Novel set of plasma proteins classifies Alzheimer's dementia in African American individuals with high accuracy

Abstract

Introduction: African American (AA) individuals are underrepresented in biomarker studies for Alzheimer's disease (AD). Biomarkers that reflect the heterogeneity of AD and achieve the greatest accuracy across populations are sorely needed. Methods: Untargeted proteome measurements were obtained using the SomaScan 7k platform to identify novel plasma biomarkers for AD in AA participants with clinical diagnoses of AD dementia (n = 181) and cognitively unimpaired (CU, n = 142). Machine learning was used to identify a set of plasma proteins that yielded the best classification accuracy. Results: A set of 36 proteins achieved an area under the curve (AUC) of 0.94 to classify AD dementia versus CU, a 16% improvement over age, sex, and apolipoprotein E (APOE). This finding was replicated in multiple plasma and brain datasets (AUCs 0.73-0.97). Our findings underscore the importance of matrisome and cerebrovascular dysfunction in AD pathophysiology. Discussion: This study demonstrates the potential of biomarker discovery through untargeted plasma proteomics and machine learning. Highlights: Conducted large-scale plasma proteomics in Alzheimer's disease (AD) versus cognitively unimpaired controls. Machine learning biomarker discovery was replicated in an independent cohort. Novel set of proteins distinguishes AD versus controls with high accuracy (area under the curve [AUC] = 0.94). Achieved reproducibility across multiple replication cohorts (AUC = 0.73-0.97). Network analyses implicates matrisome biology and cerebrovascular dysfunction.