Purpose: Ectopic fat in non-adipose tissues is linked to higher chronic-disease risk. Using the UK Biobank, this study examined associations of device-measured physical activity (PA) and sedentary time with ectopic fat. Methods: Liver and pancreas proton density fat fraction (PDFF), and anterior-thigh skeletal muscle fat infiltration (MFI) were assessed by magnetic resonance imaging. Reallocations among accelerometer derived time spent sedentary and in light intensity PA (LPA), and moderate-to-vigorous intensity PA (MVPA) were examined using isotemporal substitution. Multi-variable linear regression was employed with stepwise adjustment for sex, age, height, socioeconomic factors, and counts of cancer and non-cancer illnesses. Sex*activity interaction terms were then added to the models, and sex-stratified analyses were conducted where interactions were significant. Results: The final sample comprised 9,213 participants with liver PDFF, 6,521 participants with pancreatic PDFF, and 9,197 participants with MFI (mean age ~64 years). Replacing 30 minutes of sedentary time with LPA was associated with lower ectopic fat, with relative percentage differences in fully adjusted models of -2.2% [-2.7%, -1.8%] for liver PDFF, 1.9% [-2.4%, -1.3%] for pancreas PDFF, and -0.5% [-0.7%, -0.4%] for MFI. Substituting sedentary time with MVPA showed greater differences (-9.9% [11.0%, 8.7%], 7.9% [-9.2%, -6.6%], and -3.6 % [-3.9%, -3.2%], respectively). Replacing LPA with MVPA was also associated with lower ectopic fat (-7.6% [-8.9%, -6.4%], -6.0% [7.5%, -4.6%], and -3.0% [-3.4%, -2.6%] for liver PDFF, pancreas PDFF, and MFI. Associations were stronger in females for liver and pancreas PDFF and MFI when sedentary time was replaced with LPA or MVPA. Conclusions: Reallocation of sedentary time or lower-intensity PA to higher intensity PA is linked to lower ectopic fat. These relationships are stronger in women than men.

Oxyntomodulin (OXM) is a gut-derived peptide hormone with anorexigenic properties that reduce food intake and increase energy expenditure. This study investigated the effect of a single bout of moderate-intensity running on OXM concentrations and whether OXM response were associated with appetite perceptions and energy intake. Twenty healthy adults (10 men, 10 women; age: 25 ± 4 years; BMI: 22.2 ± 2.0 kg m-2) completed two randomised crossover trials: (1) 60-min treadmill running at 70% of peak oxygen uptake, and (2) rest control. Plasma OXM concentrations were measured at baseline (0 min), immediately post-exercise/rest, and every 30 min until 210 min. Appetite ratings were assessed throughout. Energy intake was measured from an ad libitum meal provided at 120 min. Linear mixed-effect models were used to examine the main effect of trial, time, and their interaction. OXM concentrations increased after the meal in both trials (p < 0.001), with no main effect of trial (p = 0.413) or trial-by-time interaction (p = 0.748). Pre-lunch OXM time-averaged incremental area under the curve (iAUC) was higher following exercise compared with control (0-120 min, mean difference = 16 pg mL-1·min, p = 0.006), whereas no difference was observed across the full sampling period (0-210 min; p = 0.265). Exercise increased fullness at 60 min (mean difference = 13 mm, p = 0.019) and reduced relative energy intake compared with control (mean difference = 2056 kJ, p < 0.001). No associations were observed between OXM iAUC and appetite perceptions or energy intake (p ≥ 0.098). Moderate-intensity running did not alter OXM concentrations. Changes in OXM were not associated with appetite perceptions or energy intake, suggesting that OXM may not play a central role in exercise-induced appetite regulation. Research across different exercise modes, intensities, and durations is warranted.

