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Determining Causality and Quantifying Contribution to Risk Assessment

Svati H. Shah, MD, MHS; James A. de Lemos, MD

JAMA. 2009;302(1):92-93.

	Since this article does not have an abstract, we have provided the first 150 words of the full text and any section headings.

Personalized medicine aims to use individual biological signals to improve disease detection, risk stratification, and therapeutic selection. Such a strategy holds great appeal to practitioners, who are increasingly disillusioned with the "one size fits all" approach that characterizes contemporary medicine. Key to achieving these goals is integration of genetics and biomarkers with currently available clinical data. While the model of personalized medicine is appealing, many questions remain about which markers are diagnostic, prognostic, or pathologic.

Studies combining genetics and biomarkers may help establish causality of a biomarker with disease. This may have particular clinical relevance when the biomarker is potentially modifiable, in which case a biomarker-guided targeted treatment strategy may be feasible. To establish this causal link, investigators have traditionally used observational data showing an association between biomarker and outcome. However, it is often unclear which associated variable is cause . . . [Full Text of this Article]

Author Affiliations: Division of Cardiology, Department of Medicine, and Center for Human Genetics, Duke University Medical Center, Durham, North Carolina (Dr Shah); and Department of Medicine, Division of Cardiology, University of Texas Southwestern Medical Center, Dallas (Dr de Lemos).

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