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 amongst multiple-autoantibody-positive individuals is highly heterogenous. Accurate prediction of the most rapidly progressing individuals is crucial for efficient and informative clinical trials, and identification of candidates most likely to benefit from disease modification. This is increasingly relevant with the recent success in delaying clinical disease in pre-symptomatic subjects using immunotherapy, and as the field moves towards 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 pre-clinical 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.

中文翻译：

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使用胰岛自身抗体进行1型糖尿病预测的进展：超越简单计数。

胰岛自身抗体是诊断1型糖尿病的关键标志。自发现以来，他们还因其在症状出现之前识别高危人群的潜力而受到认可。迄今为止，使用自身抗体的风险预测是​​基于自身抗体的数量。有力的证据表明，几乎所有多重自身抗体阳性的人都将发展为临床疾病。然而，纵向研究表明，多种自身抗体阳性的个体之间的进展速度是高度异质的。对进展最快的个体的准确预测对于有效和翔实的临床试验以及确定最有可能从疾病改良中受益的候选者至关重要。这与最近成功使用免疫疗法延缓症状发生前受试者的临床疾病以及随着该领域向基于人群的筛查的成功越来越相关。除了简单的分类计数之外，还有许多研究针对胰岛自身抗体的预测潜力进行研究。出现的预测特征包括分子特异性，例如表位靶标和亲和力；纵向模式，例如滴度变化和自身抗体回复；以及针对自身抗体和受试者年龄的序列依赖性风险谱。这些见解是数十年来的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，更具体的临床前分期所需的粒度。