The age-related decline in resting energy expenditure in humans is due to the loss of fat-free mass and to alterations in its metabolically active components

Divergence in aerobic capacity and energy expenditure influence metabolic tissue mitochondrial protein synthesis rates in aged rats

Age-associated declines in aerobic capacity promote the development of various metabolic diseases. In rats selectively bred for high/low intrinsic aerobic capacity, greater aerobic capacity reduces susceptibility to metabolic disease while increasing longevity. However, little remains known how intrinsic aerobic capacity protects against metabolic disease, particularly with aging. Here, we tested the effects of aging and intrinsic aerobic capacity on systemic energy expenditure, metabolic flexibility and mitochondrial protein synthesis rates using 24-month-old low-capacity (LCR) or high-capacity runner (HCR) rats. Rats were fed low-fat diet (LFD) or high-fat diet (HFD) for eight weeks, with energy expenditure (EE) and metabolic flexibility assessed utilizing indirect calorimetry during a 48 h fast/re-feeding metabolic challenge. Deuterium oxide (D2O) labeling was used to assess mitochondrial protein fraction synthesis rates (FSR) over a 7-day period. HCR rats possessed greater EE during the metabolic challenge. Interestingly, HFD induced changes in respiratory exchange ratio (RER) in male and female rats, while HCR female rat RER was largely unaffected by diet. In addition, analysis of protein FSR in skeletal muscle, brain, and liver mitochondria showed tissue-specific adaptations between HCR and LCR rats. While brain and liver protein FSR were altered by aerobic capacity and diet, these effects were less apparent in skeletal muscle. Overall, we provide evidence that greater aerobic capacity promotes elevated EE in an aged state, while also regulating metabolic flexibility in a sex-dependent manner. Modulation of mitochondrial protein FSR by aerobic capacity is tissue-specific with aging, likely due to differential energetic requirements by each tissue.

Dietary Intakes of Community-Dwelling Adults in the United States across Older Adulthood: National Health and Nutrition Examination Survey 2015-March 2020

BACKGROUND

Dietary guidance is set on the basis of age and life stage and defines older adults as ≥60 y. Yet, little is known about if and/or how diet quality differs beyond the age of 60.

OBJECTIVE

The objective of this study was to compare the dietary intakes of 60-69 (n = 2079), 70-79 (n = 1181), and 80+ y old (n = 644) noninstitutionalized men and women in the United States using the Healthy Eating Index 2015 (HEI) and the What We Eat in America food categories.

METHODS

Data were obtained from National Health and Nutrition Examination Survey 2015-2016 and 2017-March 2020. HEI and component scores were calculated using the population ratio method. Population estimates for dietary intake were calculated as the average reported over 2 separate nonconsecutive 24-h dietary recalls.

RESULTS

In men and women, the reported energy intake was lower among the 80+ y olds (kcal/d men-80+: 1884 ± 30, 70-79: 2022 ± 33, 60-69: 2142 ± 39; women-80+: 1523 ± 36; 70-79: 1525 ± 33, 60-69: 1650 ± 25; P-trend < 0.001). Total HEI scores did not differ significantly across the 3 age categories, but the 80+ y olds had significantly lower scores for the green vegetables and beans component than the 60-69 y olds [men-mean (95% confidence interval): 2.0 (1.5, 2.5) compared with 3.4 (2.6, 4.1); women-2.3 (1.8, 2.8) compared with 4.4 (3.7, 5.0)]. In women, the percentage of daily calories from protein was significantly lower in the 80+ y olds than in the 60-69 and 70-79 y olds (12.9% ± 0.6%, compared with 17.0% ± 0.9% and 15.6% ± 0.6%, respectively). Protein intake did not differ significantly among the 3 age groups in men. The 80+ y old men and women reported consuming a significantly higher percentage of calories from snacks and sweets compared with the 60-69 y olds (men-80+: 18.1% ± 0.8%, 60-69: 15.4% ± 0.7%; women-80+: 19.6% ± 0.8%, 60-69: 15.5% ± 0.7%).

CONCLUSION

The diet of 80+ y olds differed from that of 60-69 y olds in some key components, including energy, snacks and sweets, protein, and green vegetables. Future research is needed to determine if there are health-related consequences to these differences.

Dietary intake and visceral adiposity in older adults: The Multiethnic Cohort Adiposity Phenotype study

Background

There are established links between the accumulation of body fat as visceral adipose tissue (VAT) and the risk of developing obesity-associated metabolic disease. Previous studies have suggested that levels of intake of specific foods and nutrients are associated with VAT accumulation after accounting for total energy intake.

Objective

This study assessed associations between a priori selected dietary factors on VAT quantified using abdominal magnetic resonance imaging.

Methods

The cross-sectional Multiethnic Cohort Adiposity Phenotype Study included n = 395 White, n = 274 Black, n = 269 Native Hawaiian, n = 425 Japanese American and n = 358 Latino participants (mean age = 69 years ± 3 SD). Participants were enrolled stratified on sex, race, ethnicity and body mass index. General linear models were used to estimate the mean VAT area (cm2) for participants categorized into quartiles based on their dietary intake of selected foods/nutrients adjusting for age, sex, racial and ethnic groups, the total percentage fat from whole-body dual energy X-ray absorptiometry and total energy.

Results

There were significant inverse associations with VAT for dietary intake of total vegetables, total fruits (including juice), cereals, whole grains, calcium, copper and dietary fiber (p-trend ≤0.04). Positive trends were observed for VAT for participants who reported higher intake of potatoes, total fat and saturated fatty acids (SFA) (p-trend ≤0.02). Foods/nutrients that met the multiple testing significance threshold were total fruits, whole grains, copper, dietary fiber and SFA intake.

Conclusions

These results highlight foods and nutrients including SFA, total fruit, whole grains, fiber and copper as potential candidates for future research to inform dietary guidelines for the prevention of chronic disease among older adults.

Postcontraction [acetylcarnitine] reflects interindividual variation in skeletal muscle ATP production patterns in vivo

In addition to its role in substrate selection (carbohydrate vs. fat) for oxidative metabolism in muscle, acetylcarnitine production may be an important modulator of the energetic pathway by which ATP is produced. A combination of noninvasive magnetic resonance spectroscopy measures of cytosolic acetylcarnitine and ATP production pathways was used to investigate the link between [acetylcarnitine] and energy production in vivo. Intracellular metabolites were measured in the vastus lateralis muscle of eight males (mean: 28.4 yr, range: 25-35) during 8 min of incremental, dynamic contractions (0.5 Hz, 2-min stages at 6%, 9%, 12%, and 15% maximal torque) that increased [acetylcarnitine] approximately fivefold from resting levels. ATP production via oxidative phosphorylation, glycolysis, and the creatine kinase reaction was calculated based on phosphorus metabolites and pH. Spearman rank correlations indicated that postcontraction [acetylcarnitine] was positively associated with both absolute (mM) and relative (% total ATP) glycolytic ATP production (rs = 0.95, P = 0.001; rs = 0.93, P = 0.002), and negatively associated with relative (rs = -0.81, P = 0.02) but not absolute (rs = -0.14, P = 0.75) oxidative ATP production. Thus, acetylcarnitine accumulated more when there was a greater reliance on "nonoxidative" glycolysis and a relatively lower contribution from oxidative phosphorylation, reflecting the fate of pyruvate in working skeletal muscle. Furthermore, these data indicate striking interindividual variation in responses to the energy demand of submaximal contractions. Overall, the results of this preliminary study provide novel evidence of the coupling in vivo between ATP production pathways and the carnitine system.NEW & NOTEWORTHY Production of acetylcarnitine from acetyl-CoA and free carnitine may be important for energy pathway regulation in contracting skeletal muscle. Noninvasive magnetic resonance spectroscopy was used to investigate the link between acetylcarnitine and energy production in the vastus lateralis muscle during dynamic contractions (n = 8 individuals). A positive correlation between acetylcarnitine accumulation and "nonoxidative" glycolysis and an inverse relationship with oxidative phosphorylation, provides novel evidence of the coupling between ATP production and the carnitine system in vivo.

