Marie-Pierre St-Onge

Assessing Adiposity: A Scientific Statement From the American Heart Association

The prevalence of obesity in the United States and the world has risen to epidemic/pandemic proportions. This increase has occurred despite great efforts by healthcare providers and consumers alike to improve the health-related behaviors of the population and a tremendous push from the scientific community to better understand the pathophysiology of obesity. This epidemic is all the more concerning given the clear association between excess adiposity and adverse health consequences such as cardiovascular disease (CVD) and type 2 diabetes mellitus (T2DM). The risks associated with overweight/obesity are primarily related to the deposition of adipose tissue, which leads to excess adiposity or body fatness. Furthermore, weight loss, specifically loss of body fat, is associated with improvement in obesity-related comorbidities. Before weight loss interventions can be recommended, however, patients must be assessed for their adiposity-related risk. Unfortunately, healthcare providers and systems have not done a good job of assessing for excess adiposity even in its simplest form, such as measuring body mass index (BMI). It is for these reasons that we must emphasize the importance of assessing adiposity in clinical practices. Although it can be argued that the entire population should be targeted as an important public health issue with a goal of prevention of weight gain and obesity, there are currently so many “at risk” individuals that simple strategies to identify and treat those individuals are necessary. We must identify those individuals at highest risk of comorbidities in order to identify those who might benefit the most from aggressive weight management. This scientific statement will first briefly review the epidemiology of obesity and its related comorbidities, supporting the need for improved assessment of adiposity in daily clinical practice. This will be followed by a discussion of some of the challenges and issues associated with assessing adiposity and then by a review …

New bioimpedance analysis system: improved phenotyping with whole-body analysis.

Objective: Bioimpedance analysis (BIA) is a potential field and clinical method for evaluating skeletal muscle mass (SM) and %fat. A new BIA system has 8-(two on each hand and foot) rather than 4-contact electrodes allowing for rapid ‘whole-body’ and regional body composition evaluation.Design: This study evaluated the 50 kHz BC-418 8-contact electrode and TBF-310 4-contact electrode foot–foot BIA systems (Tanita Corp., Tokyo, Japan).Subjects: There were 40 subject evaluations in males (n=20) and females (n=20) ranging in age from 6 to 64 y. BIA was evaluated in each subject and compared to reference lean soft-tissue (LST) and %fat estimates in the appendages and remainder (trunk+head) provided by dual-energy X-ray absorptiometry (DXA). Appendicular LST (ALST) estimates from both BIA and DXA were used to derive total body SM mass.Results: The highest correlation between total body LST by DXA and impedance index (Ht2/Z) by BC-418 was for the foot–hand segments (r=0.986; left side only) compared to the arm (r=0.970–0.979) and leg segments (r=0.942–0.957)(all P<0.001). The within- and between-day coefficient of variation for %fat and ALST evaluated in five subjects was <1% and ∼1–3.7%, respectively. The correlations between 8-electrode predicted and DXA appendicular (arms, legs, total) and trunk+head LST were strong and highly significant (all r⩾0.95, P<0.001) and group means did not differ across methods. Skeletal muscle mass calculated (Kim equation) from total ALST by DXA (X±s.d.)(23.7±9.7 kg) was not significantly different and highly correlated with BC-418 estimates (25.2±9.6 kg; r=0.96, P<0.001). There was a good correlation between total body %fat by 8-electrode BIA vs DXA (r=0.87, P<0.001) that exceeded the corresponding association with 4-electrode BIA (r=0.82, P<0.001). Group mean segmental %fat estimates from BC-418 did not differ significantly from corresponding DXA estimates. No between-method bias was detected in the whole body, ALST, and skeletal muscle analyses.Conclusions: The new 8-electrode BIA system offers an important new opportunity of evaluating SM in research and clinical settings. The additional electrodes of the new BIA system also improve the association with DXA %fat estimates over those provided by the conventional foot–foot BIA.

