Lipoprotein-associated phospholipase A2:: a new biomarker for cardiovascular risk assessment and potential therapeutic target

Lipoprotein-associated phospholipase (Lp-PL)A(2) is a recently described and potentially useful plasma biomarker associated with cardiovascular disease. The enzyme, originally named platelet-activating factor acetylhydrolase (PAF-AH), has two prominent biological activities. First, it inactivates the prominent proinflammatory mediator PAF-AH. Second, Lp-PLA(2) hydrolyzes oxidatively modified polyunsaturated fatty acids producing lysophosphatidylcholine (LysoPC) and oxidized nonesterified fatty acids (OxNEFA). OxNEFA have potent monocyte chemotactic activity and LysoPC upregulates inflammatory mediators, including cytokines, adhesion molecules and the chemotactic mediator MCP-1. Whereas the first activity may be considered antiatherogenic, the prevailing consensus is that Lp-PLA2 is positively associated with coronary disease. Initial evidence for this came largely from the West of Scotland Coronary Prevention Study Group (WOSCOPS) in which Lp-PLA(2) was compared among 580 cases and 1160 age-matched controls. In addition, the quantitative contribution of Lp-PLA(2) to risk assessment was assessed in a substudy of the Atherosclerosis Risk in Communities (ARIC) study. Although positively correlated with disease, the addition of Lp-PLA(2) did not appreciably enhance risk prediction beyond the model employing traditional risk factors. Thus, population screening for subclinical disease using Lp-PLA(2) does not appear to be warranted. Presently, the most useful application of Lp-PLA(2) testing is to adjust individual risk assessment for those patients found to be at borderline risk using traditional models. In this regard, the marker appears to be particularly useful for gauging risk among patients with metabolic syndrome or diabetes. There is observational evidence that Lp-PLA(2) may be a useful guide for therapeutic efficacy, but prospective evaluation will be required. Considering the large number of biomarkers currently under evaluation, it is probable that useful additions to existing risk models may be found in combinatorial models.