Biomedical Engineering Reference
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54% qualified for at least one of these medications while 18% qualified for both.
Using this 18% cutoff, the 18% of the subjects with the highest calculated heart
disease risk were also identified using the developed risk model. Applying both
drugs to the high-risk group (one third the size of the guidelines group) achieved
the same reduction in population risk (about one fourth) as applying the drugs to
the guideline groups and required only half as many prescriptions. Intermediate
results were found when an intervention group was identified by a combination of
both high risk and high levels of risk factors. In this simulation, identifying patients
by heart disease risk level resulted in substantially fewer people being treated with
fewer drugs and achieving a similar reduction in disease risk.
The benefit of a biomarker-based risk model is that the newest risk factor infor-
mation can be incorporated into the model and a more accurate assessment of risk
made. The cost of the biomarkers is an added cost not encountered in the guide-
lines-based approach. However, this increase is more than compensated for by the
44% reduction in cardiovascular disease events in the high-risk group.
A study was conducted to determine the association of physical activity and
body mass index (BMI) with novel and traditional cardiovascular biomarkers in
women (
Mora et al. 2006b
). Biomarkers used in the study included hsCRP, fibrino-
gen, soluble ICAM-1, homocysteine, LDL and HDL cholesterol, total cholesterol,
apolipoprotein A-1 and B100, lipoprotein-a, and creatinine. Lower levels of physi-
cal activity and higher levels of BMI were independently associated with adverse
levels of nearly all lipid and inflammatory biomarkers. High BMI showed stronger
associations with these biomarkers than physical inactivity. However, within BMI
categories, physical activity was generally associated with more favorable cardio-
vascular biomarker levels than inactivity.
Molecular Signature Analysis in Management
of Cardiovascular Diseases
In cardiovascular disorders, microarray studies have largely focused on gene discovery,
identifying differentially expressed genes characteristic of diverse disease states,
through which novel genetic pathways and potential therapeutic targets may be
elucidated. However, gene expression profiling may also be used to identify a pattern
of genes (a molecular signature) that serves as a biomarker for clinically relevant
parameters (Kittleson and Hare
2005
). Molecular signature analysis (MSA) accu-
rately predicts the etiologic basis of heart failure and cardiac transplant rejection.
These early studies provide valuable proof of concept for future work using MSA.
The ultimate potential application of transcriptome-based MSA is individualization
of the management of patients with structural heart disease, arrhythmias, and heart
failure. A patient with a newly diagnosed cardiac disease could, through molecular
signature analysis, be offered an accurate assessment of prognosis and how indi-
vidualized medical therapy could affect the outcome.
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