1
|
Lah JJ, Tian G, Risk BB, Hanfelt JJ, Wang L, Zhao L, Hales CM, Johnson ECB, Elmor MB, Malakauskas SJ, Heilman C, Wingo TS, Dorbin CD, Davis CP, Thomas TI, Hajjar IM, Levey AI, Parker MW. Lower Prevalence of Asymptomatic Alzheimer's Disease Among Healthy African Americans. Ann Neurol 2024; 96:463-475. [PMID: 38924596 DOI: 10.1002/ana.26960] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2023] [Revised: 03/25/2024] [Accepted: 04/04/2024] [Indexed: 06/28/2024]
Abstract
OBJECTIVE Alzheimer's disease (AD) is believed to be more common in African Americans (AA), but biomarker studies in AA populations are limited. This report represents the largest study to date examining cerebrospinal fluid AD biomarkers in AA individuals. METHODS We analyzed 3,006 cerebrospinal fluid samples from controls, AD cases, and non-AD cases, including 495 (16.5%) self-identified black/AA and 2,456 (81.7%) white/European individuals using cutoffs derived from the Alzheimer's Disease Neuroimaging Initiative, and using a data-driven multivariate Gaussian mixture of regressions. RESULTS Distinct effects of race were found in different groups. Total Tauand phospho181-Tau were lower among AA individuals in all groups (p < 0.0001), and Aβ42 was markedly lower in AA controls compared with white controls (p < 0.0001). Gaussian mixture of regressions modeling of cerebrospinal fluid distributions incorporating adjustments for covariates revealed coefficient estimates for AA race comparable with 2-decade change in age. Using Alzheimer's Disease Neuroimaging Initiative cutoffs, fewer AA controls were classified as biomarker-positive asymptomatic AD (8.0% vs 13.4%). After adjusting for covariates, our Gaussian mixture of regressions model reduced this difference, but continued to predict lower prevalence of asymptomatic AD among AA controls (9.3% vs 13.5%). INTERPRETATION Although the risk of dementia is higher, data-driven modeling indicates lower frequency of asymptomatic AD in AA controls, suggesting that dementia among AA populations may not be driven by higher rates of AD. ANN NEUROL 2024;96:463-475.
Collapse
Affiliation(s)
- James J Lah
- Department of Neurology, Emory University School of Medicine, Emory Brain Health Center, Atlanta, GA, USA
- Emory Goizueta Alzheimer's Disease Research Center, Emory University, Atlanta, GA, USA
- Center for Neurodegenerative Disease, Emory University, Atlanta, GA, USA
| | - Ganzhong Tian
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Benjamin B Risk
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - John J Hanfelt
- Emory Goizueta Alzheimer's Disease Research Center, Emory University, Atlanta, GA, USA
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Liangkang Wang
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Liping Zhao
- Emory Goizueta Alzheimer's Disease Research Center, Emory University, Atlanta, GA, USA
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, USA
| | - Chadwick M Hales
- Department of Neurology, Emory University School of Medicine, Emory Brain Health Center, Atlanta, GA, USA
- Emory Goizueta Alzheimer's Disease Research Center, Emory University, Atlanta, GA, USA
- Center for Neurodegenerative Disease, Emory University, Atlanta, GA, USA
| | - Erik C B Johnson
- Department of Neurology, Emory University School of Medicine, Emory Brain Health Center, Atlanta, GA, USA
- Emory Goizueta Alzheimer's Disease Research Center, Emory University, Atlanta, GA, USA
- Center for Neurodegenerative Disease, Emory University, Atlanta, GA, USA
| | - Morgan B Elmor
- Department of Neurology, Emory University School of Medicine, Emory Brain Health Center, Atlanta, GA, USA
- Emory Goizueta Alzheimer's Disease Research Center, Emory University, Atlanta, GA, USA
| | - Sarah J Malakauskas
- Department of Neurology, Emory University School of Medicine, Emory Brain Health Center, Atlanta, GA, USA
- Emory Goizueta Alzheimer's Disease Research Center, Emory University, Atlanta, GA, USA
| | - Craig Heilman
- Department of Neurology, Emory University School of Medicine, Emory Brain Health Center, Atlanta, GA, USA
- Emory Goizueta Alzheimer's Disease Research Center, Emory University, Atlanta, GA, USA