Does inhibitory control spill over to eating behaviors? Two preregistered studies of inhibitory spillover effects on food intake and reactions to food stimuli

Overweight and obesity are worldwide conditions associated with detrimental medical and psychosocial outcomes. As inhibitory control deficits are thought to contribute to weight gain, they are a worthwhile target for new approaches. Previous research has shown that the execution of inhibitory control in one domain leads to a concurrent increase of inhibitory control in another domain, an effect denoted as inhibitory spillover effect (ISE). Therefore, we assumed that exertion of inhibitory control in a food-unrelated domain in overweight and normal weight individuals will decrease food intake in a simultaneous bogus taste test (BTT; study 1) as well as increase food-specific response inhibition ability in a stop signal task (SST; study 2). We assumed stronger effects in overweight individuals. In both studies ISE was induced via cognitive priming and compared to a neutral condition in a group of overweight (OW: n = 46 for study 1, n = 46 for study 2) and normal weight (NW: n = 46 for study 1, n = 46 for study 2) individuals. In the ISE condition with an inhibitory control priming task, participants had to learn and retain control-related words while simultaneously performing a BTT (study 1) or SST (study 2). In the neutral condition, participants followed the same protocol, albeit memorizing neutral (i.e., control-unrelated) words. There was no significant interaction of weight group × cognitive priming condition neither regarding food intake (study 1) nor regarding food-related response inhibition (study 2). Cognitive priming, as implemented in the present studies, does not instigate an ISE strong enough to improve inhibitory control during food intake or food-related response inhibition. Relevant practical and theoretical aspects as well as implications for future research on the ISE are discussed.

Resting Energy Expenditure in the Critically Ill and Healthy Elderly-A Retrospective Matched Cohort Study

The use of indirect calorimetry to measure resting energy expenditure (mREE) is widely recommended as opposed to calculating REE (cREE) by predictive equations (PE). The aim of this study was to compare mREE with cREE in critically ill, mechanically ventilated patients aged ≥ 75 years and a healthy control group matched by age, gender and body mass index. The primary outcome was the PE accuracy rate of mREE/cREE, derived using Bland Altman plots. Secondary analyses included linear regression analyses for determinants of intraindividual mREE/cREE differences in the critically ill and interindividual mREE differences in the matched healthy cohort. In this retrospective study, 90 critically ill patients (median age 80 years) and 58 matched healthy persons were included. Median mREE was significantly higher in the critically ill (1457 kcal/d) versus the healthy cohort (1351 kcal/d), with low PE accuracy rates (21% to 49%). Independent predictors of mREE/cREE differences in the critically ill were body temperature, heart rate, FiO₂, hematocrit, serum sodium and urea. Body temperature, respiratory rate, and FiO₂ were independent predictors of interindividual mREE differences (critically ill versus healthy control). In conclusion, the commonly used PE in the elderly critically ill are inaccurate. Respiratory, metabolic and energy homeostasis variables may explain intraindividual mREE/cREE as well as interindividual mREE differences.

Changes in 24-h energy expenditure, substrate oxidation, and body composition following resistance exercise and a high protein diet via whey protein supplementation in healthy older men

PURPOSE

To investigate changes in 24-h energy expenditure (EE), substrate oxidation, and body composition following resistance exercise (RE) and a high protein diet via whey protein supplementation (alone and combined) in healthy older men.

METHODS

In a pooled groups analysis, 33 healthy older men [(mean ± SE) age: 67 ± 1 years; BMI: 25.4 ± 0.4 kg/m2] were randomized to either RE (2×/week; n = 17) or non-exercise (n = 16) and either a high protein diet via whey protein supplementation (PRO, 2 × 25 g whey protein isolate/d; n = 17) or control (CON, 2 × 23.75 g maltodextrin/d; n = 16). An exploratory sub-analysis was also conducted between RE+CON (n = 8) and RE+PRO (n = 9). At baseline and 12 weeks, participants resided in respiration chambers for measurement of 24-h EE and substrate oxidation and wore an accelerometer for 7 days for estimation of free-living EE.

RESULTS

Resistance exercise resulted in greater increases in fat-free mass (1.0 ± 0.3 kg), resting metabolic rate [(RMR) 36 ± 14 kcal/d], sedentary EE (60 ± 33 kcal/d), and sleeping metabolic rate [(SMR) 45 ± 7 kcal/d] compared to non-exercise (p < 0.05); however, RE decreased activity energy expenditure in free-living (-90 ± 25 kcal/d; p = 0.049) and non-exercise activity inside the respiration chamber (-1.9 ± 1.1%; p = 0.049). PRO decreased fat mass [(FM) -0.5 ± 0.3 kg], increased overnight protein oxidation (30 ± 6 g/d), and decreased 24-h protein balance (-20 ± 4 g/d) greater than CON (p < 0.05). RE+PRO decreased FM (-1.0 ± 0.5 kg) greater than RE+CON (p = 0.04).

CONCLUSION

Resistance exercise significantly increased RMR, SMR, and sedentary EE in healthy older men, but not total EE. PRO alone and combined with RE decreased FM and aided body weight maintenance. This study was registered at clinicaltrials.gov as NCT03299972.

Resting Oxygen Uptake Value of 1 Metabolic Equivalent of Task in Older Adults: A Systematic Review and Descriptive Analysis

BACKGROUND

It is important for sport scientists and health professionals to have estimative methods for energy demand during different physical activities. The metabolic equivalent of task (MET) provides a feasible approach for classifying activity intensity as a multiple of the resting metabolic rate (RMR). RMR is generally assumed to be 3.5 mL of oxygen per kilogram of body mass per minute (mL O2 kg-1 min-1), a value that has been criticized and considered to be overestimated in the older adult population. However, there has been no comprehensive effort to review available RMR estimations, equivalent to 1 MET, obtained in the older adult population.

OBJECTIVE

The aim of this review was to examine the existing evidence reporting measured RMR values in the older adult population and to provide descriptive estimates of 1 MET.

METHODS

A systematic review was conducted by searching PubMed, Web of Science, Scopus, CINAHL, SPORTDiscus, and Cochrane Library, from database inception to July 2021. To this end, original research studies assessing RMR in adults ≥ 60 years old using indirect calorimetry and reporting results in mL O2 kg-1 min-1 were sought.

RESULTS

Twenty-three eligible studies were identified, including a total of 1091 participants (426 men). All but two studies reported RMR values lower than the conventional 3.5 mL O2 kg-1 min-1. The overall weighted average 1 MET value obtained from all included studies was 2.7 ± 0.6 mL O2 kg-1 min-1; however, when considering best practice studies, this value was 11% lower (2.4 ± 0.3 mL O2 kg-1 min-1).

CONCLUSION

Based on the results of this systematic review, we would advise against the application of the standard value of 1 MET (3.5 mL O2 kg-1 min-1) in people ≥ 60 years of age and encourage the direct assessment of RMR using indirect calorimetry while adhering to evidence-based best practice recommendations. When this is not possible, assuming an overall value of 2.7 mL O2 kg-1 min-1 might be reasonable. Systematic review registration: International Prospective Register of Systematic Reviews on 30 September 2020, with registration number CRD42020206440.

