Jugular venous flow dynamics during acute weightlessness.

During spaceflight, fluids shift headward, causing internal jugular vein (IJV) distension and altered hemodynamics, including stasis and retrograde flow, that may increase the risk of thrombosis. This study's purpose was to determine the effects of acute exposure to weightlessness (0-G) on IJV dimensions and flow dynamics. We used two-dimensional (2-D) ultrasound to measure IJV cross-sectional area (CSA) and Doppler ultrasound to characterize venous blood flow patterns in the right and left IJV in 13 healthy participants (6 females) while 1) seated and supine on the ground, 2) supine during 0-G parabolic flight, and 3) supine during level flight (at 1-G). On Earth, in 1-G, moving from seated to supine posture increased CSA in both left (+62 [95% CI: +42 to 81] mm2, P < 0.0001) and right (+86 [95% CI: +58 to 113] mm2, P < 0.00012) IJV. Entry into 0-G further increased IJV CSA in both left (+27 [95% CI: +5 to 48] mm2, P = 0.02) and right (+30 [95% CI: +0.3 to 61] mm2, P = 0.02) relative to supine in 1-G. We observed stagnant flow in the left IJV of one participant during 0-G parabolic flight that remained during level flight but was not present during any imaging during preflight measures in the seated or supine postures; normal venous flow patterns were observed in the right IJV during all conditions in all participants. Alterations to cerebral outflow dynamics in the left IJV can occur during acute exposure to weightlessness and thus, may increase the risk of venous thrombosis during any duration of spaceflight.NEW & NOTEWORTHY The absence of hydrostatic pressure gradients in the vascular system and loss of tissue weight during weightlessness results in altered flow dynamics in the left internal jugular vein in some astronauts that may contribute to an increased risk of thromboembolism during spaceflight. Here, we report that the internal jugular veins distend bilaterally in healthy participants and that flow stasis can occur in the left internal jugular vein during acute weightlessness produced by parabolic flight.

Assessment of Vascular Function in Response to High-Fat and Low-Fat Ground Beef Consumption in Men.

Red meat is stigmatized as an unhealthy protein choice; however, its impacts on vascular function have not been evaluated. We aimed to measure the vascular impact of adding either low-fat (~5% fat) ground beef (LFB) or high-fat (~25% fat) ground beef (HFB) to a habitual diet in free-living men. Twenty-three males (39.9 ± 10.8 years, 177.5 ± 6.7 cm, 97.3 ± 25.0 kg) participated in this double-blind crossover study. Assessment of vascular function and aerobic capacity were measured at entry and in the last week of each intervention and washout period. Participants then completed two 5-week dietary interventions (LFB or HFB; 5 patties/week) in a randomized order with a 4-week washout. Data were analyzed via 2 × 2 repeated-measures ANOVA (p < 0.05). The HFB intervention improved FMD relative to all other time points, while lowering systolic (SBP) and diastolic blood pressure (DBP) relative to entry. Neither the HFB nor the LFB altered pulse wave velocity. The addition of either low- or high-fat ground beef did not negatively alter vascular function. In fact, consuming HFB improved FMD and BP values, which may be mediated by lowering LDL-C concentrations.

Consuming High-Fat and Low-Fat Ground Beef Depresses High-Density and Low-Density Lipoprotein Cholesterol Concentrations, and Reduces Small, Dense Low-Density Lipoprotein Particle Abundance.

We hypothesized that consumption of high-fat (HF) ground beef (24% fat) would not affect plasma concentrations of high-density lipoprotein cholesterol (HDL-C) or low-density lipoprotein (LDL-C), whereas low-fat (LF) ground beef (5% fat) would decrease HDL-C and LDL-C concentrations. In a randomized 2-period crossover, controlled feeding trial, 25 men (mean age and body mass index, 40 years and 31.2) consumed 115-g HF or LF patties, 5/week for 5 weeks with a 4-week washout. The HF treatment increased % energy from fat (p = 0.006) and saturated fat (p = 0.004) and tended (p = 0.060) to depress % energy from carbohydrates. The HF and LF treatments decreased the plasma concentrations of HDL-C (p = 0.001) and LDL-C (p = 0.011). Both ground beef treatments decreased the abundance of HDL3a and increased the abundance of HDL3 (p ≤ 0.003); the LF treatment also decreased the abundance of HDL2b and HDL2a (p ≤ 0.012). The HF and LF treatments decreased the abundance of LDL3 and LDL4 (p ≤ 0.024) and the HF treatment also decreased LDL5 (p = 0.041). Contrary to our hypothesis, the HF treatment decreased plasma HDL-C and LDL-C concentrations despite increased saturated fat intake, and both treatments decreased the abundance of smaller, denser LDL subfractions.

