Familial Dysbetalipoproteinemia (FD)
We study cardiovascular disease risk in human populations using non-traditional statistical approaches especially graphical exploratory data analysis techniques for multivariable assessment of novel blood and genetic biomarkers of risk. Our approach is based upon the notion that current limitations on the implementation of personalized medicine can be broached by identification of patient subgroups with common underlying pathophysiologic features. Such subgroups, we believe, would manifest particular combinations of biomarkers of risk that generally will be different dependent on different underlying pathophysiologic mechanisms. As such, we have developed a 3-dimensional graphical exploratory data analysis tool called "outcome event mapping" (OEM) that allows identification of high-risk patient subgroups.
As an example, we demonstrated for recurrent coronary events a completely unexpected high-risk subgroup of individuals characterized by high levels of HDL cholesterol ("the good cholesterol") and high levels of C-reactive protein (CRP), a marker of systemic inflammation. The high CRP underscores the important role of inflammation in the dysfunctional transformation of HDL from a form that is normally protective to a form that is pathologic and generates cardiovascular disease risk.
In addition, we are continuing our efforts to utilize advanced exploratory data analysis approaches for biomarker assessment. Most recently, collaborative efforts are underway to develop and utilize the technique of Bayesian network analysis (BNA) to approach large databases of biomarkers to provide novel mechanistic insights on disease-associated pathways.