Long-term effects of job loss on male health: BMI and health behaviors

Employment is one of the most critical determinants of health and health behaviors for adults. This study focuses on Ukraine and measures how an involuntary job loss - defined as job loss due to business closures, reorganizations, bankruptcies, or privatization - affects BMI, being overweight or obese, smoking, alcohol consumption, and physical activity. There are three reasons to study Ukraine in the aftermath of an enormous economic transition that resulted in employment contraction as high as 40 % compared to 1990. First, nearly all published studies on the relationship between job loss and health and health behaviors have been on developed countries, meaning that our study fills the gap in the literature on transition economies. Second, the job losses that we study are plausibly exogenous and affected a significant share of the population. Third, the longitudinal survey follows individuals for up to 10 years starting from 2003, allowing us to capture the long-term effects of past job loss on outcomes at a specific point in time and their trajectories across the life cycle. Applying growth-curve models, we show that past involuntary job loss significantly alters the age trajectories of all considered outcomes at both extensive and intensive margins.

Prediction of Individual Basal Rate Profiles From Patient Characteristics in Type 1 Diabetes on Insulin Pump Therapy

BACKGROUND

Basal rate profiles in patients with type 1 diabetes on insulin pump therapy are subject to enormous inter-individual heterogeneity. Tools to predict basal rates based on clinical characteristics may facilitate insulin pump therapy.

METHODS

Data from 339 consecutive in-patients with adult type 1 diabetes on insulin pump therapy were collected. Basal rate tests were performed over 24 hours. A mathematical algorithm to predict individual basal rate profiles was generated by relating the individual insulin demand to selected clinical characteristics in an exploratory cohort of 170 patients. The predicted insulin pump profiles were validated in a confirmatory cohort of 169 patients.

FINDINGS

Basal rates (0.27 ± 0.01 IU.d^-1.kg^-1) showed circadian variations with peaks corresponding to the "dawn" and "dusk" phenomena. Age, gender, duration of pump treatment, body-mass-index, HbA_1c, and triacylglycerol concentrations largely predicted the individual basal insulin demand per day (IU/d; exploratory vs prospective cohorts: r² = 0.518, P < .0001). Model-predicted and actual basal insulin rates were not different (exploratory cohort: Δ 0.1 (95% CI -0.9; 1.0 U/d; P = .95; prospective cohort: Δ -0.5 (95% CI -1.5; 0.6 IU/d; P = .46). Similarly, precise predictions were possible for each hour of the day. Actual and predicted "dawn" index correlated significantly in the exploratory but not in the confirmatory cohort.

INTERPRETATION

Clinical characteristics predict 52% of the variation in individual basal rate profiles, including their diurnal fluctuations. The multivariate regression model can be used to initiate or optimize insulin pump treatment in patients with type 1 diabetes.

What Is the Impact of Energy Expenditure on Energy Intake?

Coupling energy intake (EI) to increases in energy expenditure (EE) may be adaptively, compensatorily, or maladaptively leading to weight gain. This narrative review examines if functioning of the homeostatic responses depends on the type of physiological perturbations in EE (e.g., due to exercise, sleep, temperature, or growth), or if it is influenced by protein intake, or the extent, duration, timing, and frequency of EE. As different measures to increase EE could convey discrepant neuronal or humoral signals that help to control food intake, the coupling of EI to EE could be tight or loose, which implies that some ways to increase EE may have advantages for body weight regulation. Exercise, physical activity, heat exposure, and a high protein intake favor weight loss, whereas an increase in EE due to cold exposure or sleep loss likely contributes to an overcompensation of EI, especially in vulnerable thrifty phenotypes, as well as under obesogenic environmental conditions, such as energy dense high fat—high carbohydrate diets. Irrespective of the type of EE, transient elevations in the metabolic rate seem to be general risk factors for weight gain, because a subsequent decrease in energy requirement is not compensated by an adequate adaptation of appetite and EI.

Impact of Energy Turnover on the Regulation of Energy and Macronutrient Balance

Energy turnover, defined as the average daily total metabolic rate, can be normalized for basal metabolic rate in order to compare physical activity level between individuals, whereas normalization of energy turnover for energy intake (energy flux) allows investigation of its impact on regulation of energy partitioning independent of energy balance. Appetite sensations better correspond to energy requirements at a high compared with a low energy turnover. Adaptation of energy intake to habitual energy turnover may, however, contribute to the risk of weight gain associated with accelerated growth, pregnancy, detraining in athletes, or after weight loss in people with obesity. The dose-response relationship between energy turnover and energy intake as well as the metabolic effects of energy turnover varies with the habitual level of physical activity and the etiology of energy turnover (e.g., cold-induced thermogenesis, growth, or lactation; aerobic vs. anaerobic exercise). Whether a high energy turnover due to physical activity or exercise may compensate for adverse effects of overfeeding or an unhealthy diet needs to be further investigated using the concept of energy flux. In summary, the beneficial effects of a high energy turnover on regulation of energy and macronutrient balance facilitate the prevention and treatment of obesity and associated metabolic risk.

Resting Energy Expenditure: From Cellular to Whole-Body Level, a Mechanistic Historical Perspective

The basis of heat generated by the human body has been a source of speculation and research for more than 2,000 years. Basal heat production, now usually referred to as resting energy expenditure (REE), is currently recognized as deriving from biochemical reactions at subcellular and cellular levels that are expressed in the energy expended by the body's 78 organs and tissues. These organs and tissues, and the 11 systems to which they belong, influence body size and shape. Connecting these subcellular-/cellular-level reactions to organs and tissues, and then on to body size and shape, provides a comprehensive understanding of individual differences in REE, a contemporary topic of interest in obesity research and clinical practice. This review critically examines these linkages, their association with widely used statistical and physiological REE prediction formulas, and often-unappreciated aspects of measuring basal heat production in humans.

Measured and Predicted Resting Energy Expenditure in Malnourished Older Hospitalized Patients: A Cross-Sectional and Longitudinal Comparison

A number of equations have been proposed to predict resting energy expenditure (REE). The role of nutritional status in the accuracy and validity of the REE predicted in older patients has been paid less attention. We aimed to compare REE measured by indirect calorimetry (IC) and REE predicted by the Harris–Benedict formula in malnourished older hospitalized patients. Twenty-three malnourished older patients (age range 67–93 years, 65% women) participated in this prospective longitudinal observational study. Malnutrition was defined as Mini Nutritional Assessment Long Form (MNA-SF) score of less than 17. REE was measured (REE_measured) and predicted (REE_predicted) on admission and at discharge. REE_predicted within ±10% of the REE_measured was considered as accuracy. Nutritional support was provided to all malnourished patients during hospitalization. All patients were malnourished with a median MNA-LF score of 14. REE_measured and REE_predicted increased significantly during 2-week nutritional therapy (+212.6 kcal and +19.5 kcal, respectively). Mean REE_predicted (1190.4 kcal) was significantly higher than REE_measured (967.5 kcal) on admission (p < 0.001). This difference disappeared at discharge (p = 0.713). The average REE_predicted exceeded the REE_measured on admission and at discharge by 29% and 11%, respectively. The magnitude of difference between REE_measured and REE_predicted increased along with the degree of malnutrition (r = 0.42, p = 0.042) as deviations ranged from −582 to +310 kcal/day in severe to mildly malnourished patients, respectively. REE_predicted by the Harris–Benedict formula is not accurate in malnourished older hospitalized patients. REE measured by IC is considered precise, but it may not represent the true energy requirements to recover from malnutrition. Therefore, the effect of malnutrition on measured REE must be taken into account when estimating energy needs in these patients.

