Identification of Novel Biomarkers for Alzheimer's Disease and Related Dementias Using Unbiased Plasma Proteomics

Lacar, B.; Ferdosi, S.; Alavi, A.; Stukalov, A.; Venkataraman, G. R.; de Geus, M.; Dodge, H.; Wu, C.-Y.; Kivisakk, P.; Das, S.; Guturu, H.; Hyman, B.; Batzoglou, S.; Arnold, S. E.; Siddiqui, A.

doi:10.1101/2024.01.05.574446

bioRxiv · 10.1101/2024.01.05.574446

Identification of Novel Biomarkers for Alzheimer's Disease and Related Dementias Using Unbiased Plasma Proteomics

Lacar, B.·Ferdosi, S.·Alavi, A.·Stukalov, A.·Venkataraman, G. R.·de Geus, M.·Dodge, H.·Wu, C.-Y.·Kivisakk, P.·Das, S.·Guturu, H.·Hyman, B.·Batzoglou, S.·Arnold, S. E.·Siddiqui, A.

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Abstract

Alzheimers disease (AD) and related dementias (ADRD) is a complex disease with multiple pathophysiological drivers that determine clinical symptomology and disease progression. These diseases develop insidiously over time, through many pathways and disease mechanisms and continue to have a huge societal impact for affected individuals and their families. While emerging blood-based biomarkers, such as plasma p-tau181 and p-tau217, accurately detect Alzheimer neuropthology and are associated with faster cognitive decline, the full extension of plasma proteomic changes in ADRD remains unknown. Earlier detection and better classification of the different subtypes may provide opportunities for earlier, more targeted interventions, and perhaps a higher likelihood of successful therapeutic development. In this study, we aim to leverage unbiased mass spectrometry proteomics to identify novel, blood-based biomarkers associated with cognitive decline. 1,786 plasma samples from 1,005 patients were collected over 12 years from partcipants in the Massachusetts Alzheimers Disease Research Center Longitudinal Cohort Study. Patient metadata includes demographics, final diagnoses, and clinical dementia rating (CDR) scores taken concurrently. The ProteographTM Product Suite (Seer, Inc.) and liquid-chromatography mass-spectrometry (LC-MS) analysis were used to process the plasma samples in this cohort and generate unbiased proteomics data. Data-independent acquisition (DIA) mass spectrometry results yielded 36,259 peptides and 4,007 protein groups. Linear mixed effects models revealed 138 differentially abundant proteins between AD and healthy controls. Machine learning classification models for AD diagnosis identified potential candidate biomarkers including MBP, BGLAP, and APoD. Cox regression models were created to determine the association of proteins with disease progression and suggest CLNS1A, CRISPLD2, and GOLPH3 as targets of further investigation as potential biomarkers. The Proteograph workflow provided deep, unbiased coverage of the plasma proteome at a speed that enabled a cohort study of almost 1,800 samples, which is the largest, deep, unbiased proteomics study of ADRD conducted to date.

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Lacar, B., Ferdosi, S., Alavi, A., Stukalov, A., Venkataraman, G. R., de Geus, M., Dodge, H., Wu, C.-Y., Kivisakk, P., Das, S., Guturu, H., Hyman, B., Batzoglou, S., Arnold, S. E., Siddiqui, A.. 2024-01-08. Identification of Novel Biomarkers for Alzheimer's Disease and Related Dementias Using Unbiased Plasma Proteomics. https://doi.org/10.1101/2024.01.05.574446

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