New directions in cardiovascular risk assessment: the role of secondary risk stratification markers

A S Wierzbicki1

  • 1Consultant in Metabolic Medicine/Chemical Pathology, St. Thomas' Hospital Campus, Lambeth Palace Road, London SE1 7EH, UK. anthony.wierzbicki@kcl.ac.uk

Insights

Current cardiovascular disease (CVD) risk screening identifies only 70% of at-risk individuals. Research is exploring additional markers like family history and lipoprotein (a) to improve CVD event prediction.

Area of Science:

  • Cardiology
  • Preventive Medicine
  • Biomarker Research

Background:

  • Current cardiovascular disease (CVD) risk screening utilizes multivariate methods and calculators from epidemiological studies.
  • These methods identify approximately 70% of individuals at risk for CVD events, necessitating improved predictive capacity.

Purpose of the Study:

  • To evaluate the potential of incorporating additional risk factors and novel biomarkers into existing CVD risk screening protocols.
  • To enhance the identification of individuals at risk for cardiovascular events.

Main Methods:

  • Review of existing literature on established and emerging CVD risk factors.
  • Assessment of evidence for adding family history, lipoprotein (a), C-reactive protein, N-terminal pro-B-type natriuretic peptide, coronary artery calcium score, carotid intima-media thickness, ankle-brachial index, and high-sensitivity troponins.
  • Consideration of studies focusing on intermediate-risk groups and the general population.

Main Results:

  • Family history of premature CVD is a well-established addition.
  • Lipoprotein (a) and C-reactive protein show promise for specific populations and general use, respectively.
  • N-terminal pro-B-type natriuretic peptide and coronary artery calcium score significantly enhance risk prediction.
  • Data for carotid intima-media thickness and ankle-brachial index are less robust; high-sensitivity troponins show potential but require more data.

Conclusions:

  • Additional biomarkers can improve CVD risk classification beyond conventional methods.
  • Large-scale meta-analyses of pooled patient data are crucial to determine optimal additional markers and their application groups.
  • Further research is needed to validate emerging markers for widespread CVD risk assessment.

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