Short sleep duration increases energy intakes but does not change energy expenditure in normal-weight individuals

BACKGROUND Evidence suggests a relation between short sleep duration and obesity. OBJECTIVE We assessed energy balance during periods of short and habitual sleep in normal-weight men and women. DESIGN Fifteen men and 15 women aged 30-49 y with a body mass index (in kg/m(2)) of 22-26, who regularly slept 7-9 h/night, were recruited to participate in this crossover inpatient study. All participants were studied under short (4 h/night) and habitual (9 h/night) sleep conditions, in random order, for 5 nights each. Food intake was measured on day 5, and energy expenditure was measured with the doubly labeled water method over each period. RESULTS Participants consumed more energy on day 5 during short sleep (2813.6 ± 593.0 kcal) than during habitual sleep (2517.7 ± 593.0 kcal; P = 0.023). This effect was mostly due to increased consumption of fat (20.7 ± 37.4 g; P = 0.01), notably saturated fat (8.7 ± 20.4 g; P = 0.038), during short sleep. Resting metabolic rate (short sleep: 1455.4 ± 129.0 kcal/d; habitual sleep: 1486.5 ± 129.5 kcal/d; P = 0.136) and total energy expenditure (short sleep: 2589.2 ± 526.5 kcal/d; habitual sleep: 2611.1 ± 529.0 kcal/d; P = 0.832) did not differ significantly between sleep phases. CONCLUSIONS Our data show that a reduction in sleep increases energy and fat intakes, which may explain the associations observed between sleep and obesity. If sustained, as observed, and not compensated by increased energy expenditure, the dietary intakes of individuals undergoing short sleep predispose to obesity. This trial is registered at clinicaltrials.gov as NCT00935402.

Body composition changes with aging: The cause or the result of alterations in metabolic rate and macronutrient oxidation?

It has been well documented that as individuals age, body composition changes, even in the absence of changes in body weight. Studies have shown that fat mass increases and muscle mass decreases with age. However, it is unclear why such changes occur. Resting metabolic rate (RMR) and substrate oxidation rates have been examined with aging. It has been proposed that reductions in RMR and fat oxidation may lead to changes in body composition. Alternatively, changes in body composition with aging may lead to reductions in RMR. The purpose of this review is to provide an overview of the literature surrounding the impact of aging on RMR and substrate oxidation. Although long-term longitudinal studies are lacking, most cross-sectional studies or short-term longitudinal studies show a reduction in RMR with aging that cannot be explained by changes in body composition including loss in fat-free mass, where the latter includes atrophy or decreases in the mass of high metabolic rate organs. There is indirect evidence suggesting that the metabolic rate of individual organs is lower in older compared with younger individuals. With aging, we conclude that reductions in the mass of individual organs/tissues and in tissue-specific organ metabolic rate contribute to a reduction in RMR that in turn promotes changes in body composition favoring increased fat mass and reduced fat-free mass.

Sleep restriction leads to increased activation of brain regions sensitive to food stimuli

BACKGROUND Epidemiologic evidence shows an increase in obesity concurrent with a reduction in average sleep duration among Americans. Although clinical studies propose that restricted sleep affects hormones related to appetite, neuronal activity in response to food stimuli after restricted and habitual sleep has not been investigated. OBJECTIVE The objective of this study was to determine the effects of partial sleep restriction on neuronal activation in response to food stimuli. DESIGN Thirty healthy, normal-weight [BMI (in kg/m²): 22-26] men and women were recruited (26 completed) to participate in a 2-phase inpatient crossover study in which they spent either 4 h/night (restricted sleep) or 9 h/night (habitual sleep) in bed. Each phase lasted 6 d, and functional magnetic resonance imaging was performed in the fasted state on day 6. RESULTS Overall neuronal activity in response to food stimuli was greater after restricted sleep than after habitual sleep. In addition, a relative increase in brain activity in areas associated with reward, including the putamen, nucleus accumbens, thalamus, insula, and prefrontal cortex in response to food stimuli, was observed. CONCLUSION The findings of this study link restricted sleep and susceptibility to food stimuli and are consistent with the notion that reduced sleep may lead to greater propensity to overeat.

