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Fasting can be divided into intermittent short-term fasting (ISTF; frequent fasts lasting <24 h) and long-term fasting (LTF; fasts lasting >24 h and up to several weeks). ISTF extends lifespan and ameliorates a wide range of diseases in animal models. ISTF and ketogenic diets (KDs) reduce oxidative stress by direct actions on the mitochondria and by inducing the expression of antioxidant enzymes. The ketone body β-hydroxybutyrate suppresses inflammation by inhibiting the nucleotide oligomerization domain-like receptor family, pyrin domain-containing 3 (NLRP3) inflammasome. Human clinical trials demonstrated disease-modifying effects of ISTF in obesity, diabetes, and chronic inflammatory disorders. Frequent switching between ketogenic and nonketogenic states may optimize health and resilience. Intermittent short-term fasting (ISTF) and ketogenic diets (KDs) exert overlapping but not identical effects on cell metabolism, function, and resilience. Whereas health benefits of KD are largely mediated by the ketone bodies (KBs), ISTF engages additional adaptive physiological responses. KDs act mainly through inhibition of histone deacetylases (HDACs), reduction of oxidative stress, improvement of mitochondria efficiency, and control of inflammation. Mechanisms of action of ISTF include stimulation of autophagy, increased insulin and leptin sensitivity, activation of AMP-activated protein kinase (AMPK), inhibition of the mechanistic target of rapamycin (mTOR) pathway, bolstering mitochondrial resilience, and suppression of oxidative stress and inflammation. Frequent switching between ketogenic and nonketogenic states may optimize health by increasing stress resistance, while also enhancing cell plasticity and functionality. Intermittent short-term fasting (ISTF) and ketogenic diets (KDs) exert overlapping but not identical effects on cell metabolism, function, and resilience. Whereas health benefits of KD are largely mediated by the ketone bodies (KBs), ISTF engages additional adaptive physiological responses. KDs act mainly through inhibition of histone deacetylases, reduction of oxidative stress, improvement of mitochondria efficiency, and control of inflammation. Mechanisms of action of ISTF include stimulation of autophagy, increased insulin and leptin sensitivity, activation of AMP-activated protein kinase (AMPK), inhibition of the mechanistic target of rapamycin (mTOR) pathway, bolstering mitochondrial resilience, and suppression of oxidative stress and inflammation. Frequent switching between ketogenic and nonketogenic states may optimize health by increasing stress resistance, while also enhancing cell plasticity and functionality. [ABSTRACT FROM AUTHOR]

Raw bioelectrical impedance measurements are often used as a prognosticator of health status because of their association with disease states and malnutrition. Although studies consistently show the effect of physical characteristics on bioelectrical impedance, few investigations describe the effect of race, particularly for Black adults, and many bioelectrical impedance standards were produced from primarily White adults almost 2 decades ago. Therefore, this study sought to evaluate the racial differences in bioelectrical impedance measurements using bioimpedance spectroscopy between non-Hispanic White and non-Hispanic Black adults matched for age, sex, and body mass index. We hypothesized that Black adults would have a lower phase angle from higher resistance and lower reactance compared with White adults. One hundred non-Hispanic White (n = 50) and non-Hispanic Black (n = 50) males (n = 34) and females (n = 66) matched for sex, age, and body mass index completed this cross-sectional study. Participants underwent several anthropometric assessments, including height, weight, waist circumference, hip circumference, bioimpedance spectroscopy, and dual-energy X-ray absorptiometry. Bioelectrical impedance measures of resistance, reactance, phase angle, and impedance were all collected at frequencies of 5, 50, and 250 kHz and bioelectrical impedance vector analysis was performed using 50-kHz data. There were no significant differences for any anthropometric variable between Black and White participants in the total sample or by sex groups. In addition, there were no significant racial differences for any bioelectrical impedance assessment, including bioelectrical impedance vector analysis. Differences in bioelectrical impedance are likely not a function of race between Black and White adults and concerns regarding its utility should not be based on this characteristic. After accounting for all relevant physical attributes known to influence bioelectrical impedance measurements, assessments, including bioimpedance vector analyses, were not significantly different between non-Hispanic Black and White adults. Concerns regarding its utility for Black adults should not be based on race. The absence of race-dependent bioelectrical values may improve clinical confidence and prognostic value in these techniques and limit racial health disparities for Black adults. BMI, body mass index. [Display omitted] [ABSTRACT FROM AUTHOR]

