This review summarizes the effects of different types of intermittent fasting (IF) on human cardiometabolic health, with a focus on energy metabolism. First, we discuss the coordinated metabolic adaptations (energy expenditure, hormonal changes and macronutrient oxidation) occurring during a 72 h fast. We then discuss studies investigating the effects of IF on cardiometabolic health, energy expenditure and substrate oxidation. Finally, we discuss how IF may be optimized by combining it with exercise. In general, IF regimens improve body composition, ectopic fat, and classic cardiometabolic risk factors, as compared to unrestricted eating, especially in metabolically unhealthy participants. However, it is still unclear whether IF provides additional cardiometabolic benefits as compared to continuous daily caloric restriction (CR). Most studies found no additional benefits, yet some preliminary data suggest that IF regimens may provide cardiometabolic benefits in the absence of weight loss. Finally, although IF and continuous daily CR appear to induce similar changes in energy expenditure, IF regimens may differentially affect substrate oxidation, increasing protein and fat oxidation. Future tightly controlled studies are needed to unravel the underlying mechanisms of IF and its role in cardiometabolic health and energy metabolism.

This study aimed to investigate the influence of different exercise training modalities [(i) a concurrent training based on physical activity recommendation from the World Health Organization group (PAR group), (ii) a high intensity interval training group (HIIT group), and (iii) a high intensity interval training adding whole-body electromyostimulation group (WB-EMS group)] on physical fitness in sedentary middle-aged adults. A total of 89 (52.7% women) middle-aged sedentary adults (53.7 ± 5.1 years old) were enrolled in the FIT-AGING study. Cardiorespiratory fitness was determined by a maximum treadmill test using indirect calorimetry. Lower, upper, and core body muscular strength were assessed by an isokinetic strength test, by the handgrip strength test, and by several core strength endurance tests, respectively. All the exercise types induced similar increases on cardiorespiratory fitness (Δ VO₂max ≥ 11%, Δ maximal heart rate ≥ 8%, and Δ total test duration ≥ 14%; all P ≤ 0.034), as well as on muscular strength (Δ extension and flexion peak torque ≥ 10%, Δ total hand grip ≥ 3%, Δ core strength endurance tests ≥ 20%; all P ≤ 0.050) compared with a control group. In conclusion, our results suggest that a 12-week structured exercise intervention improves physical fitness regardless of the training program in sedentary middle-aged adults. Despite slightly greater improvements in some physical fitness variables, the changes observed in the WB-EMS group were not superior to the other exercise programs.

The validity of most commercially available metabolic cart is mostly unknown. Thus, we aimed to determine the accuracy, precision, within-subject reproducibility, and concordance of RMR and RER measured by four commercially available metabolic carts [Cosmed Q-NRG, Vyaire Vyntus CPX, Maastricht Instruments Omnical, and Medgraphics Ultima CardiO2]. Further, we studied whether a previously proposed simulation-based post-calorimetric calibration of cart readouts [individual calibration control evaluation (ICcE)] modify the RMR and RER reproducibility and concordance. Three experiments simulating different RMR and RER by controlled pure gas (N₂ and CO₂) infusions were conducted on 5 non-consecutive days. Moreover, 30-min methanol burns were per- formed on 3 non-consecutive days. Lastly, the RMR and RER of 29 young non-ventilated adults (11 women; 25 ± 4 years-old; BMI: 24.1 ± 3.2 kg/m²) were assessed twice using each instrument, 24 hours apart, under standardized conditions. The Omnical presented the lowest measurement error for RER (Omnical = 1.7 ± 0.9%; Vyntus = 4.5 ± 2.0%; Q-NRG = 6.6 ± 1.9%; Ultima = 6.8 ± 6.5%) and EE (Omnical = 1.5 ± 0.5%; Q- NRG = 2.5 ± 1.3%; Ultima = 10.7 ± 11.0%; Vyntus = 13.8 ± 5.0%) in all in vitro experiments (controlled pure gas infusions and methanol burns). In humans, the 4 metabolic carts provided discordant RMR and RER estimations (all P < 0.001). No differences were detected in RMR within-subject reproducibility (P = 0.058; Q-NRG inter-day coefficient of variance = 3.6 ± 2.5%; Omnical = 4.8 ± 3.5%; Vyntus = 5.0 ± 5.6%; Ultima = 5.7 ± 4.6%), although the Ultima CardiO2 provided larger RER inter-day differences (4.6 ± 3.5%) than the other carts (P = 0.001; Omnical = 1.9 ± 1.7%; Vyntus = 2.1 ± 1.3%; Q-NRG = 2.4 ± 2.1%). The ICcE procedure did not modify the RMR or RER concordance and did not reduce the inter-day differences in any of the carts. The 4 metabolic carts provided discordant measurements of RMR and RER. Overall, the Omnical provides more accurate and precise estimations of RMR and RER than the Q-NRG, Vyntus and Ultima CardiO2, and might be considered the best for assessing RMR and RER in non-ventilated humans. Finally, our results do not support the use of an ICcE procedure.

