Mixture of regressions with multivariate responses for discovering subtypes in Alzheimer's biomarkers with detection limits.
Mixture of regressions with multivariate responses for discovering subtypes in Alzheimer's biomarkers with detection limits.
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回归与多变量响应的混合，用于发现具有检测限的阿尔茨海默病生物标志物的亚型。
DOI：
10.1080/26941899.2024.2309403
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发表时间：
2024
期刊：
影响因子：
--
通讯作者：
Risk,BenjaminB
中科院分区：
文献类型：
--
作者：
Tian,Ganzhong;Hanfelt,John;Lah,James;Risk,BenjaminB
除了尸检，没有诊断阿尔茨海默病(AD)的金标准，这促使了无监督学习的使用。混合回归是一种无监督的方法，可以同时从多个生物标记物中识别集群，同时学习集群内的人口统计效应。AD的脑脊液(CSF)生物标记物有检测极限，这带来了额外的挑战。我们对来自Emory Goizueta阿尔茨海默病研究中心和Emory Healthy Brain Study的3000多名参与者应用了具有多元截断高斯分布的混合回归(也称为删失的多元高斯回归或多变量Tobit回归的混合)，以检测脑脊液中淀粉样β蛋白1-42(Abeta42)、总tau蛋白和磷酸化tau蛋白的已知检测下限。我们解决了文献中关于回归与截断多变量高斯分布的混合的三个空白：软件可用性；推断；以及聚类准确性。我们发现了三个倾向于与AD组、正常对照组和非AD病理相一致的簇。脑脊液图谱因种族、性别和遗传标记ApoE4的不同而不同，这突显了在有检测极限的非监督学习中考虑人口统计因素的重要性。值得注意的是，在类AD组中，非裔美国人的tau负担明显较低。
There is no gold standard for the diagnosis of Alzheimer’s disease (AD), except for autopsies, which motivates the use of unsupervised learning. A mixture of regressions is an unsupervised method that can simultaneously identify clusters from multiple biomarkers while learning 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 3000 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 the 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.
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