Visual body composition (VBC) estimates produced from smartphone-based artificial intelligence represent a user-friendly and convenient way to automate body composition remotely and without the inherent geographical and monetary restrictions of other body composition methods. However, there are limited studies that have assessed the reliability and agreement of this method and thus, the aim of this study was to evaluate VBC estimates compared to a 4-compartment (4C) criterion model. A variety of body composition assessments were conducted across 184 healthy adult participants (114 F, 70 M) including dual-energy X-ray absorptiometry and bioimpedance spectroscopy for utilization in the 4C model and automated assessments produced from two smartphone applications (Amazon Halo®, HALO; and myBVI®) using either Apple® or Samsung® phones. Body composition components were compared to a 4C model using equivalence testing, root mean square error (RMSE), and Bland–Altman analysis. Separate analyses by sex and racial/ethnic groups were conducted. Precision metrics were conducted for 183 participants using intraclass correlation coefficients (ICC), root mean squared coefficients of variation (RMS-%CV) and precision error (PE). Only %fat produced from HALO devices demonstrated equivalence with the 4C model although mean differences for HALO were <±1.0 kg for FM and FFM. RMSEs ranged from 3.9% to 6.2% for %fat and 3.1–5.2 kg for FM and FFM. Proportional bias was apparent for %fat across all VBC applications but varied for FM and FFM. Validity metrics by sex and specific racial/ethnic groups varied across applications. All VBC applications were reliable for %fat, fat mass (FM), and fat-free mass (FFM) with ICCs ≥0.99, RMS-%CV between 0.7% and 4.3%, and PEs between 0.3% and 0.6% for %fat and 0.2–0.5 kg for FM and FFM including assessments between smartphone types. Smartphone-based VBC estimates produce reliable body composition estimates but their equivalence with a 4C model varies by the body composition component being estimated and the VBC being employed. VBC estimates produced by HALO appear to have the lowest error, but proportional bias and estimates by sex and race vary across applications. [ABSTRACT FROM AUTHOR]

Chronobiology plays a crucial role in modulating many physiologic systems in which there is nutritional synergism with meal timing. Given that intermittent fasting (IF) has grown as a flexible dietary method consisting of delayed or early eating windows, this scoping review addresses the effects of IF protocols on metabolism as they relate to clinical nutrition and the circadian system. Although nocturnal habits are associated with circadian misalignments and impaired cardiometabolic profile—and nutritional physiology is better orchestrated during the day—most findings are based on animal experiments or human studies with observational designs or acute meal tests. Well-controlled randomized clinical trials employing IF protocols of delayed or early eating windows have sometimes demonstrated clinical benefits, such as improved glycemic and lipid profiles, as well as weight loss. However, IF does not appear to be more effective than traditional diets at the group level, and its effects largely depend on energy restriction. Thus, efforts must be made to identify patient biological rhythms, preferences, routines, and medical conditions before individual dietary prescription in clinical practice. [ABSTRACT FROM AUTHOR]

Introduction/Background : Few investigations have sought to explain discrepancies between dual-energy X-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA) body composition estimates. The purpose of this analysis was to explore physiological and anthropometric predictors of discrepancies between DXA and BIA total and segmental body composition estimates. Methodology : Assessments via DXA (GE Lunar Prodigy) and single-frequency BIA (RJL Systems Quantum V) were performed in 179 adults (103 F, 76 M, age: 33.6 ± 15.3 yr; BMI: 24.9 ± 4.3 kg/m2). Potential predictor variables for differences between DXA and BIA total and segmental fat mass (FM) and lean soft tissue (LST) estimates were obtained from demographics and laboratory techniques, including DXA, BIA, bioimpedance spectroscopy, air displacement plethysmography, and 3-dimensional optical scanning. To determine meaningful predictors, Bayesian robust regression models were fit using a t -distribution and regularized hierarchical shrinkage "horseshoe" prior. Standardized model coefficients (β) were generated, and leave-one-out cross validation was used to assess model predictive performance. Results: LST hydration (i.e., total body water:LST) was a predictor of discrepancies in all FM and LST variables (|β|: 0.20–0.82). Additionally, extracellular fluid percentage was a predictor for nearly all outcomes (|β|: 0.19–0.40). Height influenced the agreement between whole-body estimates (|β|: 0.74–0.77), while the mass, length, and composition of body segments were predictors for segmental LST estimates (|β|: 0.23–3.04). Predictors of segmental FM errors were less consistent. Select sex-, race-, or age-based differences between methods were observed. The accuracy of whole-body models was superior to segmental models (leave-one-out cross-validation-adjusted R 2 of 0.83–0.85 for FM TOTAL and LST TOTAL vs. 0.20–0.76 for segmental estimates). For segmental models, predictive performance decreased in the order of: appendicular lean soft tissue, LST LEGS , LST TRUNK and FM LEGS , FM ARMS , FM TRUNK , and LST ARMS. Conclusions : These findings indicate the importance of LST hydration, extracellular fluid content, and height for explaining discrepancies between DXA and BIA body composition estimates. These general findings and quantitative interpretation based on the presented data allow for a better understanding of sources of error between 2 popular segmental body composition techniques and facilitate interpretation of estimates from these technologies. [ABSTRACT FROM AUTHOR]

