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Journal ArticleDOI

Body composition and energy intake — skeletal muscle mass is the strongest predictor of food intake in obese adolescents: The HEARTY trial

TL;DR: The hypothesis that the magnitude of the body's lean tissue is related to absolute levels of EI in a sample of inactive adolescents with obesity is supported.
Abstract: There has been renewed interest in examining the relationship between specific components of energy expenditure and the overall influence on energy intake (EI). The purpose of this cross-sectional analysis was to determine the strongest metabolic and anthropometric predictors of EI. It was hypothesized that resting metabolic rate (RMR) and skeletal muscle mass would be the strongest predictors of EI in a sample of overweight and obese adolescents. 304 post-pubertal adolescents (91 boys, 213 girls) aged 16.1 (±1.4) years with body mass index at or above the 95th percentile for age and sex OR at or above the 85th percentile plus an additional diabetes risk factor were measured for body weight, RMR (kcal/day) by indirect calorimetry, body composition by magnetic resonance imaging (fat free mass (FFM), skeletal muscle mass, fat mass (FM), and percentage body fat), and EI (kcal/day) using 3 day food records. Body weight, RMR, FFM, skeletal muscle mass, and FM were all significantly correlated with EI (p < 0.00...

Summary (1 min read)

Introduction

  • Running Title: Body composition and energy intake in obese adolescents Trial Registration: ClinicalTrials.
  • Applied Physiology, Nutrition, and Metabolism Draft 2 Abstract Purpose:.
  • It was hypothesized that resting metabolic rate (RMR) and skeletal muscle mass would be the strongest predictors of EI in a sample of overweight and obese adolescents.

2.1 Subjects

  • The educational level of parents of most participants was high, with 78% of mothers and 67% of fathers having completed some university or community college.
  • This study received approval from the Research Ethics Boards at the Children’s Hospital of Eastern Ontario (protocol #05/04E) and the Ottawa Hospital Research Institute (protocol #2004219-01H).

2.2. Design and Procedure

  • For the baseline assessments the research coordinator performed a complete medical, drug, and physical activity history as well as a physical examination.
  • All participants and the individuals in their households most involved in food preparation attended an initial visit with the dietitian to discuss weight and diet history, fast food consumption and current eating habits.
  • Body weight, height, waist circumference, body composition and RMR were also measured.
  • Total skeletal muscle mass is defined by fat-free skeletal muscle due to the absence of intramuscular fat included in the calculation.
  • Under the supervision of a registered dietitian, energy intake was assessed using 3-day self-reported food logs.

3. Results

  • Participant characteristics are reported in Table 1.
  • The authors primary hypothesis was partially confirmed in that skeletal muscle mass was the strongest predictor of energy intake even after adjustment for age, sex, height, and physical activity levels, whereas RMR was not significantly related to energy intake under these same adjustments .
  • Taken together, the authors demonstrate that, similar to several other studies of adults (Blundell et al.
  • Outside of these intervention points it is understood that at the low(ered) end of body fatness leptin signaling stimulates food intake and reduces energy expenditure, whereas, the opposite effects on feeding and energy expenditure are noted with high(er) body fatness, albeit to a lesser extent.

7. References

  • Applied Physiology, Nutrition, and Metabolism Draft 21 appetite in obese individuals.
  • Linear regression analysis demonstrating the independent associations between subjects’ characteristics (independent variables) and energy intake (dependent variables), presented unadjusted and adjusted for age, sex, height, and physical activity.

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Draft
Body Composition and Energy Intake
Skeletal Muscle Mass
is the Strongest Predictor of Food Intake in Obese
Adolescents: The HEARTY Trial
Journal:
Applied Physiology, Nutrition, and Metabolism
Manuscript ID
apnm-2015-0479.R1
Manuscript Type:
Article
Date Submitted by the Author:
16-Nov-2015
Complete List of Authors:
Cameron, Jameason; Children's Hospital of Eastern Ontario, Healthy Active
Living and Obesity
Sigal, Ronald; University of Calgary,
Kenny, Glen; University of Ottawa,
Alberga, Angela; University of Calgary, Werklund School of Education
Prud'Homme, Denis; Montfort Hospital
Phillips, Penny; The Ottawa Hospital Research Institute, Clinical
Epidemiology Program
Doucette, Steve; Dalhousie University, Capital District Health Authority
Community Health and Epidemiology
Goldfield, Gary; Childrens Hospital of Eastern Ontario
Keyword:
body composition, fat free mass < body composition, dietary intake <
energy regulation, adolescent obesity, magnetic resonance imaging
https://mc06.manuscriptcentral.com/apnm-pubs
Applied Physiology, Nutrition, and Metabolism

