Key result
Computational lipoprotein modeling improves dyslipidemia diagnosis compared with classical plasma markers.
At present, several measurement technologies are available for generating highly detailed concentration–size profiles of lipoproteins, offering increased diagnostic potential. Computational models are useful in aiding the interpretation of these complex datasets and making the data more accessible for clinical diagnosis. They do so by calculating hitherto inaccessible biological parameters that underlie the profile. Their application results in new markers that have been demonstrated to improve diagnosis of dyslipidemias compared with the classical plasmamarkers, LDL-C, HDL-C and total triglycerides. Whether the new diagnostic markers contribute to cardiovascular and diabetes risk prediction is currently under investigation.
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Graaf et al. (2011) conducted a review in Dyslipidemias. Computational models for analyzing lipoprotein profiles vs. Classical plasma markers (LDL-C, HDL-C, and total triglycerides) was evaluated. Computational models calculating underlying biological parameters from lipoprotein profiles improve the diagnosis of dyslipidemias compared with classical plasma markers.
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