BACKGROUND:While fat-free mass (FFM), fat mass (FM), and resting metabolic rate (RMR) influence energy intake, their specific roles as homoeostatic and hedonic drivers of eating-and factors moderating these relationships-are uncertain. This study examined associations of FFM, FM, and RMR with a comprehensive set of appetite-related outcomes in adults. METHODS:130 participants (58% male; age: 25.0 ± 8.5 years; BMI: 24.0 ± 3.7 kg/m²; mean ± SD) completed assessments of RMR (indirect-calorimetry), body composition (air-displacement plethysmography), energy intake (laboratory-based), appetite-related hormones (fasted and meal-stimulated), appetite (visual analogue scales), food reward (Leeds Food Preference Questionnaire [LFPQ]), food cravings (Control of Eating Questionnaire), taste and smell perception. GLM examined associations between exposure and outcome variables, controlling for demographic and lifestyle factors. Effect modification was assessed via interaction terms and stratified analyses with median splits for continuous variables. RESULTS:RMR was positively associated with energy intake (P < 0.001), while FFM and FM were not. The positive RMR-energy intake association was stronger in individuals who were younger, more active, smokers, and with lower BMI (P ≤ 0.020). FFM was positively associated with postprandial peptide-YY (PYY) and hunger, craving control and smell sensitivity (P ≤ 0.026). FM was positively associated with fasting PYY and leptin, and inversely associated with postprandial PYY and hunger, craving control, and taste sensitivity (P ≤ 0.047). RMR was inversely associated with fasting ghrelin (P < 0.001). No consistent associations were observed between body composition, RMR and food reward (LFPQ). Several associations were moderated by age, sex, BMI, physical activity and smoking. CONCLUSIONS:RMR is a key driver of energy intake, with sex, BMI, physical activity and smoking status moderating the relationship. Body composition (FFM and FM) is linked to distinct variations in homoeostatic and hedonic components of appetite, particularly gut hormone responses, craving control and taste perception.

BACKGROUND/OBJECTIVES:Sleeve gastrectomy (SG) improves glycaemic control; however, it also markedly accelerates gastric emptying, which can lead to exaggerated postprandial glucose and insulin excursions and, in some cases, postprandial hyperinsulinaemic hypoglycaemia. In non-surgical populations, fat preloads can reduce postprandial glycaemia by slowing gastric emptying, but their effects after SG are unclear. METHODS:Ten adults >1-year post-SG completed a randomised, open-label, crossover study involving two mixed-meal tolerance tests (MMTTs), preceded (-30 min) by either a moderate, fat-dominant preload (28 g Brazil nuts) or 100 mL water (control). Blood samples were collected over three hours to assess plasma glucose, insulin, c-peptide, and total glucagon-like peptide-1 (GLP-1). Hypoglycaemia and dumping symptoms were assessed using validated questionnaires. Nadir plasma glucose concentration was the primary outcome. RESULTS:Nadir plasma glucose was identical between conditions (both 3.8 mmol/L; 95% CI: -0.4, 0.3, p = 0.849), and neither peak glucose nor overall postprandial glucose exposure (incremental area under the curve iAUC0-180 min) differed between the preload and water conditions. Insulin and c-peptide concentrations immediately before the MMTT were higher after the fat-dominant preload (both p < 0.001). Overall insulin and c-peptide responses during the MMTT (iAUC0-180 min) remained comparable between conditions (95% CI -225, 2665; p = 0.442 and -67,787, 70,263; 0.968), but peak values for both hormones were higher after the preload (95% CI 2.9, 79.1, p = 0.038 and 17.3, 2402.7, p = 0.040, respectively). Total GLP-1 was also elevated prior to the MMTT (95% CI 1.6, 22.8, p = 0.028), while its early and overall postprandial responses did not differ (both p > 0.05). Ratings of hypoglycaemia and dumping symptoms were similar for both study arms. DISCUSSION:A moderate, fat-dominant preload consumed before a mixed meal did not alter nadir or overall postprandial glucose in adults without diabetes after SG. However, the preload was associated with higher peak insulin secretion, a finding that should be interpreted with caution, as the study was not powered for secondary outcomes. Given the unique gastrointestinal physiology after SG, further research is needed to determine how different nutrient compositions or timing approaches influence postprandial glucose homeostasis in this population.