Draft
1
Body Composition and Energy Intake— Skeletal Muscle Mass is the Strongest
Predictor of Food Intake in Obese Adolescents: The HEARTY Trial
Cameron, Jameason D
1,2
, Sigal, Ronald J
2,3,4
, Kenny, Glen P
2,4
,
Alberga, Angela S
3
,
Prud’homme, Denis
2,5
,
Phillips, Penny
4
, Doucette, Steve
6
, Goldfield, Gary
1
,2
1
Children’s Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada;
2
University of Ottawa, Ottawa, ON, Canada;
3
University of Calgary, Calgary, AB,
Canada;
4
Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa,
ON, Canada;
5
Institut de recherche de l’Hôpital Montfort, Ottawa, ON, Canada;
6
Dalhousie University, Halifax, NS, Canada
Address correspondence to: Gary Goldfield, Healthy Active Living & Obesity (HALO)
Research Group, CHEO Research Institute, 401 Smyth Rd., Ottawa, ON, K1H 8L1,
CANADA, ggoldfield@cheo.on.ca , 613-737-7600 ext. 3288.
Running Title: Body composition and energy intake in obese adolescents
Trial Registration: ClinicalTrials.Gov NCT00195858
http://clinicaltrials.gov/show/NCT00195858, September 12, 2005 (Funded by the
Canadian Institutes of Health Research).
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2
Abstract
Purpose: There has been renewed interest in examining the relationship between specific
components of energy expenditure and the overall influence on energy intake (EI). The
purpose of this cross-sectional analysis was to determine the strongest metabolic and
anthropometric predictors of EI. It was hypothesized that resting metabolic rate (RMR)
and skeletal muscle mass would be the strongest predictors of EI in a sample of
overweight and obese adolescents.
Methods: 304 post-pubertal adolescents (90 boys, 217 girls) aged 16.1 (±1.4) years with
body mass index at or above the 95
th
percentile for age and sex OR at or above the 85
th
percentile plus an additional diabetes risk factor were measured for body weight, RMR
(kcal/day) by indirect calorimetry, body composition by magnetic resonance imaging (fat
free mass [FFM], skeletal muscle mass, fat mass [FM], and %body fat), and EI (kcal/day)
using 3-day food records.
Results: Body weight, RMR, FFM, skeletal muscle mass, and FM were all significantly
correlated with EI (p<0.005). After adjusting the model for age, sex, height, and physical
activity only FFM (β =21.9, p=0.007) and skeletal muscle mass (β =25.8, p=0.02)
remained as significant predictors of EI. FFM and skeletal muscle mass also predicted
dietary protein and fat intake (p<0.05), but not carbohydrate intake.
Conclusions: With skeletal muscle mass being the best predictor of EI, our results
support the hypothesis that the magnitude of the body’s lean tissue is related to absolute
levels of EI in a sample of inactive adolescents with obesity.
Key Words: Body composition, fat free mass, energy intake, adolescent obesity,
magnetic resonance imaging,
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3
1.Introduction
Energy balance and energy homeostasis are theoretical concepts that provide a
heuristic approach to understand feeding—one of the most integrated of all animal
behaviors—and to further help guide an explanation of human obesity (Cameron and
Doucet 2007). In practice, however, at the individual level humans do not balance energy
intake and energy expenditure on a day-to-day basis (Dulloo et al. 2012), which results in
a daily standard deviation of approximately 0.5% body weight (Garrow 1995). This
deviation is due to the continuous use of energy over the course of the day combined with
the episodic nature of feeding; therein there are a plethora of peripheral peptide and
hormone signals released by the gastrointestinal tract (e.g. CCK, PYY
3-36
, GLP-1, etc.)
and signals from adipose tissue (e.g. the adipokines leptin and adiponectin) that serve to
adjust intake to changing demands. Although the cross-talk of peripheral tissues and
organs with specific brain centers has been described in great detail in relation to appetite
and body weight regulation (Berthoud 2004; Morton et al. 2006), there are still many
questions that remain unanswered. To be sure, in the controlled setting of the laboratory
and without exogenous pharmacologic administration of appetite hormones, very small
effects or null findings are commonplace when trying to elucidate the role that these
common appetite signals have in hunger, satiety, and energy balance in humans (Doucet
and Cameron 2007; Gibbons et al. 2013; King et al. 2015; Sysko et al. 2013).
The recently developed concept of a “Dual Intervention Point Model” by
Speakman et al. (2011) describes two distinct intervention points—low and high levels of
body fatness—where between these points body fat plays a minimal role in the regulation
of food intake (Speakman et al. 2011). Outside of these intervention points it is
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4
understood that at the low(ered) end of body fatness leptin signaling stimulates food
intake and reduces energy expenditure, whereas, the opposite effects on feeding and
energy expenditure are noted with high(er) body fatness, albeit to a lesser extent. Thus,
under the circumstances described above, the drive for food may be better described by
an equal but opposite drive to balance the energy used for the physiologic and metabolic
activity of normal body functioning. In an effort to describe why, in a similar food
environment, some individuals become obese while others do not, Speakman posits that
there may be signals that are deficient—specifically protein or micronutrients—which
compel overconsumption at the individual level (Speakman 2014). Indeed, relative to
carbohydrates, fats, and ethanol, dietary protein has been shown to produce the greatest
effect on satiety and diet-induced thermogenesis in humans (Veldhorst et al. 2008), but it
remains unclear and speculative whether we consume food to leverage a minimal level of
protein. Correspondingly, in light of the literature showing no consistent relationship
between physical activity energy expenditure and energy intake (Blundell et al. 2015;
Donnelly et al. 2014; Martins et al. 2015) there has been a renewed interest in examining
the relationship between other components of energy expenditure—particularly resting
metabolic rate (RMR) and fat free mass (FFM)—and the overall influence on energy
intake (Blundell et al. 2012b; Dulloo et al. 2015). It is well-accepted that the largest
contributor to total energy expenditure is RMR, accounting for approximately 50-70% of
the variance (Johnstone et al. 2005); by convention, FFM and fat mass (FM) account for
approximately 60% and 6%, respectively, of that variance (Johnstone et al. 2005).
Furthermore, a breakdown of contributions of energy expenditure arising from FFM have
been estimated to be 20% brain, 20% liver, 11% heart, 9% gastrointestinal tract, and 20%
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Citations
More filters
Journal ArticleDOI
TL;DR: Sedentariness (physical inactivity) is positively associated with adiposity and is proposed to be a source of overconsumption and appetite dysregulation and represents a target for research.
Abstract: Energy balance is not a simple algebraic sum of energy expenditure and energy intake as often depicted in communications. Energy balance is a dynamic process and there exist reciprocal effects between food intake and energy expenditure. An important distinction is that of metabolic and behavioural components of energy expenditure. These components not only contribute to the energy budget directly, but also by influencing the energy intake side of the equation. It has recently been demonstrated that resting metabolic rate (RMR) is a potential driver of energy intake, and evidence is accumulating on the influence of physical activity (behavioural energy expenditure) on mechanisms of satiety and appetite control. These effects are associated with changes in leptin and insulin sensitivity, and in the plasma levels of gastrointestinal (GI) peptides such as glucagon-like peptide-1 (GLP-1), ghrelin and cholecystokinin (CCK). The influence of fat-free mass on energy expenditure and as a driver of energy intake directs attention to molecules emanating from skeletal tissue as potential appetite signals. Sedentariness (physical inactivity) is positively associated with adiposity and is proposed to be a source of overconsumption and appetite dysregulation. The molecular signals underlying these effects are not known but represent a target for research.

