Pharmacogenomics of alcohol response and addiction.
ABSTRACT Alcoholism is a complex psychiatric disorder that has high heritability (50-60%) and is relatively common; in the US the lifetime prevalence of alcohol dependence is 20% in men and 8% in women. Current psychosocial and pharmacological therapies have relatively modest effects. Treatment is complicated by the fact that alcoholism is often co-morbid with other disorders, including anxiety, depression, and antisocial personality disorder. Approximately 80% of alcoholics smoke cigarettes and there is considerable genetic overlap between nicotine and alcohol addiction. Convergent evidence supports the classification of alcoholics into two broad categories: type 1 - later onset with feelings of anxiety, guilt, and high harm avoidance; and type 2 - early age of onset, usually men, impulsive, antisocial, and with low levels of brain serotonin. The pharmacogenomics of alcohol response is well established; genetic variants for the principal enzymes of alcohol metabolism influence drinking behavior and protect against alcoholism. Vulnerability to alcoholism is likely to be due to multiple interacting genetic loci of small to modest effects. First-line therapeutic targets for alcoholism are neurotransmitter pathway genes implicated in alcohol use. Of particular interest are the 'reward pathway' (serotonin, dopamine, GABA, glutamate, and beta endorphin) and the behavioral stress response system (corticotrophin-releasing factor and neuropeptide Y). Common functional polymorphisms in these genes are likely to be predictive (although each with small effect) of individualized pharmacological responses. Genetic studies, including case-control association studies and genome wide linkage studies, have identified associations between alcoholism and common functional polymorphisms in several candidate genes. Meanwhile, the current pharmacological therapies for alcoholism are effective in some alcoholics but not all. Some progress has been made in elucidating the pharmacogenomic responses to these drugs, particularly in the context of the type 1/type 2 classification system for alcoholics.
SourceAvailable from: Md Mahbubur Rahman[Show abstract] [Hide abstract]
ABSTRACT: Stress can lead to headaches and fatigue, precipitate addictive be-haviors (e.g., smoking, alcohol and drug use), and lead to cardio-vascular diseases and cancer. Continuous assessment of stress from sensors can be used for timely delivery of a variety of interven-tions to reduce or avoid stress. We investigate the feasibility of continuous stress measurement via two field studies using wireless physiological sensors — a four-week study with illicit drug users (n = 40), and a one-week study with daily smokers and social drinkers (n = 30). We find that 11+ hours/day of usable data can be obtained in a 4-week study. Significant learning effect is ob-served after the first week and data yield is seen to be increasing over time even in the fourth week. We propose a framework to an-alyze sensor data yield and find that losses in wireless channel is negligible; the main hurdle in further improving data yield is the attachment constraint. We show the feasibility of measuring stress minutes preceding events of interest and observe the sensor-derived stress to be rising prior to self-reported stress and smoking events.
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ABSTRACT: Excessive, chronic, and repeated exposure to psychological stress can lead to significant health problems. However, new methods for better coping with stress that could significantly improve health and quality of life, cannot be developed and evaluated without scientifically valid datasets describing the experience of stress in everyday life. In prior research, sci-entifically valid datasets have been difficult to capture from natural environments. Sensors, which continuously capture objective information about physiology and behavior, are prone to noise and failure. In addition, aspects of every-day life (e.g., conversation, exercise, etc.) interfere with the physiological response to stress, making it difficult to tease out the effect of stress from changes in physiology. To overcome the challenges of assessing both exposures and re-sponses to stressful events, new wireless sensing systems are needed to capture scientifically valid datasets describing the experience of stress in natural environments. In this paper, we present the design and evaluation of mStress, a smartphone (Android G1) based system that con-tinuously collects and processes multi-modal measurements from six body-worn wireless sensors to infer in real-time whether the subject wearing the sensors is stressed. mStress generates prompts for timely collection of self-reports, trig-gered by real-time changes in stress level inferred by the system, to collect the subjective experience of stress when it is fresh in the participant's mind. To improve the quality of data, mStress incorporates several features including paying micro-incentives for timely completion of self-reports, real-time detection of and response to confounding factors that affect physiological signals, and real-time detection of sen-sor detachments so the participant can rectify themselves. All of this functionality occurs entirely on the mobile phone without any help from the back-end cloud. mStress was used by 23 human volunteers in a scientific study, in which each participant wore the sensors and pro-vided self-reports during their wake hours for one full day in their natural environment. The phone lasted over 14 hours. Over 200 million samples of sensor measurements were col-lected, 19,000 stress predictions were made, and 803 prompts for self-report were answered, 98% of which were completed within 7 minutes of the prompt.
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ABSTRACT: Excessive stress may lead to health problems like headache, trouble sleeping, depression and chronic diseases such as cardiovascular and cerebrovascular diseases. In this paper we present deStress, the mobile and remote stress monitoring, alleviation and management system, whose features are: firstly it is wearable and inexpensive, which uses only one wearable stress monitor sensor and a mobile phone-based application (Android OS) to monitor stress. Secondly, deStress quantitatively assesses the user's stress level continuously, not just classifies the users into stressed or non-stressed state. Thirdly, deStress provides a system for stress monitoring and management, through which the stress data could be recorded, analyzed and shared with medical professionals. Last but not least, a novel adaptive respiration-based bio-feedback approach is implemented to alleviate stress. To the best of our knowledge, deStress is the first telehealth system dedicated to mobile and remote stress monitoring, alleviation and management. Extensive experiment are conducted in 30 persons to demonstrate the feasibility and effectiveness of deStress, and the result shows that the stress level assessment of deStress correctly indicates the mental states of the users, and under the guidance of deStress the users could alleviate their stress level dramatically.Global Communications Conference (GLOBECOM), 2012 IEEE; 01/2012