Familial influences and obesity-associated metabolic risk factors contribute to the variation in resting energy expenditure: the Kiel Obesity Prevention Study

Age But Not Menopausal Status Is Linked to Lower Resting Energy Expenditure

CONTEXT

It remains uncertain whether aging before late adulthood and menopause are associated with fat-free mass and fat mass-adjusted resting energy expenditure (REEadj).

OBJECTIVES

We investigated whether REEadj differs between middle-aged and younger women and between middle-aged women with different menopausal statuses. We repeated the age group comparison between middle-aged mothers and their daughters to partially control for genotype. We also explored whether serum estradiol and FSH concentrations explain REEadj in midlife.

METHODS

We divided 120 women, including 16 mother-daughter pairs, into age groups; group I (n = 26) consisted of participants aged 17 to 21, group II (n = 35) of those aged 22 to 38, and group III (n = 59) of those aged 41 to 58 years. The women in group III were further categorized as pre- or perimenopausal (n = 19), postmenopausal (n = 30), or postmenopausal hormone therapy users (n = 10). REE was assessed using indirect calorimetry, body composition using dual-energy X-ray absorptiometry, and hormones using immunoassays.

RESULTS

The REEadj of group I was 126 kcal/day [95% confidence interval (CI): 93-160] higher than that of group III, and the REEadj of group II was 88 kcal/day (95% CI: 49-127) higher. Furthermore, daughters had a 100 kcal/day (95% CI: 63-138 kcal/day) higher REEadj than their middle-aged mothers (all P < .001). In group III, REEadj was not lower in postmenopausal women and did not vary by sex hormone concentrations.

CONCLUSIONS

We demonstrated that REEadj declines with age in women before late adulthood, also when controlling partially for genetic background, and that menopause may not contribute to this decline.

TG/HDL Ratio Is an Independent Predictor for Estimating Resting Energy Expenditure in Adults with Normal Weight, Overweight, and Obesity

Factors that determine resting energy expenditure (REE) remain under investigation, particularly in persons with a high body mass index (BMI). The accurate estimation of energy expenditure is essential for conducting comprehensive nutrition assessments, planning menus and meals, prescribing weight and chronic disease interventions, and the prevention of malnutrition. This study aimed to: (a) determine the contribution of cardiometabolic biomarkers to the inter-individual variation in REE in persons categorized by BMI; and (b) assess the contribution of these biomarkers in the prediction of REE when persons of varying BMI status were categorized by their glycemic and metabolic syndrome status. Baseline data from 645 adults enrolled in diet intervention trials included REE measured by indirect calorimetry, body composition by dual energy X-ray absorptiometry, anthropometrics, and cardiometabolic biomarkers. Multivariate linear regression modeling was conducted to determine the most parsimonious model that significantly predicted REE by BMI category, metabolic syndrome status, and glycemic status. Modeling with the traditional predictors (age, sex, height, weight) accounted for 58-63% of the inter-individual variance in REE. When including age, sex, height, weight and fat-free mass as covariates, adding TG/HDL to regression modeling accounted for 71-87% of the variance in REE. The finding that TG/HDL is an independent predictor in estimating REE was further confirmed when participants were categorized by metabolic syndrome status and by glycemic status. The clinical utility of calculating the TG/HDL ratio not only aids health care providers in identifying patients with impaired lipid metabolism but can optimize the estimation of REE to better meet therapeutic goals for weight and disease management.

The Role and Regulatory Mechanism of Brown Adipose Tissue Activation in Diet-Induced Thermogenesis in Health and Diseases

Brown adipose tissue (BAT) has been considered a vital organ in response to non-shivering adaptive thermogenesis, which could be activated during cold exposure through the sympathetic nervous system (SNS) or under postprandial conditions contributing to diet-induced thermogenesis (DIT). Humans prefer to live within their thermal comfort or neutral zone with minimal energy expenditure created by wearing clothing, making shelters, or using an air conditioner to regulate their ambient temperature; thereby, DIT would become an important mechanism to counter-regulate energy intake and lipid accumulation. In addition, there has been a long interest in the intriguing possibility that a defect in DIT predisposes one to obesity and other metabolic diseases. Due to the recent advances in methodology to evaluate the functional activity of BAT and DIT, this updated review will focus on the role and regulatory mechanism of BAT biology in DIT in health and diseases and whether these mechanisms are applicable to humans.

Impact of Protein Intake during Weight Loss on Preservation of Fat-Free Mass, Resting Energy Expenditure, and Physical Function in Overweight Postmenopausal Women: A Randomized Controlled Trial

INTRODUCTION

Weight loss in old age increases the risk of sarcopenia caused by the age-related reduction of fat-free mass (FFM). Due to the strong correlation between FFM and resting energy expenditure (REE), the maintenance of this must also be considered. Besides, the physical function (PF) must be maintained.

OBJECTIVE

The impact of protein intake on changes in FFM, REE, and PF during weight loss in overweight postmenopausal women was investigated.

METHODS

Fifty-four postmenopausal women (BMI 30.9 ± 3.4; age 59 ± 7 years) were randomized into 2 groups receiving energy-restricted diets with either 0.8 g (normal protein; NP) or 1.5 g protein/kg body weight (high protein; HP) for 12 weeks, followed by a 6-month follow-up phase with an ad libitum food intake. FFM, REE, and PF (strength, endurance, and balance) were measured at baseline, after weight loss, and after follow-up.

