cohorts for T1D risk stratification. Discovery (wet and dry lab) analysis identified 50 miRNAs associated with functional β cell loss, which is a hallmark of T1D. These miRNAs measured across n = 2,204 individuals from four contexts (4C: Australia, Denmark, Hong Kong SAR People's Republic of China, India) led to a four-context, miRNA-based dynamic risk score (DRS) that effectively stratified individuals with and without T1D. Generative artificial intelligence was used to create an enhanced four-context, miRNA-based DRS, which offered good predictive power (area under the curve = 0.84) for T1D stratification in a separate multicontext validation dataset (n = 662), and accurately predicted future exogenous insulin requirement at 1 hour of islet transplantation. In a clinical trial assessing the imatinib drug therapy, baseline miRNA signature, rather than clinical characteristics, distinguished drug responders from nonresponders at 1 year. This study harnessed machine learning/generative artificial intelligence approaches, identifying and validating a miRNA-based DRS for T1D discrimination and treatment efficacy prediction.
由于有可延缓疾病进展的药物，识别1型糖尿病（T1D）高危个体至关重要。在此，我们报告一种基于微小RNA（miRNA）的动态（对环境有反应）风险评分，该评分是利用多中心、多民族和多国（“多背景”）队列开发的，用于T1D风险分层。发现（湿实验室和干实验室）分析确定了50种与功能性β细胞丢失相关的miRNA，而功能性β细胞丢失是T1D的一个标志。在来自四个背景（4C：澳大利亚、丹麦、中国香港特别行政区、印度）的n = 2204名个体中对这些miRNA进行测量，得出了一个基于miRNA的四背景动态风险评分（DRS），该评分有效地对患有和未患有T1D的个体进行了分层。生成式人工智能被用于创建一个增强的基于miRNA的四背景DRS，在一个单独的多背景验证数据集（n = 662）中，该评分对T1D分层具有良好的预测能力（曲线下面积 = 0.84），并准确预测了胰岛移植1小时后未来对外源胰岛素的需求。在一项评估伊马替尼药物治疗的临床试验中，基线miRNA特征而非临床特征在1年时区分了药物反应者和无反应者。本研究利用机器学习/生成式人工智能方法，识别并验证了一种基于miRNA的DRS，用于T1D鉴别和治疗疗效预测。
