Comparative predictive power of serum vs plasma proteomic signatures in feto-maternal medicine

Camilo Espinosa, Said Mohammed Ali, Waqasuddin Khan, Rasheda Khanam, Jesmin Pervin, Joan T. Price, Sayedur Rahman, Tarik Hasan, Salahuddin Ahmed, Rubhana Raqib, Monjur Rahman, Shaki Aktar, Muhammad I. Nisar, Javairia Khalid, Usha Dhingra, Arup Dutta, Saikat Deb, Jeffrey S.A. Stringer, Ronald J. Wong, Gary M. ShawDavid K. Stevenson, Gary L. Darmstadt, Brice Gaudilliere, Abdullah H. Baqui, Fyezah Jehan, Anisur Rahman, Sunil Sazawal, Bellington Vwalika, Nima Aghaeepour, Martin S. Angst

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

BACKGROUND: Blood proteins are frequently measured in serum or plasma, because they provide a wealth of information. Differences in the ex vivo processing of serum and plasma raise concerns that proteomic health and disease signatures derived from serum or plasma differ in content and quality. However, little is known about their respective power to predict feto-maternal health outcomes. Predictive power is a sentinel characteristic to determine the clinical use of biosignatures. OBJECTIVE: This study aimed to compare the power of serum and plasma proteomic signatures to predict a physiological pregnancy outcome. STUDY DESIGN: Paired serum and plasma samples from 73 women were obtained from biorepositories of a multinational prospective cohort study on pregnancy outcomes. Gestational age at the time of sampling was the predicted outcome, because the proteomic signatures have been validated for such a prediction. Multivariate and cross-validated models were independently derived for serum and plasma proteins. RESULTS: A total of 1116 proteins were measured in 88 paired samples from 73 women with a highly multiplexed platform using proximity extension technology (Olink Proteomics Inc, Watertown, MA). The plasma proteomic signature showed a higher predictive power (R=0.64; confidence interval, 0.42–0.79; P=3.5×10-6) than the serum signature (R=0.45; confidence interval, 0.18–0.66; P=2.2×10-3). The serum signature was validated in plasma with a similar predictive power (R=0.58; confidence interval, 0.34–0.75; P=4.8×10-5), whereas the plasma signature was validated in serum with reduced predictive power (R=0.53; confidence interval, 0.27–0.72; P=2.6×10-4). Signature proteins largely overlapped in the serum and plasma, but the strength of association with gestational age was weaker for serum proteins. CONCLUSION: Findings suggest that serum proteomics are less informative than plasma proteomics. They are compatible with the view that the partial ex-vivo degradation and modification of serum proteins during sample processing are an underlying reason. The rationale for collecting and analyzing serum and plasma samples should be carefully considered when deriving proteomic biosignatures to ascertain that specimens of the highest scientific and clinical yield are processed. Findings suggest that plasma is the preferred matrix.

Original languageEnglish
Article number100244
JournalAJOG Global Reports
Volume3
Issue number3
DOIs
Publication statusPublished - Aug 2023

Keywords

  • biobanking
  • biomarker
  • biorepository
  • cohort study
  • gestational age
  • maternal health
  • multivariate model
  • plasma
  • prediction
  • pregnancy
  • proteins
  • proteomics
  • serum

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