Background: The Brozek and Siri formulas estimate relative adiposity (%Fat) from total body density (D b) using a 2-compartment (2C) model. Racial/ethnic differences in D b have been reported, along with subsequent errors in estimated %Fat. Objective: The primary aim of this systematic review and meta-analysis was to examine potential race/ethnic differences in the accuracy of the Brozek and Siri 2C formulas using aggregate-level data. Methods: Peer-reviewed studies available in English that provided 2C and 4C estimates of %Fat were located using searches of the PubMed (n = 150), Scopus (n = 170), and Web of Science (n = 138) online electronic databases. Random-effects models were used to determine potential differences between racial groups using a mean ES and 95% confidence intervals. Results: The cumulative results from 78 effects indicate that the relative accuracy of the Brozek equation did not vary between racial groups (between group p = 0.053). In contrast, the Siri equation slightly underestimated %Fat for Asian adults (ES WMD = −1.40%, 95%CI –2.33% to −0.46%; p = 0.004) and Black adults (ES WMD = –1.10%, 95%CI −2.11% to −0.08%; p = 0.034), with no significant differences observed in Hispanic adults (ES WMD = 0.64%, 95%CI −1.02% to 2.31%; p = 0.448) and White adults (ES WMD = 0.08%, 95%CI −0.42% to 0.57%; p = 0.766) (between group p = 0.019). Conclusion: Small, but statistically significant, error was found between racial groups when estimating %Fat using the 2C Siri equation when compared to 4C models. However, the observed error due to race/ethnicity appears to be of little clinical or practical significance when using either equation. [ABSTRACT FROM AUTHOR]

Background: Offset scanning procedures, such as reflection scanning, allow for dual-energy x-ray absorptiometry (DXA) body composition assessment of individuals who are too broad for standard scanning dimensions. However, limited information is available concerning the precision of this procedure, particularly in athletes. Methodology: Twenty-seven muscular athletes (n = 17 males, BMI: 28.8 ± 2.0 kg/m2, DXA body fat: 12.5 ± 2.7%; n = 10 females, BMI: 22.8 ± 1.6 kg/m2, DXA body fat: 19.2 ± 3.4%) underwent consecutive DXA scans on a GE Lunar Prodigy scanner using the reflection scanning technique. The fully automated output was obtained for each scan, and an additional version of each scan's output was saved after manual adjustment of regions of interest (ROI). Metrics of reliability and precision were calculated for total and regional body mass (BM), lean mass (LM), fat mass (FM) and bone mineral content (BMC). These metrics included the precision error (PE), least significant change, ΔMean, technical error of measurement, intraclass correlation coefficient, smallest worthwhile effect and minimum difference considered real. Results: Reflection scanning produced small errors for BM (PE: ∼0.5%), LM (PE < 1%) and BMC (PE: ∼1.2%), with larger errors observed for total FM (PE: ∼3%). Manual ROI adjustment produced lower errors for total BM, LM, FM, and BMC, as well as lower errors for most regional estimates. The utilization of automated ROI revealed concerns unique to reflection scanning, including unnecessary estimation of trunk composition, which appreciably increased error in this region. Regional estimates produced higher errors for all variables as compared to whole-body estimates, although which regions produced the highest errors differed between BM, LM, FM, and BMC. Conclusions: Reflection scanning allows DXA body composition assessment in individuals exceeding traditional scanning dimensions, including broad athletes. Although this procedure introduces error, it may be minimized through manual adjustment of ROIs and consistency of analysis methods. [ABSTRACT FROM AUTHOR]

Body composition assessment via 3-dimensional optical (3DO) scanning has emerged as a rapid and simple evaluation method. The aim of this study was to establish the precision of body composition estimates from four commercially available 3DO scanners and evaluate their validity as compared to a reference 4-component (4C) model. The body composition of 171 participants was assessed using four commercially-available 3DO scanners (FIT3D®, Naked Labs®, Size Stream®, and Styku®) and a 4C model utilizing data from dual-energy x-ray absorptiometry, air displacement plethysmography, and bioimpedance spectroscopy. Body composition estimates were compared via equivalence testing, Deming regression, Bland–Altman analysis, concordance correlation coefficients (CCC), root mean square error (RMSE), and related metrics. Precision metrics, including the root mean square coefficient of variation (RMS-%CV), precision error, and intraclass correlation coefficient, were generated for duplicate scans in 139 participants. All scanners produced reasonably reliable estimates, with RMS-%CV of 2.3–4.3% for body fat percentage (BF%), 2.5–4.3% for fat mass (FM), and 0.7–1.4% for fat-free mass (FFM). ICC values ranged from 0.975 to 0.996 for BF% and 0.990 to 0.999 for FM and FFM. All scanners except Styku® demonstrated equivalence with 4C, using 5% equivalence regions, and constant errors of <1% for BF% and ≤0.5 kg for FM and FFM. However, the slopes of regression lines differed from the line of identity for most scanners and variables. CCC values ranged from 0.74 to 0.90 for BF%, 0.85 to 0.95 for FM, and 0.93 to 0.97 for FFM. RMSE values ranged from 3.7 to 6.1% for BF% and 2.8–4.6 kg for FM and FFM. Bland–Altman analysis indicated proportional bias of varying magnitudes was present for all scanners. Commercially available 3DO scanners produce relatively reliable body composition estimates. Three out of four scanners demonstrated equivalence with a 4C model for assessments of BF%, FM, and FFM, although other metrics of validity varied among scanners, and proportional bias was present for all scanners. [ABSTRACT FROM AUTHOR]

