Jagim, AR, Camic, CL, Kisiolek, J, Luedke, J, Erickson, J, Jones, MT, and Oliver, JM. Accuracy of resting metabolic rate prediction equations in athletes. J Strength Cond Res 32(7): 1875-1881, 2018-The purpose of this study was to determine the accuracy of 5 different resting metabolic rate (RMR) prediction equations in male and female athletes. Twenty-two female (19.7 ± 1.4 years; 166.2 ± 5.5 cm; 63.5 ± 7.3 kg; 49.2 ± 4.3 kg of fat-free mass (FFM); 23.4 ± 4.4 body fat (BF) percent) and 28 male (20.2 ± 1.6 years; 181.9 ± 6.1 cm; 94.5 ± 16.2 kg; 79.1 ± 7.2 kg of FFM; 15.1 ± 8.5% BF) athletes were recruited to participate in 1 day of metabolic testing. Assessments comprised RMR measurements using indirect calorimetry, and body composition analyses using air displacement plethysmography. One-way repeated-measures analysis of variance with follow-up paired t tests were selected to determine differences between indirect calorimetry and 5 RMR prediction equations. Linear regression analysis was used to assess the accuracy of each RMR prediction method. An alpha level of p ≤ 0.05 was used to determine statistical significance. All the prediction equations significantly underestimated RMR while the Cunningham equation had the smallest mean difference (-165 kcals). In men, the Harris-Benedict equation was found to be the best prediction formula with the lowest root-mean-square prediction error value of 284 kcals. In women, the Cunningham equation was found to be the best prediction equation with the lowest root-mean-squared error value of 110 kcals. Resting metabolic rate prediction equations consistently seem to underestimate RMR in male and female athletes. The Harris-Benedict equation seems to be most accurate for male athletes, whereas the Cunningham equation may be better suited for female athletes.

Purpose The purpose of this cross‐sectional study is to compare measures of general health, circulating concentrations of nerve growth factor (NGF), brain derived neurotrophic factor (BDNF), c‐reactive protein (CRP), interleukin‐6 (IL‐6) and cortisol in physically active cannabis users (CU) and physically active non‐cannabis users (NU). Methods Participants (N=30; n=20 male) were defined as CU (n=15; n=10 male) if they were using cannabis products at least once a week for the past 6‐months or NU (n=15; n=10 male) if they had not used any cannabis products in the past 6‐months. Age, height, weight, and body composition were assessed. Resting heart rate (HR) was obtained using a Polar heart rate monitor and VO2max, as a measure of cardiovascular fitness, was assessed with a ParvoMedics metabolic cart using a graded treadmill protocol. Fasted and rested intravenous blood samples were collected and ELISAs were used to obtain NGF, BDNF, CRP, IL‐6, and cortisol concentrations. Data are presented as mean ± SD and analyzed with a student's t‐test (alpha=0.05). Results CU used cannabis products an average of 4.5±2.5 times per week. There were no significant differences between CU and NU age (23.4±4.4 yrs), height (177.6±8.2 cm), weight (75.7±15.5 kg), body composition, or VO2max (50.3±7.4 ml/kg/min). HR was significantly higher in CU (72.7±15.9 bpm) compared to NU (62.5±7.9 bpm) (p=0.04). Concentrations of BDNF were significantly lower in CU (5.6±0.8 ng/mL) compared to NU (6.3±0.8 ng/mL) (p=0.02). There were no differences in concentration of NGF (193.7±71.6 pg/mL), CRP (CU=1.0± 1.83; NU=0.5±0.4 mg/L), IL‐6 (1.28±0.6 pg/mL), or cortisol (19.8±7.0 ng/mL). Conclusions Although aspects of general health appeared to be similar between NU and CU groups, this study revealed elevated resting HR and reduced BDNF concentrations in CU compared to NU. Although there were no differences between groups with respect to bio‐markers of stress (IL‐6, CRP and cortisol); CU were classified as moderate risk for cardiovascular disease (CVD) compared to low CVD risk for NU as defined by CRP concentrations. This study suggests that chronic cannabis use is associated with altered central and peripheral synaptic plasticity, as well as the potential for increased CVD risk classification in physically active individuals. Support or Funding Information This research was funded by the University of Northern Colorado New Projects Program Grant, University of Northern Colorado Graduate Student Association and Natural and Health Sciences Student Grants. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

No abstract

Abstract:Background:

No abstract