RESULTS

Forty-six women completed the weight loss intervention and 29 were followed up. The weight loss was -4.6 ± 3.6 kg (HP) and -5.2 ± 3.4 kg (NP; both p < 0.001) and the weight regain during follow-up was 1.3 ± 2.8 kg (HP; p = 0.03) and 0.4 ± 2.5 kg (NP; p = 0.39), with no differences between groups. Similar decreases in FFM (-0.9 ± 1.1 [HP] vs. -1.0 ± 1.3 kg [NP]) and REE (-862 ± 569 [HP] vs. -1,000 ± 561 kJ [NP]; both p < 0.001) were observed in both groups. During follow-up, no changes in FFM were detected in either group, whereas in the NP group the REE increased again (+138 ± 296; p = 0.02). The main determinants of FFM loss were the energy deficit and the speed of weight loss. In the NP group, the Short Physical Performance Battery score improved with weight loss (+0.6 ± 0.8; p < 0.001) and handgrip strength decreased (-1.7 ± 3.4 kg; p < 0.001), whereas no changes were observed in the HP group.

CONCLUSIONS

An HP weight-loss diet without exercise had no impact on preservation of FFM and REE but may help to maintain muscle strength in postmenopausal women.

Exome Sequencing Identifies A Nonsense Variant in DAO Associated With Reduced Energy Expenditure in American Indians

BACKGROUND

Obesity and energy expenditure (EE) are heritable and genetic variants influencing EE may contribute to the development of obesity. We sought to identify genetic variants that affect EE in American Indians, an ethnic group with high prevalence of obesity.

METHODS

Whole-exome sequencing was performed in 373 healthy Pima Indians informative for 24-hour EE during energy balance. Genetic association analyses of all high-quality exonic variants (≥5 carriers) was performed, and those predicted to be damaging were prioritized.

RESULTS

Rs752074397 introduces a premature stop codon (Cys264Ter) in DAO and demonstrated the strongest association for 24-hour EE, where the Ter allele associated with substantially lower 24-hour EE (mean lower by 268 kcal/d) and sleeping EE (by 135 kcal/d). The Ter allele has a frequency = 0.5% in Pima Indians, whereas is extremely rare in most other ethnic groups (frequency < 0.01%). In vitro functional analysis showed reduced protein levels for the truncated form of DAO consistent with increased protein degradation. DAO encodes D-amino acid oxidase, which is involved in dopamine synthesis which might explain its role in modulating EE.

CONCLUSION

Our results indicate that a nonsense mutation in DAO may influence EE in American Indians. Identification of variants that influence energy metabolism may lead to new pathways to treat human obesity.

CLINICAL TRIAL REGISTRATION NUMBER

NCT00340132.

Project STARLIT: protocol of a longitudinal study of habitual sleep trajectories, weight gain, and obesity risk behaviors in college students

BACKGROUND

Obesity in the United States is a serious and preventable health concern. Previous research suggests that habitual short sleep may influence obesity-risk behaviors, such as increased caloric intake, decreased physical activity and increased engagement in sedentary activities (e.g., media consumption, computer usage). Given that existing longitudinal research studies have methodological concerns preventing conclusive interpretations, Project STARLIT was designed to address these limitations and identify future intervention targets.

METHODS

A sample of young adults (n = 300) will be recruited during the summer prior to entering college. Participants will be screened for eligibility requirements prior to the inclusion in the Time 1 assessment though phone and in-person interviews. Once enrolled, participants will complete four assessments over a two year period (i.e., approximately 8, 16 and 24 months after Time 1). Each assessment will consist of one week of data collection including both objective (i.e., habitual sleep, physical activity, body fat composition) and subjective (i.e., sleep diary, 24-h food recall, technology use, and sleep-related beliefs/behaviors) measures.

DISCUSSION

Project STARLIT is designed to address methodological concerns of previous research. In addition to clarifying the relationship between habitual short sleep and weight gain among young adults, the proposed study will identify problematic obesity risk behaviors associated with habitual short sleep (e.g., increased caloric intake, physical/sedentary activity). The results will identify prevention or intervention targets related to obesity risk.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04100967, 9/23/19, Retrospectively registered.

The pathogenesis of obesity

Body fat mass increases when energy intake exceeds energy expenditure. In the long term, a positive energy balance will result in obesity. The worldwide prevalence of obesity has increased dramatically, posing a serious threat to human health. Therefore, insight in the pathogenesis of obesity is important to identify novel prevention and treatment strategies. This review describes the physiology of energy expenditure and energy intake in the context of body weight gain in humans. We focus on the components of energy expenditure and the regulation of energy intake. Finally, we describe rare monogenetic causes leading to an impairment in central regulation of food intake and obesity.

