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      Modeling the Autonomic and Metabolic Effects of Obstructive Sleep Apnea: A Simulation Study

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          Long-term exposure to intermittent hypoxia and sleep fragmentation introduced by recurring obstructive sleep apnea (OSA) has been linked to subsequent cardiovascular disease and Type 2 diabetes. The underlying mechanisms remain unclear, but impairment of the normal interactions among the systems that regulate autonomic and metabolic function is likely involved. We have extended an existing integrative model of respiratory, cardiovascular, and sleep–wake state control, to incorporate a sub-model of glucose–insulin–fatty acid regulation. This computational model is capable of simulating the complex dynamics of cardiorespiratory control, chemoreflex and state-related control of breath-to-breath ventilation, state-related and chemoreflex control of upper airway potency, respiratory and circulatory mechanics, as well as the metabolic control of glucose–insulin dynamics and its interactions with the autonomic control. The interactions between autonomic and metabolic control include the circadian regulation of epinephrine secretion, epinephrine regulation on dynamic fluctuations in glucose and free-fatty acid in plasma, metabolic coupling among tissues and organs provided by insulin and epinephrine, as well as the effect of insulin on peripheral vascular sympathetic activity. These model simulations provide insight into the relative importance of the various mechanisms that determine the acute and chronic physiological effects of sleep-disordered breathing. The model can also be used to investigate the effects of a variety of interventions, such as different glucose clamps, the intravenous glucose tolerance test, and the application of continuous positive airway pressure on OSA subjects. As such, this model provides the foundation on which future efforts to simulate disease progression and the long-term effects of pharmacological intervention can be based.

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          Most cited references 44

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          Glucose clamp technique: a method for quantifying insulin secretion and resistance.

          Methods for the quantification of beta-cell sensitivity to glucose (hyperglycemic clamp technique) and of tissue sensitivity to insulin (euglycemic insulin clamp technique) are described. Hyperglycemic clamp technique. The plasma glucose concentration is acutely raised to 125 mg/dl above basal levels by a priming infusion of glucose. The desired hyperglycemic plateau is subsequently maintained by adjustment of a variable glucose infusion, based on the negative feedback principle. Because the plasma glucose concentration is held constant, the glucose infusion rate is an index of glucose metabolism. Under these conditions of constant hyperglycemia, the plasma insulin response is biphasic with an early burst of insulin release during the first 6 min followed by a gradually progressive increase in plasma insulin concentration. Euglycemic insulin clamp technique. The plasma insulin concentration is acutely raised and maintained at approximately 100 muU/ml by a prime-continuous infusion of insulin. The plasma glucose concentration is held constant at basal levels by a variable glucose infusion using the negative feedback principle. Under these steady-state conditions of euglycemia, the glucose infusion rate equals glucose uptake by all the tissues in the body and is therefore a measure of tissue sensitivity to exogenous insulin.
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            Hypoxia-inducible factor 1: master regulator of O2 homeostasis.

            Hypoxia-inducible factor 1 (HIF-1) is a transcription factor that mediates essential homeostatic responses to reduced O2 availability in mammals. Recent studies have provided insights into the O2-dependent regulation of HIF-1 expression, target genes regulated by HIF-1, and the effects of HIF-1 deficiency on cellular physiology and embryonic development.
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              Sleep-disordered breathing and insulin resistance in middle-aged and overweight men.

              Sleep-disordered breathing is a prevalent condition associated with impairment of daytime function and may predispose individuals to metabolic abnormalities independent of obesity. The primary objective of this study was to determine the metabolic consequences and community prevalence of sleep-disordered breathing in mildly obese, but otherwise healthy, individuals. One hundred and fifty healthy men, without diabetes or cardiopulmonary disease, were recruited from the community. Measurements included polysomnography, a multiple sleep latency test, an oral glucose tolerance test, determination of body fat by hydrodensitometry, and fasting insulin and lipids. The prevalence of sleep-disordered breathing, depending on the apnea-hypopnea index (AHI) cutoff, ranged from 40 to 60%. After adjusting for body mass index (BMI) and percent body fat, an AHI gt-or-equal, slanted 5 events/h was associated with an increased risk of having impaired or diabetic glucose tolerance (odds ratio, 2.15; 95% CI, 1.05-4.38). The impairment in glucose tolerance was related to the severity of oxygen desaturation (DeltaSa(O(2))) associated with sleep-disordered breathing. For a 4% decrease in oxygen saturation, the associated odds ratio for worsening glucose tolerance was 1.99 (95% CI, 1.11 to 3.56) after adjusting for percent body fat, BMI, and AHI. Multivariable linear regression analyses revealed that increasing AHI was associated with worsening insulin resistance independent of obesity. Thus, sleep-disordered breathing is a prevalent condition in mildly obese men and is independently associated with glucose intolerance and insulin resistance.

                Author and article information

                Front Physiol
                Front. Physio.
                Frontiers in Physiology
                Frontiers Research Foundation
                04 January 2012
                : 2
                1Biomedical Engineering Department, University of Southern California Los Angeles, CA, USA
                Author notes

                Edited by: Zhe Chen, Massachusetts Institute of Technology, USA

                Reviewed by: Luca Citi, Harvard Medical School, USA; Premananda Indic, University of Massachusetts Medical School, USA; Sandun Kodituwakku, Australian National University, Australia

                *Correspondence: Michael C. K. Khoo, Biomedical Engineering Department, University of Southern California, DRB-140, University Park, Los Angeles, CA 90089-1111, USA. e-mail: khoo@ 123456bmsr.usc.edu

                This article was submitted to Frontiers in Computational Physiology and Medicine, a specialty of Frontiers in Physiology.

                Copyright © 2012 Cheng and Khoo.

                This is an open-access article distributed under the terms of the Creative Commons Attribution Non Commercial License, which permits non-commercial use, distribution, and reproduction in other forums, provided the original authors and source are credited.

                Page count
                Figures: 11, Tables: 4, Equations: 9, References: 56, Pages: 20, Words: 13592
                Original Research


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