The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Comparison of Proteomic Assessment Methods in Multiple Cohort Studies
Open accessAbstract
Novel proteomics platforms, such as the aptamer‐based SOMAscan platform, can quantify large numbers of proteins efficiently and cost‐effectively and are rapidly growing in popularity. However, comparisons to conventional immunoassays remain underexplored, leaving investigators unsure when cross‐assay comparisons are appropriate. The correlation of results from immunoassays with relative protein quantification is explored by SOMAscan. For 63 proteins assessed in two chronic obstructive pulmonary disease (COPD) cohorts, subpopulations and intermediate outcome measures in COPD Study (SPIROMICS), and COPDGene, using myriad rules based medicine multiplex immunoassays and SOMAscan, Spearman correlation coefficients range from −0.13 to 0.97, with a median correlation coefficient of ≈0.5 and consistent results across cohorts. A similar range is observed for immunoassays in the population‐based Multi‐Ethnic Study of Atherosclerosis and for other assays in COPDGene and SPIROMICS. Comparisons of relative quantification from the antibody‐based Olink platform and SOMAscan in a small cohort of myocardial infarction patients also show a wide correlation range. Finally, cis pQTL data, mass spectrometry aptamer confirmation, and other publicly available data are integrated to assess relationships with observed correlations. Correlation between proteomics assays shows a wide range and should be carefully considered when comparing and meta‐analyzing proteomics data across assays and studies.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The detection of a cis-pQTL for a given protein measure is often used as a metric to confirm on-target affinity-probe binding12,15,16; existing studies comparing SomaScan and Olink measures have established that the detection of a cis-pQTL is a strong predictor of inter-platform correlation coefficients for a given protein12,15,16.”
From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [15]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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 [15]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The detection of a cis-pQTL for a given protein measure is often used as a metric to confirm on-target affinity-probe binding12,15,16; existing studies comparing SomaScan and Olink measures have established that the detection of a cis-pQTL is a strong predictor of inter-platform correlation coefficients for a given protein12,15,16.”
From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [15]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Here, and across prior studies, cis-pQTL presence/absence is thought to provide one predictor of on-target protein measures12,15,16.”
From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [15]
The cited source says what the claim attributes to it.
In the citing paper
“This modest overlap is unsurprising, given that correlations between proteins measured by both platforms are highly variable 52,53 , and shared genetic signals can be masked by extreme, assay-specific binding affinities 51 .”
From Plasma proteomic associations with genetics and health in the UK Biobank (2023) · cites this paper as [53]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Multi-Ethnic Study of Atherosclerosis: Objectives and Design