Resting Energy Expenditure, Insulin Resistance and UCP1 Expression in Human Subcutaneous and Visceral Adipose Tissue of Patients With Obesity

Determinants of resting energy expenditure (REE) in humans are still under investigation, especially the association with insulin resistance. Brown adipose tissue (AT) regulates energy expenditure through the activity of the uncoupling protein 1 (UCP1). White AT browning is the process by which some adipocytes within AT depots acquire properties of brown adipocytes ("brite" adipocytes) and it correlates with metabolic improvement. We analyzed determinants of REE in patients with obesity and assessed UCP1 expression as a "brite" marker in abdominal subcutaneous AT (SAT) and visceral omental AT (VAT). Clinical data, REE, free fat mass (FFM), and fat mass (FM) were determined in 209 patients with obesity. UCP1, PPARG coactivator 1 alpha (PPARGC1A), transcription factor A, mitochondrial (TFAM), T-box transcription factor 1 (TBX1), and solute carrier family 27 member 1 (SLC27A1) expression was assayed in SAT and VAT samples, obtained during sleeve gastrectomy from 62 patients with obesity. REE and body composition data were also available for a subgroup of 35 of whom. In 209 patients with obesity a multiple regression model was computed with REE as the dependent variable and sex, waist, FFM, FM, homeostasis model assessment-insulin resistance (HOMA), interleukin-6 and High Density Lipoprotein-cholesterol as the independent variables. Only FFM, FM and HOMA were independently correlated with REE (r = 0.787, AdjRsqr = 0.602). In each patient VAT displayed a higher UCP1, PPARGC1A, TFAM, TBX1, and SLC27A1 expression than SAT and UCP1 expression in VAT (UCP1-VAT) correlated with Body Mass Index (BMI) (r = 0.287, p < 0.05). Introducing UCP1-VAT in the multivariate model, we showed that FFM, HOMA, interleukin-6, High Density Lipoprotein-cholesterol, and UCP1-VAT were independent factors correlated with REE (r = 0.736, AdjRsqr = 0.612). We confirmed that REE correlates with FFM, FM and HOMA in a large cohort of patients. Our results clearly showed that UCP1-VAT expression was significantly increased in severe human obesity (BMI > 50 kg/m²) and that it behaved as an independent predictor of REE. Lastly, we suggest that an increased REE and browning in metabolically complicated severe obesity could represent an effort to counteract further weight gain.

Human energy expenditure: advances in organ-tissue prediction models

Humans expend energy at rest (REE), and this major energy exchange component is now usually estimated using statistical equations that include weight and other predictor variables. While these formulas are useful in evaluating an individual's or group's REE, an important gap remains: available statistical models are inadequate for explaining underlying organ-specific and tissue-specific mechanisms accounting for resting heat production. The lack of such systems level REE prediction models leaves many research questions unanswered. A potential approach that can fill this gap began with investigators who first showed in animals and later in humans that REE reflects the summated heat production rates of individual organs and tissues. Today, using advanced imaging technologies, REE can be accurately estimated from the measured in vivo mass of 10 organ-tissue mass components combined with their respective mass-specific metabolic rates. This review examines the next frontier of energy expenditure models and discusses how organ-tissue models have the potential not only to better predict REE but also to provide insights into how perturbations in organ mass lead to structure-function changes across other interacting organ systems. The introductory ideas advanced in this review provide a framework for future human energy expenditure modelling research.

Recent advances in understanding body weight homeostasis in humans

Presently, control of body weight is assumed to exist, but there is no consensus framework of body weight homeostasis. Three different models have been proposed, with a "set point" suggesting (i) a more or less tight and (ii) symmetric or asymmetric biological control of body weight resulting from feedback loops from peripheral organs and tissues (e.g. leptin secreted from adipose tissue) to a central control system within the hypothalamus. Alternatively, a "settling point" rather than a set point reflects metabolic adaptations to energy imbalance without any need for feedback control. Finally, the "dual intervention point" model combines both paradigms with two set points and a settling point between them. In humans, observational studies on large populations do not provide consistent evidence for a biological control of body weight, which, if it exists, may be overridden by the influences of the obesogenic environment and culture on personal behavior and experiences. To re-address the issue of body weight homeostasis, there is a need for targeted protocols based on sound concepts, e.g. lean rather than overweight subjects should be investigated before, during, and after weight loss and weight regain. In addition, improved methods and a multi-level-multi-systemic approach are needed to address the associations (i) between masses of individual body components and (ii) between masses and metabolic functions in the contexts of neurohumoral control and systemic effects. In the future, simplifications and the use of crude and non-biological phenotypes (i.e. body mass index and waist circumference) should be avoided. Since changes in body weight follow the mismatch between tightly controlled energy expenditure at loosely controlled energy intake, control (or even a set point) is more likely to be about energy expenditure rather than about body weight itself.

The Cardiometabolic Burden of Self-Perceived Obesity: A Multilevel Analysis of a Nationally Representative Sample of Korean Adults

Emerging evidence has shown that self-perception of overweight/obese status is associated with unfavorable cardiometabolic outcomes, above and beyond actual body weight. Given the lack of research among Asian populations, we examined the association between weight perception and metabolic syndrome (MetS) and cardiometabolic risks among Koreans. Data from the 2010–2015 Korea National Health and Nutrition Examination Survey, including women (N = 12,181) and men (N = 9,448) aged 19–65 years, were analyzed. Weight status perception was measured by participants’ self-evaluation of their body size (“very/slightly obese,” “normal,” and “very/slightly thin”). Overall, 23.2% of women and 28.7% of men had MetS. Our cross-sectional multilevel logistic analyses showed a significant positive association between self-perceived obesity (vs. perceived normal weight) and MetS, independent of BMI and sociodemographic/behavioral/medical conditions, with a stronger association detected among men (OR = 1.38, p < 0.05) than women (OR = 1.22, p < 0.05), confirmed by a statistically significant interaction. Additionally, perceived obesity was associated with high blood pressure (OR = 1.27, p < 0.05) and high triglycerides (OR = 1.38, p < 0.05) among men and low high-density lipoprotein cholesterol (OR = 1.15, p < 0.05) among women. While further prospective research is needed, our findings suggest that perception of being obese may be an unfavorable indicator of cardiometabolic health among Koreans regardless of actual body weight.

