The prevalence of concomitant proteinopathies and heterogeneous clinical symptoms in neurodegenerative diseases hinders the identification of individuals who might be candidates for a particular intervention. Here, by applying an unsupervised clustering algorithm to post-mortem histopathological data from 895 patients with degeneration in the central nervous system, we show that six non-overlapping disease clusters can simultaneously account for tau neurofibrillary tangles, α-synuclein inclusions, neuritic plaques, inclusions of the transcriptional repressor TDP-43, angiopathy, neuron loss and gliosis. We also show that membership to the six transdiagnostic disease clusters, which explains more variance in cognitive phenotypes than can be explained by individual diagnoses, can be accurately predicted from scores of the Mini-Mental Status Exam, protein levels in cerebrospinal fluid, and genotype at the APOE and MAPT loci, via cross-validated multiple logistic regression. This combination of unsupervised and supervised data-driven tools provides a framework that could be used to identify latent disease subtypes in other areas of medicine.

神经退行性疾病中伴随的蛋白质病和异质性临床症状的流行阻碍了对可能适合特定干预措施的个体的识别。在这里，通过对 895 名中枢神经系统变性患者的死后组织病理学数据应用无监督聚类算法，我们发现六个不重叠的疾病簇可以同时解释 tau 神经原纤维缠结、α-突触核蛋白包涵体、神经炎斑块、转录抑制因子 TDP-43 的内含物、血管病、神经元损失和神经胶质增生。我们还表明，可以通过简易精神状态检查的分数、脑脊液中的蛋白质水平和基因型来准确预测六种跨诊断疾病簇的成员资格，这解释了认知表型的差异比个体诊断所能解释的更多。 APOE和MAPT位点，通过交叉验证的多重逻辑回归。这种无监督和监督数据驱动工具的组合提供了一个可用于识别其他医学领域的潜在疾病亚型的框架。