BACKGROUND:Glucose has been implicated in the control of appetite and food intake. This study investigated whether glycaemic patterns relate to energy intake and whether appetite-related hormones mediate this relationship. METHODS:Thirty healthy young women (age: 25 ± 4 years; BMI: 21.4 ± 2.0 kg/m2) arrived at the laboratory at 8:00 AM after an overnight fast. Upon arrival, participants completed a 30 min resting period, during which the cannula was inserted, and all fasted glucose measurements were collected. Glucose was measured every 5 min using FreeStyle Libre 2™ continuous glucose monitors (CGM), and one venous plasma glucose (VPG) sample was obtained immediately after cannula insertion. Following the 30 min fasting measurement period, participants consumed a fixed breakfast and then remained in the laboratory for an additional 240 min. Glucose was monitored via CGM every 5 min and VPG was measured every 15 min after breakfast. Energy intake was assessed at 240 min using an ad-libitum homogeneous pasta meal. Subjective appetite ratings were collected fasted and every 15 min after breakfast. Appetite-related hormones, including insulin, acylated ghrelin (AG), total glucagon-like peptide-1 (GLP-1) and oxyntomodulin (OXM) were measured in the fasted state, immediately after breakfast (t = 0 min), and subsequently at 30 min intervals after breakfast until t = 240 mins. Menstrual cycle phase was recorded and included as a covariate in all analyses. Associations between glycaemic variables and satiety or energy intake were examined using generalised linear models with a gamma distribution and log link function, adjusting for BMI and menstrual cycle phase. Bootstrap-based causal mediation analyses were conducted to evaluate whether appetite-related hormones mediated any significant associations between glycaemic responses and satiety or energy intake. RESULTS:CGM-derived glucose nadir (lowest concentration) and dip (deviation of nadir from baseline as a percentage) were significantly associated with subsequent energy intake. A higher glucose nadir was associated with lower energy intake (β = -0.17, p = 0.003), whereas a greater glucose dip was associated with higher energy intake (β = 0.007; p < 0.001). When standardised, a one standard deviation (SD) increases in glucose nadir corresponded to an approximately 13% reduction in energy intake, while a one-SD increase in glucose dip corresponded to an approximately 16% increase in energy intake. Glucose nadir (CGM: β = 0.23, p = 0.041; VPG: β = 0.26, p = 0.008) and dip (CGM: β = -0.01, p = 0.006; VPG: β = -0.012, p < 0.001) derived from both CGM and VPG were also significantly associated with overall satiety measured immediately before the ad libitum lunch. A one-SD increase in glucose nadir was associated with higher satiety (CGM: 20% increase; VPG: 20% increase), whereas a one-SD increase in glucose dip was associated with lower satiety (CGM: 15% reduction; VPG: 17% reduction). Causal mediation analyses provided no evidence that insulin, acylated ghrelin, total GLP-1, or oxyntomodulin mediated these associations (p ≥ 0.09). CONCLUSIONS:Glucose dynamics including nadir and dip may influence satiety and subsequent energy intake. These data support for a role of glucose in models of appetite regulation.

PURPOSE:Glycemic variability is a potential risk factor for cardiometabolic disease. As adolescence is a critical period for establishing metabolic health, this study examined associations between daily physical activity (PA) levels, sedentary time, and glycemic variability in healthy adolescents under free-living conditions. METHODS:Eighty-seven adolescents (53 girls; mean age: 12.9 [0.8] y) wore a continuous glucose monitor (FreeStyle Libre) and an accelerometer (ActiGraph) concurrently over 8 days. Generalized estimating equations examined daily associations in participants with ≥3 days of concurrent accelerometer/continuous glucose monitor data, after adjusting for key confounders. RESULTS:Vigorous PA was consistently associated with a lower glucose coefficient of variation (exponentiated beta coefficient [exp β] = 0.898; 95% CI, 0.828-0.974; P = .010), mean amplitude of glycemic excursion (exp β = 0.889; 95% CI, 0.804-0.983; P = .022), and SD of glucose (exp β = 0.914; 95% CI, 0.845-0.988; P = .023) and a greater percentage in the 3.9 to 7.8 mmol·L-1 glucose range (exp β = 1.020; 95% CI, 1.003-1.038; P = .019). Moderate-to-vigorous PA was also inversely associated with coefficient of variation and SD of glucose (P ≤ .018). Yet, sedentary time, light PA, moderate PA, and total PA were not significantly associated with any glycemic metric (P ≥ .145). CONCLUSION:PA at moderate-to-vigorous, and especially vigorous, intensities was associated with lower glycemic variability in healthy adolescents.

