Women with ovulatory menstrual cycles show an increase in body temperature in the luteal phase, compared with follicular phase, particularly during the night. Several, albeit not all, studies reported higher energy expenditure in the luteal phase compared with follicular phase. Q 10 of biological reactions lies between 2.0 and 3.0, predicting a 7-12% increase in energy expenditure when body temperature rises by 1°C. In this study, temperature dependence of energy expenditure was assessed by comparing changes in sleeping energy expenditure and thermoregulation with menstrual cycle in 9 young females. Energy expenditure was measured using a metabolic chamber, in which sleep was recorded polysomnographically, and core body temperature and skin temperature were continuously monitored. Distal-to-proximal skin temperature gradient was assessed as an index of heat dissipation. In the luteal phase, a significant increase in average core body temperature (+0.27°C) and energy expenditure (+6.9%) were observed. Heat dissipation was suppressed during the first 2 hr of sleep in the luteal phase, compared with follicular phase. Rise in basal body temperature in the luteal phase was accompanied by increased energy expenditure and suppressed heat dissipation. The 6.9% increase in metabolic rate would require a Q 10 of 12.4 to be attributable solely to temperature (+0.27°C), suggesting that energy expenditure in the luteal phase is enhanced through the mechanism, dependent and independent of lutealphase rise in body temperature presumably reflects other effects of the sex hormones.

Mammals have circadian clocks, which consist of the central clock in the suprachiasmatic nucleus and the peripheral clocks in the peripheral tissues. The effect of exercise on phase of peripheral clocks have been reported in rodents but not in humans. Continuous sampling is necessary to assess the phase of the circadian rhythm of peripheral clock gene expressions. It has been assumed that the expression of the genes in leukocyte may be “an accessible window to the multiorgan transcriptome.” The present study aimed to examine whether exercise affects the level and phase of clock gene expression in human leukocytes. Eleven young men participated in three trials, in which they performed a single bout of exercise at 60% V̇o2max for 1 h beginning either at 0700 (morning exercise) or 1600 (afternoon exercise) or no exercise (control). Blood samples were collected at 0600, 0900, 1200, 1500, 1800, 2100, and 2300 and at 0600 the next morning, to assess diurnal changes of clock gene expression in leukocytes. Brain and muscle ARNT-like protein 1 ( Bmal1) expression level increased after morning and afternoon exercise, and Cryptochrome 1 ( Cry1) expression level increased after morning exercise. Compared with control trial, acrophase of Bmal1 expression tended to be earlier in morning exercise trial and later in afternoon exercise trial. Acrophase of Cry1 expression was earlier in morning exercise trial but not affected by afternoon exercise. Circadian locomotor output cycles kaput ( Clock), Period 1–3 ( Per1–3), and Cry2 expression levels and those acrophases were not affected by exercise. The present results suggest a potential role of a single bout of exercise to modify peripheral clocks in humans. NEW & NOTEWORTHY The present study showed that a single bout of exercise affected peripheral clock gene expression in human leukocytes and the effect of exercise depended on when it was performed. Brain and muscle ARNT-like protein 1 ( Bmal1) expression was increased after exercises performed in the morning and afternoon. Cryptochrome 1 ( Cry1) expression was also increased after the morning exercise. The effect of exercise on acrophase of Bmal1 depended on the time of the exercise: advanced after morning exercise and delayed after afternoon exercise.

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<b><i>Introduction:</i></b> Young and early middle-aged office workers spend most of the day sitting or sleeping. Few studies have used a metabolic chamber to report sitting resting energy expenditure (REE) or sleeping metabolic rate (SMR) estimation equations. This study aimed to develop novel equations for estimating sitting REE and SMR, and previously published equations for SMR were compared against measured values. <b><i>Methods:</i></b> The relationships among sitting REE, SMR, and body composition measured in clinical trials were analyzed. The body composition (fat-free mass [FFM] and fat mass) and energy metabolism of 85 healthy young and early middle-aged Japanese individuals were measured using dual-energy X-ray absorptiometry and a metabolic chamber, respectively. Novel estimate equations were developed using stepwise multiple regression analysis. Estimates of SMR using a new equation and 2 published equations were compared against measured SMR. <b><i>Results:</i></b> The sitting mREE and mSMR were highly correlated (<i>r</i> = 0.756, <i>p</i> &#x3c; 0.01). The new FFM-based estimate accounted for 50.4% of the variance in measured sitting REE (mREE) and 82.3% of the variance in measured SMR (mSMR). The new body weight-based estimate accounted for 49.3% of the variance in sitting mREE and 82.2% of the variance in mSMR. Compared with mSMR, the SMR estimate using an FFM-based published equation was slightly underestimated. <b><i>Conclusion:</i></b> These novel body weight- and FFM-based equations may help estimate sitting REE and SMR in young and early middle-aged adults. Previous SMR estimated FFM-based equations were slightly underestimated against measured SMR; however, we confirmed the previous SMR estimate equations could be useful. This finding suggests that sitting REE and SMR can be easily estimated from individual characteristics and applied in clinical settings.

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Study objectiveTraditionally, age-related deterioration of sleep architecture in older individuals has been evaluated by visual scoring of polysomnographic (PSG) recordings with regard to total sleep time and latencies. In the present study, we additionally compared the non-REM sleep (NREM) stage and delta, theta, alpha, and sigma wave stability between young and older subjects to extract features that may explain age-related changes in sleep.MethodsPolysomnographic recordings were performed in 11 healthy older (72.6 ± 2.4 years) and 9 healthy young (23.3 ± 1.1 years) females. In addition to total sleep time, the sleep stage, delta power amplitude, and delta, theta, alpha, and sigma wave stability were evaluated by sleep stage transition analysis and a novel computational method based on a coefficient of variation of the envelope (CVE) analysis, respectively.ResultsIn older subjects, total sleep time and slow-wave sleep (SWS) time were shorter whereas wake after sleep onset was longer. The number of SWS episodes was similar between age groups, however, sleep stage transition analysis revealed that SWS was less stable in older individuals. NREM sleep stages in descending order of delta power were: SWS, N2, and N1, and delta power during NREM sleep in older subjects was lower than in young subjects. The CVE of the delta-band is an index of delta wave stability and showed significant differences between age groups. When separately analyzed for each NREM stage, different CVE clusters in NREM were clearly observed between young and older subjects. A lower delta CVE and amplitude were also observed in older subjects compared with young subjects in N2 and SWS. Additionally, lower CVE values in the theta, alpha and sigma bands were also characteristic of older participants.ConclusionThe present study shows a decrease of SWS stability in older subjects together with a decrease in delta wave amplitude. Interestingly, the decrease in SWS stability coincided with an increase in short-term delta, theta, sigma, and alpha power stability revealed by lower CVE. Loss of electroencephalograms (EEG) variability might be a useful marker of brain age.