Associations of nicotidamide-N-methyltransferase, FTO, and IRX3 genetic variants with body mass index and resting energy expenditure in Mexican subjects

The enzyme nicotidamide-N-methyltransferase (NNMT) regulates adipose tissue energy expenditure through increasing nicotinamide adenosine dinucleotide (NAD⁺) content. NNMT methylates nicotinamide to N¹-methylnicotidamide (MNA-1) using S-adenosyl methionine. The rs694539 NNMT polymorphism is associated with non-alcoholic steatohepatitis, and rs1941404 is associated with hyperlipidemia. The rs1421085 FTO is related to poor eating behaviors, and rs3751723 IRX3 is associated with obesity. To investigate the association of rs694539 and rs1941404 NNMT, rs140285 FTO and rs3751723 IRX3 polymorphisms with MNA-1 concentrations, resting energy expenditure (REE) and BMI, we included clinically healthy Mexican subjects 30 to 50 years old, 100 subjects (35 men/65 women) with BMI > 30 kg/m² and 100 subjects (32 men/68 women) with BMI < 25 kg/m². Glucose, lipid profile, insulin, leptin, acylated ghrelin, and MNA-1 (LC–MS) were quantified. Resting energy expenditure (REE) was estimated using indirect calorimetry with a Fitmate instrument. Genotyping was performed using PCR–RFLP, and allelic discrimination was examined using TaqMan probes. MNA-1 concentrations and REE were significantly higher in obese subjects. Subjects with the rs694539AA NNMT genotype (recessive model) had lower weight, BMI, and REE. BMI showed an association with HDL-C, triglycerides, MNA-1, acetylated ghrelin, leptin, insulin concentrations, HOMA-IR, REE, and rs1421085. Subjects with the TC or CC genotypes of rs1421085 FTO showed 6 kg and 2 units of BMI more than did those with the TT wild type. The CG of the rs1421085 and rs3751723 haplotypes was associated with BMI. These findings showed that BMI was strongly associated with REE, rs1421085 FTO and the CG rs1421085 FTO and rs3751723 IRX3 haplotypes. We used the GMDR approach in obesity phenotype to show the interaction of four SNPs and metabolic variables.

Selective androgen receptor modulators: the future of androgen therapy?

Selective androgen receptor modulators (SARMs) are small molecule drugs that function as either androgen receptor (AR) agonists or antagonists. Variability in AR regulatory proteins in target tissues permits SARMs to selectively elicit anabolic benefits while eschewing the pitfalls of traditional androgen therapy. SARMs have few side effects and excellent oral and transdermal bioavailability and may, therefore, represent viable alternatives to current androgen therapies. SARMs have been studied as possible therapies for many conditions, including osteoporosis, Alzheimer’s disease, breast cancer, stress urinary incontinence (SUI), prostate cancer (PCa), benign prostatic hyperplasia (BPH), male contraception, hypogonadism, Duchenne muscular dystrophy (DMD), and sarcopenia/muscle wasting/cancer cachexia. While there are no indications for SARMs currently approved by the Food and Drug Administration (FDA), many potential applications are still being explored, and results are promising. In this review, we examine the literature assessing the use of SARMS for a number of indications.

Is There a Contribution of Structural Brain Phenotypes to the Variance in Resting Energy Expenditure before and after Weight Loss in Overweight Females?

Brain gray (GM) and white matter (WM) are associated with resting energy expenditure (REE). The impact of weight loss on GM and WM masses, as well as on their associations with REE and the ratio between body and brain metabolism, i.e., encephalic measure (EM)), are unknown. Longitudinal data of 69 female Caucasian subjects (age range 19–69 years) with detailed information on fat mass (FM), fat free mas (FFM), GM, WM and REE. Mean weight loss was 14.5 ± 11.9 kg with changes in FM (−12.9 ± 9.8 kg), FFM (−1.7 ± 4.8 kg) and REE (−159 ± 191 kcal/24 h) (all p < 0.05). With weight loss, there were no changes in GM and WM. Before and after weight loss, FFM was the main determinant of REE (r² = 0.483 and 0.413; p < 0.05). After weight loss, GM added to the variances in REE (3.6%), REE_adjFFM (6.1%) and the REE on FFM residuals (6.6%). In addition, before and after weight loss GM explained 25.0% and 10.0% of the variances in EM (p < 0.05). Weight loss had no effect on volumes of GM and WM. After weight loss, both, GM added to the variances of REE, REE on FFM residuals and EM.

Metabolic Determinants of Weight Gain in Humans

One of the fundamental challenges in obesity research is to identify subjects prone to weight gain so that obesity and its comorbidities can be promptly prevented or treated. The principles of thermodynamics as applied to human body energetics demonstrate that susceptibility to weight gain varies among individuals as a result of interindividual differences in energy expenditure and energy intake, two factors that counterbalance one another and determine daily energy balance and, ultimately, body weight change. This review focuses on the variability among individuals in human metabolism that determines weight change. Conflicting results have been reported about the role of interindividual differences in energy metabolism during energy balance in relation to future weight change. However, recent studies have shown that metabolic responses to acute, short-term dietary interventions that create energy imbalance, such as low-protein overfeeding or fasting for 24 hours, may reveal the underlying metabolic phenotype that determines the degree of resistance to diet-induced weight loss or the propensity to spontaneous weight gain over time. Metabolically "thrifty" individuals, characterized by a predilection for saving energy in settings of undernutrition and dietary protein restriction, display a minimal increase in plasma fibroblast growth factor 21 concentrations in response to a low-protein overfeeding diet and tend to gain more weight over time compared with metabolically "spendthrift" individuals. Similarly, interindividual variability in the causal relationship between energy expenditure and energy intake ("energy sensing") and in the metabolic response to cold exposure (e.g., brown adipose tissue activation) seems, to some extent, to be indicative of individual propensity to weight gain. Thus, an increased understanding and the clinical characterization of phenotypic differences in energy metabolism among individuals (metabolic profile) may lead to new strategies to prevent weight gain or improve weight-loss interventions by targeted therapies on the basis of metabolic phenotype and susceptibility to obesity in individual persons.

Are Raw BIA Variables Useful for Predicting Resting Energy Expenditure in Adults with Obesity?

This study aimed to develop and validate new predictive equations for resting energy expenditure (REE) in a large sample of subjects with obesity also considering raw variables from bioimpedance-analysis (BIA). A total of 2225 consecutive obese outpatients were recruited and randomly assigned to calibration (n = 1680) and validation (n = 545) groups. Subjects were also split into three subgroups according to their body mass index (BMI). The new predictive equations were generated using two models: Model 1 with age, weight, height, and BMI as predictors, and Model 2 in which raw BIA variables (bioimpedance-index and phase angle) were added. Our results showed that REE was directly correlated with all anthropometric and raw-BIA variables, while the correlation with age was inverse. All the new predictive equations were effective in estimating REE in both sexes and in the different BMI subgroups. Accuracy at the individual level was high for specific group-equation especially in subjects with BMI > 50 kg/m². Therefore, new equations based on raw-BIA variables were as accurate as those based on anthropometry. Equations developed for BMI categories did not substantially improve REE prediction, except for subjects with a BMI > 50 kg/m². Further studies are required to verify the application of those formulas and the role of raw-BIA variables for predicting REE.

Age-associated changes of resting energy expenditure, body composition and fat distribution in Chinese Han males

Age-related alterations in whole body composition, particularly, reduced fat free mass (FFM) and increased fat mass (FM), lead to a progressive decline in resting energy expenditure (REE). Similarly, regional body composition and fat distribution changes with age might also contribute to an overall lower REE. This study investigated the influence of age on REE, regional body composition and fat distribution, including subcutaneous fat (SF) and visceral fat (VF), in a Chinese Han population as well as their contributions to age-related changes in REE. One hundred and two males aged 31-83 years old underwent dual-energy X-ray absorptiometry (DXA) which measured whole body and regional FM and FFM. SF and VF were measured by magnetic resonance imaging (MRI) and REE by indirect calorimetry. Age was significantly negatively correlated with REE (r = -0.37), total FFM (r = -0.25), upper limbs FFM (r = -0.32), lower limbs FFM (r = -0.34) and showed positive association with trunk FFM (β=0.926). FM, SF and VF decreased in older age groups after an initial rise up to 55-65 years. REE correlated positively to FM, FFM, SF, VF and showed significant association with age (β = -0.254) independent of age-associated changes in body composition. The regional alterations in body composition with age were explained by changes in trunk FFM (β = 0.926). Age-related decline in REE were not solely due to alterations in FM and FFM. Therefore, the changes in regional body composition, fat distribution and REE which occur during aging could be explained by disparities in race, ethnicity, diet, physical activity, and lower specific metabolic rates of FFM components.

