Islet autoantibodies are key markers for the diagnosis of type 1 diabetes. Since their discovery, they have also been recognized for their potential to identify at-risk individuals prior to symptoms. To date, risk prediction using autoantibodies has been based on autoantibody number; it has been robustly shown that nearly all multiple-autoantibody-positive individuals will progress to clinical disease. However, longitudinal studies have demonstrated that the rate of progression among multiple-autoantibody-positive individuals is highly heterogenous. Accurate prediction of the most rapidly progressing individuals is crucial for efficient and informative clinical trials and for identification of candidates most likely to benefit from disease modification. This is increasingly relevant with the recent success in delaying clinical disease in presymptomatic subjects using immunotherapy, and as the field moves toward population-based screening. There have been many studies investigating islet autoantibody characteristics for their predictive potential, beyond a simple categorical count. Predictive features that have emerged include molecular specifics, such as epitope targets and affinity; longitudinal patterns, such as changes in titer and autoantibody reversion; and sequence-dependent risk profiles specific to the autoantibody and the subject’s age. These insights are the outworking of decades of prospective cohort studies and international assay standardization efforts and will contribute to the granularity needed for more sensitive and specific preclinical staging. The aim of this review is to identify the dynamic and nuanced manifestations of autoantibodies in type 1 diabetes, and to highlight how these autoantibody features have the potential to improve study design of trials aiming to predict and prevent disease.

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使用胰岛自身抗体预测 1 型糖尿病的进展：超越简单的计数

胰岛自身抗体是诊断 1 型糖尿病的关键标志物。自发现以来，它们也因其在出现症状之前识别高危个体的潜力而得到认可。迄今为止，使用自身抗体的风险预测一直基于自身抗体数量。已经有力地表明，几乎所有多重自身抗体阳性的个体都会发展为临床疾病。然而，纵向研究表明，多种自身抗体阳性个体之间的进展速度是高度异质的。准确预测进展最快的个体对于高效和信息丰富的临床试验以及识别最有可能从疾病改变中受益的候选人至关重要。这与最近在使用免疫疗法延迟症状前受试者的临床疾病方面取得的成功以及随着该领域转向基于人群的筛查而越来越相关。除了简单的分类计数之外，已经有许多研究调查了胰岛自身抗体特征的预测潜力。已经出现的预测特征包括分子特异性，例如表位靶点和亲和力；纵向模式，例如滴度变化和自身抗体逆转；以及特定于自身抗体和受试者年龄的序列依赖性风险概况。这些见解是数十年前瞻性队列研究和国际化验标准化工作的成果，将有助于更敏感和具体的临床前分期所需的粒度。