01.03.2012 | Original Article | Ausgabe 2/2012
Development of a model for prediction of coronary atherosclerotic regression: evaluation of high-density lipoprotein cholesterol level and peripheral blood monocyte count
Heart and Vessels
- Shigemasa Tani, Michiaki Matsumoto, Takeo Anazawa, Hirofumi Kawamata, Shingo Furuya, Hiroshi Takahashi, Kiyoshi Iida, Takehiko Washio, Narimichi Kumabe, Masashi Kobori, Ken Nagao, Atsushi Hirayama
Monocytes and high-density lipoprotein cholesterol (HDL-C) play important roles in the process of coronary atherosclerosis. We hypothesized that a reasonable predictive model of coronary plaque regression might be constructed using the change in the peripheral monocyte count and the serum HDL-C level. The plaque volume, as assessed by volumetric intravascular ultrasound, was measured at the baseline and after 6 months of pravastatin therapy in 114 patients with coronary artery disease. After 6 months of pravastatin therapy, a significant decrease of the plaque volume by 9.9% (p < 0.0001, vs. baseline) was observed; furthermore, a corresponding increase of the serum HDL-C level and decrease of the peripheral blood monocyte count were also seen (12.5%, p < 0.01 and −7.3%, p < 0.0001). In a multivariate regression analysis using the serum lipids and traditional risk factors as the covariates, the increase in the serum HDL-C (β −0.56, p < 0.0001) and the decrease in monocyte count (β 0.23, p = 0.03) were identified as independent predictors of the plaque regression. A model for the prediction of plaque regression according to whether the achieved the change in (Δ) monocyte count and ΔHDL-C were above or below the median values was prepared. Among the four groups, the group with ΔHDL-C ≥8.8% and Δmonocyte count ≤−8.6% showed the largest plaque regression (−20.4%), and the group with ΔHDL-C <8.8% and Δmonocyte count >−8.6% showed the increase of the plaque volume (2.6%). In view of the inflammatory nature of atherosclerosis, the model constructed using the two predictors may be a useful model for the prediction of plaque regression.