Contribution of structural brain phenotypes to the variance in resting energy expenditure in healthy Caucasian subjects

Brain gray (GM) and white matter (WM) volumes are related to weight changes. The impact of structural variations in GM and WM on the variance in resting energy expenditure (REE) and the REE-on-fat-free mass (FFM) association is unknown. The aim of this study was to address this in healthy Caucasian subjects. Cross-sectional data analysis of 493 healthy Caucasian subjects (age range 6–80 years; 3 age groups) was conducted with comprehensive information on FFM, organ and tissue masses, and detailed brain composition as assessed by whole body magnetic resonance imaging and REE (assessed by indirect calorimetry). REE was calculated (REEc) using organ and tissue masses times their specific metabolic rates. FFM was the major determinant of REE (70.6%); individual masses of liver, total brain, and heart explained a further 2.1% of the variance in REE. Replacing total brain with GM and WM did not change the total R2. Nevertheless, GM added more to the variance in REE (5.6%) and corresponding residuals (12.5%) than did total brain. Additionally, up to 12% was explained by age and sex (<2%). There was a systematic bias between REE and REEc with positive values in younger subjects but negative values in older ones. This bias remained after substituting the specific metabolic rate of brain with the specific metabolic rates of GM and WM. In healthy Caucasian subjects, GM and WM contributed to the variance in REE. Detailed brain structures do not explain the bias between REE and REEc.

NEW & NOTEWORTHY Detailed brain composition (gray and white matter) contributed to the variances of resting energy expenditure (REE) and REE-on-fat-free mass residuals. Gray matter explained most of the variances, and for future studies on energy expenditure, brain compartments should be analyzed separately with regard to their different energy needs.

Development of selective androgen receptor modulators (SARMs)

The Androgen Receptor (AR), a member of the steroid hormone receptor family, plays important roles in the physiology and pathology of diverse tissues. AR ligands, which include circulating testosterone and locally synthesized dihydrotestosterone, bind to and activate the AR to elicit their effects. Ubiquitous expression of the AR, metabolism and cross reactivity with other receptors limit broad therapeutic utilization of steroidal androgens. However, the discovery of selective androgen receptor modulators (SARMs) and other tissue-selective nuclear hormone receptor modulators that activate their cognate receptors in a tissue-selective manner provides an opportunity to promote the beneficial effects of androgens and other hormones in target tissues with greatly reduced unwanted side-effects. In the last two decades, significant resources have been dedicated to the discovery and biological characterization of SARMs in an effort to harness the untapped potential of the AR. SARMs have been proposed as treatments of choice for various diseases, including muscle-wasting, breast cancer, and osteoporosis. This review provides insight into the evolution of SARMs from proof-of-concept agents to the cusp of therapeutic use in less than two decades, while covering contemporary views of their mechanisms of action and therapeutic benefits.

Energy expenditure in the etiology of human obesity: spendthrift and thrifty metabolic phenotypes and energy-sensing mechanisms

The pathogenesis of human obesity is the result of dysregulation of the reciprocal relationship between food intake and energy expenditure (EE), which influences daily energy balance and ultimately leads to weight gain. According to principles of energy homeostasis, a relatively lower EE in a setting of energy balance may lead to weight gain; however, results from different study groups are contradictory and indicate a complex interaction between EE and food intake which may differentially influence weight change in humans. Recently, studies evaluating the adaptive response of one component to perturbations of the other component of energy balance have revealed both the existence of differing metabolic phenotypes ("spendthrift" and "thrifty") resulting from overeating or underfeeding, as well as energy-sensing mechanisms linking EE to food intake, which might explain the propensity of an individual to weight gain. The purpose of this review is to debate the role that human EE plays on body weight regulation and to discuss the physiologic mechanisms linking EE and food intake. An increased understanding of the complex interplay between human metabolism and food consumption may provide insight into pathophysiologic mechanisms underlying weight gain, which may eventually lead to prevention and better treatment of human obesity.

Dihydrocapsiate improved age-associated impairments in mice by increasing energy expenditure

Metabolic dysfunction is associated with aging and results in age-associated chronic diseases, including type 2 diabetes mellitus, cardiovascular disease, and stroke. Hence, there has been a focus on increasing energy expenditure in aged populations to protect them from age-associated diseases. Dihydrocapsiate (DCT) is a compound that belongs to the capsinoid family. Capsinoids are capsaicin analogs that are found in nonpungent peppers and increase whole body energy expenditure. However, their effect on energy expenditure has been reported only in young populations, and to date the effectiveness of DCT in increasing energy expenditure in aged populations has not been investigated. In this study, we investigated whether DCT supplementation in aged mice improves age-associated impairments. We obtained 5-wk-old and 1-yr-old male C57BL/6J mice and randomly assigned the aged mice to two groups, resulting in a total of three groups: 1) young mice, 2) old mice, and 3) old mice supplemented with 0.3% DCT. After 12 wk of supplementation, blood and tissue samples were collected and analyzed. DCT significantly suppressed age-associated fat accumulation, adipocyte hypertrophy, and liver steatosis. In addition, the DCT treatment dramatically suppressed age-associated increases in hepatic inflammation, immune cell infiltration, and oxidative stress. DCT exerted these suppression effects by increasing energy expenditure linked to upregulation of both the oxidative phosphorylation gene program and fatty acid oxidation in skeletal muscle. These results indicate that DCT efficiently improves age-associated impairments, including liver steatosis and inflammation, in part by increasing energy expenditure via activation of the fat oxidation pathway in skeletal muscle.

Resting Energy Expenditure in Adults with Becker's Muscular Dystrophy

PURPOSE

The purpose of this study was: 1) To compare Resting energy expenditure (REE) in adult males with Becker's Muscular Dystrophy (BeMD, n = 21, 39 ±12 years) and healthy controls (CTRL, n = 12, 37 ±12 years) 2) Determine whether other physiological parameters correlate with REE in BeMD, and 3) Compare current prediction methods of REE with measured REE.

METHODS

REE was calculated via indirect calorimetry using continuous, expired gas analysis following an overnight fast. Fat free mass (FFM) and fat mass were measured by bioelectrical impedance. B-mode ultrasound measured Tibialis Anterior (TA) and Gastrocnemius Medialis (GM) anatomical cross sectional area (ACSA). The Bone Specific Physical Activity Questionnaire measured physical activity.

RESULTS

No difference in REE was found between CTRL and BeMD groups (1913 ±203 & 1786 ±324 Kcal respectively). Other physiological comparisons showed increased fat mass (+54%), decreased TA ACSA (-42%), increased GM ACSA (+25%) as well as reduced respiratory function (FVC -28%; FEV1-27%) in BeMD adults compared to controls. REE estimated from prediction equations (Schofield's) in Muscular Dystrophy were different from measured REE (P<0.05, bias = -728kcal), while the Mifflin equation was no different from measured REE (r2 = 0.58, Bias = -8kcal). Within the present BeMD, REE predicted from FFM (REE = FFM x 34.57-270; r2 = 0.85) and body mass (REE = BM x 15.65 + 421.5; r2 = 0.66), were not different from measured REE (bias equals 0 and 0.2kcals, respectively).

CONCLUSIONS

Despite no differences in REE between CTRL and BeMD adults, increased fat masses highlights the requirement for explicit nutritional guidelines, as well as maintenance of physical activity levels, where possible. Prediction equations are frequently used in clinical settings, however these have been shown to be less accurate in BeMD; therefore, the equations proposed here should be used where possible.

