The Effect of Preanalytical Conditions on Blood-Based Inflammation Biomarkers

Blood-based inflammation biomarkers have potential for diagnostic, prognostic, and predictive testing, but preanalytical processing conditions can affect biomarker levels. We investigated how needle-to-freezer time, centrifugation timing, and tube types influence inflammation biomarkers. Twenty subjects donated 21 different blood samples in collection tubes, including plasma and serum types. Ninety-two biomarkers from each sample were analyzed using the Olink Target 96 Inflammation panel. We compared biomarker concentrations across different preanalytical variables to a reference standard tube. Intraclass, Pearson, and Spearman's correlation coefficients were calculated. We also assessed the impact of these conditions on age-related associations with biomarkers. Across the preprocessing protocol/blood matrix combinations, 38%-83%, 50%-87%, and 47%-79% of proteins showed good to excellent correlations in intraclass, Pearson, and Spearman analyses, respectively. Eighteen proteins differed by >0.5 NPX units between test and reference protocols. Among 30 comparisons of age-related biomarker associations showing <i>p</i> ≤ 0.05 at baseline, 12 (40%) maintained a <i>p</i> ≤ 0.05 across all needle-to-freezer times. Many proteins in the Olink Target 96 Inflammation panel exhibited robust stability across various preanalytical conditions, indicating that blood-based inflammation biomarkers are suitable for testing across different blood specimen types. Further studies are needed to evaluate the impact of long-term storage.

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Publication Details

Journal
Journal of Proteome Research
Published
2025-06-18
DOI
https://doi.org/10.1021/acs.jproteome.5c00225
Citations
1
Primary Topic
Inflammatory Biomarkers in Disease Prognosis
Type
article
Field-Weighted Citation Impact
1.05

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article

The Effect of Preanalytical Conditions on Blood-Based Inflammation Biomarkers

Norma Diaz-Mayoral, Yukiko Yano, Christian C. Abnet, Paul S. Albert et al.
1 citations
Journal of Proteome Research
Inflammatory Biomarkers in Disease Prognosis
1.05
article

The Effect of Preanalytical Conditions on Blood-Based Inflammation Biomarkers

Norma Diaz-Mayoral, Yukiko Yano, Christian C. Abnet, Paul S. Albert, Stephanie J. Weinstein, Katrina Wyatt, Alaina H. Shreves, Nicolas Wentzensen, Michelle Brotzman, Amanda Black, Thomas J. O'Donnell, Nicole Gerlanc, Mia M. Gaudet
article en
1 citations

Abstract

Blood-based inflammation biomarkers have potential for diagnostic, prognostic, and predictive testing, but preanalytical processing conditions can affect biomarker levels. We investigated how needle-to-freezer time, centrifugation timing, and tube types influence inflammation biomarkers. Twenty subjects donated 21 different blood samples in collection tubes, including plasma and serum types. Ninety-two biomarkers from each sample were analyzed using the Olink Target 96 Inflammation panel. We compared biomarker concentrations across different preanalytical variables to a reference standard tube. Intraclass, Pearson, and Spearman's correlation coefficients were calculated. We also assessed the impact of these conditions on age-related associations with biomarkers. Across the preprocessing protocol/blood matrix combinations, 38%-83%, 50%-87%, and 47%-79% of proteins showed good to excellent correlations in intraclass, Pearson, and Spearman analyses, respectively. Eighteen proteins differed by >0.5 NPX units between test and reference protocols. Among 30 comparisons of age-related biomarker associations showing <i>p</i> ≤ 0.05 at baseline, 12 (40%) maintained a <i>p</i> ≤ 0.05 across all needle-to-freezer times. Many proteins in the Olink Target 96 Inflammation panel exhibited robust stability across various preanalytical conditions, indicating that blood-based inflammation biomarkers are suitable for testing across different blood specimen types. Further studies are needed to evaluate the impact of long-term storage.

Journal of Proteome ResearchVol. 24(7)
National Institutes of Health (US), University of Oxford (GB), Nuffield Health (GB), Bioprocess Control (Sweden) (SE), Frederick National Laboratory for Cancer Research (US), Cancer Genetics (United States) (US), Division of Cancer Epidemiology and Genetics
National Cancer Institute
Good health and well-being
Openalex Percentile: Top 21%
Inflammatory Biomarkers in Disease Prognosis
1.05
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