To compare acute effects of continuous moderate-intensity exercise (CME) and low-volume high-intensity interval exercise (LV-HIIE) on appetite responses between South Asians and white Europeans with non-diabetic hyperglycaemia. Thirteen white Europeans and 10 South Asians (age 50–74 years) completed three, 5-h experimental conditions (CME, LV-HIIE, control) in randomised sequences. Standardised meals were provided at 0 and 3 h. Exercise involved a 25-min LV-HIIE or 35-min CME bout that ended at 2 h. Subjective appetite perceptions and appetite-related hormones (glucagon-like peptide-1 (GLP-1), peptide YY (PYY), acylated ghrelin (AG)) were measured at 0, 0.5, 1, 2, 3, 3.5, 4 and 5 h. Time-averaged total area under the curve (TAUC; 1–5 h) was analysed, adjusted for age, sex, and pre-intervention time-averaged TAUC (0–1 h). Total GLP-1 was higher in LV-HIIE (mean difference [95% CI] 4.3 [1.5, 7.1] pmol/L h) and CME (4.5 [1.4, 7.7] pmol/L h) versus control (condition effect P = 0.01), but exercise had no effect on the other outcomes in the whole study population (P ≥ 0.28). Appetite responses to exercise were similar between ethnicities for total GLP-1 and total PYY (interaction P ≥ 0.11), but subtle differences emerged for AG and overall appetite (interaction P ≤ 0.02). AG was higher and overall appetite lower in LV-HIIE versus CME in South Asians whilst overall appetite was higher in LV-HIIE versus CME in white Europeans, but neither exercise bout was different to control. Single LV-HIIE and CME bouts increased total GLP-1 in individuals with non-diabetic hyperglycaemia, but exercise-related appetite responses were not strongly modulated by exercise intensity or ethnicity.

Roux-en-Y gastric bypass (RYGB) induces substantial weight loss and changes in glucose homeostasis, partly through alterations in gastrointestinal anatomy and nutrient absorption. Sodium-glucose co-transporters-1 (SGLT-1) facilitate intestinal glucose absorption, a process that may be compromised post-RYGB. Animal studies suggest that sodium supplementation to a meal may enhance SGLT-1 activity post-RYGB. In this study, we investigated whether adding sodium chloride (NaCl) to a carbohydrate-rich meal affects glucose homeostasis in people after RYGB. In this open-label, randomized, crossover study, eleven adults without diabetes post-RYGB (mean age 57.3 ± 11.3 years, BMI 35.1 ± 7.5 kg/m2, 64

OBJECTIVES:Altered appetite-related gut hormone concentrations may reflect a physiological adaptation facilitating weight regain after weight loss. This review investigates hormonal changes after weight loss achieved through calorie restriction (CR), exercise (EX), or both combined (CREX). METHODS:A systematic search of PubMed (MEDLINE), EMBASE, SPORTDiscus, Cochrane Library, Web of Science, and ClinicalTrials.gov was conducted to identify randomised controlled trials (RCTs) and non-RCTs reporting in a fasting state either pre- and post-intervention appetite-related hormone concentrations or the changes therein after weight loss. The hormones examined were ghrelin, peptide tyrosine tyrosine (PYY), glucagon-like peptide -1 (GLP-1), and cholecystokinin (CCK), in their total and/or active form. Standardised mean differences (SMD) were extracted as the effect size. RESULTS:127 studies were identified: 19 RCTs, 108 non-RCTs, 1305 and 4725 participants, respectively. In response to weight loss induced by CR, EX or CREX, the meta-analysis revealed an increase in total ghrelin from both RCTs (SMD: 0.55, 95% CI: 0.07-1.04) and non-RCTs (SMD: 0.24, 95% CI: 0.14-0.35). A decrease in acylated ghrelin was identified for RCTs (SMD: -0.58, 95% CI: -1.09 to -0.06) but an increase was observed for non-RCTs (SMD: 0.15, 95% CI: 0.03 to 0.27). Findings also revealed a decrease in PYY (total PYY: SMD: -0.17, 95%CI: -0.28 to -0.06; PYY3-36: SMD: -0.17, 95%CI: -0.32 to -0.02) and active GLP-1 (SMD: -0.16, 95% CI: -0.28 to -0.05) from non-RCTs. Changes in hormones did not differ among the three interventions when controlling for weight loss. Meta-regression indicated that greater weight loss was associated with a greater increase in total ghrelin. CONCLUSIONS:Weight loss induced by CR, EX, or CREX elicits an increase in total ghrelin, but varied responses in other appetite-related hormones. The extent of weight loss influences changes in appetite-related gut hormone concentrations.

