Benaroya Research Institute at Virginia Mason, Seattle, Washington, USA.
Vaccines and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
JCI Insight. 2019 Dec 5;4(23):126917. doi: 10.1172/jci.insight.126917.
At diagnosis, most people with type 1 diabetes (T1D) produce measurable levels of endogenous insulin, but the rate at which insulin secretion declines is heterogeneous. To explain this heterogeneity, we sought to identify a composite signature predictive of insulin secretion, using a collaborative assay evaluation and analysis pipeline that incorporated multiple cellular and serum measures reflecting β cell health and immune system activity. The ability to predict decline in insulin secretion would be useful for patient stratification for clinical trial enrollment or therapeutic selection. Analytes from 12 qualified assays were measured in shared samples from subjects newly diagnosed with T1D. We developed a computational tool (DIFAcTO, Data Integration Flexible to Account for different Types of data and Outcomes) to identify a composite panel associated with decline in insulin secretion over 2 years following diagnosis. DIFAcTO uses multiple filtering steps to reduce data dimensionality, incorporates error estimation techniques including cross-validation and sensitivity analysis, and is flexible to assay type, clinical outcome, and disease setting. Using this novel analytical tool, we identified a panel of immune markers that, in combination, are highly associated with loss of insulin secretion. The methods used here represent a potentially novel process for identifying combined immune signatures that predict outcomes relevant for complex and heterogeneous diseases like T1D.
在诊断时，大多数 1 型糖尿病（T1D）患者都能产生可测量水平的内源性胰岛素，但胰岛素分泌下降的速度存在异质性。为了解释这种异质性，我们试图确定一个预测胰岛素分泌的综合特征，使用了一种协作检测评估和分析管道，其中包含了多个反映β细胞健康和免疫系统活动的细胞和血清测量指标。预测胰岛素分泌下降的能力对于临床试验入组或治疗选择的患者分层将非常有用。从新诊断为 T1D 的受试者的共享样本中测量了 12 项合格检测的分析物。我们开发了一种计算工具（DIFAcTO，Data Integration Flexible to Account for different Types of data and Outcomes），以确定与诊断后 2 年内胰岛素分泌下降相关的综合指标。DIFAcTO 使用多个过滤步骤来降低数据维度，包括交叉验证和敏感性分析等误差估计技术，并且对检测类型、临床结果和疾病状况具有灵活性。使用这种新颖的分析工具，我们确定了一组免疫标志物，它们联合起来与胰岛素分泌的丧失高度相关。这里使用的方法代表了一种识别与复杂和异质性疾病（如 T1D）相关的联合免疫特征的潜在新过程。
