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. 2021;79(3):1327-1344.
doi: 10.3233/JAD-201318.

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

Mostafa J Khan  1 , Heather Desaire  2 , Oscar L Lopez  3   4 , M Ilyas Kamboh  4   5   6 , Renã A S Robinson  1   7   8   9   10
Affiliations

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

Mostafa J Khan et al. J Alzheimers Dis. 2021.

Erratum in

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; Black; biomarker; discovery; disparities; machine learning; plasma; proteomics; race.

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Figures

Fig. 1.
Fig. 1.
Overview of the plasma proteomics workflow. Samples from four study groups-African American/Black Alzheimer’s disease (AD) and cognitively normal (CN), non-Hispanic White AD and CN-were obtained from the University of Pittsburgh ADRC. Samples were divided into Set 1 (N = 73) and Set 2 (N = 40) for this study. Samples were randomized into eight batches for Set 1 and four batches for Set 2. There was one QC pool sample in each batch and representation of one sample from each study group in each batch. The samples were randomly assigned TMT channels for both experiments. The experimental workflow was maintained the same except for the digestion step, where in solution digestion was used for Set 1, while FASP digestion was employed in Set 2. The plasma samples were immunodepleted of the six most abundant proteins, followed by proteolytic digestion. This was followed by isobaric tagging using either TMT 10/11 plex labels, followed by high pH reversed-phase fractionation. The resulting peptides were loaded into an Ultimate 3000 RPLC system coupled to an Orbitrap Fusion Lumos mass spectrometer for LC-MS, MS/MS and MS3 analysis. Example representative MS3 reporter ion spectra for TMT-10 plex sample (Set 1) and TMT-11 plex sample (Set 2) is also provided, demonstrating analysis of multiple samples using a single injection.
Fig. 2.
Fig. 2.
Summary of the number of identified proteins in both sample sets. On the left, are the number of high confidence identified proteins as a function of missing channels for TMT reporter ions. Values are provided for Set 1 and Set 2. On the right are Venn diagrams, displaying the overlap in common proteins at each level from Set 1 and Set 2.
Fig. 3.
Fig. 3.
Volcano plots of differentially-expressed proteins between Alzheimer’s disease (AD) and cognitively normal individuals (CN) for the entire set of samples in a) Set 1 (N = 39 AD, N = 34 CN); b) data from the non-Hispanic White group only, Set 1 (N = 19 AD, N = 18 CN); and c) data from the African American/Black group only, Set 1 (N = 20 AD, N = 16 CN). Red circles coincide with proteins higher in AD compared to CN, while green circles coincide with proteins lower in AD. CNDP1, Beta-Ala-His dipeptidase; KRT9, Keratin type I cytoskeletal 9; APOL1, Apolipoprotein L1; ADIPOQ, Adiponectin; KRT1, Keratin type II cytoskeletal 1; APOC3, Apolipoprotein C3; MMRN2, Multimerin-2; AFM, Afamin; SAA1, Serum amyloid A-1 protein; SAA4, Serum amyloid A-4 protein; DPH, Dopamine beta-hydroxylase; APOE, Apolipoprotein E.
Fig. 4.
Fig. 4.
Histogram displaying classification accuracy for predicting AD in Set 1: N = 73 samples and Set 2: N = 40 samples. Blue bars: Accuracy determined when only the four differentially expressed proteins (beta-ala-his dipeptidase, keratin type I cytoskeletal 9, apolipoprotein L1, and adiponectin) are included in the model. Orange bars: Additional improvement in accuracy when clinical variables (age, sex, education, and APOE) are also included in the model.
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