Missing Data in Randomized Clinical Trials for Weight Loss: Scope of the Problem, State of the Field, and Performance of Statistical Methods

Background Dropouts and missing data are nearly-ubiquitous in obesity randomized controlled trails, threatening validity and generalizability of conclusions. Herein, we meta-analytically evaluate the extent of missing data, the frequency with which various analytic methods are employed to accommodate dropouts, and the performance of multiple statistical methods. Methodology/Principal Findings We searched PubMed and Cochrane databases (2000–2006) for articles published in English and manually searched bibliographic references. Articles of pharmaceutical randomized controlled trials with weight loss or weight gain prevention as major endpoints were included. Two authors independently reviewed each publication for inclusion. 121 articles met the inclusion criteria. Two authors independently extracted treatment, sample size, drop-out rates, study duration, and statistical method used to handle missing data from all articles and resolved disagreements by consensus. In the meta-analysis, drop-out rates were substantial with the survival (non-dropout) rates being approximated by an exponential decay curve (e−λt) where λ was estimated to be .0088 (95% bootstrap confidence interval: .0076 to .0100) and t represents time in weeks. The estimated drop-out rate at 1 year was 37%. Most studies used last observation carried forward as the primary analytic method to handle missing data. We also obtained 12 raw obesity randomized controlled trial datasets for empirical analyses. Analyses of raw randomized controlled trial data suggested that both mixed models and multiple imputation performed well, but that multiple imputation may be more robust when missing data are extensive. Conclusion/Significance Our analysis offers an equation for predictions of dropout rates useful for future study planning. Our raw data analyses suggests that multiple imputation is better than other methods for handling missing data in obesity randomized controlled trials, followed closely by mixed models. We suggest these methods supplant last observation carried forward as the primary method of analysis.

Short sleep duration, glucose dysregulation and hormonal regulation of appetite in men and women.

STUDY OBJECTIVE To determine the hormonal effects of reducing sleep duration under controlled feeding conditions. DESIGN Randomized, crossover study. SETTING Inpatient. PARTICIPANTS Twenty-seven normal weight, 30- to 45-yr-old men and women habitually sleeping 7-9 hr/night. INTERVENTION PARTICIPANTS WERE STUDIED UNDER TWO SLEEP CONDITIONS: short (4 hr in bed) or habitual (9 hr in bed) sleep. A controlled diet was provided for each 4-day study period. MEASUREMENTS AND RESULTS Fasting blood samples were obtained daily and frequent blood samples were obtained throughout day 4. The main outcomes measures included glucose, insulin, leptin, ghrelin, adiponectin, total glucagon-like peptide 1 (GLP-1) and peptide YY(3-36) (PYY(3-36)) concentrations. Body weights were reduced by 2.2 ± 0.4 lb and 1.7 ± 0.4 lb during the habitual and short sleep phases, respectively (both P < 0.0001). There was no effect of sleep duration on glucose, insulin, and leptin profiles (all P > 0.05). Ghrelin and GLP-1 responses differed by sex. Short sleep increased fasting (P = 0.054) and morning (08:00-12:00) (P = 0.042) total ghrelin in men but not women. The reverse was observed for GLP-1: afternoon levels (12:30-19:00) were lower (P = 0.016) after short sleep compared with habitual sleep in women but not men. CONCLUSIONS These data suggest that, in the context of negative energy balance, short sleep does not lead to a state of increased insulin resistance, but may predispose to overeating via separate mechanisms in men and women. CLINICAL TRIAL INFORMATION Trial registration on http://www.clinicaltrials.gov. #NCT00935402.

The role of sleep duration in the regulation of energy balance: effects on energy intakes and expenditure.

