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2009:
Mallat Ziad; Simon Tabassome; Benessiano Joelle; Clément Karine; Taleb Soraya; Wareham Nicholas J; Luben Robert; Khaw Kay-Tee; Tedgui Alain; Boekholdt S Matthijs
Retinol-binding protein 4 and prediction of incident coronary events in healthy men and women.
The Journal of clinical endocrinology and metabolism 2009;
94(
1):.
CONTEXT: Recent studies reported that retinol-binding protein 4 (RBP4) has a causal role in insulin resistance and suggested that its circulating levels may predict cardiovascular disease. However, the latter assumption has not yet been tested. OBJECTIVE: We assessed the value of RBP4 measurement in the prediction of incident coronary artery disease (CAD). DESIGN: We conducted a nested case-control study of incident CAD (n = 1036 cases vs. n = 1889 controls) selected from among 25,336 participants of the EPIC-Norfolk study. SETTING: Healthy men and women, aged between 45 and 79 yr, were recruited from age-sex registers of general practices in Norfolk. PATIENTS AND OTHER PARTICIPANTS: Participants completed a baseline questionnaire survey between 1993 and 1997, attended a clinic visit, and were followed for an average of 6 yr. Cases (n = 1036) were participants who developed CAD during the follow-up. Controls (n = 1889) matched by age, sex, and enrollment time remained free of any CAD during follow-up. MAIN OUTCOMES MEASURE: Risk of incident fatal or nonfatal CAD according to RBP4 quartiles was assessed. RESULTS: RBP4 levels were higher in cases than in controls. RBP4 levels correlated weakly with body mass index, waist-to-hip ratio, systolic and diastolic blood pressure, and total and low-density lipoprotein-cholesterol and were inversely associated with C-reactive protein concentrations. The strongest correlation was found with triglycerides. The risk of incident CAD was associated with increasing quartiles of RBP4 levels (P = 0.03). However, adjustment for cardiovascular risk factors abolished this association. CONCLUSIONS: Measurement of serum RBP4 does not provide added value for predicting CAD risk beyond traditional risk factors.
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