This study examined associations of cigarette smoking with appetite perceptions, appetite-related hormones, food preferences and eating traits. In a cross-sectional matched-pair cohort design, 25 participants who smoke cigarettes and 25 who do not were matched 1:1 by age, sex, ethnicity, and BMI. Across two visits, participants' food preferences (Leeds Food Preference Questionnaire), cravings (Control of Eating Questionnaire), and eating traits (Three-Factor Eating Questionnaire) were assessed. Fasting and postprandial appetite perceptions (visual analogue scales) were also assessed during a 4-h mixed-meal tolerance test (MM-TT), while fasting leptin and fasting and postprandial acylated ghrelin and total peptide-YY (PYY) were measured for 2 h postprandially. Group differences in study outcomes were analysed using generalised linear models. After adjustment (age and BMI), explicit liking and wanting for high-fat foods and cravings for savoury foods were higher in participants who smoke versus those who do not (P ≤ 0.065; d ≥ 0.52). Cognitive restraint was lower, while disinhibition was higher in participants who smoke compared to those who do not (P ≤ 0.014; d ≥ 0.69). Smoking was also associated with lower fasting acylated ghrelin and lower postprandial total PYY (P ≤ 0.041; d ≥ 0.58), whereas fasting leptin was similar between groups (P = 0.821; d = 0.06). Additionally, participants who smoke had higher fasting perceived fullness (P = 0.021; d = 0.65), while no other fasting or postprandial differences were identified for other appetite perceptions (hunger, prospective food consumption, satisfaction; P ≥ 0.373; d ≤ 0.25). In conclusion, cigarette smoking is associated with altered food preferences and less favourable eating traits, while more subtle differences may exist in appetite perceptions and appetite-related hormones.

Adolescent girls often skip breakfast due to time constraints and reduced morning appetite. This study examined the acute impact of breakfast consumption timing v. breakfast omission (BO) on glycaemic and insulinaemic responses to lunch in infrequent breakfast-consuming girls. Fifteen girls (13·1 (sd 0·8) years) completed three conditions in a randomised crossover design: early-morning breakfast consumption (EM-BC; 08.30), mid-morning breakfast consumption (MM-BC; 10.30) and BO. A standardised lunch was provided at 12.30, followed by a 2-h post-lunch observation period. Blood and expired gas samples were collected periodically. Linear mixed models with Cohen's d effect sizes compared outcomes between conditions. Pre-lunch glucose and insulin incremental AUC (iAUC) were higher in the breakfast conditions v. BO (P ≤ 0·009), with no differences between breakfast conditions. MM-BC reduced post-lunch glucose iAUC by 36 % and 25 % compared with BO and EM-BC, respectively (P < 0·001, d = 0·92-1·44). A moderate, non-significant 15 % reduction in post-lunch glucose iAUC was seen with EM-BC v. BO (P = 0·077, d = 0·52). These reductions occurred without changes in post-lunch insulinemia (P ≥ 0·323) and were accompanied by increased post-lunch carbohydrate oxidation compared with BO (P ≤ 0·018, d = 0·58-0·75); with no differences between EM-BC and MM-BC. MM-BC lowered glycaemic response over the experimental period compared with BO (P = 0·033, d = 0·98) and EM-BC (P = 0·123, d = 0·93), with no difference between EM-BC and BO. Compared with BO, both breakfast conditions lowered post-lunch glycaemic responses with mid-morning breakfast eliciting a greater second-meal effect than early-morning breakfast. These findings indicate the breakfast-to-lunch meal interval may be a crucial factor affecting postprandial glycaemia in infrequent breakfast-consuming girls.

