Diet composition and energy intake in humans James Stubbs, R; Horgan, Graham; Robinson, Eric ...
Philosophical transactions of the Royal Society of London. Series B. Biological sciences,
10/2023, Volume:
378, Issue:
1888
Journal Article
Peer reviewed
Open access
Absolute energy from fats and carbohydrates and the proportion of carbohydrates in the food supply have increased over 50 years. Dietary energy density (ED) is primarily decreased by the water and ...increased by the fat content of foods. Protein, carbohydrates and fat exert different effects on satiety or energy intake (EI) in the order protein > carbohydrates > fat. When the ED of different foods is equalized the differences between fat and carbohydrates are modest. Covertly increasing dietary ED with fat, carbohydrate or mixed macronutrients elevates EI, producing weight gain and vice versa. In more naturalistic situations where learning cues are intact, there appears to be greater compensation for the different ED of foods. There is considerable individual variability in response. Macronutrient-specific negative feedback models of EI regulation have limited capacity to explain how availability of cheap, highly palatable, readily assimilated, energy-dense foods lead to obesity in modern environments. Neuropsychological constructs including food reward (liking, wanting and learning), reactive and reflective decision making, in the context of asymmetric energy balance regulation, give more comprehensive explanations of how environmental superabundance of foods containing mixtures of readily assimilated fats and carbohydrates and caloric beverages elevate EI through combined hedonic, affective, cognitive and physiological mechanisms. This article is part of a discussion meeting issue 'Causes of obesity: theories, conjectures and evidence (Part II)'.
Due to relationships between diet and health including obesity, there is a need to examine the explanatory power of factors that motivate people to (over or under) eat. In a previous investigation, a ...four-factor subscale-based model of eating behaviour traits (EBTs) was developed which identified individual differences in psychological factors influencing motivations to eat and some residual uncertainties. The current study used a data-driven and theory-driven approach, including individual items to refine and extend previous EBT models. The aim was to examine and validate the domain structure of a framework for EBTs. The analysis used two samples including a representative sample of the UK population (n = 2010, 51% female, 49% male, 18 – 88 years), and members of a weight management program (n = 2317, 96.6% female, 2.8% male, 21 – 84 years), who completed 5 questionnaires including 10 EBTs. The results found some support for a 6-factor model, encompassing reactive eating, negative emotional eating, positive emotional eating, restricted eating, homeostatic eating, and body-food choice congruence (data-driven model) or eating for health (theory-driven model). There were differences between the data-driven model and the theory-driven model regarding the 6th factor. Additionally, the data-driven model did not distinguish between eating for pleasure and reactive eating. The models demonstrated that the eating behaviour factors were significantly associated with BMI category. Overall, this research contributes to a more structured understanding of the dimensions of motivation underlying EBTs, emphasising the utility of this framework for identifying at-risk individuals and tailoring interventions to meet specific individual needs.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Summary
At present, it is unclear whether eating behavior traits (EBT) predict objectively measured short‐term energy intake (EI) and longer‐term energy balance as estimated by body mass index (BMI). ...This systematic review examined the impact of EBT on BMI and laboratory‐based measures of EI in adults (
≥18 years) in any BMI category, excluding self‐report measures of EI. Articles were searched up until 28th October 2021 using MEDLINE, PsycINFO, EMBASE and Web of Science. Sixteen EBT were identified and the association between 10 EBT, EI and BMI were assessed using a random‐effects meta‐analysis. Other EBT outcomes were synthesized qualitatively. Risk of bias was assessed with the mixed methods appraisal tool. A total of 83 studies were included (mean BMI = 25.20 kg/m2, mean age = 27 years and mean sample size = 70). Study quality was rated moderately high overall, with some concerns in sampling strategy and statistical analyses. Susceptibility to hunger (n = 6) and binge eating (n = 7) were the strongest predictors of EI. Disinhibition (n = 8) was the strongest predictor of BMI. Overall, EBT may be useful as phenotypic markers of susceptibility to overconsume or develop obesity (PROSPERO: CRD42021288694).
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BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SBCE, SBMB, UL, UM, UPUK
Eating Behaviour Traits (EBTs) are psychological constructs developed to explain patterns of eating behaviour, including factors that motivate people to (over or under) eat. There is a need to align ...and clarify their unique contributions and harmonise the understanding they offer for human eating behaviour. Therefore, the current study examined whether 18 commonly cited EBTs could be explained by underlying, latent factors (domains of eating behaviour). An exploratory factor analysis (EFA) was used to identify latent factors, and these factors were validated using a confirmatory factor analysis (CFA). 1279 participants including the general public and members of a weight management programme were included in the analysis (957 females, 317 males, 3 others, 2 prefer not to say), with a mean age of 54 years (median = 57 years, SD = 12.03) and a mean BMI of 31.93 kg/m2 (median = 30.86, SD = 6.00). The participants completed 8 questionnaires which included 18 commonly cited EBTs and the dataset was split at random with a 70/30 ratio to conduct the EFA (n = 893) and CFA (n = 383). The results supported a four-factor model which indicated that EBTs can be organised into four domains: reactive, restricted, emotional, and homeostatic eating. The four-factor model also significantly predicted self-reported BMI and weight change. Future research should test whether this factor structure is replicated in more diverse populations, and including other EBTs, to advance these domains of eating as a unifying framework for studying individual differences in human eating behaviour.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The impact of exercise on food reward is increasingly being discussed as an interplay between executive function (EF), homeostasis and mechanisms promoting or undermining intentional behaviour ...change. Integrating current knowledge of neurocognitive processes encompassing cognitive and affective networks within an energy balance framework will provide a more comprehensive account. Reward circuitry affected by recreational drugs and food overlap. Therefore the underlying processes explaining changes in drug-taking behaviour may offer new insights into how exercise affects the reward value of recreational drugs and food. EF is important for successful self-regulation, and training EF may boost inhibitory control in relation to food- and drug-related reward. Preclinical and clinical observations suggest that reward-seeking can transfer within and between categories of reward. This may have clinical implications beyond exercise improving metabolic health in people with obesity to understanding therapeutic responses to exercise in people with neurocognitive deficits in non-food reward-based decision making such as drug dependence.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Dynamic changes in body composition which occur during weight loss may have an influential role on subsequent energy balance behaviors and weight.
