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NIDA Proteomics Center
>
Investigators
> Rajita Sinha
Stress-Related Biomarkers of
Relapse Vulnerability in Addictive Disorders.
Rajita Sinha, Department of Psychiatry
Yale University Stress
plays a central role in both the development of addiction and the risk for
subsequent relapse to chronic drug taking after addicted individuals have
achieved an initial period of sobriety. The focus of our translational
neuroscience research program is to understand stress-related mechanisms that
influence addictive processes. These research studies involve examining changes
in brain and peripheral stress circuits associated with acute and protracted
drug and alcohol withdrawal states, modeling drug craving in the laboratory and
examining stress-related factors that influence relapse risk in alcohol, cocaine
and marijuana abusing subjects. Thus far, our studies have therefore focused
primarily on behavioral and neural systems level and molecular (genetic)
analysis of stress effects on addictive behavior in humans. The proposed new
studies will focus on identifying and validating stress-related biomarkers of
relapse vulnerability in addictive disorders. Specific aims for the proposed
study include:
1. Identifying candidate
biomarkers for stress-related vulnerability to relapse to cocaine and alcohol
abuse.
2. Assessing the relationship
between candidate biomarkers and psychological, physiological, and biochemical
indices of stress during exposure to stress and drug cues in addicted samples
(cocaine, alcohol).
3. Validating candidate biomakers
of sress-related vulnerability in a new cohort of addicted subjects ((cocaine,
alcohol; n=30/group) as compared to healthy non-addicted control volunteers
(n=30).
4. Determining whether specific
treatment interventions targeting stress-induced vulnerability to relapse affect
expression of candidate biomarkers, and if so, whether this correlates with an
improvement in clinical outcome.
In these studies we will use
Liquid chromatography/Fourier transform ion cyclotron resonance mass
spectrometry (LC-FTICR MS) to assess protein expression in addicted and healthy
control subjects. Newly developed statistical algorithms including Random
Forests classification and regression will be used to identify those alterations
in protein expression that show the greatest association with clinical outcome
and psychological, physiological, and biochemical indices of stress. |