Appetite control is a topic which attracts widespread interest given its importance to energy balance and obesity. In this research area, the mixed-meal tolerance test (MM-TT) has emerged as an ‘appetite regulation assay’, facilitating the dynamic assessment of appetite parameters (e.g. subjective appetite perceptions, appetite-related hormones, food reward) in response to an individual meal. The MM-TT is commonly employed in observational and experimental studies to examine population differences and intervention effects. Problematically, no practice standard exists for the MM-TT and protocols vary widely. This presents a challenge for researchers designing new MM-TTs and hampers the comparability of findings. Therefore, within this narrative review we sought to identify and discuss key methodological considerations inherent within a MM-TT. The scope of our review extends to evaluating participant familiarisation and methodological standardisation practices, test meal characteristics, appetite perception assessment, blood sampling techniques, measurement of appetite-related hormones and data handling/analysis. A checklist has been devised to summarise relevant methodological issues identified within this review. This checklist can be used as a tool by researchers to facilitate MM-TT design and promote greater standardisation/comparability between studies. This review highlights the need for broader standardisation of MM-TT procedures to support consistency across future research. Additional research is needed to strengthen the evidence base on which various recommendations are made, particularly relating to participant familiarisation and methodological standardisation practices. Additional scrutiny of less common outcomes employed in MM-TTs (not addressed here), such as diet-induced thermogenesis, gastric emptying and ad libitum energy intake, is also needed.

This study examined whether supplementation with collagen peptides (CP) affects appetite and post-exercise energy intake in healthy active females. In this randomised, double-blind cross-over study, fifteen healthy females (23 (sd 3) years) consumed 15 g/d of CP or a taste matched non-energy control (CON) for 7 d. On day 7, participants cycled for 45 min at ∼55 % Wmax, before consuming the final supplement. Sixty-min post supplementation an ad libitum meal was provided, and energy intake recorded. Subjective appetite sensations were measured daily for 6 d (pre- and 30 min post-supplement) and pre (0 min) to 280 min post-exercise on day 7. Blood glucose and hormone concentrations (total ghrelin, glucagon-like peptide-1 (GLP-1), and peptide YY (PYY), cholecystokinin (CCK), dipeptidyl peptidase-4 (sDPP-4), leptin, and insulin) were measured fasted at baseline (day 0), then pre-breakfast (0 min), post-exercise (100 min), post-supplement (115, 130, 145, 160 min) and post-meal (220, 280 min) on day 7. Ad libitum energy intake was ∼10 % (∼41 kcal) lower in the CP trial (P = 0·037). There was no difference in gastrointestinal symptoms or subjective appetite sensations throughout the trial (P ≥ 0·412). Total plasma GLP-1 (AUC, CON: 6369 (sd 2330); CP: 9064 (sd 3021) pmol/l; P < 0·001) and insulin (+80 % at peak) were higher after CP (P < 0·001). Plasma ghrelin and leptin were lower in CP (condition effect; P ≤ 0·032). PYY, CCK and glucose were not different between CP and placebo (P ≥ 0·100). CP supplementation following exercise increased GLP-1 and insulin concentrations and reduced ad libitum energy intake at a subsequent meal in physically active females.

Crossover randomized controlled trials (RCTs) are common in exercise and nutrition sciences. Since researchers randomize participants to different sequences of the treatment and comparator/control conditions, crossover RCTs are powerful for detecting mean treatment effects under certain circumstances. We aim to review the information that can be derived from crossover RCTs about treatment response heterogeneity-a fundamental issue in precision medicine for tailoring treatments to individuals. After covering the fundamental design issues, we describe the variance components that underlie observed data. The crucial person-by-treatment variance component can be quantified from a repeated or "replicate" crossover RCT by exposing participants to multiple cycles of trial conditions. As a type of n-of-1 trial, replicate crossover RCTs have important design and statistical power considerations, which we describe. By synthesizing findings from our six published replicate crossover RCTs, we also compare various data analysis approaches. We find general agreement between these approaches, and a link between within-person consistency of response and the detection of person-by-treatment interactions. We postulate that a paired "variance comparison," for example, the Pitman-Morgan test, provides some preliminary information regarding response heterogeneity from a typical single-cycle crossover RCT. Nevertheless, underlying assumptions are critical, rendering these comparisons as merely exploratory until an n-of-1 or replicate crossover RCT is undertaken. Multiple-cycle n-of-1 trials and replicate crossover RCTs are underused but are informative for treatment response heterogeneity. However, these trials are still only one component of the process for predicting individual magnitude of response from any personal traits, which is the "holy grail" of personalized treatment.