The Impact of Multimorbidity on Resting Metabolic Rate in Community-Dwelling Women over a Ten-Year Period: A Cross-Sectional and Longitudinal Study

OBJECTIVES

Ageing has been associated with increasing multimorbidity. This study investigated whether the number of diseases is a predictor of resting metabolic rate (RMR) and its long-term changes in community-dwelling elderly women.

SUBJECTS AND DESIGN

Cross-sectional and longitudinal data, obtained over ten years with repeated follow-ups, from 180 women aged 60 - 86 years and with a BMI of 18 - 43 kg/m2 at baseline were analyzed.

MEASUREMENTS

RMR was measured using indirect calorimetry and body composition by bioelectrical impedance analysis. Diagnosed diseases were assessed by a questionnaire comprising 23 disease categories. Subjects with 0 - 2 diseases were classified as relatively healthy and with > 2 diseases as multimorbid.

RESULTS

At baseline, relatively healthy (N = 75) and multimorbid (N = 105) women did not differ in RMR. During the ten-year follow-up, the median (range) number of diseases increased from 2 (0 - 2) to 4 (0 - 8) in relatively healthy and from 5 (3 - 11) to 7 (3 - 15) in multimorbid women. In the longitudinal analyses, only women who were multimorbid at baseline showed a significant increase in RMR of 31 kJ/d per additional disease (P = 0.015), adjusted for fat-free mass, fat mass, waist circumference and age.

CONCLUSION

Increasing multimorbidity in community-dwelling women is associated with an increase in RMR independently of body composition and age.

Metabolic Equivalent in Adolescents, Active Adults and Pregnant Women

“Metabolic Equivalent” (MET) represents a standard amount of oxygen consumed by the body under resting conditions, and is defined as 3.5 mL O₂/kg × min or ~1 kcal/kg × h. It is used to express the energy cost of physical activity in multiples of MET. However, universal application of the 1-MET standard was questioned in previous studies, because it does not apply well to all individuals. Height, weight and resting metabolic rate (RMR, measured by indirect calorimetry) were measured in adolescent males (n = 50) and females (n = 50), women during pregnancy (gestation week 35–41, n = 46), women 24–53 weeks postpartum (n = 27), and active men (n = 30), and were compared to values predicted by the 1-MET standard. The RMR of adolescent males (1.28 kcal/kg × h) was significantly higher than that of adolescent females (1.11 kcal/kg × h), with or without the effects of puberty stage and physical activity levels. The RMR of the pregnant and post-pregnant subjects were not significantly different. The RMR of the active normal weight (0.92 kcal/kg × h) and overweight (0.89 kcal/kg × h) adult males were significantly lower than the 1-MET value. It follows that the 1-MET standard is inadequate for use not only in adult men and women, but also in adolescents and physically active men. It is therefore recommended that practitioners estimate RMR with equations taking into account individual characteristics, such as sex, age and Body Mass Index, and not rely on the 1-MET standard.

Age-Dependent Changes in Resting Energy Expenditure (REE): Insights from Detailed Body Composition Analysis in Normal and Overweight Healthy Caucasians

Age-related changes in organ and tissue masses may add to changes in the relationship between resting energy expenditure (REE) and fat free mass (FFM) in normal and overweight healthy Caucasians. Secondary analysis using cross-sectional data of 714 healthy normal and overweight Caucasian subjects (age 18–83 years) with comprehensive information on FFM, organ and tissue masses (as assessed by magnetic resonance imaging (MRI)), body density (as assessed by Air Displacement Plethysmography (ADP)) and hydration (as assessed by deuterium dilution (D₂O)) and REE (as assessed by indirect calorimetry). High metabolic rate organs (HMR) summarized brain, heart, liver and kidney masses. Ratios of HMR organs and muscle mass (MM) in relation to FFM were considered. REE was calculated (REEc) using organ and tissue masses times their specific metabolic rates. REE, FFM, specific metabolic rates, the REE-FFM relationship, HOMA, CRP, and thyroid hormone levels change with age. The age-related decrease in FFM explained 59.7% of decreases in REE. Mean residuals of the REE-FFM association were positive in young adults but became negative in older subjects. When compared to young adults, proportions of MM to FFM decreased with age, whereas contributions of liver and heart did not differ between age groups. HOMA, TSH and inflammation (plasma CRP-levels) explained 4.2%, 2.0% and 1.4% of the variance in the REE-FFM residuals, but age and plasma T3-levels had no effects. HMR to FFM and MM to FFM ratios together added 11.8% on to the variance of REE-FFM residuals. Differences between REE and REEc increased with age, suggesting age-related changes in specific metabolic rates of organs and tissues. This bias was partly explained by plasmaT3-levels. Age-related changes in REE are explained by (i) decreases in fat free mass; (ii) a decrease in the contributions of organ and muscle masses to FFM; and (iii) decreases in specific organ and tissue metabolic rates. Age-dependent changes in the REE-FFMassociation are explained by composition of FFM, inflammation and thyroid hormones.

Nitrogen Balance and Protein Requirements for Critically Ill Older Patients

Critically ill older patients with sarcopenia experience greater morbidity and mortality than younger patients. It is anticipated that unabated protein catabolism would be detrimental for the critically ill older patient. Healthy older subjects experience a diminished response to protein supplementation when compared to their younger counterparts, but this anabolic resistance can be overcome by increasing protein intake. Preliminary evidence suggests that older patients may respond differently to protein intake than younger patients during critical illness as well. If sufficient protein intake is given, older patients can achieve a similar nitrogen accretion response as younger patients even during critical illness. However, there is concern among some clinicians that increasing protein intake in older patients during critical illness may lead to azotemia due to decreased renal functional reserve which may augment the propensity towards worsened renal function and worsened clinical outcomes. Current evidence regarding protein requirements, nitrogen balance, ureagenesis, and clinical outcomes during nutritional therapy for critically ill older patients is reviewed.

Gender-Specific Associations in Age-Related Changes in Resting Energy Expenditure (REE) and MRI Measured Body Composition in Healthy Caucasians

BACKGROUND

The effect of gender as well as gender-specific changes of fat free mass (FFM) and its metabolic active components (muscle mass and organ masses [OMs]) and fat mass (FM) on age-related changes in resting energy expenditure (REE) are not well defined. We hypothesized that there are gender differences in (1) the age-specific onset of changes in detailed body composition (2); the onset of changes in body composition-REE associations with age.

METHODS

Using a cross-sectional magnetic resonance imaging database of 448 Caucasian participants (females and males) with comprehensive data on skeletal muscle (SM) mass, adipose tissue (AT), OMs, and REE.

RESULTS

We observed gender-specific differences in the onset of age-related changes in metabolic active components and REE. Declines in body composition and REE started earlier in females than in males for SM (29.4 vs 39.6 years), AT (38.2 vs 49.9 years), OM (34.7 vs 45.7 years), and REE (31.9 vs 36.8 years). The age-related decrease of AT was significantly higher in females than in males (-5.69kg/decade vs -0.59kg/decade). In females adjusted REEmFFM&FM (resting energy expenditure measured adjusted for FFM and FM) and REEmSM/OM/AT (resting energy expenditure measured adjusted for skeletal muscle and organ mass and adipose tissue) decreased by -145 kJ/d/decade and -604.8 kJ/d/ decade after the age of 35.2 respectively 34.3 years. SM was main determinant of REEm in females (R (2) = .67) and males (R (2) = .66) with remaining variance mainly explained by kidney mass (R (2) = .07) in females and liver mass (R (2) = .09) in males.

CONCLUSION

We concluded that gender affects the age-related changes in body composition as well as changes in body composition-REE relationship. This trial was registered at linicaltrials.gov as NCT01737034.

