Predictive Accuracy of the Nelson Equation via BodPod Compared to Commonly Used Equations to Estimate Resting Metabolic Rate in Adults

International Journal of Exercise Science 14(2): 1166-1177, 2021. Indirect calorimetry (IC) is considered the gold standard for assessing resting metabolic rate (RMR). However, many people do not have access to IC devices and use prediction equations for RMR estimation. Equations using fat free mass (FFM) as a predictor have been developed to estimate RMR, as a strong relationship exists between FFM and RMR. One such equation is the Nelson equation which is used by the BodPod (BP). Yet, there is limited evidence whether the Nelson equation is superior to other common equations to predict RMR. To examine the agreement between predicted RMR from common RMR equations and the BP, and RMR measured via IC. Data from 48 healthy volunteers who completed both the BP and IC were collected. Agreement between RMR measured by BP, common regression equations, and indirect caloriometry was evaluated using repeated measures ANOVA, Bland-Altman analysis and root mean square error (RMSE). Predicted RMR values from common equations and BP were significantly different from IC with the exception of the World Health Organization (WHO) equation. Large limits of agreement and RMSE values demonstrate a large amount of error at the individual level. Despite the use of FFM, the Nelson equation does not appear to be superior to other common RMR equations. Although the WHO equation presented the best option within our sample, all equations performed poorly at the individual level. Clinicians should be aware that prediction equations may significantly under- or overestimate RMR compared to IC and when an accurate value of RMR is required, IC is recommended

Energy Status Differentially Modifies Feeding Behavior and POMCARC Neuron Activity After Acute Treadmill Exercise in Untrained Mice

Emerging evidence identifies a potent role for aerobic exercise to modulate activity of neurons involved in regulating appetite; however, these studies produce conflicting results. These discrepancies may be, in part, due to methodological differences, including differences in exercise intensity and pre-exercise energy status. Consequently, the current study utilized a translational, well-controlled, within-subject, treadmill exercise protocol to investigate the differential effects of energy status and exercise intensity on post-exercise feeding behavior and appetite-controlling neurons in the hypothalamus. Mature, untrained male mice were exposed to acute sedentary, low (10m/min), moderate (14m/min), and high (18m/min) intensity treadmill exercise in a randomized crossover design. Fed and 10-hour-fasted mice were used, and food intake was monitored 48h. post-exercise. Immunohistochemical detection of cFOS was performed 1-hour post-exercise to determine changes in hypothalamic NPY/AgRP, POMC, tyrosine hydroxylase, and SIM1-expressing neuron activity concurrent with changes in food intake. Additionally, stains for pSTAT3tyr705 and pERKthr202/tyr204 were performed to detect exercise-mediated changes in intracellular signaling. Results demonstrated that fasted high intensity exercise suppressed food intake compared to sedentary trials, which was concurrent with increased anorexigenic POMC neuron activity. Conversely, fed mice experienced augmented post-exercise food intake, with no effects on POMC neuron activity. Regardless of pre-exercise energy status, tyrosine hydroxylase and SIM1 neuron activity in the paraventricular nucleus was elevated, as well as NPY/AgRP neuron activity in the arcuate nucleus. Notably, these neuronal changes were independent from changes in pSTAT3tyr705 and pERKthr202/tyr204 signaling. Overall, these results suggest fasted high intensity exercise may be beneficial for suppressing food intake, possibly due to hypothalamic POMC neuron excitation. Furthermore, this study identifies a novel role for pre-exercise energy status to differentially modify post-exercise feeding behavior and hypothalamic neuron activity, which may explain the inconsistent results from studies investigating exercise as a weight loss intervention

Behavioral and Transcriptome Profiling of Heterozygous Rab10 Knock-Out Mice

This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.A central question in the field of aging research is to identify the cellular and molecular basis of neuroresilience. One potential candidate is the small GTPase, Rab10. Here, we used Rab101/ mice to investigate the molecular mecha-nisms underlying Rab10-mediated neuroresilience. Brain expression analysis of 880 genes involved in neurodegener-ation showed that Rab101/ mice have increased activation of pathways associated with neuronal metabolism, structural integrity, neurotransmission, and neuroplasticity compared with their Rab101/1 littermates. Lower activation was observed for pathways involved in neuroinflammation and aging. We identified and validated several differentially expressed genes (DEGs), including Stx2, Stx1b, Vegfa, and Lrrc25 (downregulated) and Prkaa2, Syt4, and Grin2d (upregulated). Behavioral testing showed that Rab101/ mice perform better in a hippocampal-dependent spatial task (object in place test), while their performance in a classical conditioning task (trace eyeblink classical condition-ing, TECC) was significantly impaired. Therefore, our findings indicate that Rab10 differentially controls the brain cir-cuitry of hippocampal-dependent spatial memory and higher-order behavior that requires intact cortex-hippocampal circuitry. Transcriptome and biochemical characterization of these mice suggest that glutamate ionotropic receptor NMDA type subunit 2D (GRIN2D or GluN2D) is affected by Rab10 signaling. Further work is needed to evaluate whether GRIN2D mediates the behavioral phenotypes of the Rab101/ mice. We conclude that Rab101/ mice de-scribed here can be a valuable tool to study the mechanisms of resilience in Alzheimer’s disease (AD) model mice and to identify novel therapeutical targets to prevent cognitive decline associated with normal and pathologic aging.ECU Open Access Publishing Support Fun

Accuracy of 5 Common Age-Predicted Maximal Heart Rate Equations

International Journal of Exercise Science 13(7): 1242-1250, 2020. Age-predicted maximal heart rate (APMHR) is an essential measure for healthcare professionals in determining cardiovascular response to exercise testing, exertion, and prescription. Although multiple APMHR prediction equations have been validated for specific populations, the accuracy of each within a general population requires testing. We aimed to determine which APMHR equation (Fox, Gellish, Gulati, Tanaka, Arena, Astrand, Nes, Fairbarn) most accurately predicts max heart rate (HRmax) in a general population. HRmax from 99 graded treadmill exercise tests (GXT) were measured. GXTs ended upon volitional fatigue and were only included for analysis if RER \u3e 1.10. Individual paired t-test were performed to determine if significant differences existed between measured and predicted HRmax,along with root mean square errors for each equation. Bland-Altman plots were constructed to determine agreement between equations and measured HRmax. Significant differences between measured and predicted HRmax were found for the Gulati, Astrand, Nes, and Fairbarn (male) equations (p \u3c 0.05). Bland-Altman plots revealed wide limits of agreement for all nine APMHR equations, suggesting poor agreement between measured and predicted HRmax. Proportional bias indicates that prediction equations under and overestimated HRmax in individuals with lower and higher measured HRmax, respectively, with the exception of the Fox equation. All equations used in this study show poor agreement between measured HRmax and APMHR. The Fox equation may represent the best option for a general population as it is less likely to under or overestimate based on individual HRmax. Individuals should use data from GXTs to determine HRmax when applicable to ensure accuracy