A four-compartment (4C) model quantifies fat, water, mineral and residual. As such, 4C models are more accurate than two-compartment (2C) models based off cadaver reference values (RV), which necessitate assumptions regarding fat-free mass (FFM) characteristics. Nonetheless, research has yet to determine whether the FFM characteristics of Hispanics are similar to non-Hispanic Caucasians and RV. The aim of this analysis was to compare the FFM characteristics of Hispanics to non-Hispanic Caucasians and cadaver RV. Data from 2 separate research centers were pooled to create a sample of 100 and 119 Hispanic males and females (age: 18–54 yrs; BMI: 16.46–42.27 kg/m2), respectively, and 47 and 55 non-Hispanic Caucasian males and females (age: 18–54 yrs; BMI: 16.00–36.67 kg/m2), respectively (n = 331). A 4C model was determined using bioimpedance analysis for hydration, dual energy X-ray absorptiometry for mineral, and air displacement plethysmography for body density (4C-ADP). FFM was calculated via the 4C-ADP and FFM characteristics (i.e., density [D FFM ], water [TBW:FFM], bone mineral [Mo:FFM], and residual [R:FFM]) were compared between sexes and ethnicities using a one-way ANOVA and against RV via a one sample t -test. In Hispanics, all FFM characteristics significantly differed from cadaver RV (all p < 0.05). In contrast, D FFM and TBW:FFM of non-Hispanic Caucasians were similar to cadaver RV for both sexes (all p > 0.05). Moreover, the R:FFM of non-Hispanic Caucasian females did not differ from cadaver RV (p = 0.403) whereas all other comparisons were significantly different (all p < 0.05). Sex comparisons within Hispanic participants revealed FFM characteristics were similar between males and females other than Mo:FFM (p < 0.001) whereas all FFM characteristics were similar between non-Hispanic Caucasian males and females (all p > 0.05). All of the ethnicity comparisons within males were statistically significant (all p < 0.05). Moreover, ethnicity comparisons within females were statistically significant for all comparisons other than Mo:FFM (p = 0.258). The observed differences in FFM characteristics of Hispanics as compared to non-Hispanics Caucasians and reference values indicate that allied health professionals should employ appropriate caution when estimating body composition via 2C models in Hispanic populations. [ABSTRACT FROM AUTHOR]

Common body composition estimation techniques necessitate assumptions of uniform fat-free mass (FFM) characteristics, although variation due to sex, race, and body characteristics may occur. National Health and Nutrition Examination Survey data from 1999 to 2004, during which paired dual-energy x-ray absorptiometry (DXA) and bioimpedance spectroscopy assessments were performed, were used to estimate FFM characteristics in a sample of 4619 US adults. Calculated FFM characteristics included the density and water, bone mineral, and residual content of FFM. A rapid 4-component model was also produced using DXA and bioimpedance spectroscopy data. Study variables were compared across sex, race/ethnicity, body mass index (BMI), and age categories using multiple pairwise comparisons. A general linear model was used to estimate body composition after controlling for other variables. Statistical analyses accounted for 6-year sampling weights and complex sampling design of the National Health and Nutrition Examination Survey and were based on 5 multiply imputed datasets. Differences in FFM characteristics across sex, race, and BMI were observed, with notable dissimilarities between men and women for all outcome variables. In racial/ethnic comparisons, non-Hispanic blacks most commonly presented distinct FFM characteristics relative to other groups, including greater FFM density and proportion of bone mineral. Body composition errors between DXA and the 4-component model were significantly influenced by sex, age, race, and BMI. In conclusion, FFM characteristics, which are often assumed in body composition estimation methods, vary due to sex, race/ethnicity, and weight status. The variation of FFM characteristics in diverse populations should be considered when body composition is evaluated. [ABSTRACT FROM AUTHOR]