Background: Ageing is associated with sleep pattern changes and body composition changes, which are related to several diseases. Purpose: This study aimed to analyse the association between sleep quality and an extensive set of body composition parameters (waist-hip ratio, body mass index, bone mineral content, bone mineral density, lean mass, lean mass index, fat mass, fat mass percentage, fat mass index, visceral adipose tissue) and sleep quality in sedentary middle-aged adults. We also aimed to evaluate whether the possible associations accord between subjective and objective measurements of sleep quality. Methods: 74 (39 women) middle-aged sedentary adults (40–65 years old) participated in the present study. The sleep quality was assessed using the Pittsburgh sleep quality index (PSQI) scale and accelerometers. A PSQI global score more than 5 indicates poor sleep quality. Weight, height, waist and hip circumferences were measured, and body mass index and waist-hip ratio were also calculated. Body composition was assessed with a dual-energy X-ray absorptiometry scanner. Results: The PSQI global score was negatively associated with bone mineral content, bone mineral density, lean mass, lean mass index and positively associated with fat mass percentage. No association was found between accelerometer parameters and body composition variables. Conclusion: We showed that a subjective poor sleep quality was negatively associated with bone mineral content (BMC), bone mineral density (BMD), lean mass and lean mass index (LMI) whereas was positively associated with fat mass percentage in middle-aged adults. We also observed that these associations did not accord with objective sleep quality measurements.

To determine the acute effect of a single high‐intensity interval training (HIIT) session on testosterone and cortisol levels in healthy individuals, a systematic search of studies was conducted in MEDLINE and Web of Science databases from inception to February 2020. Meta‐analyses were performed to establish the acute effect of HIIT on testosterone and cortisol levels immediately after a single HIIT session; after 30 min and 60 min (primary outcomes); and after 120 min, 180 min, and 24 h (secondary outcomes, only for pre‐post intervention groups). Potential effect‐size modifiers were assessed by meta‐regression analyses and analyses of variance. Study quality was assessed using the Cochrane's risk of bias tool and the Physiotherapy Evidence Database scale. The meta‐analyses of 10 controlled studies (213 participants) and 50 pre‐post intervention groups (677 participants) revealed a significant increase in testosterone immediately after a single HIIT session (d = 0.92 and 0.52, respectively), which disappeared after 30 min (d = 0.18 and −0.04), and returned to baseline values after 60 min (d = −0.37 and −0.16). Significant increases of cortisol were found immediately after (d = 2.17 and 0.64), after 30 min (d = 1.62 and 0.67) and 60 min (d = 1.32 and 0.27). Testosterone and cortisol levels decreased significantly after 120 min (d = −0.48 and −0.95, respectively) and 180 min (d = −0.29 and −1.08), and returned to baseline values after 24 h (d = 0.14 and −0.02). HIIT components and participant's characteristics seem to moderate the effect sizes. In conclusion, testosterone and cortisol increase immediately after a single HIIT session, then drop below baseline levels, and finally return to baseline values after 24 h. This meta‐analysis provides a better understanding of the acute endocrine response to a single HIIT session, which would certainly be valuable for both clinicians and coaches in the prescription of exercise programs to improve health and performance. Testosterone and cortisol may be used as sensitive biomarkers to monitor the anabolic and catabolic response to HIIT.