Pharmacogenomics, Pharmacoproteomics, and Pharmacometabolomics and Personalized Medicine: An Overview

Raghavachari, Nalini; Guček, Marjan

doi:10.1007/978-81-322-1184-6_1

Cited by 1 publication

(1 citation statement)

References 82 publications

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“…By employing a systems biology framework, multi-omic datasests provide the ability to understand the underlying disease network-associated biological processes [16]. The systems biology approach aims to characterize a large and diverse set of molecules within an illness or individual by examining entire biological systems, not just individual components, allowing the assessment of interactions among levels of cellular pathology, ranging from DNA to circulating metabolites [17][18][19]. This approach has the potential to provide a more comprehensive characterization of illnesses, to track underlying biological dysregulation before clinical symptoms develop or worsen, to lead to the identification of improved diagnostic markers, and to allow for the discovery of novel targets for treatment [20].…”

Section: Introductionmentioning

confidence: 99%

Multi-omic biomarker identification and validation for diagnosing warzone-related post-traumatic stress disorder

et al. 2019

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Post-traumatic stress disorder (PTSD) impacts many veterans and active duty soldiers, but diagnosis can be problematic due to biases in self-disclosure of symptoms, stigma within military populations, and limitations identifying those at risk. Prior studies suggest that PTSD may be a systemic illness, affecting not just the brain, but the entire body. Therefore, disease signals likely span multiple biological domains, including genes, proteins, cells, tissues, and organism-level physiological changes. Identification of these signals could aid in diagnostics, treatment decision-making, and risk evaluation. In the search for PTSD diagnostic biomarkers, we ascertained over one million molecular, cellular, physiological, and clinical features from three cohorts of male veterans. In a discovery cohort of 83 warzone-related PTSD cases and 82 warzone-exposed controls, we identified a set of 343 candidate biomarkers. These candidate biomarkers were selected from an integrated approach using (1) data-driven methods, including Support Vector Machine with Recursive Feature Elimination and other standard or published methodologies, and (2) hypothesis-driven approaches, using previous genetic studies for polygenic risk, or other PTSD-related literature. After reassessment of ~30% of these participants, we refined this set of markers from 343 to 28, based on their performance and ability to track changes in phenotype over time. The final diagnostic panel of 28 features was validated in an independent cohort (26 cases, 26 controls) with good performance (AUC = 0.80, 81% accuracy, 85% sensitivity, and 77% specificity). The identification and validation of this diverse diagnostic panel represents a powerful and novel approach to improve accuracy and reduce bias in diagnosing combat-related PTSD.

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