There is no gold standard for the diagnosis of Alzheimer's disease (AD), except from autopsies. Unsupervised learning can provide insight into the pathophysiology of AD. A mixture of regressions can simultaneously identify clusters from multiple biomarkers while accounting for within-cluster demographic effects. Cerebrospinal fluid (CSF) biomarkers for AD have detection limits, which create additional challenges. We apply a mixture of regressions with a multivariate truncated Gaussian distribution (also called a censored multivariate Gaussian mixture of regressions or a mixture of multivariate tobit regressions) to over 3,000 participants from the Emory Goizueta Alzheimer's Disease Research Center and Emory Healthy Brain Study to examine amyloid-beta peptide 1-42 (Abeta42), total tau protein and phosphorylated tau protein in CSF with known detection limits. We address three gaps in the literature on mixture of regressions with a truncated multivariate Gaussian distribution: software availability; inference; and clustering accuracy. We discovered three clusters that tend to align with an AD group, a normal control profile and non-AD pathology. The CSF profiles differed by race, gender and the genetic marker ApoE4, highlighting the importance of considering demographic factors in unsupervised learning with detection limits. Notably, African American participants in the AD-like group had significantly lower tau burden.
翻译:除了解剖外,诊断阿尔茨海默氏病(AD)没有黄金标准,诊断阿尔茨海默氏病(AD)没有金本位,未经监督的学习可以深入了解AD病的病理生理学。一种回归混合物既可以同时识别多种生物标志的群集,同时又可以计算组内的人口影响。甲状腺素(CSF)生物标志具有检测限度,这造成了额外的挑战。我们采用多种变式脱轨高斯分布(也称为多变式高斯回归混合或多变式比位回归混合)的回归混合组合。我们从Emory Goizueta阿尔茨海默症研究中心和Emory健康脑研究的3 000多名参与者那里,可以同时识别多生物群内生物群内生物群内生物群内生物群内生物群内生物群内生物群内生物群内生物群内生物群内生物群内生物群内生物群内生物群内生物群内生物群内生物检测极限。我们用多种变式的多变式高斯拉比分布的文献中的三个差距:软件可获取性;不易被推断;不精确;和分组的分类内生物群内生物群内生物群内生物群内生物群内生物群内生物群内生物群内生物群内生物群内生物群内生物群内生物群内生物群内生物群内分。</s>