Bild, D. E. (2002) · American Journal of Epidemiology · doi:10.1093/aje/kwf113 · Cited by 3,942
Genomic atlas of the human plasma proteome
Sun, B. B., Maranville, J. C., Peters, J. E., Stacey, D., Staley, J. R., Blackshaw, J., Burgess, S., Jiang, T., Paige, E., Surendran, P., et al. (2018) · Nature · doi:10.1038/s41586-018-0175-2 · Cited by 2,268
Assarsson, E., Lundberg, M., Holmquist, G., Björkesten, J., Bucht Thorsen, S., Ekman, D., Eriksson, A., Rennel Dickens, E., Ohlsson, S., Edfeldt, G., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0095192 · Cited by 1,874
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
Genetic epidemiology of copd (copdgene) study design
Regan, E. A., Hokanson, J. E., Murphy, J. R., Make, B., Lynch, D. A., Beaty, T. H., Curran-Everett, D., Silverman, E. K., & Crapo, J. D. (2011) · COPD: Journal of Chronic Obstructive Pulmonary Disease · doi:10.3109/15412550903499522 · Cited by 1,313
Connecting genetic risk to disease end points through the human blood plasma proteome
Suhre, K., Arnold, M., Bhagwat, A. M., Cotton, R. J., Engelke, R., Raffler, J., Sarwath, H., Thareja, G., Wahl, A., DeLisle, R. K., et al. (2017) · Nature Communications · doi:10.1038/ncomms14357 · Cited by 800
Ethnic Differences in Coronary Calcification: The Multi-Ethnic Study of Atherosclerosis (MESA)
Bild, D., Detrano, R., Peterson, D., Guerci, A., Liu, K., Shahar, E., Ouyang, P., Jackson, S. A., & Saad, M. (2005) · Circulation · doi:10.1161/01.cir.0000157730.94423.4b · Cited by 773
Fit-for-Purpose Method Development and Validation for Successful Biomarker Measurement
Lee, J. W., Devanarayan, V., Barrett, Y. C., WEINER, R. S., Allinson, J. L., Fountain, S. T., Keller, S., Weinryb, I., Green, M., Duan, L., et al. (2006) · Pharmaceutical Research · doi:10.1007/s11095-005-9045-3 · Cited by 771
Co-regulatory networks of human serum proteins link genetics to disease
Emilsson, V., Ilkov, M., Lamb, J. R., Finkel, N., Gudmundsson, E. F., Pitts, R., Hoover, H., Gudmundsdottir, V., Horman, S. R., Aspelund, T., et al. (2018) · Science · doi:10.1126/science.aaq1327 · Cited by 680
Plasma protein patterns as comprehensive indicators of health
Williams, S. A., Kivimaki, M., Langenberg, C., Hingorani, A. D., Casas, J. P., Bouchard, C., Jonasson, C., Sarzynski, M. A., Shipley, M. J., Alexander, L., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0665-2 · Cited by 506
Celli, B. R., Locantore, N., Yates, J., Tal‐Singer, R., Miller, B. E., Bakke, P. S., Calverley, P. M. A., Coxson, H. O., Crim, C. C., Edwards, L. D., et al. (2012) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201110-1792oc · Cited by 435
Design of the Subpopulations and Intermediate Outcomes in COPD Study (SPIROMICS): Table 1
Couper, D. J., LaVange, L. M., Han, M. K., Barr, R. G., Bleecker, E. R., Hoffman, E. A., Kanner, R. E., Kleerup, E. C., Martínez, F. J., Woodruff, P. G., et al. (2013) · Thorax · doi:10.1136/thoraxjnl-2013-203897 · Cited by 415
Assessment of Variability in the SOMAscan Assay
Candia, J., Cheung, F., Kotliarov, Y., Fantoni, G., Sellers, B. A., Griesman, T., Huang, J., Stuccio, S., Zingone, A., Ryan, B. M., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-14755-5 · Cited by 399
Antibody-Based Protein Multiplex Platforms: Technical and Operational Challenges
Ellington, A. A., Kullo, I. J., Bailey, K. R., & Klee, G. G. (2009) · Clinical Chemistry · doi:10.1373/clinchem.2009.127514 · Cited by 314