Segmental estimates add specificity to body composition evaluation and could potentially have greater health and function implications than whole-body estimates alone. The aim of this study was to quantify the level of agreement between total and segmental fat mass (FM) and lean soft tissue (LST) estimates from dual-energy x-ray absorptiometry (DXA) and single-frequency bioelectrical impedance analysis (SFBIA). Assessments via DXA (GE® Lunar Prodigy) and SFBIA (RJL Systems® Quantum V) were performed in 179 adults (103 F, 76 M; 30% racial/ethnic minorities). Total and segmental FM and LST estimates were compared in the entire sample, females, and males using null hypothesis significance testing (NHST; via paired-samples t-tests), equivalence testing with 5% equivalence regions, Bland–Altman analysis with linear regression, and additional error metrics. In females and the entire sample, all LST variables except LST ARMS exhibited equivalence between methods, despite statistically significant differences via NHST for most variables. In males, only estimates of LST TOTAL and appendicular lean soft tissue (ALST) were equivalent between methods. LST variables exhibited minimal proportional bias. All FM variables failed to exhibit equivalence, and most FM variables were underestimated by SFBIA. The magnitude of relative errors for FM generally appeared larger in males than females. Proportional bias was observed for FM LEGS and FM ARMS , as well as FM TOTAL in females only. ALST estimates were equivalent between DXA and SFBIA in all analyses, did not differ between methods based on NHST, exhibited relatively low errors, and displayed no proportional bias. In the context of the present study, DXA and SFBIA LST estimates appear to exhibit better overall agreement than FM estimates. Additionally, overall agreement between SFBIA and DXA may be superior in females as compared to males. The relatively strong agreement between ALST estimates indicates potential utility of SFBIA for clinical applications, such as evaluation of sarcopenia. Further investigation into the explanatory physiological (e.g. hydration) and anthropometric (e.g. segment lengths, circumferences, and volumes) variables predicting individual discrepancies between DXA and BIA is warranted. [ABSTRACT FROM AUTHOR]

Lipid profile screening is crucial for the prevention, evaluation and treatment of cardiovascular (CV) disease (CVD). Small dense low-density lipoprotein-cholesterol (sdLDL-C) is an emerging biomarker associated with CVD and several comorbidities. The aim of this literature review is to discuss the potential importance of sdLDL-C as a surrogate biomarker for managing CVD by explaining its pathophysiology and promising treatments. The current synthesis demonstrates the impact of sdLDL-C on CV ailments, which are related to arterial pathologies and dysregulated lipid profiles. Several drug classes used for the treatment of dyslipidemia decrease the sdLDL-C concentrations. For instance, statins, fibrates, ezetimibe, nicotinic acid, resin and orlistat are pharmacological sdLDL-C-lowering agents. Regarding nutritional strategies, simple carbohydrate types, such as fructose, are common in Western diets and should be reduced or avoided due to their potential in increasing synthesis of sdLDL-C subclasses. Dairy products, avocado, pistachios, soy-based diet (except for hydrogenated soybean oil) and corn oil seem to be suitable food choices for a therapeutic diet aiming to control sdLDL-C concentrations. However, thus far dietary supplementation with omega-3 fatty acids is unsubstantiated for decreasing sdLDL-C concentration. In conclusion, coupled with the traditional lipid profile, measurement or even the estimation of sdLDL-C as a routine screening should be encouraged, whereas more insights into the control of sdLDL-C are imperative. Appropriate clinical reference ranges for sdLDL-C are also needed. [ABSTRACT FROM AUTHOR]

The validity of dual-energy x-ray absorptiometry (DXA) and multifrequency bioelectrical impedance analysis (MFBIA) for detecting changes in fat mass (FM), fat-free mass (FFM), and body fat percentage (BF%) was evaluated, as compared to a rapid 4-component (4C) model, in 31 females completing 8 weeks of resistance training. Analyses were performed in all participants (ALL) and in subgroups that gained FFM but lost FM (R subgroup) or gained both FFM and FM (G subgroup). It was hypothesized that methods would comparably detect changes in ALL, but discrepancies would occur in subgroup analysis. Changes in body composition did not significantly differ between 4C, DXA, and MFBIA. Equivalence testing indicated that similar changes were detected by DXA and MFBIA, compared to 4C, for ΔFFM in all analyses and ΔBF% in ALL and R subgroup. ΔFM was equivalent to 4C only in R subgroup for DXA and G subgroup for MFBIA. For ΔFM and ΔBF%, DXA and MFBIA produced similar magnitude errors in ALL. However, DXA exhibited lower error in R subgroup, whereas MFBIA exhibited lower error in G subgroup. For ΔFFM, DXA and MFBIA exhibited relatively similar errors in ALL and R subgroup, although MFBIA displayed proportional bias and weaker correlations with 4C than DXA. In G subgroup, MFBIA exhibited lower errors and a higher correlation with 4C ΔFFM than DXA. Although both DXA and MFBIA may have utility for estimating body composition changes during FFM accretion, DXA may be superior during simultaneous FM loss, whereas MFBIA may produce lower error during simultaneous FM gain. [ABSTRACT FROM AUTHOR]