Sub-optimal sleep, whether insufficient, excessive, or poor-quality, is an independent risk factor for obesity, largely through influencing energy intake via altered appetite and reward processing. Less is known about its influence on real-world dietary behaviours. We examined associations of self-reported sleep quality and duration with dietary eating behaviours in a large UK adult cohort. 27,263 adults (median (interquartile range): age, 51.0 (16.0) years; BMI, 25.2 (5.3) kg/m2; 40.5 % female) completed a standardised health assessment, including self-reported sleep quality (1-10 scale) and duration. Thirteen eating behaviours broadly reflecting emotional/reward-driven eating, dietary disinhibition, food preferences, and meal patterns were assessed via questionnaire. Regression models examined associations between sleep characteristics and eating behaviours, adjusting for age, sex, socioeconomic status, assessment year, and region. Odds ratios (OR) are presented for ordinal/binary outcomes and rate ratios (RR) for count outcomes. Poor sleep quality and short sleep duration were associated with an eating profile suggestive of heightened emotional/reward-driven eating and reduced dietary restraint. This included higher odds/frequency of eating out of boredom, stress, or anger, overeating, skipping meals, and consuming energy-dense foods (OR/RR range: 1.08-3.50, P ≤ 0.018). Long sleep duration was linked to higher emotional eating (OR range: 1.16-1.19, P < 0.001) but showed fewer signs of impulsivity or disinhibited intake. Some behaviours, like adding sugar to food and snacking, were not consistently related to sleep characteristics. In conclusion, short and poor-quality sleep are associated with eating patterns that may increase obesity risk. Interventions targeting sleep extension and quality could support healthier dietary behaviours and appetite regulation.

Self-reported physical activity is associated with lower brain food cue responsiveness in reward-related regions, but relationships utilizing objective physical activity measurement tools have not been explored. This cross-sectional study examined whether device-measured moderate-to-vigorous intensity physical activity and sedentary time are related to neural responses to visual food cues using functional magnetic resonance imaging. Fifty-one healthy adults (30 men, 21 women; mean ± SD: age 26 ± 6 years; body mass index 24.1 ± 3.0 kg/m 2 ) underwent a functional magnetic resonance imaging scan after an overnight fast while viewing images of high/very high-energy density foods (HED), very low/low-energy density foods (LED) and non-food objects. Free-living moderate-to-vigorous intensity physical activity and sedentary time were measured for seven consecutive days using an ActiGraph wGT3X-BT and activPAL4 accelerometer, respectively. Associations of behavioural variables with brain food cue reactivity were examined in regression models controlling for physiological and behavioural covariates. After adjusting for age, sex, body mass index and device weartime, moderate-to-vigorous intensity physical activity was negatively associated with reactivity to LED versus non-food cues in the precentral gyrus, hippocampus, posterior insula, and amygdala, which may diminish inhibitory-related responses towards healthier lower energy value foods. Time spent in moderate-to-vigorous intensity physical activity was positively associated with reactivity to LED versus non-food cues in the dorsal striatum, a region implicated in food motivation. A positive association was identified between sedentary time and reactivity to HED versus non-food cues in the dorsal division of the posterior cingulate gyrus that has been implicated in attention allocation. These findings suggest that moderate-to-vigorous intensity physical activity may enhance the appeal of and motivation to consume LED foods, whereas sedentary time may promote attention towards HED foods, highlighting the potential for engaging in greater physical activity and less sedentary time to positively influence the central (brain) appetite control system.

Background: Physical activity, sedentary behaviour, and sleep are interdependent components of the 24 h movement profile that may influence appetite control. While acute exercise can alter appetite perceptions and food reward, less is known about how reallocating time between daily behaviours affects appetite outcomes under free-living conditions. Methods: We applied isotemporal-substitution modelling in a cross-sectional study of 130 young, healthy, active adults. Accelerometer-derived estimates of sedentary time, light physical activity (LPA), moderate-to-vigorous physical activity (MVPA), and sleep were analysed in relation to energy intake (food diaries, laboratory meals), subjective appetite perceptions, appetite-related hormones (acylated ghrelin, PYY, leptin), and psychological traits, including food reward (Leeds Food Preference Questionnaire, LFPQ), food cravings (Control of Eating Questionnaire, CoEQ), and eating behaviour traits (Three-Factor Eating Questionnaire, TFEQ). Results: Reallocating 30 min/day of sedentary time to MVPA was associated with higher energy intake in free-living (+113 kcal/day, 95% CI: 34-192) and laboratory settings (+120 kcal/day, 95% CI: 55-185), along with greater postprandial hunger and prospective food consumption, reduced fullness, elevated fasting acylated ghrelin, and lower postprandial PYY. No associations were observed for reallocations to LPA or sleep. Furthermore, sedentary time reallocations were unrelated to leptin or psychological eating traits assessed by the LFPQ, CoEQ, or TFEQ. Conclusions: In this population, reallocating sedentary time to MVPA was linked to physiological and behavioural compensation consistent with elevated energy demands, whereas reallocating to LPA or sleep showed no associations. Trait-level eating behaviours were unaffected, suggesting MVPA influences appetite primarily through acute physiological rather than enduring cognitive or hedonic pathways.

