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Adv Ther. 2006 Nov-Dec;23(6):1040-51.

Reward deficiency syndrome in obesity: a preliminary cross-sectional trial with a Genotrim variant.

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Department of Physiology & Pharmacology, Wake Forest University School of Medicine, Winston-Salem, North Carolina 27157-1083, USA.


Obesity is the second largest preventable cause of death in the United States. Even though it was classified as a disease in 1985, traditionally, obesity has been treated primarily as a behavioral problem that requires only modifications in diet and exercise. Similar to research on obesity, clinical studies have elucidated the role of biologic and genetic factors in alcoholism and other conditions previously classified as behavioral. These studies showed that behavioral adjustments alone may not address underlying genetic causes. We hypothesize that biologic and genetic factors must be addressed synergistically while behavioral modifications are implemented to adequately treat obese patients. We hypothesize that a predisposition to glucose craving and obesity is due to inadequate dopaminergic activity in the reward center of the brain. This defect drives individuals to engage in activities of behavioral excess, which, in turn, enhance brain dopamine function. Consumption of large quantities of alcohol or carbohydrates (carbohydrate bingeing) stimulates production and usage of dopamine within the brain; the term reward deficiency syndrome (RDS) may be used to categorize such biologic influences on behavior. We propose that a novel approach to nutritional supplementation may be required to target the role of RDS in obesity. In this regard, GenoTrim, a DNA-customized nutritional solution, has been developed and is currently under investigation in several clinical studies. Through its mechanism of action, GenoTrim addresses the genetic influence of RDS on obesity. In this cross-sectional study, 24 subjects were studied after they had completed a case report format questionnaire. For this assessment, we used a novel assessment tool-a path analysis. This statistical regression model is used to (1) examine the effectual relationships between various systems within a multisystem matrix, and (2) measure the contributory roles of those relationships in obesity, enabling the development of targeted and effective therapeutic interventions.

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