Lipoprotein biomarkers and risk of cardiovascular disease
Sandhu, P. K., Musaad, S. M. A., Remaley, A. T., Buehler, S. S., Strider, S., Derzon, J. H., ... Christenson, R. H. (2016). Lipoprotein biomarkers and risk of cardiovascular disease: A laboratory medicine best practices (LMBP) systematic review. The Journal of Applied Laboratory Medicine: An AACC Publication, 1(2), 214-229.
BACKGROUND: Controversy exists about the incremental utility of nontraditional lipid biomarkers [e.g., apolipoprotein (apo) B, apo A-I, and non-HDL-C] in improving cardiovascular disease (CVD) risk prediction when added to a conventional model of traditional risk factors (e.g., total cholesterol, LDL cholesterol, HDL cholesterol, sex, age, smoking status, and blood pressure). Here we present a systematic review that was conducted to assess the use of nontraditional lipid biomarkers including apo B, apo A-I, apo B/A-I ratio, and non-HDL-C in improving CVD risk prediction after controlling for the traditional risk factors in populations at risk for cardiovascular events.
CONTENT: This systematic review used the Laboratory Medicine Best Practices (LMBP™) A-6 methods. A total of 9 relevant studies published before and including July 2015 comprised the evidence base for this review. Results from this systematic review indicated that after the adjustment for standard nonlipid and lipid CVD risk factors, nontraditional apolipoprotein biomarkers apo B (overall effect = relative risk: 1.31; 95% CI, 1.22-1.40; 4 studies) and apo B/apo A-I ratio (overall effect = relative risk: 1.31; 95% CI, 1.11-1.38; 7 studies) resulted in significant improvement in long-term CVD risk assessment.
SUMMARY: Available evidence showed that nontraditional lipid biomarkers apo B and apo B/apo I ratio can improve the risk prediction for cardiovascular events after controlling for the traditional risk factors for the populations at risk. However, because of insufficient evidence, no conclusions could be made for the effectiveness of apo A-I and non-HDL-C lipid markers to predict the CVD events, indicating a need for more research in this field.