Comparison of Multiplex Immunoassay Platforms
Fu, Q., Zhu, J., & Eyk, J. E. V. (2009) · Clinical Chemistry · doi:10.1373/clinchem.2009.135087 · Cited by 217
Biochemistry and Biology of GDF11 and Myostatin
Walker, R. G., Poggioli, T., Katsimpardi, L., Buchanan, S. M., Oh, J., Wattrus, S. J., Heidecker, B., Fong, Y. W., Rubin, L. L., Ganz, P., et al. (2016) · Circulation Research · doi:10.1161/circresaha.116.308391 · Cited by 217
Ngo, D., Sinha, S., Shen, D., Kuhn, E. W., Keyes, M. J., Shi, X. D., Benson, M. D., O’Sullivan, J., Keshishian, H., Farrell, L. A., et al. (2016) · Circulation · doi:10.1161/circulationaha.116.021803 · Cited by 214
Self-citationSmith, J. G., & Gerszten, R. E. (2017) · Circulation · doi:10.1161/circulationaha.116.025446 · Cited by 211
Self-citationStability and reproducibility of proteomic profiles measured with an aptamer-based platform
Kim, C. H., Tworoger, S. S., Stampfer, M. J., Dillon, S. T., Gu, X., Sawyer, S. J., Chan, A. T., Libermann, T. A., & Eliassen, A. H. (2018) · Scientific Reports · doi:10.1038/s41598-018-26640-w · Cited by 170
Crowther, J. R. (2009) · Methods in molecular biology · doi:10.1007/978-1-60327-254-4 · Cited by 167
Common Genetic Polymorphisms Influence Blood Biomarker Measurements in COPD
Sun, W., Kechris, K. J., Jacobson, S. R., Drummond, M., Hawkins, G. A., Yang, J., Chen, T., Quibrera, P. M., Anderson, W. H., Barr, R. G., et al. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006011 · Cited by 140
Self-citationChua, W., Purmah, Y., Cardoso, V. R., Gkoutos, G. V., Tull, S. P., Neculau, G., Thomas, M. R., Kotecha, D., Lip, G. Y. H., Kirchhof, P. F., et al. (2018) · European Heart Journal · doi:10.1093/eurheartj/ehy815 · Cited by 140
Biomarkers Predictive of Exacerbations in the SPIROMICS and COPDGene Cohorts
Keene, J., Jacobson, S. R., Kechris, K. J., Kinney, G. L., Foreman, M. G., Doerschuk, C. M., Make, B. J., Curtis, J. L., Rennard, S. I., Barr, R. G., et al. (2016) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201607-1330oc · Cited by 134
Self-citationThe value of blood cytokines and chemokines in assessing COPD
Bradford, E., Jacobson, S. R., Varasteh, J., Comellas, A. P., Woodruff, P. G., O’Neal, W. K., DeMeo, D. L., Li, X., Kim, V., Cho, M., et al. (2017) · Respiratory Research · doi:10.1186/s12931-017-0662-2 · Cited by 118
Self-citationTin, A., Yu, B., Ma, J., Masushita, K., Daya, N., Hoogeveen, R. C., Ballantyne, C. M., Couper, D., Rebholz, C. M., Grams, M. E., et al. (2019) · The Journal of Applied Laboratory Medicine · doi:10.1373/jalm.2018.027086 · Cited by 106
Evaluation of Multiplexed Cytokine and Inflammation Marker Measurements: a Methodologic Study
Chaturvedi, A. K., Kemp, T. J., Pfeiffer, R. M., Biancotto, A., Williams, M. C., Munuo, S., Purdue, M. P., Hsing, A. W., Pinto, L. A., McCoy, J. P., et al. (2011) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-11-0221 · Cited by 98
Penn‐Nicholson, A., Hraha, T. H., Thompson, E. G., Sterling, D., Mbandi, S. K., Wall, K. M., Fisher, M., Suliman, S., Shankar, S., Hanekom, W. A., et al. (2019) · PLoS Medicine · doi:10.1371/journal.pmed.1002781 · Cited by 96
Circulating proteins as predictors of cardiovascular mortality in end-stage renal disease
