Why Inclusion Matters for Alzheimer’s Disease Biomarker Discovery in Plasma

Article type: Research Article

Authors: Khan, Mostafa J.^a | Desaire, Heather^b | Lopez, Oscar L.^{c; d} | Kamboh, M. Ilyas^{d; e; f} | Robinson, Renã A.S.^{a; g; h; i; j; *}

Affiliations: [a] Department of Chemistry, Vanderbilt University, Nashville, TN, USA | [b] Department of Chemistry, University of Kansas, Lawrence, KS, USA | [c] Department of Neurology, University of Pittsburgh, Pittsburgh, PA, USA | [d] Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA | [e] Department of Human Genetics, University of Pittsburgh, Pittsburgh, PA, USA | [f] Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA | [g] Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA | [h] Vanderbilt Institute of Chemical Biology, Vanderbilt University, Nashville, TN, USA | [i] Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, TN, USA | [j] Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA

Correspondence: [*] Correspondence to: Prof. Renã A. S. Robinson, Department of Chemistry, Vanderbilt University, 5423 Stevenson Center, Nashville, TN 37235, USA. Tel.:+1 615 343 0129; E-mail: [email protected].

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.

Keywords: African American, Alzheimer’s disease, biomarker, Black, discovery, disparities, machine learning, plasma, proteomics, race

DOI: 10.3233/JAD-201318

Journal: Journal of Alzheimer's Disease, vol. 79, no. 3, pp. 1327-1344, 2021