Objectives: To conduct a systematic review and a meta-analysis of studies exploring the effects of caffeine and/or sodium bicarbonate on performance in the Yo-Yo test.Design: Systematic review and meta-analysis.Methods: A total of six databases were searched, and random-effects meta-analyses were performed examining the isolated effects of caffeine and sodium bicarbonate on performance in the Yo-Yo test.Results: After reviewing 988 search records, 15 studies were included. For the effects of caffeine on performance in the Yo-Yo test, the meta-analysis indicated a significant favoring of caffeine as compared with the placebo conditions (p=0.022; standardized mean difference [SMD]=0.17; 95% CI: 0.08, 0.32; +7.5%). Subgroup analyses indicated that the effects of caffeine were significant for the level 2 version of the Yo-Yo test, but not level 1. Four out of the five studies that explored the effects of sodium bicarbonate used the level 2 version of the Yo-Yo test. The pooled SMD favored the sodium bicarbonate condition as compared with the placebo/control conditions (p=0.007; SMD: 0.36; 95% CI: 0.10, 0.63; +16.0%).Conclusions: This review demonstrates that isolated ingestion of caffeine and sodium bicarbonate enhances performance in the Yo-Yo test. Given these ergogenic effects, the intake of caffeine and sodium bicarbonate before the Yo-Yo test needs to be standardized (i.e., either restricted or used in the same way before each testing session). Furthermore, the results suggest that individuals competing in sports involving intermittent exercise may consider supplementing with caffeine or sodium bicarbonate for acute improvements in performance. [ABSTRACT FROM AUTHOR]

Background A very limited amount of research has examined intermittent fasting (IF) programs, such as time-restricted feeding (TRF), in active populations. Objective Our objective was to examine the effects of TRF, with or without β-hydroxy β-methylbutyrate (HMB) supplementation, during resistance training (RT). Methods This study employed a randomized, placebo-controlled, reduced factorial design and was double-blind with respect to supplementation in TRF groups. Resistance-trained females were randomly assigned to a control diet (CD), TRF, or TRF plus 3 g/d HMB (TRFHMB). TRF groups consumed all calories between 1200 h and 2000 h, whereas the CD group ate regularly from breakfast until the end of the day. All groups completed 8 wk of supervised RT and consumed supplemental whey protein. Body composition, muscular performance, dietary intake, physical activity, and physiological variables were assessed. Data were analyzed prior to unblinding using mixed models and both intention-to-treat (ITT) and per protocol (PP) frameworks. Results Forty participants were included in ITT, and 24 were included in PP. Energy and protein intake (1.6 g/kg/d) did not differ between groups despite different feeding durations (TRF and TRFHMB: ∼7.5 h/d; CD: ∼13 h/d). Comparable fat-free mass (FFM) accretion (+2% to 3% relative to baseline) and skeletal muscle hypertrophy occurred in all groups. Differential effects on fat mass (CD: +2%; TRF: −2% to −4%; TRFHMB: −4% to −7%) were statistically significant in the PP analysis, but not ITT. Muscular performance improved without differences between groups. No changes in physiological variables occurred in any group, and minimal side effects were reported. Conclusions IF, in the form of TRF, did not attenuate RT adaptations in resistance-trained females. Similar FFM accretion, skeletal muscle hypertrophy, and muscular performance improvements can be achieved with dramatically different feeding programs that contain similar energy and protein content during RT. Supplemental HMB during fasting periods of TRF did not definitively improve outcomes. This study was prospectively registered at clinicaltrials.gov as NCT03404271. [ABSTRACT FROM AUTHOR]

Abstract The purpose of this study was to compare body fat estimates and fat-free mass (FFM) characteristics produced by multicompartment models when utilizing either dual energy X-ray absorptiometry (DXA) or single-frequency bioelectrical impedance analysis (SF-BIA) for bone mineral content (BMC) in a sample of physically active adults. Body fat percentage (BF%) was estimated with 5-compartment (5C), 4-compartment (4C), 3-compartment (3C), and 2-compartment (2C) models, and DXA. The 5C-Wang with DXA for BMC (i.e., 5C-Wang DXA) was the criterion. 5C-Wang using SF-BIA for BMC (i.e., 5C-Wang BIA), 4C-Wang DXA (DXA for BMC), 4C-Wang BIA (BIA for BMC), and 3C-Siri all produced values similar to 5C-Wang DXA (r > 0.99; total error [TE] < 0.83%; standard error of estimate < 0.67%; 95% limits of agreement [LOAs] < ±1.35%). The 2C models (2C-Pace, 2C-Siri, and 2C-Brozek) and DXA each produced similar standard error of estimate and 95% LOAs (2.13%–3.12% and ±4.15%–6.14%, respectively). Furthermore, 3C-Lohman DXA (underwater weighing for body volume and DXA for BMC) and 3C-Lohman BIA (underwater weighing for body volume and SF-BIA for BMC) produced the largest 95% LOAs (±5.94%–8.63%). The FFM characteristics (i.e., FFM density, water/FFM, mineral/FFM, and protein/FFM) for 5C-Wang DXA and 5C-Wang BIA were each compared with the "reference body" cadavers of Brozek et al. 5C-Wang BIA FFM density differed significantly from the "reference body" in women (1.103 ± 0.007 g/cm3; p < 0.001), but no differences were observed for 5C-Wang DXA or either 5C model in men. Moreover, water/FFM and mineral/FFM were significantly lower in men and women when comparing 5C-Wang DXA and 5C-Wang BIA with the "reference body," whereas protein/FFM was significantly higher (all p ≤ 0.001). 3C-Lohman BIA and 3C-Lohman DXA produced error similar to 2C models and DXA and are therefore not recommended multicompartment models. Although more advanced multicompartment models (e.g., 4C-Wang and 5C-Wang) can utilize BIA-derived BMC with minimal impact on body fat estimates, the increased accuracy of these models over 3C-Siri is minimal. [ABSTRACT FROM AUTHOR]