Normalizing resting energy expenditure across the life course in humans: challenges and hopes

Whole-body daily energy expenditure is primarily due to resting energy expenditure (REE). Since there is a high inter-individual variance in REE, a quantitative and predictive framework is needed to normalize the data. Complementing the assessment of REE with data normalization makes individuals of different sizes, age, and sex comparable. REE is closely correlated with body mass suggesting its near constancy for a given mass and, thus, a linearity of this association. Since body mass and its metabolic active components are the major determinants of REE, they have been implemented into allometric modeling to normalize REE for quantitative differences in body weight and/or body composition. Up to now, various size and allometric scale laws are used to adjust REE for body mass. In addition, the impact of the anatomical and physical properties of individual body components on REE has been quantified in large populations and for different age groups. More than 80% of the inter-individual variance in REE is explained by FFM and its composition. There is evidence that the impact of individual organs on REE varies between age groups with a higher contribution of brain and visceral organs in children/adolescents compared with adults where skeletal muscle mass contribution is greater than in children/adolescents. However, explaining REE variations by FFM and its composition has its own limitations (inter-correlations of organs/tissues). In future, this could be overcome by re-describing the organ-to-organ variation using principal components analysis and then using the scores on the components as predictors in a multiple regression analysis.

Associations Between C-Reactive Protein, Insulin Sensitivity, and Resting Metabolic Rate in Adults: A Mediator Analysis

Objective: Long-term positive energy balance promotes the development of obesity, a main risk factor for type 2 diabetes mellitus (T2DM). While an association between increased resting metabolic rate (RMR) and insulin sensitivity (IS) was shown previously, the underlying mechanisms remain unclear. Aim of the mediator analysis was to investigate the role of inflammation within the association between RMR and IS. Methods: Anthropometric, clinical, and lifestyle data were collected according to standard operating procedures. RMR was measured using indirect calorimetry. Homeostasis model assessment for insulin resistance (HOMA-IR) was used as an IS parameter and C-reactive protein (CRP) was measured to represent the inflammatory status. Statistical analyses were performed using SPSS. Results: The analysis included 782 adults (517 females) with a mean age of 32.4 ± 12.0 years and a mean body mass index (BMI) of 24.6 ± 5.2 kg/m². Regression analysis indicated a significant evidence for associations between RMR and HOMA-IR (ß = 39.3 ± 7.3 kcal/d; p ≤ 0.001) and CRP and HOMA-IR (ß = 0.5 ± 0.1; p ≤ 0.001) after adjustment for fat-free mass, sex, age, and study site. Results of the mediator analysis did not support the hypothesis that CRP is a mediator for the association between RMR and HOMA-IR. These results did not change after participant stratification according to sex or BMI. Conclusion: A significant evidence for an association between RMR and IS was shown in a large cohort. However, the inflammatory status, determined via CRP levels, was not a mediator within this association.

Association of gene coding variation and resting metabolic rate in a multi-ethnic sample of children and adults

Background

Resting metabolic rates (RMR) vary across individuals. Understanding the determinants of RMR could provide biological insight into obesity and its metabolic consequences such as type 2 diabetes and cardiovascular diseases.

Methods

The present study measured RMR using reference standard indirect calorimetry and evaluated genetic variations from an exome array in a sample of children and adults (N = 262) predominantly of African and European ancestry with a wide range of ages (10 – 67 years old) and body mass indices (BMI; 16.9 – 56.3 kg/m² for adults, 15.1 – 40.6 kg/m2 for children).

Results

Single variant analysis for RMR identified suggestive loci on chromosomes 15 (rs74010762, TRPM1, p-value = 2.7 × 10−6), 1 (rs2358728 and rs2358729, SH3D21, p-values < 5.8x10−5), 17 (AX-82990792, DHX33, 5.5 × 10−5) and 5 (rs115795863 and rs35433829, C5orf33 and RANBP3L, p-values < 8.2 × 10−5). To evaluate the effect of low frequency variations with RMR, we performed gene-based association tests. Our most significant locus was SH3D21 (p-value 2.01 × 10−4), which also contained suggestive results from single-variant analyses. A further investigation of all variants within the reported genes for all obesity-related loci from the GWAS catalog found nominal evidence for association of body mass index (BMI- kg/m²)-associated loci with RMR, with the most significant p-value at rs35433754 (TNKS, p-value = 0.0017).

Conclusions

These nominal associations were robust to adjustment for BMI. The most significant variants were also evaluated using phenome-wide association to evaluate pleiotropy, and genetically predicted gene expression using the summary statistics implicated loci related to in obesity and body composition. These results merit further examination in larger cohorts of children and adults.

Electronic supplementary material

The online version of this article (doi:10.1186/s40608-017-0145-5) contains supplementary material, which is available to authorized users.

