Circulating progenitor cell count for cardiovascular risk stratification: a pooled analysis

Gian Paolo Fadini1, Shoichi Maruyama, Takenori Ozaki

  • 1Department of Clinical and Experimental Medicine, University of Padova Medical School, Padova, Italy. gianpaolofadini@hotmail.com

Plos One
|July 17, 2010
PubMed

Insights

Reduced circulating progenitor cells (CPC) identify high-risk cardiovascular patients. Combining CPC count with high-sensitivity C-reactive protein (hsCRP) improves cardiovascular risk prediction, especially for major adverse cardiovascular events (MACE).

Area of Science:

  • Cardiovascular Research
  • Vascular Biology
  • Biomarker Discovery

Background:

  • Circulating progenitor cells (CPC) are vital for blood vessel health.
  • Low CPC counts are linked to increased cardiovascular disease risk.
  • The role of CPC in cardiovascular risk stratification, especially with inflammation, requires further investigation.

Purpose of the Study:

  • To evaluate if CPC count enhances cardiovascular risk stratification.
  • To determine if low-grade inflammation, measured by hsCRP, modulates the predictive value of CPC.
  • To assess the combined predictive power of CPC and hsCRP for major adverse cardiovascular events (MACE).

Main Methods:

  • Pooled data from 4 longitudinal studies (1,057 patients).
  • Assessed CPC counts, hsCRP levels, and cardiovascular risk factors.
  • Utilized Cox proportional hazard analyses to evaluate MACE prediction models.

Main Results:

  • CPC count independently predicted MACE, even after adjusting for hsCRP.
  • Models including CPC showed improved discrimination (IDI) and reclassification (NRI) of MACE risk.
  • A significant interaction was observed: low CPC combined with high hsCRP markedly increased MACE risk.

Conclusions:

  • Reduced CPC count identifies high-risk individuals for short-term MACE.
  • The combination of low CPC and elevated hsCRP offers superior cardiovascular risk prediction.
  • CPC count serves as a valuable biomarker for cardiovascular risk stratification in high-risk populations.
Abstract