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Background: Fat-free mass (FFM) has been shown to be positively associated with hunger and energy intake, an association mediated by resting metabolic rate (RMR). However, FFM comprises a heterogeneous group of tissues with distinct metabolic rates, and it remains unknown how specific high-metabolic rate organs contribute to the degree of perceived hunger. Objective: To examine whether FFM and its anatomical components were associated with fasting hunger when assessed at the tissue-organ level. Design: Body composition (quantitative magnetic resonance and magnetic resonance imaging), RMR and whole-body glucose oxidation (indirect calorimetry), HOMA-index as a marker of insulin sensitivity, nitrogen balance and fasting hunger (visual analogue scales) were assessed in 21 healthy males (age = 25 ± 3y; BMI = 23.4 ± 2.1 kg/m2) after 3 days of controlled energy balance. Results: FFM (rs = 0.39; p = 0.09), RMR (rs = 0.52; p = 0.02) and skeletal muscle mass (rs = 0.57; p = 0.04), but not fat mass (rs = −0.01; p = 0.99), were positively associated with fasting hunger. The association between the combined mass of high-metabolic rate organs (i.e., brain, liver, kidneys and heart; rs = 0.58; p = 0.006) and fasting hunger was stronger than with FFM as a uniform body component. The strongest individual association was between liver mass and fasting hunger (rs = 0.51; p = 0.02). No associations were observed between glucose parameters, markers of insulin sensitivity and fasting hunger. The encephalic measure, an index of brain-to-body energy allocation, was negatively associated with fasting hunger (rs = −0.51; p = 0.02). Conclusions: Fasting hunger was more strongly associated with the combined mass of high-metabolic rate organs than with FFM as a uniform body component, highlighting the importance of integrating individual tissue-organ masses and their functional correlates into homeostatic models of human appetite. The association between liver mass and fasting hunger may reflect its role in ensuring the brain's basal energy needs are met.

Background Up to 30% of community-based older adults report reduced appetite and energy intake (EI), but previous research examining the underlying physiological mechanisms have focused on the mechanisms that suppress eating rather than the hunger drive and EI. Objective To examine the associations between fat-free mass (FFM), physical activity (PA), total daily energy expenditure (TDEE) and self-reported EI in older adults. Methods The present study was a secondary analysis of The Interactive Diet and Activity Tracking in AARP Study. Body composition (deuterium dilution), PA (accelerometry) and TDEE (doubly labelled water) were measured in 590 older adults (age = 63.1 ± 5.9 years; BMI = 28.1 ± 4.9 kg/m2). Total daily EI was estimated from a single 24-hour dietary recall (EIsingle; ± one month of PA and TDEE measurement) and the mean of up to six recalls over a 12-month period (EImean), with mis-reporters classified using the 95% confidence intervals between EImean and TDEE. Results After controlling for age and sex, linear regression demonstrated that FFM and TDEE predicted EI when estimated from a single 24-hour dietary recall (p < 0.05), the mean of up to six dietary recalls (p < 0.05) and after the removal of those classified as under-reporters (p < 0.001). Age moderated the associations between FFM and EIsingle (p < 0.001), FFM and EImean (p < 0.001), and TDEE with EIsingle (p = 0.016), with associations becoming weaker across age quintiles. Conclusions These data suggest that total daily EI is proportional to FFM and TDEE, but not fat mass, in older adults. These associations may reflect an underling drive to eat that influences daily food intake. While the associations between FFM or TDEE and EI existed across all age quintiles, these associations weakened with increasing age. Trial Registration The Interactive Diet and Activity Tracking in AARP (IDATA) Study was registered at clinicaltrials.gov as: NCT03268577 (http://www.clinicaltrials.gov).