Short sleep duration and obesity are common occurrence in todays society. An extensive literature from cross-sectional and longitudinal epidemiological studies shows a relationship between short sleep and prevalence of obesity and weight gain. However, causality cannot be inferred from such studies. Clinical intervention studies have examined whether reducing sleep in normal sleepers, typically sleeping 7-9 h/night, can affect energy intake, energy expenditure, and endocrine regulators of energy balance. The aim of this review is to evaluate studies that have assessed food intake, energy expenditure, and leptin and ghrelin levels after periods of restricted and normal sleep. Most studies support the notion that restricting sleep increases food intake, but the effects on energy expenditure are mixed. Differences in methodology and component of energy expenditure analyzed may account for the discrepancies. Studies examining the effects of sleep on leptin and ghrelin have provided conflicting results with increased, reduced, or unchanged leptin and ghrelin levels after restricted sleep compared to normal sleep. Energy balance of study participants and potential sex differences may account for the varied results. Studies should strive for constant energy balance and feeding schedules when assessing the role of sleep on hormonal profile. Although studies suggest that restricting sleep may lead to weight gain via increased food intake, research is needed to examine the impact on energy expenditure and endocrine controls. Also, studies have been of short duration, and there is little knowledge on the reverse question: does increasing sleep duration in short sleepers lead to negative energy balance?

Sleep restriction increases the neuronal response to unhealthy food in normal-weight individuals.

Context:Sleep restriction alters responses to food. However, the underlying neural mechanisms for this effect are not well understood.Objective:The purpose of this study was to determine whether there is a neural system that is preferentially activated in response to unhealthy compared with healthy foods.Participants:Twenty-five normal-weight individuals, who normally slept 7–9 h per night, completed both phases of this randomized controlled study.Intervention:Each participant was tested after a period of five nights of either 4 or 9 h in bed. Functional magnetic resonance imaging (fMRI) was performed in the fasted state, presenting healthy and unhealthy food stimuli and objects in a block design. Neuronal responses to unhealthy, relative to healthy food stimuli after each sleep period were assessed and compared.Results:After a period of restricted sleep, viewing unhealthy foods led to greater activation in the superior and middle temporal gyri, middle and superior frontal gyri, left inferior parietal lobule, orbitofrontal cortex, and right insula compared with healthy foods. These same stimuli presented after a period of habitual sleep did not produce marked activity patterns specific to unhealthy foods. Further, food intake during restricted sleep increased in association with a relative decrease in brain oxygenation level-dependent (BOLD) activity observed in the right insula.Conclusion:This inverse relationship between insula activity and food intake and enhanced activation in brain reward and food-sensitive centers in response to unhealthy foods provides a model of neuronal mechanisms relating short sleep duration to obesity.

Increased sweetened beverage intake is associated with reduced milk and calcium intake in 3- to 7-year-old children at multi-item laboratory lunches.

Dietary survey data show that intake of sugar-sweetened beverages is negatively associated with intake of milk, but these findings have yet to be confirmed by laboratory feeding studies. The objectives of the present study were to analyze childrens intake across two laboratory-based ad libitum lunches to (a) investigate the relationships between intake of sweetened beverages, milk, and calcium, and (b) explore relationships between beverage consumption and child age and weight status. Data were extracted from a cohort of 126 3- to 7-year-old twins from diverse ethnic backgrounds who participated in a cross-sectional study (conducted from November 1999 to September 2002) designed to determine the genetic and environmental contributions to eating and body weight. At two visits, children ate ad libitum from lunches that offered a variety of sugar-sweetened and calcium-rich beverages. Total beverage and nutrient intakes were computed from the test meals. Weight, height, and waist circumference were assessed on the final visit. Regression analyses tested the associations among intake of sweetened beverages, calcium, and milk (primary aim), and whether these variables were associated with child age and weight status (secondary aim). Sweetened beverage intake was negatively correlated with both milk (P<0.01) and calcium (P<0.01) intakes, and these relationships remained after controlling for age, sex, and ethnicity (P<0.01). Child age was negatively associated with milk intake (r=-0.22, P<0.01) but positively associated with intake of sweetened beverages (r=0.27, P<0.01). Results support the notion that sugar-sweetened beverages displace milk in a single meal, and this phenomenon may vary with child age. Due to the cross-sectional nature of this study, future investigations are needed to determine the long-term implications of this consumption pattern. The possibility that limiting sweetened beverages may help optimize dietary calcium during childhood is a topic that merits further research.