| | - Thomas S Wingo
- Department of Neurology, Emory University School of Medicine, Emory Brain Health Center, Atlanta, GA, USA
- Emory Goizueta Alzheimer's Disease Research Center, Emory University, Atlanta, GA, USA
- Center for Neurodegenerative Disease, Emory University, Atlanta, GA, USA
| | - Cornelya D Dorbin
- Department of Neurology, Emory University School of Medicine, Emory Brain Health Center, Atlanta, GA, USA
- Emory Goizueta Alzheimer's Disease Research Center, Emory University, Atlanta, GA, USA
| | - Crystal P Davis
- Department of Neurology, Emory University School of Medicine, Emory Brain Health Center, Atlanta, GA, USA
- Emory Goizueta Alzheimer's Disease Research Center, Emory University, Atlanta, GA, USA
| | - Tiffany I Thomas
- Department of Neurology, Emory University School of Medicine, Emory Brain Health Center, Atlanta, GA, USA
- Emory Goizueta Alzheimer's Disease Research Center, Emory University, Atlanta, GA, USA
| | - Ihab M Hajjar
- Department of Neurology, Emory University School of Medicine, Emory Brain Health Center, Atlanta, GA, USA
- Emory Goizueta Alzheimer's Disease Research Center, Emory University, Atlanta, GA, USA
| | - Allan I Levey
- Department of Neurology, Emory University School of Medicine, Emory Brain Health Center, Atlanta, GA, USA
- Emory Goizueta Alzheimer's Disease Research Center, Emory University, Atlanta, GA, USA
- Center for Neurodegenerative Disease, Emory University, Atlanta, GA, USA
| | - Monica W Parker
- Department of Neurology, Emory University School of Medicine, Emory Brain Health Center, Atlanta, GA, USA
- Emory Goizueta Alzheimer's Disease Research Center, Emory University, Atlanta, GA, USA
| |
Collapse
|
2
|
Wang WL, Castro LM, Li HJ, Lin TI. Mixtures of t $$ t $$ factor analysers with censored responses and external covariates: An application to educational data from Peru. THE BRITISH JOURNAL OF MATHEMATICAL AND STATISTICAL PSYCHOLOGY 2024; 77:316-336. [PMID: 38095333 DOI: 10.1111/bmsp.12329] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2023] [Revised: 10/21/2023] [Accepted: 11/16/2023] [Indexed: 04/10/2024]
Abstract
Analysing data from educational tests allows governments to make decisions for improving the quality of life of individuals in a society. One of the key responsibilities of statisticians is to develop models that provide decision-makers with pertinent information about the latent process that educational tests seek to represent. Mixtures oft $$ t $$ factor analysers (MtFA) have emerged as a powerful device for model-based clustering and classification of high-dimensional data containing one or several groups of observations with fatter tails or anomalous outliers. This paper considers an extension of MtFA for robust clustering of censored data, referred to as the MtFAC model, by incorporating external covariates. The enhanced flexibility of including covariates in MtFAC enables cluster-specific multivariate regression analysis of dependent variables with censored responses arising from upper and/or lower detection limits of experimental equipment. An alternating expectation conditional maximization (AECM) algorithm is developed for maximum likelihood estimation of the proposed model. Two simulation experiments are conducted to examine the effectiveness of the techniques presented. Furthermore, the proposed methodology is applied to Peruvian data from the 2007 Early Grade Reading Assessment, and the results obtained from the analysis provide new insights regarding the reading skills of Peruvian students.
Collapse
Affiliation(s)
- Wan-Lun Wang
- Department of Statistics and Institute of Data Science, National Cheng Kung University, Tainan, Taiwan
| | - Luis M Castro
- Department of Statistics, Pontificia Universidad Católica de Chile, Santiago, Chile
- Center for the Discovery of Structures in Complex Data, Santiago, Chile
| | - Huei-Jyun Li
- Institute of Statistics, National Chung Hsing University, Taichung, Taiwan
| | - Tsung-I Lin
- Institute of Statistics, National Chung Hsing University, Taichung, Taiwan
- Department of Public Health, China Medical University, Taichung, Taiwan
| |
Collapse
|