title: Development of a model for prediction of coronary atherosclerotic regression: evaluation of high-density lipoprotein cholesterol level and peripheral blood monocyte count.
abstract: 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.
authors.affiliation: Department of Cardiology, Nihon University Surugadai Hospital, 1-8-13 Kanda-Surugadai, Chiyoda-ku, Tokyo 101-8309, Japan. tanishigem@yahoo.co.jp
authors.forename: Shigemasa
authors.initials: S
authors.lastname: Tani
authors.forename: Michiaki
authors.initials: M
authors.lastname: Matsumoto
authors.forename: Takeo
authors.initials: T
authors.lastname: Anazawa
authors.forename: Hirofumi
authors.initials: H
authors.lastname: Kawamata
authors.forename: Shingo
authors.initials: S
authors.lastname: Furuya
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Takahashi
authors.forename: Kiyoshi
authors.initials: K
authors.lastname: Iida
authors.forename: Takehiko
authors.initials: T
authors.lastname: Washio
authors.forename: Narimichi
authors.initials: N
authors.lastname: Kumabe
authors.forename: Masashi
authors.initials: M
authors.lastname: Kobori
authors.forename: Ken
authors.initials: K
authors.lastname: Nagao
authors.forename: Atsushi
authors.initials: A
authors.lastname: Hirayama
mesh_terms: D000368:Aged; D015906:Angioplasty, Balloon, Coronary; D015415:Biomarkers / Q000097:blood; D008076:Cholesterol, HDL / Q000097:blood*; D003324:Coronary Artery Disease / Q000097:blood* / Q000175:diagnosis / Q000000981:diagnostic imaging / Q000628:therapy*; D005260:Female; D006801:Humans; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors / Q000627:therapeutic use*; D007564:Japan; D007958:Leukocyte Count; D008297:Male; D008875:Middle Aged; D009000:Monocytes; D015999:Multivariate Analysis; D017035:Pravastatin / Q000627:therapeutic use*; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012044:Regression Analysis; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors; D016896:Treatment Outcome; D018084:Ultrasonography, Interventional
chemical_list: D015415:Biomarkers; D008076:Cholesterol, HDL; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors; D017035:Pravastatin