The aim of this article is to consider the effect of ...proportionate changes in body composition during weight loss on subsequent changes in appetite and weight outcomes at 26 wk in individuals engaged in a weight loss maintenance intervention.
A subgroup of the Diet, Obesity, and Genes (DiOGenes) study (n = 209) was recruited from 3 European countries. Participants underwent an 8-wk low-calorie diet (LCD) resulting in ≥8% body weight loss, during which changes in body composition (by DXA) and appetite (by visual analog scale appetite perceptions in response to a fixed test meal) were measured. Participants were randomly assigned into 5 weight loss maintenance diets based on protein and glycemic index content and followed up for 26 wk. We investigated associations between proportionate fat-free mass (FFM) loss (%FFML) during weight loss and 1) weight outcomes at 26 wk and 2) changes in appetite perceptions.
During the LCD, participants lost a mean ± SD of 11.2 ± 3.5 kg, of which 30.4% was FFM. After adjustment, there was a tendency for %FFML to predict weight regain in the whole group (β: 0.041; 95% CI: −0.001, 0.08; P = 0.055), which was significant in men (β: 0.09; 95% CI: 0.02, 0.15; P = 0.009) but not women (β: 0.01; 95% CI: −0.04, 0.07; P = 0.69). Associations between %FFML and change in appetite perceptions during weight loss were inconsistent. The strongest observations were in men for hunger (r = 0.69, P = 0.002) and desire to eat (r = 0.61, P = 0.009), with some tendencies in the whole group and no associations in women.
Our results suggest that composition of weight loss may have functional importance for energy balance regulation, with greater losses of FFM potentially being associated with increased weight regain and appetite. This trial was registered at clinicaltrials.gov as NCT00390637.
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CMK, GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•FFM and RMR can be considered as major determinants of energy intake in humans.•EE generates a drive to eat to support vital physiological functions.•Activity EE shows high individual variability ...and creates a more variable drive.•Appetite control can be conceptualized within an energy balance framework.•Tonic and episodic processes interact within an appetite control system.
The drive to eat is a component of appetite control, independent of the omnivorous habit of humans, and separate from food choice, satiety and food reward. The drive forms part of the tonic component of appetite and arises from biological needs; it is distinct from episodic aspects of appetite which are heavily influenced by culture and the environment (and which reflect the omnivorous habit). It is proposed that the tonic drive to eat reflects a need state generated by metabolic energy expenditure (EE) required to maintain the functioning and integrity of vital organs. Specifically, the tonic drive is quantitatively associated with fat-free mass (FFM) and resting metabolic rate (RMR). A rational proposition is that high metabolic rate organs (such as heart, liver, kidneys, brain) together with skeletal muscle generate a metabolic need which drives energy intake (EI). The basic phenomenon of a relationship between FFM, RMR and EI, first published in 2011, has been substantially replicated and there are at least 14 concordant published studies carried out in 9 different countries (and 4 continents) with various ethnic groups of lean and obese humans. These studies demonstrate that FFM and RMR represent major determinants of the drive to eat, and this is rational from an evolutionary perspective. The EE of bodily movements through skeletal muscle activity (namely physical activity and exercise) represents another driver which is clearly but more weakly associated with an increase in EI. This account of appetite control, developed within an energy balance framework, 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.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Inflammation is key risk factor for several conditions in the elderly. However, the relationship between inflammation and frailty is still unclear. We investigated whether higher dietary inflammatory ...index (DII) scores were associated with higher incidence of frailty in a cohort of North Americans.
Longitudinal, with a follow-up of 8 years.
Osteoarthritis Initiative.
A total of 4421 participants with, or at high risk of, knee osteoarthritis.
DII scores were calculated using the validated Block Brief 2000 Food-Frequency Questionnaire and categorized into sex-specific quartiles. Frailty was defined as 2 out of 3 of the criteria of the Study of Osteoporotic Fracture study (ie, weight loss, inability to rise from a chair 5 times, and poor energy). The strength of the association between baseline DII score and incident frailty was assessed through a Cox's regression analysis, adjusted for potential baseline confounders, and reported as hazard ratios.
A total of 4421 community-dwelling participants (2564 female participants; mean age: 61.3 years) without frailty at baseline were identified from the Osteoarthritis Initiative. During 8 years of follow-up, 356 individuals developed frailty (8.2%). Using Cox's regression analysis, adjusting for 11 potential confounders, participants with the highest DII score (quartile 4) had a significantly higher risk of experiencing frailty (hazard ratio 1.37; 95% confidence interval 1.01-1.89; P = .04) compared with participants with the lowest DII score (quartile 1). The association between DII score and frailty was significant only in men.
Higher DII scores, indicating a more proinflammatory diet, are associated with higher incidence of frailty, particularly in men.
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