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Purpose: To compare the effects of 8-wk eccentric (ECC) versus concentric (CON) training using downhill and uphill running in rats on whole body composition, bone mineral density (BMD), and energy expenditure., Methods: Animals were randomly assigned to one of the following groups: 1) control (CTRL), 2) +15% uphill-running slope (CON), 3) -15% downhill-running slope (ECC15), and 4) -30% downhill-running slope (ECC30). Those programs enabled to achieve conditions of isopower output for CON and ECC15 and of iso-oxygen uptake (V˙O2) for CON and ECC30. Trained rats ran 45 min at 15 m·min five times per week. Total body mass, fat body mass, and lean body mass (LBM) measured through EchoMRI™, and 24-h energy expenditure including basal metabolic rate (BMR) assessed using PhenoMaster/LabMaster™ cage system were obtained before and after training. At sacrifice, the right femur was collected for bone parameters analysis., Results: Although total body mass increased in all groups over the 8-wk period, almost no change occurred for fat body mass in exercised groups (CON, -4.8 ± 6.18 g; ECC15, 0.6 ± 3.32 g; ECC30, 2.6 ± 6.01 g). The gain in LBM was mainly seen for ECC15 (88.9 ± 6.85 g) and ECC30 (101.6 ± 11.07 g). ECC was also seen to positively affect BMD. An increase in BMR from baseline was seen in exercise groups (CON, 13.9 ± 4.13 kJ·d; ECC15, 11.6 ± 5.10 kJ·d; ECC30, 18.3 ± 4.33 kJ·d) but not in CTRL one. This difference disappeared when BMR was normalized for LBM., Conclusions: Results indicate that for iso-V˙O2 training, the impact on LBM and BMD is enhanced with ECC as compared with CON, and that for isopower but lower V˙O2 ECC, an important stimulus for adaptation is still observed. This provides further insights for the use of ECC in populations with cardiorespiratory exercise limitations.

Background: Enhanced nocturnal heart rate variability (HRV) has been evoked in sleep-related breathing disorders. However, its capacity to detect obstructive sleep apnea syndrome (OSAS) has not been systematically determined. Thus, we evaluated the discriminant power of HRV parameters in a first group of patients (G1) and validated their discriminant capacity in a second group (G2)., Methods and Results: In G1, 39 of 91 patients (42.8%) were identified as diseased by polysomnography, as were 24 of 52 patients (46%) in G2. Time-domain HRV variables (SD of NN intervals [SDNN], mean of the standard deviations of all NN intervals for all consecutive 5-minute segments of the recording [SDNN index], square root of the mean of the sum of the squares of differences between adjacent normal RR intervals [r-MSSD], and SD of the averages of NN intervals in all 5-minute segments of the recording [SDANN]) were calculated for daytime and nighttime periods, as well as the differences between daytime and nighttime values (Delta[D/N]). Correlations between HRV variables and OSAS status were analyzed in G1 by use of receiver-operating characteristic (ROC) curves and logistic regression analysis. By ROC curve analysis, 7 variables were significantly associated with OSAS. After adjustment for other variables through multiple logistic regression analysis, Delta[D/N]SDNN index and Delta[D/N] r-MSSD remained significant independent predictors of OSAS, with ORs of 8.22 (95% CI, 3.16 to 21.4) and 2.86 (95% CI, 1.21 to 6.75), respectively. The classification and regression tree methodology demonstrated a sensitivity reaching 89.7% (95% CI, 73.7 to 97.7) with Delta[D/N] SDNN index and a specificity of 98.1% (95% CI, 86.4 to 100) with Delta[D/N] SDNN using appropriate thresholds. These thresholds, applied to G2, yielded a sensitivity of 83% using Delta[D/N] SDNN index and a specificity of 96.5% using Delta[D/N] SDNN., Conclusions: Time-domain HRV analysis may represent an accurate and inexpensive screening tool in clinically suspected OSAS patients and may help focus resources on those at the highest risk.