From the past to future: from energy expenditure to energy intake to energy expenditure

Although most recent research on energy balance focusses on energy intake (EI) there is still need to think about both sides of the energy balance. Current research on energy expenditure (EE) relates to metabolic adaptation to negative energy balance, mitochondrial metabolism associated with aging, obesity and type 2 diabetes mellitus, the role of EE in hunger and appetite control, non-shivering thermogenesis and brown adipose tissue activity, cellular bioenergetics as a target of obesity treatment and the evolutionary and ecological determinants of EE in humans and other primates. As far as regulation of energy balance is concerned there is recent evidence that EE rather than body weight is under tight control. Biologically, EE is maintained within a narrow physiological range. An EE-set point has been proposed as the width between the upper and lower boundaries of the individual EE range. Regulation of EE may fail in very obese patients with an EI above their upper boundary and after drastic weight loss when patients may go far below their lower EE boundary and thus are loosing control. In population studies, fat-free mass (FFM) and its composition (that is, the proportion of high to low metabolic rate organs) are major determinants of EE. It is tempting to speculate that tight biologic control of EE is related to brain energy need, which is preserved at the cost of peripheral metabolism. There is a moderate heritability of EE, which is independent of the heritability of FFM. In future, metabolic phenotyping should focus on the EE-FFM relationship rather than on EE-values alone.

Impact of dietary glycemic challenge on fuel partitioning

The 'carbohydrate-insulin theory of obesity' is used to justify popular health claims stating that carbohydrates make you fat or a high glycemic load and consumption of sugar-sweetened beverages (SSBs) and breakfast skipping increase fat gain. According to this theory, the elevated postprandial insulin secretion to a high glycemic challenge is blamed as a causal mechanism by directing nutrients away from oxidation in muscle towards storage in adipose tissue. Scientific evidence is however largely disagreeing with an adverse effect of postprandial hyperinsulinemia on fuel partitioning. Possible reasons for this disagreement are differences in insulin sensitivity and energy balance. Diet-induced hyperinsulinemia may lead to a higher fat storage only at a positive energy balance. A shift in fuel partitioning towards fat storage requires improved or maintained insulin sensitivity in adipose tissue when compared with skeletal muscle. This may be the case during refeeding (after weight loss), physical inactivity or in metabolically healthy obese subjects (relative to insulin-resistant subjects). The adverse effect of a high-glycemic diet, SSBs consumption or breakfast skipping on body weight is likely due to increased energy consumption rather than to increased fat storage.

Lower resting energy expenditure and fat oxidation in Native American and Hispanic infants born to mothers with diabetes

OBJECTIVE

To determine whether exposure to diabetes in utero affects resting energy expenditure (REE) and fuel oxidation in infants.

STUDY DESIGN

At 35 ± 5 days after birth, body composition and REE were measured in full-term offspring of Native American and Hispanic women with either well-controlled diabetes (13 girls, 11 boys) or normal healthy pregnancies (18 girls, 17 boys).

RESULTS

Control of dysglycemia during gestation in the women with diabetes mellitus met current clinical standards, shown by average glycated hemoglobin (5.9 ± 0.2%; 40.6 ± 2.3 mmol/mol). Infant body mass (offspring of women with diabetes: 4.78 ± 0.13, control offspring: 4.56 ± 0.08 kg) and body fatness (offspring of women with diabetes: 25.2 ± 0.6, control offspring: 24.2 ± 0.5 %) did not differ between groups. REE, adjusted for lean body mass, was 14% lower in offspring of women with diabetes (41.7 ± 2.3 kJ/h) than control offspring (48.6 ± 2.0, P = .025). Fat oxidation was 26% lower in offspring of women with diabetes (0.54 ± 0.05 g/h) than control offspring (0.76 ± 0.04, P < .01) but carbohydrate oxidation did not differ. Thus, fat oxidation accounted for a lower fraction of REE in the offspring of women with diabetes (49 ± 4%) than control offspring (60 ± 3%, P = .022). Mothers with diabetes were older and had higher prepregnancy body mass index than control mothers.

CONCLUSIONS

Well-controlled maternal diabetes did not significantly affect body mass or composition of offspring at 1-month old. However, infants with mothers with diabetes had reduced REE and fat oxidation, which could contribute to adiposity and future disease risk. Further studies are needed to assess the impact differences in age and higher prepregnancy body mass index.

Examining variations of resting metabolic rate of adults: a public health perspective

PURPOSE

There has not been a recent comprehensive effort to examine existing studies on the resting metabolic rate (RMR) of adults to identify the effect of common population demographic and anthropometric characteristics. Thus, we reviewed the literature on RMR (kcal·kg(-1)·h(-1)) to determine the relationship of age, sex, and obesity status to RMR as compared with the commonly accepted value for the metabolic equivalent (MET; e.g., 1.0 kcal·kg(-1)·h(-1)).

METHODS

Using several databases, scientific articles published from 1980 to 2011 were identified that measured RMR, and from those, others dating back to 1920 were identified. One hundred and ninety-seven studies were identified, resulting in 397 publication estimates of RMR that could represent a population subgroup. Inverse variance weighting technique was applied to compute means and 95% confidence intervals (CI).

RESULTS

The mean value for RMR was 0.863 kcal·kg(-1)·h(-1) (95% CI = 0.852-0.874), higher for men than women, decreasing with increasing age, and less in overweight than normal weight adults. Regardless of sex, adults with BMI ≥ 30 kg·m(-2) had the lowest RMR (<0.741 kcal·kg(-1)·h(-1)).

CONCLUSIONS

No single value for RMR is appropriate for all adults. Adhering to the nearly universally accepted MET convention may lead to the overestimation of the RMR of approximately 10% for men and almost 15% for women and be as high as 20%-30% for some demographic and anthropometric combinations. These large errors raise questions about the longstanding adherence to the conventional MET value for RMR. Failure to recognize this discrepancy may result in important miscalculations of energy expended from interventions using physical activity for diabetes and other chronic disease prevention efforts.