Accurate estimation of energy requirements of young patients

The provision of optimal nutritional care is based on accurate estimations of patients' resting energy expenditure. The latter can be calculated with the use of predictive equations or measured with indirect calorimetry (IC). Owing to their ease of use, mathematical equations have largely replaced IC in clinical practice. This article examines the limitations and predictive inaccuracy of commonly used equations in pediatrics, which may contribute to the provision of poor nutritional care and directly affect patient outcomes. In addition, the role of IC is discussed and the physiology of nutrient metabolism, in terms of energy expenditure, is reviewed.

Computer modeling of obesity links theoretical energetic measures with experimental measures of fuel use for lean and obese men

The goal of this research was to use a computational model of human metabolism to predict energy metabolism for lean and obese men. The model is composed of 6 state variables representing amino acids, muscle protein, visceral protein, glucose, triglycerides, and fatty acids (FAs). Differential equations represent carbohydrate, amino acid, and FA uptake and output by tissues based on ATP creation and use for both lean and obese men. Model parameterization is based on data from previous studies. Results from sensitivity analyses indicate that model predictions of resting energy expenditure (REE) and respiratory quotient (RQ) are dependent on FA and glucose oxidation rates with the highest sensitivity coefficients (0.6, 0.8 and 0.43, 0.15, respectively, for lean and obese models). Metabolizable energy (ME) is influenced by ingested energy intake with a sensitivity coefficient of 0.98, and a phosphate-to-oxygen ratio by FA oxidation rate and amino acid oxidation rate (0.32, 0.24 and 0.55, 0.65 for lean and obese models, respectively). Simulations of previously published studies showed that the model is able to predict ME ranging from 6.6 to 9.3 with 0% differences between published and model values, and RQ ranging from 0.79 to 0.86 with 1% differences between published and model values. REEs >7 MJ/d are predicted with 6% differences between published and model values. Glucose oxidation increases by ∼0.59 mol/d, RQ increases by 0.03, REE increases by 2 MJ/d, and heat production increases by 1.8 MJ/d in the obese model compared with lean model simulations. Increased FA oxidation results in higher changes in RQ and lower relative changes in REE. These results suggest that because fat mass is directly related to REE and rate of FA oxidation, body fat content could be used as a predictor of RQ.

Regional fat mobilization and training type on sedentary, premenopausal overweight and obese women

OBJECTIVE

Little is known about the influence of different training types on relative fat mobilization with exercise. The purpose of this study was to analyze the changes induced by aerobic training (AT), resistance (RT) or a combination of both (AT+RT) on total fat mass (TFM) and regional fat mass (RFM). Further, the relative contribution of different regions, upper limbs (UL), lower limbs (LL), and trunk (Tr), were compared.

DESIGN AND METHODS

Forty-five overweight and premenopausal women were randomized in either AT, RT or AT+RT. All training groups exercised for the same duration (60 min), 3 times per week for 5 months. Body composition was estimated using dual energy X-ray absorptiometry.

RESULTS

TFM decreased significantly in all groups (-4.6 ± 1.9 kg; -3.8 ± 2.6 kg, and -4.7 ± 3.0 kg in AT, RT, and AT+RT groups respectively; P < 0.001). The relative contribution of FM into each segment changed significantly: TrFM represented 46.6% ± 5.8% of TFM at baseline and reduced to 43.1% ± 5.5% (P < 0.001); LLFM was 39.7% ± 5.8% vs. 41.6% ± 5.7% (P < 0.01); ULFM was 11.3% ± 1.3% vs. 12.2% ± 1.4% (P < 0.01).

CONCLUSION

Training type did not influence changes of TFM and RFM. Fat mobilization came predominantly from Tr in all training protocols. These findings suggest that overweight and obese women can reduce TFM and RFM, independently of training type.

Moderate to high levels of exercise are associated with higher resting energy expenditure in community-dwelling postmenopausal women

Postmenopausal women experience an age-related decline in resting energy expenditure (REE), which is a risk factor for energy imbalance and metabolic disease. Exercise, because of its association with greater lean tissue mass and other factors, has the potential to mediate REE decline, but the relation between exercise and REE in postmenopausal women is not well characterized. This study tests the hypothesis that exercise energy expenditure (EEE) is positively associated with REE and can counter the effects of age and menopause. It involves a cross-sectional sample of 31 healthy postmenopausal women (aged 49-72 years) with habitual exercise volumes at or above levels consistent with current clinical recommendations. Subjects kept exercise diaries for 4 weeks that quantified exercise activity and were measured for body composition, maximal oxygen uptake, and REE. Multiple regression analysis was used to test for associations between EEE, age, body composition, and REE. There was a significant positive relation between EEE and lean tissue mass (fat-free mass and fat-free mass index). The relation between REE and EEE remained significant even after controlling for lean tissue mass. These results support the hypothesis that exercise is positively associated with REE and can counter the negative effects of age and menopause. They also indicate a continuous relation between exercise and REE across ranges of exercise, from moderate to high. Exercise at levels that are at or above current clinical guidelines might, in part, ameliorate the risk for energy imbalance and metabolic disease because of its positive relation with REE.

Issues in characterizing resting energy expenditure in obesity and after weight loss

Limitations of current methods: Normalization of resting energy expenditure (REE) for body composition using the 2-compartment model fat mass (FM), and fat-free mass (FFM) has inherent limitations for the interpretation of REE and may lead to erroneous conclusions when comparing people with a wide range of adiposity as well as before and after substantial weight loss.

EXPERIMENTAL OBJECTIVES

We compared different methods of REE normalization: (1) for FFM and FM (2) by the inclusion of %FM as a measure of adiposity and (3) based on organ and tissue masses. RESULTS were compared between healthy subjects with different degrees of adiposity as well as within subject before and after weight loss.

RESULTS

Normalizing REE from an "REE vs. FFM and FM equation" that (1) was derived in obese participants and applied to lean people or (2) was derived before weight loss and applied after weight loss leads to the erroneous conclusion of a lower metabolic rate (i) in lean persons and (ii) after weight loss. This is revealed by the normalization of REE for organ and tissue masses that was not significantly different between lean and obese or between baseline and after weight loss. There is evidence for an increasing specific metabolic rate of FFM with increasing %FM that could be explained by a higher contribution of liver, kidney and heart mass to FFM in obesity. Using "REE vs. FFM and FM equations" specific for different levels of adiposity (%FM) eliminated differences in REE before and after weight loss in women.

CONCLUSION

The most established method for normalization of REE based on FFM and FM may lead to spurious conclusions about metabolic rate in obesity and the phenomenon of weight loss-associated adaptive thermogenesis. Using %FM-specific REE prediction from FFM and FM in kg may improve the normalization of REE when subjects with wide differences in %FM are investigated.

The influence of shc proteins and aging on whole body energy expenditure and substrate utilization in mice

While it has been proposed that Shc family of adaptor proteins may influence aging by regulating insulin signaling and energy metabolism, the overall impact of Shc proteins on whole body energy metabolism has yet to be elucidated. Thus, the purpose of this study was to determine the influence of Shc proteins and aging on whole body energy metabolism in a mouse model under ambient conditions (22°C) and acute cold exposure (12°C for 24 hours). Using indirect respiration calorimetry, we investigated the impact of Shc proteins and aging on EE and substrate utilization (RQ) in p66 Shc−/− (ShcKO) and wild-type (WT) mice. Calorimetry measurements were completed in 3, 15, and 27 mo mice at 22°C and 12°C. At both temperatures and when analyzed across all age groups, ShcKO mice demonstrated lower 24 h total EE values than that of WT mice when EE data was expressed as either kJ per mouse, or adjusted by body weight or crude organ mass (ORGAN) (P≤0.01 for all). The ShcKO mice also had higher (P<0.05) fed state RQ values than WT animals at 22°C, consistent with an increase in glucose utilization. However, Shc proteins did not influence age-related changes in energy expenditure or RQ. Age had a significant impact on EE at 22°C, regardless of how EE data was expressed (P<0.05), demonstrating a pattern of increase in EE from age 3 to 15 mo, followed by a decrease in EE at 27 mo. These results indicate a decline in whole body EE with advanced age in mice, independent of changes in body weight (BW) or fat free mass (FFM). The results of this study indicate that both Shc proteins and aging should be considered as factors that influence energy expenditure in mice.

