ArticleClinical chemistry2023
Comparison of Proteomic Measurements Across Platforms in the Atherosclerosis Risk in Communities (ARIC) Study.
Article in Clinical chemistry, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers, 1 of them a synthesis that pooled it.
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Who cites it
56 citing papers in PubMed, 1 synthesis or guideline pooled it, 68 citations in OpenAlex.
- Plasma proteins and onset of type 2 diabetes and diabetic complications: Proteome-wide Mendelian randomization and colocalization analyses.Cell reports. Medicine · 2023Pooled it
- Plasma proteome of the EAT-Lancet diet and long-term risks of cardiovascular disease and mortality.Food & function · 2026Article
- Review
- Integrative analysis prioritizes proteins associated with renal cell carcinoma and its risk factors.JNCI cancer spectrum · 2026Article
- Proteomic Signatures of 3-Year Progression From Impaired Fasting Glucose to Diabetes: The Atherosclerosis Risk in Communities (ARIC) Study.Diabetes care · 2026Article
- Development of novel plasma proteomic biomarkers for cancer-associated thrombosis in an advanced cancer cohort.Journal of thrombosis and haemostasis : JTH · 2026Article
- Network Pharmacology Combined With Metabonomics and Transcriptomics Reveals the Mechanism ofFood science & nutrition · 2026Article
- Plasma GDF15 affects long-term dementia risk and alters neuroimmune signaling.Science advances · 2026Article
- Blood-based obesity biomarkers and their relevance for disease risk.Nature reviews. Endocrinology · 2026Review
- The Proteomic Profile of Adults With a Fontan Circulation.JACC. Advances · 2026Article
- Machine learning cross-platform proteomic imputation enables protein quality scoring and replication of epidemiological associations.bioRxiv : the preprint server for biology · 2026Article
- Leveraging Multiomic Signatures to Predict Body Composition.Advances in nutrition (Bethesda, Md.) · 2026Review
- Proteomic pathways mediating low socioeconomic status and cardiovascular events in older adults in CHS and ARIC.Atherosclerosis · 2026Article
- Senescence protein signatures predict dementia risk with causal implication for TBCA: a two-cohort study.Research square · 2026Article
- Blood proteomics: insights from public data.Genome biology · 2026Review
- Identification of distinct and shared biomarker panels in different manifestations of cerebral small-vessel disease through proteomic profiling.Nature aging · 2026Article
- Proteomic Signatures of Cardiac Dysfunction Among People With Diabetes: The Atherosclerosis Risk in Communities Study.Circulation. Heart failure · 2026Article
- Plasma Proteomic Signatures for Alzheimer's Disease: Comparable Accuracy to ATN Biomarkers and Cross-Platform Validation.Annals of clinical and translational neurology · 2026Article
- Cross-ancestry comparison of aptamer and antibody protein measures.Nature communications · 2026Article
- Olink Proteomics in Heart Failure: A Comprehensive Review of Applications From Biomarker Discovery to Pathophysiological Insights and Clinical Implementation.Reviews in cardiovascular medicine · 2026Review
Corrections and comments
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Authors and funding
13 authors at 6 institutions in 2 countries.
Funding
Abstract
backgroundThe 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.
methodsWe 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.
resultsThere 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.
conclusionsThere 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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