Author(s): Parikh NI, Vasan RS
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Abstract Biomarkers in medicine have gained immense scientific and clinical interest in recent years. Biomarkers are potentially useful in the contexts of primary, secondary and tertiary prevention. Some of the characteristics of an ideal biomarker include that they are safe and easy to measure, are associated with acceptable costs (including those of the follow-up tests), and there is scientific evidence to suggest that biomarker use/modification influences disease outcomes. Additionally, variation in biomarker levels with gender and ethnicity should be elucidated, and the biomarker should have 'good performance characteristics' (i.e., sensitivity, specificity, positive- and negative-predictive values and positive- and negative-likelihood ratios). Risk prediction scores can combine information from several different biomarkers in order to estimate an individual's risk of developing an outcome, such as disease or death. Three commonly employed methods to test if a biomarker will add to traditional risk prediction models are model discrimination, model calibration and risk reclassification. 'Multimarker' strategies serve to integrate information from multiple biomarkers into risk prediction but may be limited by the presence of highly correlated biomarkers, economic costs and selection bias of biomarker candidates in a particular study sample. In the future, integration of biomarkers identified using emerging technologies from the 'omics fields (including genomics, proteomics, metabolomics, lipomics, ribomics and pharmacogenomics) may be useful for the 'personalization' of treatment/disease prevention.
This article was published in Biomark Med
and referenced in Journal of Biosensors & Bioelectronics