Biological variation of cardiac markers: analytical and clinical considerations

S M Ross1, C G Fraser

  • 1Ninewells Hospital and Medical School, Dundee, UK.

Insights

Biological variation in cardiac markers like total creatine kinase (TCK) and CK-MB shows high individuality. This suggests personalized monitoring is needed over population-based reference values for accurate interpretation.

Area of Science:

  • Clinical Biochemistry
  • Biomarker Analysis
  • Analytical Chemistry

Background:

  • Cardiac marker interpretation often relies on population-based reference values.
  • Understanding biological variation is crucial for accurate clinical decision-making.
  • Individual variability in biomarkers can impact diagnostic sensitivity.

Purpose of the Study:

  • To estimate analytical and biological variation components for key cardiac markers.
  • To assess the utility of population-based reference values versus individual variability.
  • To establish objective criteria for monitoring cardiac marker changes in individuals.

Main Methods:

  • Quantification of serum total creatine kinase (TCK) activity, CK-MB activity, CK-MB mass, and myoglobin concentration.
  • Analysis of within-subject and between-subject variation over a 5-day period in healthy subjects.
  • Comparison of analytical goals with biological variation to assess methodological needs.

Main Results:

  • Significant within-subject and between-subject variation was observed for most cardiac markers.
  • Methodological improvements are recommended for all markers except CK-MB mass.
  • High individual variability was noted for all cardiac markers, questioning the universal applicability of reference values.
  • Critical differences for serial result changes vary significantly between markers.

Conclusions:

  • Conventional population-based reference values may be insufficient for interpreting individual cardiac marker results.
  • The marked individuality of cardiac markers necessitates personalized monitoring strategies.
  • Objective criteria derived from biological variation data can enhance the monitoring of individuals.

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