Feldreich, T., Nowak, C., Fall, T., Carlsson, A. C., Carrero, J. J., Ripsweden, J., Qureshi, A. R., Heimbürger, O., Barany, P. F., Stenvinkel, P., et al. (2018) · Journal of Nephrology · doi:10.1007/s40620-018-0556-5 · Cited by 64
The Impact of the Glomerular Filtration Rate on the Human Plasma Proteome
Christensson, A. G., Ash, J. A., DeLisle, R. K., Gaspar, F. W., Ostroff, R. M., GRUBB, A. O., Lindström, V., Bruun, L., & Williams, S. A. (2017) · PROTEOMICS - CLINICAL APPLICATIONS · doi:10.1002/prca.201700067 · Cited by 57
O’Neal, W. K., Anderson, W. H., Basta, P. V., Carretta, E. E., Doerschuk, C. M., Barr, R. G., Bleecker, E. R., Christenson, S. A., Curtis, J. L., Han, M. K., et al. (2014) · Journal of Translational Medicine · doi:10.1186/1479-5876-12-9 · Cited by 52
Self-citationApplication of Large-Scale Aptamer-Based Proteomic Profiling to Planned Myocardial Infarctions
Jacob, J., Ngo, D., Finkel, N., Pitts, R., Gleim, S., Benson, M. D., Keyes, M. J., Farrell, L. A., Morgan, T., Jennings, L. L., et al. (2017) · Circulation · doi:10.1161/circulationaha.117.029443 · Cited by 52
Self-citationPopulation-specific coding variant underlies genome-wide association with adiponectin level
Croteau‐Chonka, D. C., Wu, Y., Li, Y., Fogarty, M. P., Lange, L. A., Kuzawa, C. W., McDade, T. W., Borja, J. B., Luo, J., AbdelBaky, O., et al. (2011) · Human Molecular Genetics · doi:10.1093/hmg/ddr480 · Cited by 44
The use of multiplex platforms for absolute and relative protein quantification of clinical material
Christiansson, L., Mustjoki, S., Simonsson, B., Olsson-Strömberg, U., Loskog, A. S. I., & Mangsbo, S. M. (2014) · EuPA Open Proteomics · doi:10.1016/j.euprot.2014.02.002 · Cited by 34
Campo, R., Rowland, J. H., Irwin, M. L., Nathan, P. C., Gritz, E. R., & Kinney, A. Y. (2011) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-11-0728 · Cited by 27
Polfus, L. M., Raffield, L. M., Wheeler, M. M., Tracy, R. P., Lange, L. A., Lettre, G., Miller, A. K., Correa, A., Bowler, R. P., Bis, J. C., et al. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy360 · Cited by 16
Self-citationBiomarkers of Key Biological Pathways in CVD
Jenny, N. S., Olson, N. C., Allison, M., Rifkin, D. E., Daniels, L. B., de Boer, I. H., Wassel, C. L., & Tracy, R. P. (2016) · Global Heart · doi:10.1016/j.gheart.2016.07.003 · Cited by 14
Self-citation
Cited by
Sorted by citations
Plasma proteomic associations with genetics and health in the UK Biobank
Sun, B. B., Chiou, J., Traylor, M., Benner, C., Hsu, Y.-H., Richardson, T. G., Surendran, P., Mahajan, A., Robins, C., Vasquez-Grinnell, S. G., et al. (2023) · Nature · doi:10.1038/s41586-023-06592-6 · Cited by 1,880
Self-citationLarge-scale plasma proteomics comparisons through genetics and disease associations
Eldjarn, G. H., Ferkingstad, E., Lund, S. H., Helgason, H., Magnusson, O. T., Gunnarsdottir, K., Olafsdottir, T. A., Halldorsson, B. V., Olason, P. I., Zink, F., et al. (2023) · Nature · doi:10.1038/s41586-023-06563-x · Cited by 583
High-throughput proteomics: a methodological mini-review
Cui, M., Cheng, C., & Zhang, L. (2022) · Laboratory Investigation · doi:10.1038/s41374-022-00830-7 · Cited by 398
A genome-wide association study of serum proteins reveals shared loci with common diseases
Guðjónsson, A., Guðmundsdóttir, V., Axelsson, G. T., Gudmundsson, E. F., Jonsson, B. G., Launer, L. J., Lamb, J. R., Jennings, L. L., Aspelund, T., Emilsson, V., et al. (2022) · Nature Communications · doi:10.1038/s41467-021-27850-z · Cited by 290
