Mixture of Regressions with Multivariate Responses for Discovering Subtypes in Alzheimer’s Biomarkers with Detection Limits
AbstractThere 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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2024-12-01
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| Series: | Data Science in Science |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/26941899.2024.2309403 |
| _version_ | 1797272085494497280 |
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| author | Ganzhong Tian John Hanfelt James Lah Benjamin B. Risk |
| author_facet | Ganzhong Tian John Hanfelt James Lah Benjamin B. Risk |
| author_sort | Ganzhong Tian |
| collection | DOAJ |
| description | AbstractThere 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. |
| first_indexed | 2024-03-07T14:20:33Z |
| format | Article |
| id | doaj.art-8ceb8c67667f4990a25db14b0e379c3a |
| institution | Directory Open Access Journal |
| issn | 2694-1899 |
| language | English |
| last_indexed | 2024-03-07T14:20:33Z |
| publishDate | 2024-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Data Science in Science |
| spelling | doaj.art-8ceb8c67667f4990a25db14b0e379c3a2024-03-06T09:17:11ZengTaylor & Francis GroupData Science in Science2694-18992024-12-013110.1080/26941899.2024.2309403Mixture of Regressions with Multivariate Responses for Discovering Subtypes in Alzheimer’s Biomarkers with Detection LimitsGanzhong Tian0John Hanfelt1James Lah2Benjamin B. Risk3Department of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, USADepartment of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, USADepartment of Neurology, Emory University School of Medicine, Atlanta, Georgia, USADepartment of Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia, USAAbstractThere 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.https://www.tandfonline.com/doi/10.1080/26941899.2024.2309403Alzheimer’s diseasecensored Gaussian mixture of regressionsclusteringfinite mixture modellatent class analysisTobit model |
| spellingShingle | Ganzhong Tian John Hanfelt James Lah Benjamin B. Risk Mixture of Regressions with Multivariate Responses for Discovering Subtypes in Alzheimer’s Biomarkers with Detection Limits Data Science in Science Alzheimer’s disease censored Gaussian mixture of regressions clustering finite mixture model latent class analysis Tobit model |
| title | Mixture of Regressions with Multivariate Responses for Discovering Subtypes in Alzheimer’s Biomarkers with Detection Limits |
| title_full | Mixture of Regressions with Multivariate Responses for Discovering Subtypes in Alzheimer’s Biomarkers with Detection Limits |
| title_fullStr | Mixture of Regressions with Multivariate Responses for Discovering Subtypes in Alzheimer’s Biomarkers with Detection Limits |
| title_full_unstemmed | Mixture of Regressions with Multivariate Responses for Discovering Subtypes in Alzheimer’s Biomarkers with Detection Limits |
| title_short | Mixture of Regressions with Multivariate Responses for Discovering Subtypes in Alzheimer’s Biomarkers with Detection Limits |
| title_sort | mixture of regressions with multivariate responses for discovering subtypes in alzheimer s biomarkers with detection limits |
| topic | Alzheimer’s disease censored Gaussian mixture of regressions clustering finite mixture model latent class analysis Tobit model |
| url | https://www.tandfonline.com/doi/10.1080/26941899.2024.2309403 |
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