Understanding the mechanisms of hunger, satiety and how nutrients affect appetite control is important for successful weight management across the lifecourse. The primary aim of this study was to describe acute appetite control across the lifecourse, comparing age groups (children, adolescents, adults, elderly), weight categories, genders and European sites (Scotland and Greece). Participants (n = 391) consumed four test drinks, varying in composition (15% (normal protein, NP) and 30% (high protein, HP) of energy from protein) and quantity (based on 100% basal metabolic rate (BMR) and 140% BMR), on four separate days in a double-blind randomized controlled study. Ad libitum energy intake (EI), subjective appetite and biomarkers of appetite and metabolism (adults and elderly only) were measured. The adults’ appetite was significantly greater than that of the elderly across all drink types (p < 0.004) and in response to drink quantities (p < 0.001). There were no significant differences in EI between age groups, weight categories, genders or sites. Concentrations of glucagon-like peptide 1 (GLP-1) and peptide YY (PYY) were significantly greater in the elderly than the adults (p < 0.001). Ghrelin and fasting leptin concentrations differed significantly between weight categories, genders and sites (p < 0.05), while GLP-1 and PYY concentrations differed significantly between genders only (p < 0.05). Compared to NP drinks, HP drinks significantly increased postprandial GLP-1 and PYY (p < 0.001). Advanced age was concomitant with reduced appetite and elevated anorectic hormone release, which may contribute to the development of malnutrition. In addition, appetite hormone concentrations differed between weight categories, genders and geographical locations.

Background: Body weight variability (BWV) is common in the general population and may act as a risk factor for obesity or diseases. The correct identification of these patterns may have prognostic or predictive value in clinical and research settings. With advancements in technology allowing for the frequent collection of body weight data from electronic smart scales, new opportunities to analyze and identify patterns in body weight data are available. Objective: This study aims to compare multiple methods of data imputation and BWV calculation using linear and nonlinear approaches Methods: In total, 50 participants from an ongoing weight loss maintenance study (the NoHoW study) were selected to develop the procedure. We addressed the following aspects of data analysis: cleaning, imputation, detrending, and calculation of total and local BWV. To test imputation, missing data were simulated at random and using real patterns of missingness. A total of 10 imputation strategies were tested. Next, BWV was calculated using linear and nonlinear approaches, and the effects of missing data and data imputation on these estimates were investigated. Results: Body weight imputation using structural modeling with Kalman smoothing or an exponentially weighted moving average provided the best agreement with observed values (root mean square error range 0.62%-0.64%). Imputation performance decreased with missingness and was similar between random and nonrandom simulations. Errors in BWV estimations from missing simulated data sets were low (2%-7% with 80% missing data or a mean of 67, SD 40.1 available body weights) compared with that of imputation strategies where errors were significantly greater, varying by imputation method. Conclusions: The decision to impute body weight data depends on the purpose of the analysis. Directions for the best performing imputation methods are provided. For the purpose of estimating BWV, data imputation should not be conducted. Linear and nonlinear methods of estimating BWV provide reasonably accurate estimates under high proportions (80%) of missing data.

The current obesogenic environment promotes physical inactivity and food consumption in excess of energy requirements, two important modifiable risk factors influencing energy balance. Habitual physical activity has been shown to impact not only energy expenditure, but also energy intake through mechanisms of appetite control. This review summarizes recent theory and evidence underpinning the role of physical activity in the homeostatic and non-homeostatic mechanisms controlling appetite. Energy intake along the spectrum of physical activity levels (inactive to highly active) appears to be J-shaped, with low levels of physical activity leading to dysregulated appetite and a mismatch between energy intake and expenditure. At higher levels, habitual physical activity influences homeostatic appetite control in a dual-process action by increasing the drive to eat through greater energy expenditure, but also by enhancing post-meal satiety, allowing energy intake to better match energy expenditure in response to hunger and satiety signals. There is clear presumptive evidence that physical activity energy expenditure can act as a drive (determinant) of energy intake. The influence of physical activity level on non-homeostatic appetite control is less clear, but low levels of physical activity may amplify hedonic states and behavioural traits favouring overconsumption indirectly through increased body fat. More evidence is required to understand the interaction between physical activity, appetite control and diet composition on passive overconsumption and energy balance. Furthermore, potential moderators of appetite control along the spectrum of physical activity, such as body composition, sex, and type, intensity and timing of physical activity, remain to be fully understood.