Relationship between submaximal oxygen uptake, detailed body composition, and resting energy expenditure in overweight subjects

OBJECTIVE

We investigated the impact of detailed body composition on aerobic fitness to determine whether regional components of fat mass have independent effects on VO2submax , and whether VO2submax and detailed body composition independently explain variation in REE.

METHODS

71 healthy adults (80% female, 20% male, BMI 28.2-43.8 kg/m(2) ) were investigated. Body composition was measured by the four-compartment model together with whole body magnetic resonance imaging (MRI) to assess high and low metabolic rate organs and regional fat depots. VO2submax was estimated at 75% of predicted maximum heart rate.

RESULTS

There was a strong association between VO2submax and FFM and all organ masses except for heart. Skeletal muscle mass accounted for 34.8% of the variance in VO2submax . In addition, subcutaneous adipose tissue (SAT) of extremities explained additional 14.4%. FFM and FM explained 71.3% of the variance in REE. Including the components of FFM and FM, the explained variance in REE increased by about 5.8%; skeletal muscle mass explained 70.0% of the variance in REE and kidney and liver masses explained additional 7.1%. VO2submax correlated with REE. Taking into account body composition, VO2submax did not add to the variance in REE.

CONCLUSION

FFM is a determinant of both VO2submax and REE. Modeling either REE or VO2submax from individual components of FFM, about 77.1% of variance in REE (by muscle, liver and kidneys mass) and 34.8% of variance in VO2submax (by skeletal muscle mass) could be explained. FM explained additional variance in REE, whereas SAT of extremities added to the variance in VO2submax only.

Best-fitting prediction equations for basal metabolic rate: informing obesity interventions in diverse populations

Basal metabolic rate (BMR) represents the largest component of total energy expenditure and is a major contributor to energy balance. Therefore, accurately estimating BMR is critical for developing rigorous obesity prevention and control strategies. Over the past several decades, numerous BMR formulas have been developed targeted to different population groups. A comprehensive literature search revealed 248 BMR estimation equations developed using diverse ranges of age, gender, race, fat-free mass, fat mass, height, waist-to-hip ratio, body mass index and weight. A subset of 47 studies included enough detail to allow for development of meta-regression equations. Utilizing these studies, meta-equations were developed targeted to 20 specific population groups. This review provides a comprehensive summary of available BMR equations and an estimate of their accuracy. An accompanying online BMR prediction tool (available at http://www.sdl.ise.vt.edu/tutorials.html) was developed to automatically estimate BMR based on the most appropriate equation after user-entry of individual age, race, gender and weight.

Brown adipose tissue in adult humans: a metabolic renaissance

Brown adipose tissue (BAT) plays a key role in energy homeostasis and thermogenesis in animals, conferring protection against diet-induced obesity and hypothermia through the action of uncoupling protein 1 (UCP1). Recent metabolic imaging studies using positron emission tomography computerized tomography (PET-CT) scanning have serendipitously revealed significant depots of BAT in the cervical-supraclavicular regions, demonstrating persistence of BAT beyond infancy. Subsequent cold-stimulated PET-CT studies and direct histological examination of adipose tissues have demonstrated that BAT is highly prevalent in adult humans. BAT activity correlates positively with increment of energy expenditure during cold exposure and negatively with age, body mass index, and fasting glycemia, suggesting regulatory links between BAT, cold-induced thermogenesis, and energy metabolism. Human BAT tissue biopsies express UCP1 and harbor inducible precursors that differentiate into UCP1-expressing adipocytes in vitro. These recent discoveries represent a metabolic renaissance for human adipose biology, overturning previous belief that BAT had no relevance in adult humans. They also have implications for the understanding of the pathogenesis and treatment of obesity and its metabolic sequelae.

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.

Acquired differences in brain responses among monozygotic twins discordant for restrained eating

We studied whether self-reported intent to exert cognitive control over eating was associated with differences in brain response to food cues, independent of genetic background. Subjects were ten pairs of identical twins in which one twin was a restrained eater and the co-twin was unrestrained, as classified by the Herman and Polivy Restraint Scale. Before and after ingestion of a milkshake, we used functional magnetic resonance imaging to measure brain response to photographs of objects, "fattening" food, and "non-fattening" food. At baseline, restrained eaters had greater activation in the left amygdala and the right thalamus in response to fattening food cues than did their unrestrained co-twins. When restrained eaters drank a milkshake, activation in response to fattening food photographs decreased across multiple brain areas, whereas activation induced by non-fattening food photographs increased. As compared to their unrestrained co-twins, restrained eaters who drank a milkshake had greater decreases in activation by fattening food images in the left amygdala and occipital lobe, and greater increases in activation by non-fattening food images in the medial orbitofrontal cortex. Because of the discordant monozygotic twin study design, the findings provide a rigorous level of support for the hypothesis that adopting an intention to restrain eating alters brain response to food cues.

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.

The effect of birth weight on low-energy diet-induced changes in body composition and substrate-energy metabolism in obese women

OBJECTIVE

The objective of this study was to examine the association of birth weight (BW) with body composition, resting metabolic rate (RMR), nonprotein respiratory quotient (NPRQ), and insulin sensitivity changes after a 12-week diet intervention program.

