Effects of long-term continuous positive airway pressure on body composition and IGF1

OBJECTIVE: To investigate the long-term effects of nasal continuous positive airway pressure (CPAP) ventilation in patients with obstructive sleep apnea syndrome (OSAS) on body composition (BC) and IGF1. DESIGN: Observational study. SUBJECTS: Seventy-eight (11 females and 67 males) OSAS patients who were compliant with CPAP (age 51+/-1.1 years) participated in the study. We assessed body mass index (BMI), total body mass (TBM), total body fat (TBF; kg) and lean body mass (LBM; kg), abdominal subcutaneous (SC) and visceral (V) fat (cm(2)), and waist circumference (WC; cm) by magnetic resonance imaging, and IGF1 (ng/ml) before and after 7.8+/-1.3 months of CPAP use of an average of 5.9+/-1.2 h. RESULTS: Women had a higher BMI, WC; TBM, TBF, and more SC fat. Men had a higher LBM and more V fat. CPAP increased WC (+2.8+/-9.6 cm, P=0.02) and LBM (2.2+/-0.5 kg, P=0.006), but not IGF1. In men, CPAP increased BMI (0.5+/-0.2 kg/m(2), P=0.02), WC (1.7+/-6.9 cm, P=0.002), TBM (1.7+/-0.4 kg, P=0.0001), LBM (1.5+/-0.4 kg, P=0.0003), SC fat (12.9+/-5.1 cm(2), P=0.02), and IGF1 (13.6+/-4.2 ng/ml, P=0.002). Compliance with CPAP increased LBM in men aged 60 years, and IGF1 increased in men aged 40-60 years only. CONCLUSIONS: Long-term CPAP increased LBM in both sexes and IGF1 in men, while fat mass remained unchanged, suggesting a sexually dimorphic response of IGF1 to CPAP. The role of the GH axis activity and age to this response is unclear. The metabolic consequences of changes in LBM are still to be determined. Future studies on the effects of CPAP on BC should include LBM as an outcome

Begleiten ja – aber nicht ohne moralische Bedenken : Studie zum freiwilligen Verzicht auf Nahrung und Flüssigkeit in der Langzeitpflege

Der freiwillige Verzicht auf Nahrung und Flüssigkeit - auch als Sterbefasten bekannt - ist in Institutionen der Langzeitpflege wohlbekannt. Aussagen von 535 Institutionen zeigen, dass 71 Prozent der Pflegenden dieses Sterben würdevoll finden

Self-Adaptation for Multi-objective Evolutionary Algorithms

Evolutionary Algorithms are a standard tool for multi-objective optimization that are able to approximate the Pareto front in a single optimization run. However, for some selection operators, the algorithm stagnates at a certain distance from the Pareto front without convergence for further iterations