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Gender differences in C-reactive protein concentrations-confirmation with two sensitive methods.

Joseph P McConnell1, Earl L Branum, Karla V Ballman

  • 1Department of Laboratory, Mayo Clinic and Foundation, Rochester, MN 55905, USA. McConnell.Joseph@mayo.edu

Clinical Chemistry and Laboratory Medicine
|March 28, 2002
PubMed
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New automated methods accurately measure C-reactive protein (CRP). Significant gender differences in hsCRP levels were observed, impacting cardiovascular risk stratification.

Area of Science:

  • Clinical Chemistry
  • Biomarkers
  • Cardiovascular Health

Background:

  • C-reactive protein (CRP) is a key biomarker for inflammation.
  • Accurate measurement of high-sensitivity CRP (hsCRP) is crucial for cardiovascular risk assessment.
  • Existing measurement methods may have variability.

Purpose of the Study:

  • To compare two new automated, highly sensitive methods for CRP measurement.
  • To investigate potential gender-based differences in hsCRP levels.
  • To assess the implications of these differences for clinical practice.

Main Methods:

  • Comparison of Kamiya K assay (immunoturbidimetric) and Behring BNII (nephelometric) methods for CRP measurement.
  • Analysis of blood samples from 230 healthcare workers (116 males, 114 females).

Related Experiment Videos

  • Validation using a larger cohort of 1,114 individuals (639 males, 475 females) from US reference services.

    • Main Results:

    • Both automated methods demonstrated high sensitivity and comparable results (regression: y=1.115x-0.0267).
    • Significant differences in hsCRP levels were observed between males and females (p=0.013 and p=0.0018).
    • Gender differences in hsCRP were confirmed in the larger cohort (p<0.0001) and were independent of age.

    Conclusions:

    • The new automated CRP assays are reliable and sensitive.
    • A significant gender disparity exists in hsCRP levels, independent of age.
    • These findings necessitate consideration of gender-specific cut-off values for cardiovascular risk stratification.