123 citations

Journal ArticleDOI
TL;DR: A loss of FFM that results from dieting or sedentarity should be viewed as a risk factor for weight regain and increased fatness not only because of the impact of the FFM deficit in lowering the maintenance energy requirement but also because the body’s attempt to restore FFM by overeating—a phenomenon referred to as ‘collateral fattening’.
Abstract: While putative feedback signals arising from adipose tissue are commonly assumed to provide the molecular links between the body's long-term energy requirements and energy intake, the available evidence suggests that the lean body or fat-free mass (FFM) also plays a role in the drive to eat. A distinction must, however, be made between a 'passive' role of FFM in driving energy intake, which is likely to be mediated by 'energy-sensing' mechanisms that translate FFM-induced energy requirements to energy intake, and a more 'active' role of FFM in the drive to eat through feedback signaling between FFM deficit and energy intake. Consequently, a loss of FFM that results from dieting or sedentarity should be viewed as a risk factor for weight regain and increased fatness not only because of the impact of the FFM deficit in lowering the maintenance energy requirement but also because of the body's attempt to restore FFM by overeating-a phenomenon referred to as 'collateral fattening'. A better understanding of these passive and active roles of FFM in the control of energy intake will necessitate the elucidation of peripheral signals and energy-sensing mechanisms that drive hunger and appetite, with implications for both obesity prevention and its management.

