Energy cost of activity and exercise in children and adolescents with cystic fibrosis.

In cystic fibrosis (CF), perturbations of total daily energy expenditure (TDEE) may be a major determinant of altered nutrition and growth. Measurement of TDEE is problematic, though the flex-heart rate method (FHRM) provides a close estimation of TDEE, as compared to the cost-prohibitive, gold standard, the double-labeled water method, and permits estimates of the energy cost of daily activities (ECA) above resting energy expenditure (REE). We hypothesize that alterations in ECA affects TDEE in CF. PURPOSE: To measure components of TDEE in adolescents with CF and normal lung function compared with controls, and to determine whether ECA can be improved by diet and exercise. METHODS: Clinically stable CF subjects (aged 9-13, n=12) and age- and gender-matched controls (n=13) had repeated measurements of TDEE by FHRM, REE, and maximal cardiopulmonary exercise testing (CPET) during a 6-week exercise and diet program. RESULTS: While the mean REE was similar in both groups, ECA was significantly lower in CF adolescents as compared to controls (p=0.02). During CPET, maximal exercise in CF was characterized by hyperventilation, which was unrelated to ventilation-perfusion mismatching. There were no changes in REE after dietary intervention. CONCLUSION: ECA in CF adolescents with normal lung function is lower when compared to healthy controls. These findings support the hypothesis that clinically stable patients with CF have inefficient energy metabolism or alternatively conserve energy during activities of daily living.     

Bayesian inference of hemodynamic changes in functional arterial spin labeling data.

The study of brain function using MRI relies on acquisition techniques that are sensitive to different aspects of the hemodynamic response contiguous to areas of neuronal activity. For this purpose different contrasts such as arterial spin labeling (ASL) and blood oxygenation level dependent (BOLD) functional MRI techniques have been developed to investigate cerebral blood flow (CBF) and blood oxygenation, respectively. Analysis of such data typically proceeds by separate, linear modeling of the appropriate CBF or BOLD time courses. In this work an approach is developed that provides simultaneous inference on hemodynamic changes via a nonlinear physiological model of ASL data acquired at multiple echo times. Importantly, this includes a significant contribution by changes in the static magnetization, M, to the ASL signal. Inference is carried out in a Bayesian framework. This is able to extract, from dual-echo ASL data, probabilistic estimates of percentage changes of CBF, R(2) (*), and the static magnetization, M. This approach provides increased sensitivity in inferring CBF changes and reduced contamination in inferring BOLD changes when compared with general linear model approaches on single-echo ASL data. We also consider how the static magnetization, M, might be related to changes in CBV by assuming the same mechanism for water exchange as in vascular space occupancy.