Many diets employ regular periods of fasting that extend beyond a typical overnight fast (i.e. intermittent fasting [IF]). Evaluation of acute fasting responses provides information concerning the potential theoretical rationale for IF. The purpose of the present investigation was to assess the test-retest reliability of hunger-related variables during 24-hour fasts and the relationship between these variables and body composition, as well as subsequent energy intake (EI) after fasting. Eleven participants (6 F, 5 M) completed two 24-hour fasts after being provided a 3-day standardized weight-maintenance diet. From 16 to 24 h of fasting, participants were directly observed and provided hourly assessments of hunger, desire to eat (DTE), prospective food consumption (PFC), fullness and energy. After the fast, participants were allowed ad libitum food consumption, and compensation was calculated as EI relative to weight-maintenance energy needs. Test-retest reliability for hunger-related assessments at particular durations of fasting was evaluated using intraclass correlation coefficients (ICC), changes in dependent variables were evaluated using ANOVA with repeated measures, and relationships between variables were explored using bivariate correlations. At 16 h of fasting, the ICCs for all hunger-related assessments were statistically significant (r = 0.67–0.91; p ≤ 0.05). However, as the fast progressed, reliability varied substantially. When averaged across the nine measurements, the ICCs were: 0.81 (fullness), 0.74 (PFC), 0.67 (energy), 0.44 (DTE) and 0.36 (hunger). Body fat percentage was significantly correlated with changes in PFC (r = 0.62, p = 0.04), hunger (r = 0.66, p = 0.03), DTE (r = 0.71, p = 0.02), and fullness (r = −0.63, p = 0.04), but not energy (r = −0.16, p = 0.64). Average EI compensation was only 60% of weight-maintenance needs, but substantial variability was observed (7 to 110% compensation). Compensation was significantly correlated with changes in PFC (r = 0.72, p = 0.01), hunger (r = 0.63, p = 0.04) and DTE (r = 0.60, p = 0.05), but not fullness (r = 0.58, p = 0.06) or energy (r = 0.35, p = 0.29). Compensation and body fat percentage were not correlated (r = 0.03, p = 0.94). The percent of energy intake from fat and protein increased after the fast (29.9 to 37.3% and 13.8 to 16.8%; p < 0.05), while the percent of energy intake from carbohydrate decreased (56.4 to 46.0%; p = 0.02). These results may have implications for IF programs. It is possible that the implementation of multiple “test fasts,” in which subjective variables and subsequent energy intake are evaluated, could be used to identify candidates who may be more likely to benefit from an IF program. [ABSTRACT FROM AUTHOR]

Dual-energy x-ray absorptiometry (DXA) and bioelectrical impedance analysis (BIA) are common methods of body composition assessment, but the agreement between these methods varies. Bias between DXA and single-frequency BIA was evaluated at 6 different time points in 48 active male and female adults consuming standardized high- and low-carbohydrate diets. It was hypothesized that fixed and proportional biases exist between DXA and BIA but that the extent of bias does not differ based on sex. Substantial fixed bias was present for estimates obtained by DXA and BIA, and both men and women exhibited proportional bias for fat mass (FM) and fat-free mass (FFM). The magnitude of bias was greater in women, and only women exhibited proportional bias for body fat percentage. In individuals with less FM, the mean difference between DXA and BIA was high, indicating that BIA underestimated FM relative to DXA. However, in individuals with greater FM, better agreement was seen. Correspondingly, the mean difference in FFM estimates was greater in individuals with less FFM. In some individuals with high quantities of FM or FFM relative to the sample, the relationship between devices was reversed such that BIA overestimated FM and underestimated FFM. The degree of disagreement between DXA and BIA varies substantially based on body size and sex such that all-encompassing statements regarding the comparability of these technologies cannot presently be made. [ABSTRACT FROM AUTHOR]

