INTRODUCTIONAlzheimer's disease (AD) is the most common form of dementia in the elderly. Given that AD neuropathology begins decades before symptoms, there is a dire need for effective screening tools for early detection of AD to facilitate early intervention.METHODSHere, we used tree‐based and deep learning methods to train polyomic prediction models for AD affection status and age at onset, employing genomic, proteomic, metabolomic, and drug use data from UK Biobank. We used SHAP to determine the feature's importance.RESULTSOur best‐performing polyomic model achieved an area under the receiver operating characteristics curve (AUROC) of 0.87. We identified GFAP and CXCL17 proteins to be the strongest predictors of AD, besides apolipoprotein E (APOE) alleles. Increasing the number of cases by including “AD‐by‐proxy” cases did not improve AD prediction.DISCUSSIONAmong the four modalities, genomics, and proteomics were the most informative modality based on AUROC (area under the receiver operating characteristic curve). Our data suggest that two blood‐based biomarkers (glial fibrillary acidic protein [GFAP] and CXCL17) may be effective for early presymptomatic prediction of AD.Highlights We developed a polyomic model to predict AD and age‐at‐onset using omics and medication use data from EHR. We identified GFAP and CXCL17 proteins to be the strongest predictors of AD, besides APOE alleles. “AD‐by‐proxy” cases, if used in training, do not improve AD prediction. Proteomics was the most informative modality overall for affection status and AAO prediction.

中文翻译：

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使用机器学习模型评估多组风险以预测阿尔茨海默病


引言阿尔茨海默病 （AD） 是老年人最常见的痴呆形式。鉴于 AD 神经病理学在症状出现前几十年就开始了，因此迫切需要有效的筛查工具来早期发现 AD，以促进早期干预。方法我们使用基于树的深度学习方法，利用来自英国生物样本库的基因组、蛋白质组学、代谢组学和药物使用数据，训练多组学预测模型，用于 AD 影响状态和发病年龄。我们使用 SHAP 来确定该功能的重要性。结果我们表现最好的多组学模型实现了 0.87 的受试者工作特征曲线下面积 （AUROC）。我们发现 GFAP 和 CXCL17 蛋白是除载脂蛋白 E （APOE） 等位基因外最强的 AD 预测因子。通过包括 “AD-by‐proxy” 病例来增加病例数并没有改善 AD 预测。讨论基于 AUROC （受试者工作特征曲线下面积） 的四种模式、基因组学和蛋白质组学是信息量最大的模式。我们的数据表明，两种基于血液的生物标志物 （神经胶质纤维酸性蛋白 [GFAP] 和 CXCL17）可能对 AD 的早期症状前预测有效。亮点 我们开发了一个多组学模型，使用来自 EHR 的组学和药物使用数据来预测 AD 和发病年龄。我们发现 GFAP 和 CXCL17 蛋白是除 APOE 等位基因外最强的 AD 预测因子。如果在训练中使用“AD by‐proxy”情况，则不会改进 AD 预测。蛋白质组学是总体上对情感状态和 AAO 预测信息量最大的模式。