alexa From physiology to neuroendocrinology: a reappraisal of risk factors of body weight gain in humans.
Bioinformatics & Systems Biology

Bioinformatics & Systems Biology

Journal of Computer Science & Systems Biology

Author(s): Tataranni PA

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Abstract Obesity results from a chronic imbalance between energy intake and energy expenditure. However, the biological mechanism(s) underlying possible alterations of energy balance is(are) still poorly defined. Advances in the understanding of body weight regulation in humans are represented by the discovery of: a) metabolic risk factors of body weight gain (i.e. low resting energy expenditure, low level of physical activity, high carbohydrate-to-lipid oxidation rate); b) the role of the autonomic nervous system in the control of energy metabolism and nutrient partitioning; and c) leptin, a previously unknown hormone produced by the adipocyte which seems to be quite involved in the complex neurohormonal regulation of energy balance. In view of these discoveries, current models of human body weight regulation concord on the existence of crosstalks between central nervous system and peripheral tissues. The brain monitors the nutritional status of the body (using several peripheral afferent signals including leptin) and reacts to nutritional changes by modulating the activity of its neurohormonal efferent signaling systems (autonomic nervous systems and endocrine organs). A low sympathetic nervous system (SNS) activity and a relative plasma leptin deficiency have been shown to predict body weight gain. Furthermore, plasma concentration of leptin and activity of the SNS seem to regulate each other. This paper reviews the evidence that previously described metabolic risk factors of body weight gain (i.e., low resting energy expenditure, low level of physical activity, and high carbohydrate-to-lipid oxidation rate) may in fact be the phenotypic expression of a dysfunctional leptin-SNS activity body weight regulatory loop.
This article was published in Diabetes Metab and referenced in Journal of Computer Science & Systems Biology

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