97 citations

Journal ArticleDOI
TL;DR: Although further research is needed to define normative data and cut points for the low muscle mass and strength phenotype, the use of such non-invasive medical monitoring is a promising strategy to identify early abnormalities and prevent low Muscle mass in adulthood.

80 citations

Journal ArticleDOI
TL;DR: This account of appetite control is consistent with the apparent inexorable escalation of fatness in individual humans, and for the progressive increase in the prevalence of obesity which, among other factors, reflects the difficulty of managing the biological drive to eat.

55 citations

Journal ArticleDOI
TL;DR: Energy intake is important for the maintenance of muscle mass and the relationship between energy intake and sarcopenia in elderly patients with type 2 diabetes (T2D) has been unclear.
Abstract: BACKGROUND Energy intake is important for the maintenance of muscle mass. The relationship between energy intake and sarcopenia in elderly patients with type 2 diabetes (T2D) has been unclear. METHODS Using a brief-type self-administered diet history questionnaire we assessed habitual food and nutrient intake of patients with T2D aged ≥65 years, all of whom were Japanese and physically active, taking part in the KAMOGAWA-DM cohort study. Patients' body composition was evaluated by bioimpedance analysis. Sarcopenia was defined as having both a grip strength of <26 kg for men and <18 kg for women and a skeletal muscle mass index of <7.0 kg/m2 for men and <5.7 kg/m2 for women. Logistic regression analyses were used to investigate the effect of energy intake on the presence of sarcopenia in this cross-sectional study of 391 patients (205 men, 186 women). RESULTS Fifty-five patients (14.1%) were diagnosed as having sarcopenia. Energy intake was significantly lower in patients with sarcopenia than without sarcopenia (mean ± SD [n = 366] 1498.8 ± 389.4 vs 1786.2 ± 706.7 kcal/d, respectively; P = 0.016). After adjusting for age, sex, exercise, smoking status, HbA1c, and body mass index, patients' energy intake (per 100 kcal) was negatively associated with the presence of sarcopenia (odds ratio 0.86; 95% confidence interval 0.78-0.95; P = 0.001). CONCLUSION Energy intake was negatively associated with the presence of sarcopenia in elderly patients with T2D.

51 citations

References
More filters
Journal ArticleDOI
26 Feb 2014-JAMA
TL;DR: Overall, there was no significant change from 2003-2004 through 2011-2012 in high weight for recumbent length among infants and toddlers, obesity in 2- to 19-year-olds, or obesity in adults.
Abstract: Importance More than one-third of adults and 17% of youth in the United States are obese, although the prevalence remained stable between 2003-2004 and 2009-2010. Objective To provide the most recent national estimates of childhood obesity, analyze trends in childhood obesity between 2003 and 2012, and provide detailed obesity trend analyses among adults. Design, Setting, and Participants Weight and height or recumbent length were measured in 9120 participants in the 2011-2012 nationally representative National Health and Nutrition Examination Survey. Main Outcomes and Measures In infants and toddlers from birth to 2 years, high weight for recumbent length was defined as weight for length at or above the 95th percentile of the sex-specific Centers for Disease Control and Prevention (CDC) growth charts. In children and adolescents aged 2 to 19 years, obesity was defined as a body mass index (BMI) at or above the 95th percentile of the sex-specific CDC BMI-for-age growth charts. In adults, obesity was defined as a BMI greater than or equal to 30. Analyses of trends in high weight for recumbent length or obesity prevalence were conducted overall and separately by age across 5 periods (2003-2004, 2005-2006, 2007-2008, 2009-2010, and 2011-2012). Results In 2011-2012, 8.1% (95% CI, 5.8%-11.1%) of infants and toddlers had high weight for recumbent length, and 16.9% (95% CI, 14.9%-19.2%) of 2- to 19-year-olds and 34.9% (95% CI, 32.0%-37.9%) of adults (age-adjusted) aged 20 years or older were obese. Overall, there was no significant change from 2003-2004 through 2011-2012 in high weight for recumbent length among infants and toddlers, obesity in 2- to 19-year-olds, or obesity in adults. Tests for an interaction between survey period and age found an interaction in children ( P = .03) and women ( P = .02). There was a significant decrease in obesity among 2- to 5-year-old children (from 13.9% to 8.4%; P = .03) and a significant increase in obesity among women aged 60 years and older (from 31.5% to 38.1%; P = .006). Conclusions and Relevance Overall, there have been no significant changes in obesity prevalence in youth or adults between 2003-2004 and 2011-2012. Obesity prevalence remains high and thus it is important to continue surveillance.