Pre-exercise meal frequency is commonly believed to impact exercise performance, but little is known about its impact on resistance training. This study investigated the impact of breakfast consumption on afternoon resistance training performance in habitual breakfast consumers and nonconsumers. A randomized, crossover study was conducted in Lubbock, TX between November 2021 and May 2022. Thirty-nine resistance-trained male (n = 20) and female (n = 19) adults (mean ± SD age 23.0 ± 4.7 years) who habitually consumed (≥5 d/wk; n = 19) or did not consume (≥5 d/wk; n = 20) breakfast completed the study. After the establishment of 1-repetition maximums at the first visit, participants completed 2 additional visits, each of which included 4 sets of barbell back squat, bench press, and deadlift, using 80% of their 1-repetition maximum after either consuming breakfast and lunch or the same food at lunch only. Repetitions, along with average and peak average concentric velocity and power, were measured for all repetitions throughout each exercise session. Visual analog scales were used to assess feelings of fatigue, energy, focus, hunger, desire to eat, and fullness throughout each exercise session. Data were analyzed using linear mixed-effects models. No interactions or main effects involving condition or habitual breakfast consumption were observed for resistance training outcomes, although sex differences were noted. Male participants performed significantly fewer repetitions on sets 2, 3, and 4 (P <.014) for total repetitions, on sets 2 and 4 for barbell back squat (P <.023), and set 4 for deadlift (P =.006), with no observed differences between sexes for bench press repetitions. Male participants displayed reductions in average power across all sets and exercises except deadlift. These data suggest that alterations in pre-exercise meal frequency may not influence afternoon resistance training performance provided similar total nutritional intake is consumed. [ABSTRACT FROM AUTHOR]

• Our outcomes indicated that intermittent fasting may produce improvements in body composition by decreasing body mass, body mass index, fat mass, and body fat percentage while preserving fat-free mass in adults performing resistance training. This systematic review and meta-analysis evaluated the influence of intermittent fasting (IF) in combination with resistance training (RT) on body composition outcomes. Studies examining IF vs. non-IF diets in individuals performing RT, published up to February 2021, were identified through PubMed, the Cochrane Library, Web of Science, Embase, and SCOPUS databases. Eight studies, including 221 participants were analyzed using a random-effects model to calculate weighted mean differences (WMDs) with 95% confidence intervals (CIs). Results indicated that IF had a significant effect on body mass (WMD = -2.08 kg; 95% CI: -3.04, -1.13), fat mass (WMD = -1.36 kg; 95% CI: -1.94, -0.78), body mass index (WMD = -0.52 kg/m2; 95% CI: -0.85, -0.19), and body fat percentage (WMD = -1.49%; 95% CI: -2.24, -0.74) relative to non-IF diets, without a significant effect for fat-free mass (WMD = -0.27 kg; 95% CI: -0.82, 0.28). The present systematic review and meta-analysis demonstrates potentially beneficial effects of IF in combination with RT for reducing body mass and body fat relative to non-IF control diets, with similar preservation of fat-free mass. [ABSTRACT FROM AUTHOR]

Due to the important role of follistatin (FLST), myostatin (MSTN) and growth differentiation factor 11 (GDF11) in muscle mass regulation; alterations in the FLST to MSTN ratio (F:M) may result in muscle mass changes in response to different concurrent training (CT) order. This study investigated the influence of 8 weeks of CT order on body composition and serum concentrations of FLST, MSTN, their ratio (F:M) and GDF11 in sarcopenic elderly men. Thirty sarcopenic elderly men (age = 64.3 ± 3.5 years) were randomly assigned into one of three groups, endurance followed by resistance training (E + R; n = 10), resistance followed by endurance training (R + E; n = 10) or control (C; n = 10). Serum concentrations of muscle regulatory markers, body composition, maximum rate of oxygen consumption (VO 2max), and upper and lower body strength were evaluated at baseline and after 8 weeks. The training protocol consisted of three training sessions per week for eight weeks. There were significant group-by-time interactions (P < 0.05) for FLST, MSTN, GDF11 and F:M ratio. FLST (E + R = 187 pg/mL and R + E = 292 pg/mL) and F:M ratio (E + R = 0.20 and R + E = 0.27) significantly increased (P < 0.05) while MSTN (E + R = −308 pg/mL and R + E = −294 pg/mL) and GDF11 (E + R = −12 pg/mL and R + E = −10 pg/mL) significantly decreased (P < 0.05) following eight weeks in the E + R and R + E compared to no changes in the C group. In addition, there were significant group x time interactions (P < 0.01) for weight, BMI, body fat percentage (BFP), skeletal muscle mass (SMM), VO 2max , upper body strength, and lower body strength. BFP (E + R = −1.5% and R + E = −2%) significantly decreased (P < 0.01) while weight (E + R = 2.4 kg and R + E = 1.1 kg), BMI (E + R = 0.8 kg/m2 and R + E = 0.3 kg/m2), SMM (E + R = 0.7 kg and R + E = 0.5 kg), VO 2max (E + R = 2.0 mL/kg/min and R + E = 1.8 mL/kg/min), upper body strength (E + R = 6.9 kg and R + E = 2.3 kg), and lower body strength (E + R = 9.8 kg and R + E = 4.4 kg) significantly increased (P < 0.01) in the E + R and R + E compared to no changes in the C group. CT increases the F:M ratio and FLST as well as reducing MSTN and GDF11 in sarcopenic elderly men. Additionally, CT improved weight, body composition, muscle mass, function, and aerobic fitness. Notably, these results after CT were achieved irrespective of endurance and resistance exercise order in this population. [ABSTRACT FROM AUTHOR]