Comparison of Proteomic Measurements Across Platforms in the Atherosclerosis Risk in Communities (ARIC) Study
Open accessAbstract
Background: The plasma proteome can be quantified using different types of highly multiplexed technologies, including aptamer-based and proximity-extension immunoassay methods. There has been limited characterization of how these protein measurements correlate across platforms and with absolute measures from targeted immunoassays. Methods: We assessed the comparability of (a) highly multiplexed aptamer-based (SomaScan v4; Somalogic) and proximity-extension immunoassay (OLINK Proseek® v5003; Olink) methods in 427 Atherosclerosis Risk in Communities (ARIC) Study participants (Visit 5, 2011–2013), and (b) 18 of the SomaScan protein measurements against targeted immunoassays in 110 participants (55 cardiovascular disease cases, 55 controls). We calculated Spearman correlations (r) between the different measurements and compared associations with case-control status. Results: There were 417 protein comparisons (366 unique proteins) between the SomaScan and Olink platforms. The average correlation was r = 0.46 (range: −0.21 to 0.97; 79 [19%] with r ≥ 0.8). For the comparison of SomaScan and targeted immunoassays, 6 of 18 assays (growth differentiation factor 15 [GDF15], interleukin-1 receptor-like 1 [ST2], interstitial collagenase [MMP1], adiponectin, leptin, and resistin) had good correlations (r ≥ 0.8), 2 had modest correlations (0.5 ≤ r < 0.8; osteopontin and interleukin-6 [IL6]), and 10 were poorly correlated (r < 0.5; metalloproteinase inhibitor 1 [TIMP1], stromelysin-1 [MMP3], matrilysin [MMP7], C-C motif chemokine 2 [MCP1], interleukin-10 [IL10], vascular cell adhesion protein 1 [VCAM1], intercellular adhesion molecule 1 [ICAM1], interleukin-18 [IL18], tumor necrosis factor [TNFα], and visfatin) overall. Correlations for SomaScan and targeted immunoassays were similar according to case status. Conclusions: There is variation in the quantitative measurements for many proteins across aptamer-based and proximity-extension immunoassays (approximately 1/2 showing good or modest correlation and approximately 1/2 poor correlation) and also for correlations of these highly multiplexed technologies with targeted immunoassays. Design and interpretation of protein quantification studies should be informed by the variation across measurement techniques for each protein.
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Existing comparisons of SomaScan and Olink platforms have shown that some protein measures are not concordant, with median inter-platform correlation = 0.38-0.50, which challenges the accuracy of protein associations observed.
“However, existing comparisons of SomaScan and Olink platforms have shown that some protein measures are not concordant (median inter-platform correlation = 0.38–0.50)11–16, which challenges the accuracy of protein associations observed, especially as there is no “gold-standard” reference measure for most proteins.”
From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [14]
The Results (page unavailable) report poor SomaScan–Olink correlations for 47% of protein pairs, and the Discussion cautions that divergent measurements complicate interpretation of biological associations. The Discussion reports a prior comparison with median r=0.38, but this paper’s own median is 0.53, outside the cited 0.38–0.50 range. It also cautions that disagreement does not establish which assay is more accurate. (LLM, conf=0.86)
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Cites
Aptamer-Based Multiplexed Proteomic Technology for Biomarker Discovery
Gold, L., Ayers, D., Bertino, J., Bock, C., Bock, A., Brody, E. N., Carter, J., Dalby, A. B., Eaton, B. E., Fitzwater, T., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015004 · Cited by 1,754 · see citing works
Synergistic insights into human health from aptamer- and antibody-based proteomic profiling
Pietzner, M., Wheeler, E., Carrasco-Zanini, J., Kerrison, N. D., Oerton, E., Koprulu, M., Luan, J., Hingorani, A. D., Williams, S. A., Wareham, N. J., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-27164-0 · Cited by 234 · see citing works
Comparison of Proteomic Assessment Methods in Multiple Cohort Studies
Raffield, L. M., Dang, H., Pratte, K. A., Jacobson, S., Gillenwater, L. A., Ampleford, E., Barjaktarevic, I., Basta, P., Clish, C. B., Comellas, A. P., et al. (2020) · PROTEOMICS · doi:10.1002/pmic.201900278 · Cited by 184 · see citing works
Flagged first, then by citations
Research works printed but not linked: 15
- [2]Assarsson E, Lundberg M, Holmquist G, Björkesten J, Bucht Thorsen S, Ekman D, Eriksson A, Homogenous 96-Plex PEA Immunoassay Exhibiting High Sensitivity, Specificity, and Excellent Scalability. PloS One 2014; 9: e95192. 24755770 PMC3995906
