Regulation of oxygen consumption by vasomotion.

OBJECTIVE To describe a stochastic model of the variability and heterogeneity of blood flow through the microcirculation, and to show the ability of vasomotion to vary oxygen consumption at a steady blood flow. METHODS The description of vasomotion is based on whether each microvessel is open for blood flow or closed. Over a unit time period, let alpha be the probability that a given vessel is open and will remain open, beta be the probability that an open vessel will close, nu be the probability that a closed vessel will remain closed, and mu be the probability that a closed vessel will become open. Two main parameters that characterize such a scheme are: the fraction of open microvessels [n o= mu/(beta+mu)], and the rate of vasomotion defined as the rate of switching between open and closed microvessels (R=beta+mu). A model of O2 transport to tissues is based on the following assumptions: (a) the flux of O2 is due to passive diffusion, (b) the amount of O2 dissolved in tissue is negligible as compared with that contained in arterial blood, and (c) aerobic metabolism is proportional to the delivery of O2. RESULTS The stochastic model substantiates the possibility that vasomotion can control O2 consumption. The rate of vasomotion activity can change O2 consumption by 2-8-fold, depending on the fraction of open microvessels. CONCLUSION A stochastic description of blood flow through the microcirculation system demonstrates that vasomotion rate could be a factor influencing O2 consumption.

[1]  A. Guyton,et al.  Textbook of Medical Physiology , 1961 .

[2]  A. Baron,et al.  Interaction between insulin sensitivity and muscle perfusion on glucose uptake in human skeletal muscle: evidence for capillary recruitment. , 2000, Diabetes.

[3]  William Feller,et al.  An Introduction to Probability Theory and Its Applications , 1951 .

[4]  R. Pittman,et al.  Influence of Microvascular Architecture on Oxygen Exchange in Skeletal Muscle , 1995, Microcirculation.

[5]  M. Intaglietta,et al.  Local tissue oxygenation by statistically distributed sources. , 1992, Microvascular research.

[6]  William Feller,et al.  An Introduction to Probability Theory and Its Applications , 1951 .

[7]  J. Knuuti,et al.  Muscle fractal vascular branching pattern and microvascular perfusion heterogeneity in endurance‐trained and untrained men , 2003, The Journal of physiology.

[8]  Bharat B. Biswal,et al.  SPONTANEOUS FLUCTUATIONS IN CEREBRAL OXYGEN SUPPLY , 1998 .

[9]  M Intaglietta,et al.  Evidence of flowmotion induced changes in local tissue oxygenation. , 1993, International journal of microcirculation, clinical and experimental.

[10]  A. Hudetz,et al.  Blood Flow in the Cerebral Capillary Network: A Review Emphasizing Observations with Intravital Microscopy , 1997, Microcirculation.

[11]  J. L. Frierson,et al.  Capillary lengths, anastomoses, and estimated capillary transit times in skeletal muscle. , 1977, The American journal of physiology.

[12]  B. Zweifach.  Functional behavior of the microcirculation , 1961 .

[13]  Exercise alters fractal dimension and spatial correlation of pulmonary blood flow in the horse. , 2000, Journal of applied physiology.

[14]  E. M. Renkin,et al.  B. W. Zweifach Award lecture. Regulation of the microcirculation. , 1985, Microvascular research.

[15]  H H Lipowsky,et al.  Capillary recruitment in response to tissue hypoxia and its dependence on red blood cell deformability. , 1999, American journal of physiology. Heart and circulatory physiology.

[16]  A. Popel,et al.  A computational study of the effect of vasomotion on oxygen transport from capillary networks. , 2001, Journal of theoretical biology.