7,532 citations


"Body composition and energy intake ..." refers background in this paper

  • ...The fact that 32% of North American youth are either overweight or obese (Ogden et al. 2014; Roberts et al. 2012) indicates that there is still a need to understand not only the behavioral factors involved in the act of eating, but also the physiological signals that promote feeding and…...

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  • ...The fact that 32% of North American youth are either overweight or obese (Ogden et al. 2014; Roberts et al. 2012) indicates that there is still a need to understand not only the behavioral factors involved in the act of eating, but also the physiological signals that promote feeding and overconsumption....

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TL;DR: An overview of the endocrine functions of adipose tissue can be found in this paper, where the authors highlight the adverse metabolic consequences of both adipose excess and deficiency, and propose a more rational therapy for these increasingly prevalent disorders.
Abstract: Adipose tissue is a complex, essential, and highly active metabolic and endocrine organ. Besides adipocytes, adipose tissue contains connective tissue matrix, nerve tissue, stromovascular cells, and immune cells. Together these components function as an integrated unit. Adipose tissue not only responds to afferent signals from traditional hormone systems and the central nervous system but also expresses and secretes factors with important endocrine functions. These factors include leptin, other cytokines, adiponectin, complement components, plasminogen activator inhibitor-1, proteins of the renin-angiotensin system, and resistin. Adipose tissue is also a major site for metabolism of sex steroids and glucocorticoids. The important endocrine function of adipose tissue is emphasized by the adverse metabolic consequences of both adipose tissue excess and deficiency. A better understanding of the endocrine function of adipose tissue will likely lead to more rational therapy for these increasingly prevalent disorders. This review presents an overview of the endocrine functions of adipose tissue.

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TL;DR: Several ways in which the calculation of metabolic rate by indirect calorimetry can be simplified are described and how the effect of protein metabolism can be included with a minimum of trouble are shown.
Abstract: A curious fact in the estimation of metabolic rate by indirect calorimetry is that the normal 'exact' method of calculation is so cumbersome that the effect of protein metabolism is commonly ignored. Moreover, the total respiratory quotient is used to assign to the oxygen consumed a calorie value which is appropriate only to the non-protein respiratory quotient. This paper describes several ways in which the calculation can be simplified and shows how the effect of protein metabolism can be included with a minimum of trouble. The derivation of the calorie value of 1 litre of oxygen is the first step.

5,080 citations


"Body composition and energy intake ..." refers methods in this paper

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21 Sep 2006-Nature
TL;DR: This new information provides a biological context within which to consider the global obesity epidemic and identifies numerous potential avenues for therapeutic intervention and future research.
Abstract: The capacity to adjust food intake in response to changing energy requirements is essential for survival. Recent progress has provided an insight into the molecular, cellular and behavioural mechanisms that link changes of body fat stores to adaptive adjustments of feeding behaviour. The physiological importance of this homeostatic control system is highlighted by the severe obesity that results from dysfunction of any of several of its key components. This new information provides a biological context within which to consider the global obesity epidemic and identifies numerous potential avenues for therapeutic intervention and future research.

2,263 citations


"Body composition and energy intake ..." refers background in this paper

  • ...Although the cross-talk of peripheral tissues and organs with specific brain centers has been described in great detail in relation to appetite and body weight regulation (Berthoud 2004; Morton et al. 2006), there are still many questions that remain unanswered....

    [...]

Frequently Asked Questions (1)
Q1. What have the authors contributed in "Body composition and energy intake— skeletal muscle mass is the strongest predictor of food intake in obese adolescents: the hearty trial" ?

In this paper, the strongest metabolic and anthropometric predictors of energy intake ( EI ) in overweight and obese adolescents were determined.