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Personalized Cardiovascular Disease Prediction and Treatment-A Review of Existing Strategies and Novel Systems Medicine Tools
KTH, Centres, Science for Life Laboratory, SciLifeLab. KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. Chalmers University, Sweden.
2016 (English)In: Frontiers in Physiology, ISSN 1664-042X, E-ISSN 1664-042X, Vol. 7, 2Article, review/survey (Refereed) PublishedText
Abstract [en]

Cardiovascular disease (CVD) continues to constitute the leading cause of death globally. CVD risk stratification is an essential tool to sort through heterogeneous populations and identify individuals at risk of developing CVD. However, applications of current risk scores have recently been shown to result in considerable misclassification of high-risk subjects. In addition, despite long standing beneficial effects in secondary prevention, current CVD medications have in a primary prevention setting shown modest benefit in terms of increasing life expectancy. A systems biology approach to CVD risk stratification may be employed for improving risk-estimating algorithms through addition of high-throughput derived omics biomarkers. In addition, modeling of personalized benefit-of-treatment may help in guiding choice of intervention. In the area of medicine, realizing that CVD involves perturbations of large complex biological networks, future directions in drug development may involve moving away from a reductionist approach toward a system level approach. Here, we review current CVD risk scores and explore how novel algorithms could help to improve the identification of risk and maximize personalized treatment benefit. We also discuss possible future directions in the development of effective treatment strategies for CVD through the use of genome-scale metabolic models (GEMs) as well as other biological network-based approaches.

Place, publisher, year, edition, pages
Frontiers Media , 2016. Vol. 7, 2
Keyword [en]
patient stratification, risk estimation, metabolism, systems medicine, systems biology, network medicine
National Category
Physiology
Identifiers
URN: urn:nbn:se:kth:diva-182148DOI: 10.3389/fphys.2016.00002ISI: 000368587200001ScopusID: 2-s2.0-84962649780OAI: oai:DiVA.org:kth-182148DiVA: diva2:904968
Funder
Science for Life Laboratory - a national resource center for high-throughput molecular bioscienceNovo NordiskEU, FP7, Seventh Framework Programme, HEALTH-F4-2012-305312/METACARDISKnut and Alice Wallenberg Foundation
Note

QC 20160220

Available from: 2016-02-20 Created: 2016-02-16 Last updated: 2016-02-20Bibliographically approved

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