Computer simulation of preflight blood volume reduction as a countermeasure to fluid shifts in space flight

Fluid shifts in weightlessness may cause a central volume expansion, activating reflexes to reduce the blood volume. Computer simulation was used to test the hypothesis that preadaptation of the blood volume prior to exposure to weightlessness could counteract the central volume expansion due to fluid shifts and thereby attenuate the circulatory and renal responses resulting in large losses of fluid from body water compartments. The Guyton Model of Fluid, Electrolyte, and Circulatory Regulation was modified to simulate the six degree head down tilt that is frequently use as an experimental analog of weightlessness in bedrest studies. Simulation results show that preadaptation of the blood volume by a procedure resembling a blood donation immediately before head down bedrest is beneficial in damping the physiologic responses to fluid shifts and reducing body fluid losses. After ten hours of head down tilt, blood volume after preadaptation is higher than control for 20 to 30 days of bedrest. Preadaptation also produces potentially beneficial higher extracellular volume and total body water for 20 to 30 days of bedrest

Space sickness predictors suggest fluid shift involvement and possible countermeasures

Preflight data from 64 first time Shuttle crew members were examined retrospectively to predict space sickness severity (NONE, MILD, MODERATE, or SEVERE) by discriminant analysis. From 9 input variables relating to fluid, electrolyte, and cardiovascular status, 8 variables were chosen by discriminant analysis that correctly predicted space sickness severity with 59 pct. success by one method of cross validation on the original sample and 67 pct. by another method. The 8 variables in order of their importance for predicting space sickness severity are sitting systolic blood pressure, serum uric acid, calculated blood volume, serum phosphate, urine osmolality, environmental temperature at the launch site, red cell count, and serum chloride. These results suggest the presence of predisposing physiologic factors to space sickness that implicate a fluid shift etiology. Addition of a 10th input variable, hours spent in the Weightless Environment Training Facility (WETF), improved the prediction of space sickness severity to 66 pct. success by the first method of cross validation on the original sample and to 71 pct. by the second method. The data suggest that WETF training may reduce space sickness severity

Prediction of space sickness in astronauts from preflight fluid, electrolyte, and cardiovascular variables and Weightless Environmental Training Facility (WETF) training

Nine preflight variables related to fluid, electrolyte, and cardiovascular status from 64 first-time Shuttle crewmembers were differentially weighted by discrimination analysis to predict the incidence and severity of each crewmember's space sickness as rated by NASA flight surgeons. The nine variables are serum uric acid, red cell count, environmental temperature at the launch site, serum phosphate, urine osmolality, serum thyroxine, sitting systolic blood pressure, calculated blood volume, and serum chloride. Using two methods of cross-validation on the original samples (jackknife and a stratefied random subsample), these variables enable the prediction of space sickness incidence (NONE or SICK) with 80 percent sickness and space severity (NONE, MILD, MODERATE, of SEVERE) with 59 percent success by one method of cross-validation and 67 percent by another method. Addition of a tenth variable, hours spent in the Weightlessness Environment Training Facility (WETF) did not improve the prediction of space sickness incidences but did improve the prediction of space sickness severity to 66 percent success by the first method of cross-validation of original samples and to 71 percent by the second method. Results to date suggest the presence of predisposing physiologic factors to space sickness that implicate fluid shift etiology. The data also suggest that prior exposure to fluid shift during WETF training may produce some circulatory pre-adaption to fluid shifts in weightlessness that results in a reduction of space sickness severity

Physiologic mechanisms of circulatory and body fluid losses in weightlessness identified by mathematical modeling

Central volume expansion due to fluid shifts in weightlessness is believed to activate adaptive reflexes which ultimately result in a reduction of the total circulating blood volume. However, the flight data suggests that a central volume overdistention does not persist, in which case some other factor or factors must be responsible for body fluid losses. We used a computer simulation to test the hypothesis that factors other than central volume overdistention are involved in the loss of blood volume and other body fluid volumes observed in weightlessness and in weightless simulations. Additionally, the simulation was used to identify these factors. The results predict that atrial volumes and pressures return to their prebedrest baseline values within the first day of exposure to head down tilt (HDT) as the blood volume is reduced by an elevated urine formation. They indicate that the mechanisms for large and prolonged body fluid losses in weightlessness is red cell hemoconcentration that elevates blood viscosity and peripheral resistance, thereby lowering capillary pressure. This causes a prolonged alteration of the balance of Starling forces, depressing the extracellular fluid volume until the hematocrit is returned to normal through a reduction of the red cell mass, which also allows some restoration of the plasma volume. We conclude that the red cell mass becomes the physiologic driver for a large 'undershoot' of the body fluid volumes after the normalization of atrial volumes and pressures

Mechanisms of hypoxic up-regulation of versican gene expression in macrophages

Hypoxia is a hallmark of many pathological tissues. Macrophages accumulate in hypoxic sites and up-regulate a range of hypoxia-inducible genes. The matrix proteoglycan versican has been identified as one such gene, but the mechanisms responsible for hypoxic induction are not fully characterised. Here we investigate the up-regulation of versican by hypoxia in primary human monocyte-derived macrophages (HMDM), and, intriguingly, show that versican mRNA is up-regulated much more highly (>600 fold) by long term hypoxia (5 days) than by 1 day of hypoxia (48 fold). We report that versican mRNA decay rates are not affected by hypoxia, demonstrating that hypoxic induction of versican mRNA is mediated by increased transcription. Deletion analysis of the promoter identified two regions required for high level promoter activity of luciferase reporter constructs in human macrophages. The hypoxia-inducible transcription factor HIF-1 has previously been implicated as a key potential regulator of versican expression in hypoxia, however our data suggest that HIF-1 up-regulation is unlikely to be principally responsible for the high levels of induction observed in HMDM. Treatment of HMDM with two distinct specific inhibitors of Phosphoinositide 3-kinase (PI3K), LY290042 and wortmannin, significantly reduced induction of versican mRNA by hypoxia and provides evidence of a role for PI3K in hypoxic up-regulation of versican expression

Virtual Patients and Sensitivity Analysis of the Guyton Model of Blood Pressure Regulation: Towards Individualized Models of Whole-Body Physiology

Mathematical models that integrate multi-scale physiological data can offer insight into physiological and pathophysiological function, and may eventually assist in individualized predictive medicine. We present a methodology for performing systematic analyses of multi-parameter interactions in such complex, multi-scale models. Human physiology models are often based on or inspired by Arthur Guyton's whole-body circulatory regulation model. Despite the significance of this model, it has not been the subject of a systematic and comprehensive sensitivity study. Therefore, we use this model as a case study for our methodology. Our analysis of the Guyton model reveals how the multitude of model parameters combine to affect the model dynamics, and how interesting combinations of parameters may be identified. It also includes a “virtual population” from which “virtual individuals” can be chosen, on the basis of exhibiting conditions similar to those of a real-world patient. This lays the groundwork for using the Guyton model for in silico exploration of pathophysiological states and treatment strategies. The results presented here illustrate several potential uses for the entire dataset of sensitivity results and the “virtual individuals” that we have generated, which are included in the supplementary material. More generally, the presented methodology is applicable to modern, more complex multi-scale physiological models