- [5]Liu RX, Thiessen-Philbrook HR, Vasan RS, Coresh J, Ganz P, Bonventre JV, Kimmel PL, Comparison of proteomic methods in evaluating biomarker-AKI associations in cardiac surgery patients. Transl Res 2021; 238: 49– 62. 34343625 PMC8572170
- [6]Kukova LZ, Mansour SG, Coca SG, de Fontnouvelle CA, Thiessen-Philbrook HR, Shlipak MG, El-Khoury JM, Comparison of Urine and Plasma Biomarker Concentrations Measured by Aptamer-Based versus Immunoassay Methods in Cardiac Surgery Patients. J Appl Lab Med 2019; 4: 331– 42. 31659071
- [7]Tin A, Yu B, Ma J, Masushita K, Daya N, Hoogeveen RC, Ballantyne CM, Reproducibility and Variability of Protein Analytes Measured Using a Multiplexed Modified Aptamer Assay. J Appl Lab Med 2019; 4: 30– 9. 31639705 PMC6814271
- [8]Lopez-Silva C, Surapaneni A, Coresh J, Reiser J, Parikh CR, Obeid W, Grams ME, Comparison of Aptamer-Based and Antibody-Based Assays for Protein Quantification in Chronic Kidney Disease. Clin J Am Soc Nephrol 2022: CJN.11700921. PMC8975030 35197258
- [10]Walker KA, Chen J, Zhang J, Fornage M, Yang Y, Zhou L, Grams ME, Large-scale plasma proteomic analysis identifies proteins and pathways associated with dementia risk. Nat Aging 2021; 1: 473– 89. PMC10154040 37118015
- [11]Candia J, Cheung F, Kotliarov Y, Fantoni G, Sellers B, Griesman T, Huang J, Assessment of Variability in the SOMAscan Assay. Sci Rep 2017; 7: 14248. 29079756 PMC5660188
- [12]Williams SA, Kivimaki M, Langenberg C, Hingorani AD, Casas JP, Bouchard C, Jonasson C, Plasma protein patterns as comprehensive indicators of health. Nat Med 2019; 25: 1851– 7. 31792462 PMC6922049
- [13]Bland JM, Altman DG. Measurement error proportional to the mean. BMJ 1996; 313: 106. 8688716 PMC2351517
- [14]McKee PA, Castelli WP, McNamara PM, Kannel WB. The natural history of congestive heart failure: the Framingham study. N Engl J Med 1971; 285: 1441– 6. 5122894
- [15]Yang J, Brody EN, Murthy AC, Mehler RE, Weiss SJ, DeLisle RK, Ostroff R, Impact of Kidney Function on the Blood Proteome and on Protein Cardiovascular Risk Biomarkers in Patients With Stable Coronary Heart Disease. J Am Heart Assoc 2020; 9: e016463. 32696702 PMC7792282
- [16]Sun BB, Maranville JC, Peters JE, Stacey D, Staley JR, Blackshaw J, Burgess S, Genomic atlas of the human plasma proteome. Nature 2018; 558: 73– 9. 29875488 PMC6697541
- [17]Petrera A, von Toerne C, Behler J, Huth C, Thorand B, Hilgendorff A, Hauck SM. Multiplatform Approach for Plasma Proteomics: Complementarity of Olink Proximity Extension Assay Technology to Mass Spectrometry-Based Protein Profiling. J Proteome Res 2021; 20: 751– 62. 33253581
- [18]Graumann J, Finkernagel F, Reinartz S, Stief T, Brödje D, Renz H, Jansen JM, Multi-platform Affinity Proteomics Identify Proteins Linked to Metastasis and Immune Suppression in Ovarian Cancer Plasma. Front Oncol 2019; 9: 1150-. 31737572 PMC6839336
- [19]Wollert KC, Kempf T, Wallentin L. Growth Differentiation Factor 15 as a Biomarker in Cardiovascular Disease. Clin Chem 2017; 63: 140– 51. 28062617
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Cross-ancestry comparison of aptamer and antibody protein measures
Nicholas, J. C., Katz, D. H., Tahir, U. A., Debban, C. L., Aguet, F., Blackwell, T., Bowler, R. P., Broadaway, K. A., Chen, J., Clish, C. B., et al. (2026) · Nature Communications · doi:10.1038/s41467-025-67814-1 · Cited by 13 · see citing works
Self-citation
- Authors
- Rooney, M. R., Chen, J., Ballantyne, C. M., Hoogeveen, R. C., Tang, O., Grams, M. E., Tin, A., Ndumele, C. E., Zannad, F., Couper, D. J., Tang, W., Selvin, E., & Coresh, J.
- Affiliations
- Johns Hopkins University (US)
- Baylor College of Medicine (US)
- University of Mississippi Medical Center (US)
- Inserm (FR)
- University of North Carolina at Chapel Hill (US)
- University of Minnesota (US)
- Corresponding author
- Rooney, Mary R
- Coresh, Josef
- ORCID
- Venue
- Clinical Chemistry
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-12-12
- Volume
- 69
- Issue
- 1
- Pages
- 68-79
- ISSN
- 0009-9147 (ISSN-L)
- 1530-8561
- PMC
- PMC9812856
- OpenAlex
- W4311200104
- Cited by
- 77
- References
- 3
- Citations by year
- 2026: 27 · 2025: 30 · 2024: 11 · 2023: 9
- FWCI
- 4.16
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Clinical Chemistry (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- HAL (submittedVersion, other-oa)
- Funders
- NHLBI
- NIH
- Department of Health and Human Services
- NIDDK
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Advanced Biosensing Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Health Sciences › Medicine › Physiology › Clinical Laboratory Practices and Quality Control
- Keywords
- plasma proteome, cardiovascular disease, atherosclerosis, inflammatory markers, matrix metalloproteinases, adipokines, growth differentiation factor 15, ST2, Atherosclerosis Risk in Communities Study
- MeSH terms
- Humans, Immunoassay, Interleukin-6, Proteomics, Atherosclerosis, Adiponectin
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