title: Monocyte count is a predictor of novel plaque formation: a 7-year follow-up study of 2610 persons without carotid plaque at baseline the Tromsø Study.
abstract: OBJECTIVE
  Activation of monocytes and differentiation into lipid-laden macrophages are fundamental events in generation of atherosclerotic lesions. There exist few data on monocyte activity and the risk for atherosclerosis. In this prospective population-based study, we examined whether monocyte count in blood is a predictor of future plaque formation in persons without pre-existing carotid atherosclerosis.
  
  
  METHODS
  At baseline, we measured monocyte count, white cell count (WCC), fibrinogen, intima-media thickness (IMT), and traditional cardiovascular risk factors in 2610 men and women aged 25 to 82 years who on ultrasound had no plaque in their right carotid artery. After 7 years of follow-up, a new ultrasound screening was performed and the number of novel plaques was grouped as none, 1 plaque, and 2 or more plaques.
  
  
  RESULTS
  In multivariate analysis, monocyte count, age, sex, total cholesterol, current smoking, systolic blood pressure, and IMT were independent predictors of novel plaque formation. No significant association was found between plaque formation and either WCC or fibrinogen. For 1 standard deviation (0.17x10(9)) increase in monocyte count, the risk of being in a higher plaque category increased by 18% (OR, 1.18; 95% CI, 1.08 to 1.29). In the highest monocyte quartile, the risk for having plaque compared with the lowest quartile was 1.85 (OR) (95% confidence interval, 1.41 to 2.43). Repeating the analysis without IMT did not change the monocyte estimate. Excluding subjects with cardiovascular disease and diabetes mellitus from analysis neither changed the monocyte estimate.
  
  
  CONCLUSIONS
  Monocyte count is an independent predictor of future plaque formation in subjects without pre-existing carotid atherosclerosis.
authors.affiliation: Institute of Community Medicine, University of Tromsø, N-9037 Tromsø, Norway. Stein.Johnsen@ism.uit.no
authors.forename: Stein Harald
authors.initials: SH
authors.lastname: Johnsen
authors.forename: Einar
authors.initials: E
authors.lastname: Fosse
authors.forename: Oddmund
authors.initials: O
authors.lastname: Joakimsen
authors.forename: Ellisiv B
authors.initials: EB
authors.lastname: Mathiesen
authors.forename: Eva
authors.initials: E
authors.lastname: Stensland-Bugge
authors.forename: Inger
authors.initials: I
authors.lastname: Njølstad
authors.forename: Egil
authors.initials: E
authors.lastname: Arnesen
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001161:Arteriosclerosis; D001772:Blood Cell Count; D002339:Carotid Arteries / Q000000981:diagnostic imaging / Q000473:pathology*; D016893:Carotid Stenosis / Q000175:diagnosis* / Q000453:epidemiology*; D002454:Cell Differentiation; D002784:Cholesterol / Q000378:metabolism; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D007962:Leukocytes / Q000166:cytology; D008264:Macrophages / Q000378:metabolism; D008297:Male; D008875:Middle Aged; D009000:Monocytes / Q000166:cytology* / Q000378:metabolism; D015999:Multivariate Analysis; D016017:Odds Ratio; D012307:Risk Factors; D012737:Sex Factors; D012907:Smoking; D013997:Time Factors; D014465:Ultrasonics; D014463:Ultrasonography
chemical_list: D002784:Cholesterol