Effects of an Acute Strength and Conditioning Training Session on Dual-Energy X-Ray Absorptiometry Results.

Lytle, JR, Stanelle, ST, Kravits, DM, Ellsworth, RL, Martin, SE, Green, JS, and Crouse, SF. Effects of an acute strength and conditioning training session on dual-energy x-ray absorptiometry results. J Strength Cond Res 34(4): 901-904, 2020-The purpose of this study was to determine whether an athletic strength and conditioning (S&C) session will alter body composition estimates of a dual-energy x-ray absorptiometry (DXA) scan. Twenty-two strength-trained individuals (15 men, 7 women, 24 ± 2 years, 174.2 ± 8.5 cm, 83.5 ± 15.0 kg) volunteered to participate in the study. Each subject underwent 2 DXA scans, before and after completion of the S&C session, which consisted of upper- and lower-body resistance exercises and interval running. Subjects consumed a free-living meal before the first scan, after which only ad libitum water intake was consumed until completing the second scan. Results were analyzed through sex by time repeated-measures analysis of variance. If no interaction effect was observed, results were next analyzed through correlated t-test (α = 0.05). Significant sex by time interactions were observed for arm total and lean mass, as well as a significant main effect of time showing a decrease in arm lean mass after the S&C session. Values before and after the S&C session that resulted in significant differences via correlated t-test are displayed in Table 1. Results revealed a significant decrease in total mass, arm and leg percent fat, and trunk lean mass, and an increase in leg lean mass.

Wearable positive end-expiratory pressure valve improves exercise performance.

We tested a PEEP (4.2 cmH2O) mouthpiece (PMP) on maximal cycling performance in healthy adults. Experiment-1, PMP vs. non-PMP mouthpiece (CON) [n  = 9 (5♂), Age = 30 ±â€¯2 yr]; Experiment-2, PMP vs. no mouthpiece (NMP) [n = 10 (7♂), Age = 27 ±â€¯1 yr]. At timepoint 1 in both experiments (mouthpiece condition randomized) subjects performed graded cycling testing (GXT) (Corival® cycle ergometer) to determine V ˙ O2peak (ml∗kg∗min -1), O2pulse (mlO2∗bt -1), GXT endurance time (GXT-T(s)), and V ˙ O2(ml∗kg∗min -1)-at-ventilatory-threshold ( V ˙ O2 @VT). At timepoint 2 72 h later, subjects completed a ventilatory-threshold-endurance-ride [VTER(s)] timed to exhaustion at V ˙ O2 @VT power (W). One week later at timepoints 3 and 4 (time-of-day controlled), subjects repeated testing protocols under the alternate mouthpiece condition. Selected results (paired T-test, p<0.05): Experiment 1 PMP vs. CON, respectively: V ˙ O2peak â€‹= â€‹45.2 â€‹± â€‹2.4 vs. 42.4 â€‹± â€‹2.3 p<0.05; V ˙ O2@VT â€‹= â€‹33.7 â€‹± â€‹2.0 vs. 32.3 â€‹± â€‹1.6; GXT-TTE â€‹= â€‹521.7 â€‹± â€‹73.4 vs. 495.3 â€‹± â€‹72.8 (p<0.05); VTER â€‹= â€‹846.2 â€‹± â€‹166.0 vs. 743.1 â€‹± â€‹124.7; O2pulse â€‹= â€‹24.5 â€‹± â€‹1.4 vs. 23.1 â€‹± â€‹1.3 (p<0.05). Experiment 2 PMP vs. NMP, respectively: V ˙ O2peak â€‹= â€‹43.3 â€‹± â€‹1.6 vs. 41.7 â€‹± â€‹1.6 (p<0.05); V ˙ O2@VT â€‹= â€‹31.1 â€‹± â€‹1.2 vs. 29.1 â€‹± â€‹1.3 (p<0.05); GXT-TTE â€‹= â€‹511.7 â€‹± â€‹49.6 vs. 486.4 â€‹± â€‹49.6 (p<0.05); VTER 872.4 â€‹± â€‹134.0 vs. 792.9 â€‹± â€‹122.4; O2pulse â€‹= â€‹24.1 â€‹± â€‹0.9 vs. 23.4 â€‹± â€‹0.9 (p<0.05). Results demonstrate that the PMP conferred a significant performance benefit to cyclists completing high intensity cycling exercise.

Predicting V[Combining Dot Above]O2max From Treadmill Performance in American-Style Football Athletes.

