Exploring the Dynamic Relationship between the Gut Microbiome and Body Composition across the Human Lifespan: A Systematic Review.

This systematic review aimed to identify different gut microbiome profiles across the human lifespan and to correlate such profiles with the body composition. PubMed, Scopus, and Cochrane were searched from inception to March 2022. Sixty studies were included in this systematic review. Overall, the gut microbiome composition in overweight participants exhibited decreased α-diversity, decreased levels of the phylum Bacteroidetes and its taxa, and increased levels of the phylum Firmicutes, its taxa, and the Firmicutes/Bacteroidetes ratio, in comparison to normal-weight participants. Other body composition parameters showed similar correlations. Fat mass and waist circumference were found to correlate positively with the Firmicutes taxa and negatively with the Bacteroidetes taxa. In contrast, lean body mass and muscle mass demonstrated a positive correlation with the Bacteroidetes taxa. Notably, these correlations were more pronounced in athletes than in obese and normal-weight individuals. The composition of the gut microbiome is evidently different in overweight individuals or athletes of all age groups, with the former tending towards decreased Bacteroidetes taxa and increased Firmicutes taxa, while a reversed relationship is observed concerning athletes. Further studies are needed to explore the dynamic relationship between energy intake, body composition, and the gut microbiome across the human lifespan.

The effect of chrono-nutritional manipulation of carbohydrate intake on sleep macrostructure: A randomized controlled trial.

BACKGROUND & AIMS: Over the years, there is a rapid increase in the prevalence of inadequate sleep and its detrimental consequences. Yet, the impact of prolonged nutritional interventions on sleep optimization remains unexplored. To examine the effect of carbohydrate manipulation combined with exercise training on sleep macro-structure. METHODS: Forty-two healthy, trained male volunteers were recruited for this study. The 4-week intervention consisted of three groups: i) Sleep Low-No Carbohydrates (SL-NCHO): participants consumed all their carbohydrate intake at regular intervals prior to evening training, ii) Sleep High-Low Glycemic Index (SH-LGI) and iii) Sleep High-High Glycemic Index (SH-HGI): Carbohydrate intake was spread throughout the day, both prior (60% of total CHO intake) and after evening training (40% of total CHO intake). The SH-LGI and SH-HGI groups differentiated by consuming either LGI or HGI foods in the evening, respectively. Alongside, participants performed a standardized exercise program combining resistance exercise and high-intensity interval training. Participants' sleep macro-structure was assessed with polysomnography, actigraphy, sleep diary, and sleep-wake questionnaires. RESULTS: Objective assessments revealed a substantial time-effect on sleep initiation, duration, and continuity. After the intervention, sleep onset latency decreased (p < 0.001), sleep duration was prolonged (p = 0.006), sleep efficiency increased (p < 0.001), and wake after sleep onset decreased (p = 0.035). Sleep macroarchitecture did not significantly change, while the percentage of REM sleep stage to the total sleep time increased over time (p < 0.01). Consistent with the objective findings, subjects reported improved subjective sleep quality (p = 0.043) and reduced daytime sleepiness (p = 0.047). CONCLUSION: The combination of a personalized dietary plan with exercise training enhances sleep initiation, sleep continuity, sleep duration, REM and N1 sleep stages, independently of carbohydrate type or timing. Lifestyle interventions should be investigated further to promote sleep quality and recovery. REGISTRATION: The trial was registered at clinicaltrials.gov as NCT05464342.

The vicious circle between physical, psychological, and physiological characteristics of shift work in nurses: a multidimensional approach.

PURPOSE: To compare physical, psychological, and physiological adaptations between rotating and morning shift health workers using objective and subjective approaches. METHODS: Forty nurses [n = 20 morning shift (MS) group; n = 20 rotating shift (RS) group] were evaluated for anthropometry, body composition, and handgrip strength. Quality of life, depression, fatigue, daytime sleepiness, and sleep quality were assessed with SF-36, Zung Self-Rating Depression Scale (SDS), Fatigue Severity Scale (FSS), Epworth Sleepiness Scale (ESS), and Pittsburgh Sleep Quality Index (PSQI), respectively. Physical activity was assessed by the International Physical Activity Questionnaire (IPAQ) and triaxial accelerometers. Sleep-related data were monitored with sleep actigraphy. Salivary melatonin levels were analyzed before/after sleep, and blood lipid profiles were measured the following morning. RESULTS: The RS group had higher mean BMI and total and abdominal fat and scored lower in the SF-36 (p < 0.01). All nurses showed reduced physical activity levels, which, in the RS group, were negatively correlated with FSS (p = 0.033) and SDS scores (p = 0.025). Poor sleep was revealed in 53% of nurses. The RS group had worse sleep quality by PSQI than the MS group (p = 0.045). PSQI scores were inversely related to SF-36 scores and positively correlated with FSS, BMI, waist circumference, and body fat (p < 0.05). CONCLUSION: RS nurses showed increased body mass and total and abdominal fat along with decreased quality of life and sleep quality compared to MS counterparts. A strong relationship was found between physical, psychological, and physiological domains. Further studies should consider workplace interventions to prevent obesity, promote physical activity, and manage poor sleeping patterns in nurses.