METHODS

A total of 78 obese (body mass index [BMI] 34.0 ± 2.8) women aged 36.7 ± 7 years volunteered to participate in a 12-week diet intervention program. We assessed body fat mass (FM), lean mass (LM), and bone mass (BM; measured by dual energy x-ray absorptiometry), RMR (measured by indirect calorimetry), fasting plasma glucose, and insulin before and after the intervention. We calculated BMI, FM to LM ratio (FM/LM), and HOMA-IR. BW and gestational age were self-reported, and the BW Z-score was calculated.

RESULTS

At baseline, the BW Z-score was positively associated with LM (p < 0.01) and RMR (p < 0.05). The BW Z-score was significantly associated with diet-induced FM (p < 0.05) and FM/LM ratio changes (p < 0.01) independently of potential confounders including weight loss. The BW Z-score was not associated with diet-induced RMR or insulin resistance changes.

CONCLUSIONS

Lower birth weight is associated with lower LM and RMR in obese women and could program a lower FM loss achievement after an energy-restriction diet intervention.

Effect of constitution on mass of individual organs and their association with metabolic rate in humans--a detailed view on allometric scaling

Resting energy expenditure (REE)-power relationships result from multiple underlying factors including weight and height. In addition, detailed body composition, including fat free mass (FFM) and its components, skeletal muscle mass and internal organs with high metabolic rates (i.e. brain, heart, liver, kidneys), are major determinants of REE. Since the mass of individual organs scales to height as well as to weight (and, thus, to constitution), the variance in these associations may also add to the variance in REE. Here we address body composition (measured by magnetic resonance imaging) and REE (assessed by indirect calorimetry) in a group of 330 healthy volunteers differing with respect to age (17–78 years), sex (61% female) and BMI (15.9–47.8 kg/m²). Using three dimensional data interpolation we found that the inter-individual variance related to scaling of organ mass to height and weight and, thus, the constitution-related variances in either FFM (model 1) or kidneys, muscle, brain and liver (model 2) explained up to 43% of the inter-individual variance in REE. These data are the first evidence that constitution adds to the complexity of REE. Since organs scale differently as weight as well as height the “fit” of organ masses within constitution should be considered as a further trait.

Energy metabolism and the metabolic syndrome: does a lower basal metabolic rate signal recovery following weight loss?

AIM

To determine whether basal metabolic rate (BMR) was causally related to MetS, and to study the role of gender in this relationship.

METHODS

Seventy-two Caucasian subjects (43 women, 29 men) had changes in basal metabolic rate (BMR), carbohydrate oxidation rate (COR), fat oxidation rate (FOR) and prevalence of the metabolic syndrome (MetS) assessed in response to weight loss.

RESULTS

There was a significant gender×MetS interaction in BMR at the start. Women with MetS had higher adjusted BMR, whilst men with MetS had lower adjusted BMR than their respective counterparts. Weight loss resulted in a significant decrease in fat mass (-5.2±0.31 kg, p=0.001), fat free mass (-2.3±0.27 kg, p=0.001), BMR (-549±58 kJ/d, p=0.001) and a decreased proportion of MetS (22/72, χ(2)=0.005). Subjects who recovered from MetS after weight loss (RMS) had ∼250 kJ/d significantly lower adjusted BMR compared to those who were never MetS (NMS, p=0.046) and those who still had MetS (MetS+, p=0.047). Regression analysis showed that change (Δ) in BMR was best determined by Δglucose×gender interaction (r(2)=23%), ΔFOR (r(2)=20.3%), ΔCOR (r(2)=19.4%) and Δtriglycerides (r(2)=7.8%).

CONCLUSIONS

There is a sexual dimorphism of BMR in MetS. Overall, the data support the notion that alterations in BMR may be central to the etiopathogenesis of MetS.

Poor prediction of resting energy expenditure in obese women by established equations

The objective of the study was to evaluate the accuracy of established prediction equations that calculate resting energy expenditure (REE) in obese women. This was a cross-sectional study. In 273 mildly to severely obese women (age, 41.7 +/- 13.2 years; body mass index, 42.8 +/- 7.0 kg/m(2)), REE was measured by indirect calorimetry (mREE), along with fat mass (FM) and fat-free mass (FFM) by bioelectrical impedance analysis. Eleven established equations were used to predict REE (pREE), with 9 equations basing on the anthropometric parameters body weight and height and 2 equations including body composition parameters (FM, FFM). All equations provided pREE values that significantly correlated with mREE (r > 0.66, P < .001), although 8 equations systematically underestimated mREE (P < .05). Of note, even the best equation was not able to accurately predict mREE with a deviation of less than +/-10% in more than 70% of the tested women. Furthermore, equations using body composition data were not superior in predicting REE as compared with equations exclusively including anthropometric variables. Multiple linear regression analyses revealed 2 new equations--one including body weight and age and another including FM, FFM, and age--that explained 56.9% and 57.2%, respectively, of variance in mREE. However, when these 2 new equations were applied to an independent sample of 33 obese women, they also provided an accurate prediction (+/-10%) of mREE in only 56.7% and 60.6%, respectively, of the women. Data show that an accurate prediction of REE is not feasible using established equations in obese women. Equations that include body composition parameters as assessed by bioelectrical impedance analysis do not increase the accuracy of prediction. Based on our results, we conclude that calculating REE by standard prediction equations does not represent a reliable alternative to indirect calorimetry for the assessment of REE in obese women.

