The effect of feeding on the respiratory activity of the sloth

The aim of the present study was to confirm whether feeding influences the resting breathing rate and to observe possible alterations in blood gas and pH levels produced by feeding in unanesthetized sloths (Bradypus variegatus). Five adult male sloths (4.1 ± 0.6 kg) were placed daily in an experimental chair for a period of at least 4 h for sitting adaptation. Five measurements were made for each sloth. However, the sloths one, two and five were studied once and the sloths three and four were studied twice. Breathing rate was determined with an impedance meter and the output signal was digitized. Arterial blood samples were collected for blood gas analysis with a BGE electrolytes analyzer and adjusted for the animal's body temperature and hemoglobin content. The data are reported as mean ± SD and were collected during the resting period (8:00-10:00 h) and during the feeding period (16:00-18:00 h). The mean breathing rate increased during mastication of ymbahuba leaves (rest: 5.0 ± 1, feeding: 10 ± 1 bpm). No significant alterations were observed in arterial pH (rest: 7.42 ± 0.05, feeding: 7.45 ± 0.03), PCO2 (rest: 35.2 ± 5.3, feeding: 33.3 ± 4.4 mmHg) or PO2 (rest: 77.5 ± 8.2, feeding: 78.4 ± 5.2 mmHg) levels. These results indicate that in unanesthetized sloths 1) feeding evokes an increase in breathing rate without a significant change in arterial pH, PCO2 or PO2 levels, and 2) the increase in breathing rate produced by feeding probably is due to the act of mastication.

Nutritional status and body composition after 6 months of patients switching from continuous ambulatorial peritoneal dialysis to automated peritoneal dialysis

Our objective was to determine if automated peritoneal dialysis (APD) leads to changes in nutritional parameters of patients treated by continuous ambulatory peritoneal dialysis (CAPD). Twenty-six patients (15 males; 50.5 ± 14.3 years) were evaluated during CAPD while training for APD and after 3 and 6 months of APD. Body fat was assessed by the sum of skinfold thickness and the other body compartments were assessed by bioelectrical impedance. During the 6-month follow-up, 12 patients gained more than 1 kg (GW group), 8 patients lost more than 1 kg (LW group), and 6 patients maintained body weight (MW group). Except for length on dialysis that was longer for the LW group compared with the GW group, no other differences were found between the groups at baseline. After 6 months on APD, the LW group had a reduction in body fat (24.5 ± 7.7 vs 22.1 ± 7.3 kg; P = 0.01), body cell mass (22.6 ± 6.2 vs 21.6 ± 5.8 kg, P = 0.02) and phase angle (5.4 ± 0.9 vs 5.1 ± 0.8 degrees, P = 0.004). In the GW group, body fat (25 ± 7.6 vs 27.2 ± 7.6 kg, P = 0.001) and body cell mass (20.1 ± 3.9 vs 20.8 ± 4.0 kg, P = 0.05) were increased. In the present study, different patterns of change in body composition were found. The length of previous dialysis treatment seems to be the most important factor in determining these nutritional modifications.

Anthropometric midarm measurements can detect systemic fat-free mass depletion in patients with chronic obstructive pulmonary disease

Our objective was to determine whether anthropometric measurements of the midarm (MA) could identify subjects with whole body fat-free mass (FFM) depletion. Fifty-five patients (31% females; age: 64.6 ± 9.3 years) with mild/very severe chronic obstructive pulmonary disease (COPD), 18 smokers without COPD (39% females; age: 49.0 ± 7.3 years) and 23 never smoked controls (57% females; age: 48.2 ± 9.6 years) were evaluated. Spirometry, muscle strength and MA circumference were measured. MA muscle area was estimated by anthropometry and MA cross-sectional area by computerized tomography (CT) scan. Bioelectrical impedance was used as the reference method for FFM. MA circumference and MA muscle area correlated with FFM and biceps and triceps strength. Receiver operating characteristic curve analysis showed that MA circumference and MA muscle area cut-off points presented sensitivity and specificity >82% to discriminate FFM-depleted subjects. CT scan measurements did not provide improved sensitivity or specificity. For all groups, there was no significant statistical difference between MA muscle area [35.2 (29.3-45.0) cm²] and MA cross-sectional area values [36.4 (28.5-43.3) cm²] and the linear correlation coefficient between tests was r = 0.77 (P < 0.001). However, Bland-Altman plots revealed wide 95% limits of agreement (-14.7 to 15.0 cm²) between anthropometric and CT scan measurements. Anthropometric MA measurements may provide useful information for identifying subjects with whole body FFM depletion. This is a low-cost technique and can be used in a wider patient population to identify those likely to benefit from a complete body composition evaluation.

Percent body fat is a better predictor of cardiovascular risk factors than body mass index

The objective of the present study was to evaluate the predictive values of percent body fat (PBF) and body mass index (BMI) for cardiovascular risk factors, especially when PBF and BMI are conflicting. BMI was calculated by the standard formula and PBF was determined by bioelectrical impedance analysis. A total of 3859 ambulatory adult Han Chinese subjects (2173 males and 1686 females, age range: 18-85 years) without a history of cardiovascular diseases were recruited from February to September 2009. Based on BMI and PBF, they were classified into group 1 (normal BMI and PBF, N = 1961), group 2 (normal BMI, but abnormal PBF, N = 381), group 3 (abnormal BMI, but normal PBF, N = 681), and group 4 (abnormal BMI and PBF, N = 836). When age, gender, lifestyle, and family history of obesity were adjusted, PBF, but not BMI, was correlated with blood glucose and lipid levels. The odds ratio (OR) and 95% confidence interval (CI) for cardiovascular risk factors in groups 2 and 4 were 1.88 (1.45-2.45) and 2.06 (1.26-3.35) times those in group 1, respectively, but remained unchanged in group 3 (OR = 1.32, 95%CI = 0.92-1.89). Logistic regression models also demonstrated that PBF, rather than BMI, was independently associated with cardiovascular risk factors. In conclusion, PBF, and not BMI, is independently associated with cardiovascular risk factors, indicating that PBF is a better predictor.