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Am Heart J ; 146(1): 62-8, 2003 Jul.
Article in English | MEDLINE | ID: mdl-12851609

ABSTRACT

BACKGROUND: Sphingolipids are emerging as important signaling molecules that may be produced by cardiac tissue during ischemic stress or as a consequence of inflammation. Because both inflammation and myocardial ischemia are associated with coronary artery disease (CAD), a study was designed to test the ability of serum sphingolipids to predict obstructive CAD. METHODS: The study consisted of 308 consecutive patients undergoing coronary angiography for all indications. The primary data points were the assessment of coronary artery stenosis with angiography and the measurements of serum sphingolipids. RESULTS: In this diverse population, serum sphingosine-1-phosphate (S1P) was a significant predictor of CAD (P <.001). Multivariate analysis with logistic regression demonstrated that serum S1P was more predictive of obstructive CAD (odds ratio = 7.61) than the traditional risk factors (age, sex, family history of CAD, diabetes mellitus, lipid profile, hypertension, etc.). A 3-variable S1PC composite score was derived by combining the power of the S1P marker with the 2 most important risk factors, age and sex. The relationship between the S1PC and CAD scores was continuous and progressive, such that patients with elevated S1PC scores had higher occurrences of obstructive CAD. S1PC was also predictive of disease severity; 53.2% of patients in the fourth S1PC quartile had 2 to 3 vessel CAD, whereas only 5.2% of patients in the first S1PC quartile had 2 to 3 vessel disease (RR = 10.2 for severity). CONCLUSIONS: Serum S1P is a remarkably strong and robust predictor of both the occurrence and severity of coronary stenosis. An S1P-based composite score may be useful as a novel, non-invasive indicator of obstructive CAD.


Subject(s)
Biomarkers/blood , Coronary Artery Disease/blood , Lysophospholipids , Sphingosine/analogs & derivatives , Sphingosine/blood , Adult , Aged , Aged, 80 and over , Analysis of Variance , Female , Humans , Logistic Models , Male , Middle Aged , Risk Factors , Severity of Illness Index
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