Advances and Utility of the Human Plasma Proteome
Deutsch, E. W., Omenn, G. S., Sun, Z., Maes, M., Pernemalm, M., Palaniappan, K. K., Letunica, N., Vandenbrouck, Y., Brun, V., Tao, S., et al. (2021) · Journal of Proteome Research · doi:10.1021/acs.jproteome.1c00657 · Cited by 246
Metadata
- Authors
- 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., Cornell, E., Curtis, J. L., Doerschuk, C., Durda, P., Emson, C., Freeman, C. M., Guo, X., Hastie, A. T., Hawkins, G. A., Herrera, J., Johnson, W. C., Labaki, W. W., Liu, Y., Masters, B., Miller, M., Ortega, V. E., Papanicolaou, G., Peters, S., Taylor, K. D., Rich, S. S., Rotter, J. I., Auer, P., Reiner, A. P., Tracy, R. P., Ngo, D., Gerszten, R. E., O'Neal, W. K., Bowler, R. P., & NHLBI Trans‐Omics for Precision Medicine (TOPMed) Consortium
- Affiliations
- University of North Carolina at Chapel Hill (US)
- National Jewish Health (US)
- Wake Forest University Health Sciences (US)
- UCLA Division of Pulmonary and Critical Care David Geffen School of Medicine University of California Los Angeles Los Angeles CA 90095
- Broad Institute (US)
- University of Iowa (US)
- University of Vermont (US)
- University of Michigan (US)
- AstraZeneca (United States) (US)
- The Lundquist Institute (US)
- ProterixBio (United States) (US)
- University of Washington (US)
- National Heart, Lung, and Blood Institute (US)
- University of Virginia (US)
- University of Wisconsin–Milwaukee (US)
- Beth Israel Deaconess Medical Center (US)
- Marsico Lung Institute University of North Carolina at Chapel Hill Chapel Hill NC 27599
- Corresponding author
- Raffield, Laura M.
- ORCID
- Raffield, Laura M.
- Dang, Hong
- Pratte, Katherine A.
- Jacobson, Sean
- Gillenwater, Lucas A.
- Basta, Patricia
- Clish, Clary B.
- Comellas, Alejandro P.
- Curtis, Jeffrey L.
- Doerschuk, Claire
- Durda, Peter
- Emson, Claire
- Freeman, Christine M.
- Guo, Xiuqing
- Hastie, Annette T.
- Johnson, W. Craig
- Labaki, Wassim W.
- Miller, Michael
- Ortega, Victor E.
- Papanicolaou, George
- Peters, Stephen
- Taylor, Kent D.
- Rich, Stephen S.
- Rotter, Jerome I.
- Auer, Paul
- Reiner, Alex P.
- Tracy, Russell P.
- Ngo, Debby
- Gerszten, Robert E.
- Bowler, Russell P.
- Venue
- PROTEOMICS
- Publisher
- Wiley
- Published
- 2020-06
- Volume
- 20
- Issue
- 12
- Pages
- e1900278-e1900278
- ISSN
- 1615-9853 (ISSN-L)
- 1615-9861
- PMC
- PMC7425176
- OpenAlex
- W3022509738
- Cited by
- 184
- References
- 36
- Citations by year
- 2026: 33 · 2025: 31 · 2024: 33 · 2023: 37 · 2022: 29 · 2021: 19 · 2020: 1
- FWCI
- 6.98
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- PROTEOMICS (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Veterans Affairs (Merit Review I01 CX000911)
- National Heart, Lung, and Blood Institute (R01 HL132947, T32 HL007749, T32 HL129982)
- 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 › Pulmonary and Respiratory Medicine › Chronic Obstructive Pulmonary Disease (COPD) Research
- Keywords
- SomaScan, aptamer-based proteomics, relative protein quantification, proteomics data integration, chronic obstructive pulmonary disease, myocardial infarction, cis-pQTL
- MeSH terms
- Smokers, Adult, Aged, Aged, 80 and over, Female, Humans, Immunoassay, Male, Middle Aged, Myocardial Infarction, Cohort Studies, Proteome, Pulmonary Disease, Chronic Obstructive, Proteomics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
