Harald Funke
Institut für Klinische Chemie und Laboratoriumsmedizin, Westfälische Wilhelms-Universität Münster, Albert-Schweitzer-Str. 33, D-48145 Münster, Germany (Tel: +49 251 83 56209, Fax: +49 251 83 56208, E-mail: gene@uni-muenster.de)
Atherosclerotic vascular diseases are important factors for morbidity and death in
developed countries. It is likely that with increasing wealth, the incidence of
atherosclerotic disease will also increase in the emerging nations.
Current programs for risk reduction are primarily targeted towards changes in lifestyle
factors. Family and twin studies have shown, however, that a major portion of
cardiovascular risk can be assigned to genetic factors. In rare cases, as in homozygous
familial hypercholesterolemia, a single gene defect alone causes early onset coronary
artery disease (CAD). More often, myocardial infarction, stroke, diabetic vasculopathy and
other forms of atherosclerosis arise from the interaction between several genes of small
effect (polygenes) and adverse environmental factors. In myocardial infarction occurring
at young age gene defects may be the leading causative factors. Despite such a prominent
role of genetics in the pathophysiology of atherosclerosis clinical risk assessment and
therapeutic decision making are still based on classical risk factors. This is largely due
to current limitations in the detection of genetic variation in large population samples.
A major obstacle in the assessment of the role
SNPs have in the prediction of CAD has been the difficulty to cluster the phenotypic
effects of SNPs from a large number of genes in an unbiased fashion. Gene expression data
provide a summary the effects the interaction of various genes and their variants have
with each other and also with external factors, such as individual lifestyle habits. The
analysis of such a synopsis with gene chip technology allows the use of much simpler
mathematical models for data analysis. Expression profiling is therefore seen as a
potentially very helpful tool in the early detection and classification of CAD and thus
offers the potential to be an invaluable aid for therapeutic decision making.
We have characterized expression profiles from
carriers of monogenic disorders which are tightly associated with premature CAD formation
and compared them to profiles from controls. This identified potential downstream
mediators of the vascular processes leading to atherosclerosis.