A Systematic Review, Meta-Analysis and Meta-Regression on the Effects of Carbohydrates on Sleep.

This study aimed to assess the effects of quantity, quality and periodization of carbohydrates consumption on sleep. PubMed, SCOPUS and Cochrane Library were searched through October 2020. Data were pooled using random-effects meta-analysis. Eleven articles were included in the meta-analysis which consisted of 27 separate nutrition trials, resulting in 16 comparison data sets (sleep quantity n = 11; sleep quality n = 5). Compared to high carbohydrate (HCI), low carbohydrate intake (LCI) moderately increased duration and proportion of N3 sleep stage (ES = 0.37; 95% CI = 0.18, 0.56; p < 0.001 and ES = 0.51; 95% CI = 0.33, 0.69; p < 0.001, respectively). HCI prolonged rapid eye movement (REM) stage duration (ES = -0.38; 95% CI = 0.05, -8.05; p < 0.001) and proportion (ES = -0.46; 95% CI = -0.83, -0.01; p < 0.001), compared to LCI. The quality of carbohydrate intake did not affect sleep stages. Meta-regression showed that the effectiveness of carbohydrate quantity and quality in sleep onset latency was significantly explained by alterations of carbohydrate intake as a percentage of daily energy intake (R2 = 25.87, p = 0.018) and alterations in the glycemic load (R2 = 50.8, p = 0.048), respectively. Alterations in glycemic load partially explained the variance of the effectiveness of carbohydrate quality in sleep efficiency (R2 = 89.2, p < 0.001) and wake after sleep onset (R2 = 64.9, p = 0.018). Carbohydrate quantity was shown to affect sleep architecture, and especially N3 and REM sleep stages. Alterations in both quantity and quality of carbohydrate intake showed a significant effect on sleep initiation. Variations in carbohydrate quality significantly affected measures of sleep continuation. Further studies are needed to assess the effect of long-term carbohydrate interventions on sleep.

Deconstructing athletes' sleep: A systematic review of the influence of age, sex, athletic expertise, sport type, and season on sleep characteristics.

PURPOSE: This systematic review aimed to describe objective sleep parameters for athletes under different conditions and address potential sleep issues in this specific population. METHODS: PubMed and Scopus were searched from inception to April 2019. Included studies measured sleep only via objective evaluation tools such as polysomnography or actigraphy. The modified version of the Newcastle-Ottawa Scale was used for the quality assessment of the studies. RESULTS: Eighty-one studies were included, of which 56 were classified as medium quality, 5 studies as low quality, and 20 studies as high quality. A total of 1830 athletes were monitored over 18,958 nights. Average values for sleep-related parameters were calculated for all athletes according to sex, age, athletic expertise level, training season, and type of sport. Athletes slept on average 7.2 ± 1.1 h/night (mean ± SD), with 86.3% ± 6.8% sleep efficiency (SE). In all datasets, the athletes' mean total sleep time was <8 h. SE was low for young athletes (80.3% ± 8.8%). Reduced SE was attributed to high wake after sleep onset rather than sleep onset latency. During heavy training periods, sleep duration and SE were on average 36 min and 0.8% less compared to pre-season and 42 min and 3.0% less compared to in-season training periods, respectively. CONCLUSION: Athletes' sleep duration was found to be short with low SE, in comparison to the general consensus for non-athlete healthy adults. Notable sleep issues were revealed in young athletes. Sleep quality and architecture tend to change across different training periods.

The effect of evening cycling at different intensities on sleep in healthy young adults with intermediate chronobiological phenotype: A randomized, cross-over trial.

This study investigated the effect of various cycling intensities on sleep-related parameters in healthy young adults with intermediate chronobiological phenotype. Ten recreationally trained male volunteers underwent an evening i) moderate-intensity continuous training (MICT; 45 min at 70% Wmax), ii) high-intensity interval training (HIIT; 10 × 1 min at 90% Wmax), iii) sprint interval training (SIT; 6 × 20 sec at 140% Wmax) or iv) a non-exercise (CON) trial in randomized, counter-balanced and crossover order. At baseline, somatometric data, maximum oxygen uptake and chronotype were evaluated. Sleep-related indices and daily activity were recorded by a multi-sensor activity monitor. Total sleep time was longer after SIT compared to CON and MICT (p < 0.05). Sleep efficiency was higher in SIT than in CON (p < 0.05). Sleep onset latency did not differ among trials. Wake after sleep onset was decreased after SIT compared to CON (p= 0.049). No differences were found for bedtime among trials. Wake time was earlier in the MICT trial compared to CON (p = 0.026). Evening cycling exercise -independently of intensity- did not impair sleep of individuals with intermediate chronobiological phenotype. Furthermore, a single SIT session improved sleep quantity and continuation of individuals with this specific chronotype.

A critical review on sleep assessment methodologies in athletic populations: factors to be considered.

