Comparative Analysis of Alzheimer’s Disease Cerebrospinal Fluid Biomarkers Measurement by Multiplex SOMAscan Platform and Immunoassay-Based Approach¹

Article type: Research Article

Authors: Timsina, Jigyasha^{a; b} | Gomez-Fonseca, Duber^{a; b} | Wang, Lihua^{a; b} | Do, Anh^{b; c} | Western, Dan^{a; b} | Alvarez, Ignacio^d | Aguilar, Miquel^d | Pastor, Pau^d | Henson, Rachel L.^{e; f} | Herries, Elizabeth^{e; f} | Xiong, Chengjie^c | Schindler, Suzanne E.^{e; f} | Fagan, Anne M.^{e; f; g} | Bateman, Randall J.^{e; f; g} | Farlow, Martin^{h; i} | Morris, John C.^{f; g} | Perrin, Richard J.^{f; j; k} | Moulder, Krista^f | Hassenstab, Jason^f | Vöglein, Jonathan^{l; m} | Chhatwal, Jasmeerⁿ | Mori, Hiroshi^o | Alzheimer’s Disease Neuroimaging Initiative² | Dominantly Inherited Alzheimer Network Consortia | Sung, Yun Ju^{a; b; c} | Cruchaga, Carlos^{a; b; e; *}

Affiliations: [a] Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA | [b] NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA | [c] Division of Biostatistics, Washington University School of Medicine, St. Louis, MO, USA | [d] Memory Disorders Unit, Department of Neurology, University Hospital Mutua Terrassa, Terrassa, Spain | [e] Hope Center for Neurologic Diseases, Washington University in St. Louis, St. Louis, MO, USA | [f] Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA | [g] SILQ Center, Washington University School of Medicine, St. Louis, MO, USA | [h] Indiana University School of Medicine, Indianapolis, IN, USA | [i] Indiana University Health, Indianapolis, IN, USA | [j] The Charles F. and Joanne Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO, USA | [k] Department of Pathology & Immunology, Washington University School of Medicine, St Louis, MO, USA | [l] Department of Neurology, Ludwig-Maximilians-Universität München, Munich, Germany | [m] German Center for Neurodegenerative Diseases (DZNE), Munich, Germany | [n] Massachusetts General Hospital, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA | [o] Dept. of Clinical Neuroscience, Osaka City University Medical School, Nagaoka Sutoku University, Japan

Correspondence: [*] Correspondence to: Dr. Carlos Cruchaga, NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO, USA. E-mail: cruchagac@wustl.edu.

Note: [1] This article received a correction notice (Erratum) with the reference: 10.3233/JAD-229020, available at https://doi.org/10.3233/JAD-229020.

Note: [2] Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database(https://adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: https://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

Abstract: Background:The SOMAscan assay has an advantage over immunoassay-based methods because it measures a large number of proteins in a cost-effective manner. However, the performance of this technology compared to the routinely used immunoassay techniques needs to be evaluated. Objective:We performed comparative analyses of SOMAscan and immunoassay-based protein measurements for five cerebrospinal fluid (CSF) proteins associated with Alzheimer’s disease (AD) and neurodegeneration: NfL, Neurogranin, sTREM2, VILIP-1, and SNAP-25. Methods:We compared biomarkers measured in ADNI (N = 689), Knight-ADRC (N = 870), DIAN (N = 115), and Barcelona-1 (N = 92) cohorts. Raw protein values were transformed using z-score in order to combine measures from the different studies. sTREM2 and VILIP-1 had more than one analyte in SOMAscan; all available analytes were evaluated. Pearson’s correlation coefficients between SOMAscan and immunoassays were calculated. Receiver operating characteristic curve and area under the curve were used to compare prediction accuracy of these biomarkers between the two platforms. Results:Neurogranin, VILIP-1, and NfL showed high correlation between SOMAscan and immunoassay measures (r > 0.9). sTREM2 had a fair correlation (r > 0.6), whereas SNAP-25 showed weak correlation (r = 0.06). Measures in both platforms provided similar predicted performance for all biomarkers except SNAP-25 and one of the sTREM2 analytes. sTREM2 showed higher AUC for SOMAscan based measures. Conclusion:Our data indicate that SOMAscan performs as well as immunoassay approaches for NfL, Neurogranin, VILIP-1, and sTREM2. Our study shows promise for using SOMAscan as an alternative to traditional immunoassay-based measures. Follow-up investigation will be required for SNAP-25 and additional established biomarkers.

Keywords: Alzheimer’s disease, assays, cerebrospinal fluid biomarkers, correlation, SOMAscan

DOI: 10.3233/JAD-220399

Journal: Journal of Alzheimer's Disease, vol. 89, no. 1, pp. 193-207, 2022