Prediction of whole-body and segmental body composition by bioelectrical impedance in morbidly obese subjects

BACKGROUND

Validated equations for body composition analysis using bioelectrical impedance (BIA) in morbidly obese (MO) subjects are scarce. Thus, our aim was todevelop new equations from physical and BIA parameters to estimate whole-body and segmental body composition inMO subjects, with dual-energy X-ray absorptiometry(DXA) as the reference method.

METHODS

A cross-sectional study on 159 Caucasian MO subjects (female 78%, age 43.5 ± 11.8 years, BMI 45.6 ± 4.9 kg/m2) divided in two groups was conducted: model building cohort (n = 110) and model validation cohort (n 0 49). Stepwise regression analysis was used to develop specific fat free mass (FFM) and fat mass (FM) equations.

RESULTS

Gender, body weight, and height2/impedance accounted, respectively, for 89.4% (p < 0.001) and 89.3% (p < 0.001) of the variability of DXA-total FFM in the two cohorts. Using the new equation, the mean difference between the DXA-FFM and BIA-FFM estimates was +0.180 kg (95% CI: -0.34 to +0.7 kg, p 0 NS), and the resulting limits of agreement were +6.76 and −6.40 kg. Similarly, good estimates of DXA truncal-, android-, and gynoid-FM from anthropometric and BIA parameters could be obtained from weight, height2/impedance, and waist and hip circumferences (respectively, R2 adjusted: 0.657, 0.776, and 0.770; p < 0.001).

CONCLUSIONS

The new equations derived from physical and BIA parameters provide accurate estimates of body composition in MO subjects.

Older poor-sleeping women display a smaller evening increase in melatonin secretion and lower values of melatonin and core body temperature than good sleepers

Melatonin concentration and core body temperature (CBT) follow endogenous circadian biological rhythms. In the evening, melatonin level increases and CBT decreases. These changes are involved in the regulation of the sleep-wake cycle. Therefore, the authors hypothesized that age-related changes in these rhythms affect sleep quality in older people. In a cross-sectional study design, 11 older poor-sleeping women (aged 62-72 yrs) and 9 older good-sleeping women (60-82 yrs) were compared with 10 younger good-sleeping women (23-28 yrs). The older groups were matched by age and body mass index. Sleep quality was assessed by the Pittsburgh Sleep Quality Index questionnaire. As an indicator of CBT, oral temperature was measured at 1-h intervals from 17:00 to 24:00 h. At the same time points, saliva samples were collected for determining melatonin levels by enzyme-linked immunosorbent assay (ELISA). The dim light melatonin onset (DLMO), characterizing the onset of melatonin production, was calculated. Evening changes in melatonin and CBT levels were tested by the Friedman test. Group comparisons were performed with independent samples tests. Predictors of sleep-onset latency (SOL) were assessed by regression analysis. Results show that the mean CBT decreased in the evening from 17:00 to 24:00 h in both young women (from 36.57°C to 36.25°C, p < .001) and older women (from 36.58°C to 35.88°C, p < .001), being lowest in the older poor sleepers (p < .05). During the same time period, mean melatonin levels increased in young women (from 16.2 to 54.1 pg/mL, p < .001) and older women (from 10.0 to 23.5 pg/mL, p < .001), being lowest among the older poor sleepers (from 20:00 to 24:00 h, p < .05 vs. young women). Older poor sleepers also showed a smaller increase in melatonin level from 17:00 to 24:00 h than older good sleepers (mean ± SD: 7.0 ± 9.63 pg/mL vs. 15.6 ± 24.1 pg/mL, p = .013). Accordingly, the DLMO occurred at similar times in young (20:10 h) and older (19:57 h) good-sleeping women, but was delayed ∼50 min in older poor-sleeping women (20:47 h). Older poor sleepers showed a shorter phase angle between DLMO and sleep onset, but a longer phase angle between CBT peak and sleep onset than young good sleepers, whereas older good sleepers had intermediate phase angles (insignificant). Regression analysis showed that the DLMO was a significant predictor of SOL in the older women (R(2) = 0.64, p < .001), but not in the younger women. This indicates that melatonin production started later in those older women who needed more time to fall asleep. In conclusion, changes in melatonin level and CBT were intact in older poor sleepers in that evening melatonin increased and CBT decreased. However, poor sleepers showed a weaker evening increase in melatonin level, and their DLMO was delayed compared with good sleepers, suggesting that it is not primarily the absolute level of endogenous melatonin, but rather the timing of the circadian rhythm in evening melatonin secretion that might be related to disturbances in the sleep-wake cycle in older people.

[Prevalence of metabolic syndrome and its associated factors in a rural area of Minas Gerais State (MG, Brazil)]

OBJECTIVE

To estimate the metabolic syndrome (MS) prevalence and its associated factors in rural communities of Minas Gerais State.

METHODS

This is a cross-sectional and populational based study, conducted in Virgem das Graças and Caju, which are located in Jequitinhonha Valley, Minas Gerais State. Information on demographic, lifestyle, anthropometric, biochemical and hemodynamic characteristics were collected of 534 adults. The MS was defined according to National Cholesterol Education Program - Adult Treatment Panel III criteria. The independent associations between co-variables and MS were evaluated using the multivariate Poisson regression with robust variances. The statistical level of significance was set at 5.0%.

RESULTS

MS was diagnosed in 14.9% of the subjects. Female sex, obesity, chronic subclinical inflammation, insulin resistance, age, and moderate alcohol consumption remained independently associated with MS.

CONCLUSIONS

In a rural population studied, MS was a Public Health concern, associated with modifiable factors. Therefore, primary preventive actions could be used to reduce the prevalence of this disease and its impact on people's health.

Body circumferences are predictors of weight adjusted resting energy expenditure in older people

OBJECTIVE

To evaluate predictors of resting energy expenditure (REE) in older people which are more comfortable for them than indirect calorimetry and which are suitable for field studies.

DESIGN

Cross-sectional study.

SETTING

Department of Human Biology, Kiel University.

PARTICIPANTS

100 (51 males, 49 females) healthy independently-living normal-weight (BMI, males 26.0±2.67 kg/m², females 25.0±3.29 kg/m²) Germans, aged 60-83 years.

MEASUREMENTS

REE, body composition, anthropometry, peak expiratory flow rate (PEF), and physical activity level were determined using indirect calorimetry, bioimpedance analysis, anthropometrics, peak-flow-meter, and standardized questionnaire, respectively. Stepwise linear multiple regression analysis was performed with REE or weight adjusted REE as dependent variables. Independent variables were body height, weight, body mass index (BMI), waist circumference, abdomen circumference, hip circumference, waist-to-hip ratio (WHR), lean body mass (LBM), PEF, and physical activity level.

RESULTS

The only significant predictor of REE was LBM in males and BMI in females. Trunk circumferences emerged as strong predictors of weight adjusted REE. Abdomen circumference and hip circumference explained in males and females 69% and 70% of variation in adjusted REE, respectively. Weaker predictors were LBM in males (R² increased from 0.69 to 0.80) as well as body height and BMI in females (R² increased from 0.70 to 0.91). Waist circumference, WHR, physical activity level, and PEF were no significant determinants of adjusted REE.

CONCLUSION

These findings demonstrate that trunk circumferences, but not WHR, are very strong predictors of weight adjusted REE in non-geriatric older people. This implies that the sex-specific use of abdomen or hip circumference in combination with LBM or body height and BMI seems to be well sufficient to predict weight adjusted REE in the aged. These measures might also be of clinical relevance, because they are more comfortable for older sick people than indirect calorimetry. Further studies are needed to test the applicability of the prediction equations to frail older populations.