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Why Inclusion Matters for Alzheimer’s Disease Biomarker Discovery in Plasma
dc.contributor.author | Khan, Mostafa J. | |
dc.contributor.author | Desaire, Heather | |
dc.contributor.author | Lopez, Oscar L. | |
dc.contributor.author | Kamboh, M. Ilyas | |
dc.contributor.author | Robinson, Renã A.S. | |
dc.date.accessioned | 2022-06-22T19:20:57Z | |
dc.date.available | 2022-06-22T19:20:57Z | |
dc.date.issued | 2021-02-02 | |
dc.identifier.citation | Khan, M. J., Desaire, H., Lopez, O. L., Kamboh, M. I., & Robinson, R. (2021). Why Inclusion Matters for Alzheimer's Disease Biomarker Discovery in Plasma. Journal of Alzheimer's disease : JAD, 79(3), 1327–1344. https://doi.org/10.3233/JAD-201318 | en_US |
dc.identifier.uri | http://hdl.handle.net/1808/32789 | |
dc.description.abstract | Background:African American/Black adults have a disproportionate incidence of Alzheimer’s disease (AD) and are underrepresented in biomarker discovery efforts. Objective:This study aimed to identify potential diagnostic biomarkers for AD using a combination of proteomics and machine learning approaches in a cohort that included African American/Black adults. Methods:We conducted a discovery-based plasma proteomics study on plasma samples (N = 113) obtained from clinically diagnosed AD and cognitively normal adults that were self-reported African American/Black or non-Hispanic White. Sets of differentially-expressed proteins were then classified using a support vector machine (SVM) to identify biomarker candidates. Results:In total, 740 proteins were identified of which, 25 differentially-expressed proteins in AD came from comparisons within a single racial and ethnic background group. Six proteins were differentially-expressed in AD regardless of racial and ethnic background. Supervised classification by SVM yielded an area under the curve (AUC) of 0.91 and accuracy of 86%for differentiating AD in samples from non-Hispanic White adults when trained with differentially-expressed proteins unique to that group. However, the same model yielded an AUC of 0.49 and accuracy of 47%for differentiating AD in samples from African American/Black adults. Other covariates such as age, APOE4 status, sex, and years of education were found to improve the model mostly in the samples from non-Hispanic White adults for classifying AD. Conclusion:These results demonstrate the importance of study designs in AD biomarker discovery, which must include diverse racial and ethnic groups such as African American/Black adults to develop effective biomarkers. | en_US |
dc.publisher | IOS Press | en_US |
dc.rights | Copyright Kahn et. al | en_US |
dc.subject | African American | en_US |
dc.subject | Alzheimer’s disease | en_US |
dc.subject | Biomarker | en_US |
dc.subject | Black | en_US |
dc.subject | Discovery | en_US |
dc.subject | Disparities | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Plasma | en_US |
dc.subject | Proteomics | en_US |
dc.subject | Race | en_US |
dc.title | Why Inclusion Matters for Alzheimer’s Disease Biomarker Discovery in Plasma | en_US |
dc.type | Article | en_US |
kusw.kuauthor | Desaire, Heather | |
kusw.kudepartment | Chemistry | en_US |
dc.identifier.doi | 10.3233/JAD-201318 | en_US |
kusw.oaversion | Scholarly/refereed, author accepted manuscript | en_US |
kusw.oapolicy | This item meets KU Open Access policy criteria. | en_US |
dc.identifier.pmid | PMC9126484 | en_US |
dc.rights.accessrights | openAccess | en_US |