Changes in resting energy expenditure and their relationship to insulin resistance and weight gain: a longitudinal study in pre-pubertal children (EarlyBird 17)

BACKGROUND & AIMS

In adults, adjustments in resting energy expenditure (REE) are used to defend energy balance against disturbance caused by over-and under-nutrition, and may be linked to changes in insulin resistance and leptin. Little is known of these associations in children. Our aim was to test the hypothesis that long-term weight gain in children is met with adaptive changes in resting energy expenditure, mediated by insulin resistance and/or leptin.

METHODS

REE by indirect calorimetry, anthropometry, body composition by DEXA, insulin resistance (HOMA-IR) and serum leptin were measured annually in 232 children from the age of 7-10 y.

RESULTS

REE rose from 7 to 10 y, and the rise exceeded that predicted by the concurrent rise in fat and fat-free mass by 184 kcal/day in the boys and by 160 kcal/day in the girls. However, there were no significant relationships in either gender between this 'excess' rise in REE and change in body composition (r < or = 0.08, p > or = 0.42). The rise in both boys and girls was associated with, but not explained by, a rise in insulin resistance (p < or = 0.002). There was no association with serum leptin (p > or = 0.32).

CONCLUSIONS

The data do not support the hypothesis of adaptive changes in REE in pre-pubertal children, and insulin resistance explains very little of the pre-pubertal rise in REE. The rise in REE beyond that explained by changes in body composition may reflect an increase in energy requirements prior to puberty.

'Functional' body composition: differentiating between benign and non-benign obesity

Recent body composition analyses, together with assessments of insulin resistance, aerobic fitness, and intima-media thickness of the common carotid artery, have shown that metabolically-benign obese subjects have a similar BMI, waist circumference, and subcutaneous abdominal fat compared with non-metabolically-benign obese subjects. Research has suggested that 25-30% of the obese population do not need either treatment or prevention of secondary disorders. Therefore, assessment of functional body composition should replace nutritional status-based risk assessments (such as the body mass index) in both metabolic research and clinical decision making. The concept of ‘functional’ body composition gives us a more sophisticated view on nutritional status, metabolism, endocrinology, and diseases. Knowledge of detailed body composition enables characterization of biomedical traits which will give functional evidence relating genetic variants.

Contribution of individual organ mass loss to weight loss-associated decline in resting energy expenditure

BACKGROUND

Weight loss leads to reduced resting energy expenditure (REE) independent of fat-free mass (FFM) and fat mass (FM) loss, but the effect of changes in FFM composition is unclear.

OBJECTIVE

We hypothesized that a decrease in REE adjusted for FFM with weight loss would be partly explained by a disproportionate loss in the high metabolic activity component of FFM.

DESIGN

Forty-five overweight and obese women [body mass index (in kg/m(2)): 28.7-46.8] aged 22-46 y followed a low-calorie diet for 12.7 +/- 2.2 wk. Body composition was measured by magnetic resonance imaging, dual-energy X-ray absorptiometry, and a 4-compartment model. REE measured by indirect calorimetry (REEm) was compared with REE calculated from detailed body-composition analysis (REEc) by using specific organ metabolic rates (ie, organ REE/mass).

RESULTS

Weight loss was 9.5 +/- 3.4 kg (8.0 +/- 2.9 kg FM and 1.5 +/- 3.1 kg FFM). Decreases in REE (-8%), free triiodothyronine concentrations (-8%), muscle (-3%), heart (-5%), liver (-4%), and kidney mass (-6%) were observed (all P < 0.05). Relative loss in organ mass was significantly higher (P < 0.01) than was the change in low metabolically active FFM components (muscle, bone, and residual mass). After weight loss, REEm - REEc decreased from 0.24 +/- 0.58 to 0.01 +/- 0.44 MJ/d (P = 0.01) and correlated with the decrease in free triiodothyronine concentrations (r = 0.33, P < 0.05). Women with high adaptive thermogenesis (defined as REEm - REEc < -0.17 MJ/d) had less weight loss and conserved FFM, liver, and kidney mass.

CONCLUSIONS

After weight loss, almost 50% of the decrease in REEm was explained by losses in FFM and FM. The variability in REEm explained by body composition increased to 60% by also considering the weight of individual organs.

Environmental and genetic risk factors in obesity

Because of its high prevalence and the associated medical and psychosocial risks, research into the causes of childhood obesity has experienced a tremendous upswing. Formal genetic data based on twin, adoption, and family studies lead to the conclusion that at least 50% of the interindividual variance of the body mass index (BMI; defined as weight in kilograms divided by height in meters squared) is due to genetic factors. As a result of the recent advent of genome-wide association studies, the first polygenes involved in body weight regulation have been detected. Each of the predisposing alleles explain a few hundred grams of body weight. More polygenes will be detected in the near future, thus for the first time allowing in-depth analyses of gene-gene and gene-environment interactions. They also will enable developmental studies to assess the effect of such alleles throughout childhood and adulthood. The recent increase in obesity prevalence rates illustrates the extreme relevance of environmental factors for body weight. Similar to polygenes, the effect sizes of most such environmental factors are likely to be small, thus rendering their detection difficult. In addition, the validation of the true causality of such factors is not a straightforward task. Important factors are socioeconomic status and television consumption. The authors conclude by briefly assessing implications for treatment and prevention of childhood obesity.