Crouse, SF, Tolson, H, Lytle, J, Johnson, KA, Martin, SE, Green, JS, Oliver, J, Carbuhn, A, Lambert, B, and Bramhall, JP. Predicting V[Combining Dot Above]O2max from treadmill performance in American-style football athletes. J Strength Cond Res 33(4): 1028-1034, 2019-Prediction equations are often used to estimate V[Combining Dot Above]O2max in the general population but are lacking for American-style football (ASF) athletes. We sought to develop a regression model to estimate V[Combining Dot Above]O2max from treadmill exercise time in ASF athletes and compare our football V[Combining Dot Above]O2max model with 2 published prediction equations (Foster et al., 1984, and Bruce, 1973). American-style football athletes (N = 472, age = 18 ± 1 year, height = 186.1 ± 8.2 cm, and body mass = 101.8 ± 20.4 kg) underwent treadmill exercise to voluntary exhaustion (Bruce protocol). Maximal exercise time was recorded in minutes (Tmin), and V[Combining Dot Above]O2max was simultaneously measured (M-V[Combining Dot Above]O2max, mlO2·kg·min) by an automated gas-analysis system. Athletes were then randomly divided into validation and cross-validation groups (n = 236). Linear regression yielded estimates of V[Combining Dot Above]O2max from Tmin as follows: validation V[Combining Dot Above]O2max = 4.012 × Tmin - 4.628 (r = 0.678, p < 0.001, and SEE = 4.07); cross-validation V[Combining Dot Above]O2max = 4.025 × Tmin - 4.693 (r = 0.661, p < 0.001, and SEE = -4.16). These equations had a cross-validation coefficient of 0.813 and a double cross-validation coefficient of 0.823. Differences between the slopes of the 2 equations were not significant (t-test, p = 0.9603). Because validation and cross-validation groups were not statistically different on any variables measured (multivariate analysis of variance, p > 0.05), all athletes were combined to yield our final prediction equation: football V[Combining Dot Above]O2max = 4.017 × Tmin - 4.644 (r = 0.670, p < 0.001, and SEE = 4.11). Repeated-measures analysis of variance demonstrated significant differences (p < 0.001) in estimates of V[Combining Dot Above]O2max among Foster (44.1 ± 6.1), Bruce (47.1 ± 5.5), and our football (45.1 ± 5.8) equations. Foster and Bruce V[Combining Dot Above]O2max estimates were also significantly different from M-V[Combining Dot Above]O2max ((Equation is included in full-text article.)diff = -0.975 and 1.995, respectively, p < 0.001). V[Combining Dot Above]O2max of ASF athletes can be reasonably estimated by our football prediction equation using maximal treadmill time as the predictor.

Predicting Energy Expenditure of an Acute Resistance Exercise Bout in Men and Women.

The energy expenditure of resistance exercise (RE) is an important consideration for exercise prescription and weight management, yet prediction models are lacking. PURPOSE: This study aimed to develop regression equations to predict energy expenditure (kcal) for RE involving each major muscle group using commonly measured demographic and exercise variables as predictors. METHODS: Fifty-two healthy, active subjects (27 men, 25 women, age 20-58 yr, height 174.1 ± 10.5 cm, weight 188.7 ± 42.6 kg, V˙O2max 36.8 ± 9.2 mL·kg⋅min) were strength tested to estimate their one-repetition maximum 1 wk before their experimental RE bout. The experimental RE bout consisted of a warm-up set followed by 2-3 sets (2-min turnover) of 8-12 reps at 60%-70% of predicted one-repetition maximum for leg press, chest press, leg curl, lat pull, leg extension, triceps push down, and biceps curl. Kilocalories were estimated from V˙O2 measured continuously throughout the RE bout via an automated metabolic cart. Total exercise volume (TV) was calculated as sets × reps × weight lifted. Multiple linear regression (stepwise removal) was used to determine the best model (highest adjusted R) to predict the kilocalorie consumption of the total workout and of the individual RE lifts. RESULTS: The derived regression equation for the net kilocalorie consumption of an RE bout was as follows: total net kilocalorie = 0.874 (height, cm) - 0.596 (age, yr) - 1.016 (fat mass, kg) + 1.638 (lean mass, kg) + 2.461 (TV × 10) - 110.742 (R = 0.773, SEE = 28.5 kcal). Significant equations were also derived for individual lifts (R = 0.62 to 0.83). CONCLUSIONS: Net energy expenditure for a total RE bout and for individual RE can be reasonably estimated in adult men and women using commonly measured demographic and RE variables.