A growing body of research focus on athletes' sleep in order to investigate the effects of sleep in sports performance and recovery or the prevalence of sleep disorders in athletes. At the same time, several sleep monitoring tools have been developed and used in athletic populations for fulfilling these purposes. This review aimed to provide critical assessment to the most used by athletes' methodological approaches and compared them with the gold standard approach. Advantages and disadvantages of the various sleep monitoring tools were critically discussed. Literature related to aspects of athletes' sleep was reviewed. From the shortlisted studies, several factors that seem to affect sleep in athletes were identified using objective methods such as polysomnography/electroencephalography and actigraphy. These factors were associated to sleep (eg such as sleep environment, familiarization procedures and napping) and daily habits (eg nutrition, fluid consumption, alcohol and caffeine intake, tobacco use). The selected studies that evaluated sleep objectively were screened according the reporting rates of these variables. The majority of the screened studies were found to underreport these variables. Practical issues were addressed and recommendations about reporting sleep-related factors were made in order to improve studies' quality assessment and allow for more robust comparisons between studies.

Dietary restraint is associated with adiposity and repeated attempts of food avoidance since early adolescence.

Body composition and fat distribution are predictors of health and are largely affected by eating behavior. The current study aimed to explore the adoption of dietary restraint since early adolescence, its relationship with objective physical traits, and the association with energy intake, and targeted food groups or items. Eight-four healthy volunteers (males n = 46; females n = 38), 14-24 years old, were interviewed to assess segmental body composition, using bioelectrical impendence analysis, and dietary patterns, using rigid dietary restraint questionnaire (RC-16) and the elements of persistent desire or repeated unsuccessful attempts to quit certain foods, and provided a 3-day food record. Results showed that both sexes were equally engaged in rigid dietary restraint. Higher RC-16 scores were associated with higher odds of being overweight, overfat or having increased abdominal fat, and lower energy reporting. Significant differences were also found between those trying to eliminate food items from their diet and those who did not, in Body Mass Index for-age z-score, and % body fat, but not energy intake. Seventy-two participants mentioned having problems with cutting down foods, while fitness instructors, social environment, or online sources were noted to further promote food avoidance. Repeated unsuccessful elimination attempts, and the total number of foods to avoid were associated with RC-16 score as well. These findings indicate that dietary restraint is practiced since early adolescence by both sexes, and may affect body composition and adiposity. Further studies are needed to understand how attitudes towards food and failure of elimination may shape dietary behavior.

Effects of Dehydration on Archery Performance, Subjective Feelings and Heart Rate during a Competition Simulation.

This study aimed to investigate the effect of dehydration on archery performance, subjective feelings and heart rate response. Ten national level archers performed two archery competition simulations, once under euhydration (EUH) and once in a dehydrated state (DEH), induced by 24-h reduced fluid intake. Hydration status was verified prior to each trial by urine specific gravity (USG ≥ 1.025). Archery score was measured according to official archery regulations. Subjective feelings of thirst, fatigue and concentration were recorded on a visual analogue scale. Heart rate was continuously monitored during the trials. Archery performance was similar between trials (p = 0.155). During DEH trial (USG 1.032 ± 0.005), the athletes felt thirstier (p < 0.001), more fatigued (p = 0.041) and less able to concentrate (p = 0.016) compared with the EUH trial (USG 1.015 ± 0.004). Heart rate during DEH at baseline (85 ± 5 b∙min-1) was higher (p = 0.021) compared with EUH (78 ± 6 b∙min-1) and remained significantly higher during the latter stages of the DEH compared to EUH trial. In conclusion, archery performance over 72 arrows was not affected by dehydration, despite the induced psychological and physiological strain, revealed from decreased feeling of concentration, increased sensation of fatigue and increased heart rate during the DEH trial.

Effects of High vs. Low Glycemic Index of Post-Exercise Meals on Sleep and Exercise Performance: A Randomized, Double-Blind, Counterbalanced Polysomnographic Study.

The aim of the current study was to investigate the effect of the glycemic index of post-exercise meals on sleep quality and quantity, and assess whether those changes could affect the next day's exercise performance. Following a baseline/familiarization phase, 10 recreationally trained male volunteers (23.2 ± 1.8 years) underwent two double-blinded, randomized, counterbalanced crossover trials. In both trials, participants performed sprint interval training (SIT) in the evening. Post-exercise, participants consumed a meal with a high (HGI) or low (LGI) glycemic index. Sleep parameters were assessed by a full night polysomnography (PSG). The following morning, exercise performance was evaluated by the countermovement jump (CMJ) test, a visual reaction time (VRT) test and a 5-km cycling time trial (TT). Total sleep time (TST) and sleep efficiency were greater in the HGI trial compared to the LGI trial (p < 0.05), while sleep onset latency was shortened by four-fold (p < 0.05) and VRT decreased by 8.9% (p < 0.05) in the HGI trial compared to the LGI trial. The performance in both 5-km TT and CMJ did not differ between trials. A moderate to strong correlation was found between the difference in TST and the VRT between the two trials (p < 0.05). In conclusion, this is the first study to show that a high glycemic index meal, following a single spring interval training session, can improve both sleep duration and sleep efficiency, while reducing in parallel sleep onset latency. Those improvements in sleep did not affect jumping ability and aerobic endurance performance. In contrast, the visual reaction time increased proportionally to sleep improvements.