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There is no gold standard for the diagnosis of Alzheimer's disease (AD),except from autopsies. Unsupervised learning can provide insight into thepathophysiology of AD. A mixture of regressions can simultaneously identifyclusters from multiple biomarkers while accounting for within-clusterdemographic effects. Cerebrospinal fluid (CSF) biomarkers for AD have detectionlimits, which create additional challenges. We apply a mixture of regressionswith a multivariate truncated Gaussian distribution (also called a censoredmultivariate Gaussian mixture of regressions or a mixture of multivariate tobitregressions) to over 3,000 participants from the Emory Goizueta Alzheimer'sDisease Research Center and Emory Healthy Brain Study to examine amyloid-betapeptide 1-42 (Abeta42), total tau protein and phosphorylated tau protein in CSFwith known detection limits. We address three gaps in the literature on mixtureof regressions with a truncated multivariate Gaussian distribution: softwareavailability; inference; and clustering accuracy. We discovered three clustersthat tend to align with an AD group, a normal control profile and non-ADpathology. The CSF profiles differed by race, gender and the genetic markerApoE4, highlighting the importance of considering demographic factors inunsupervised learning with detection limits. Notably, African Americanparticipants in the AD-like group had significantly lower tau burden.

Mixture of regressions with multivariate responses for discovering subtypes in Alzheimer's biomarkers with detection limits