Study Objectives Using the necessary replicate-crossover design, we investigated whether there is interindividual variability in home-assessed sleep in response to acute exercise.Methods Eighteen healthy men (mean [SD]: 26[6] years) completed two identical control (8 hour laboratory rest, 08:45-16:45) and two identical exercise (7 hour laboratory rest; 1 hour laboratory treadmill run [62(7)% peak oxygen uptake], 15:15-16:15) trials in randomized sequences. Wrist-worn actigraphy (MotionWatch 8) measured home-based sleep (total sleep time, actual wake time, sleep latency, and sleep efficiency) two nights before (nights 1 and 2) and three nights after (nights 3-5) the exercise/control day. Pearson's correlation coefficients quantified the consistency of individual differences between the replicates of control-adjusted exercise responses to explore: (1) immediate (night 3 minus night 2); (2) delayed (night 5 minus night 2); and (3) overall (average post-intervention minus average pre-intervention) exercise-related effects. Within-participant linear mixed models and a random-effects between-participant meta-analysis estimated participant-by-trial response heterogeneity.Results For all comparisons and sleep outcomes, the between-replicate correlations were nonsignificant, ranging from trivial to moderate (r range = -0.44 to 0.41, p >= .065). Participant-by-trial interactions were trivial. Individual differences SDs were small, prone to uncertainty around the estimates indicated by wide 95% confidence intervals, and did not provide support for true individual response heterogeneity. Meta-analyses of the between-participant, replicate-averaged condition effect revealed that, again, heterogeneity (tau) was negligible for most sleep outcomes.Conclusions Control-adjusted sleep in response to acute exercise was inconsistent when measured on repeated occasions. Interindividual differences in sleep in response to exercise were small compared with the natural (trial-to-trial) within-subject variability in sleep outcomes.Clinical trials information https://clinicaltrials.gov/study/NCT05022498. Registration number: NCT05022498.

BACKGROUND AND AIMS:Smokers typically have a lower body mass index (BMI) than non-smokers, while smoking cessation is associated with weight gain. In pre-clinical research, nicotine in tobacco smoking suppresses appetite and influences subsequent eating behaviour; however, this relationship is unclear in humans. This study measured the associations of smoking with different eating and dietary behaviours. DESIGN:A cross-sectional analysis of data from health assessments conducted between 2004 and 2022. SETTING:An independent healthcare-based charity within the United Kingdom. PARTICIPANTS:A total of 80 296 men and women (mean ± standard deviation [SD]: age, 43.0 ± 10.4 years; BMI, 25.7 ± 4.2 kg/m2; 62.5% male) stratified into two groups based on their status as a smoker (n = 6042; 7.5%) or non-smoker (n = 74 254; 92.5%). MEASUREMENTS:Smoking status (self-report) was the main exposure, while the primary outcomes were selected eating and dietary behaviours. Age, sex and socioeconomic status (index of multiple deprivation [IMD]) were included as covariates and interaction terms, while moderate-to-vigorous exercise and sleep quality were included as covariates only. FINDINGS:Smokers had lower odds of snacking between meals and eating food as a reward or out of boredom versus non-smokers (all odds ratio [OR] ≤ 0.82; P < 0.001). Furthermore, smokers had higher odds of skipping meals, going more than 3 h without food, adding salt and sugar to their food, overeating and finding it hard to leave something on their plate versus non-smokers (all OR ≥ 1.06; P ≤ 0.030). Additionally, compared with non-smokers, smoking was associated with eating fried food more times per week (rate ratio [RR] = 1.08; P < 0.001), eating fewer meals per day, eating sweet foods between meals and eating dessert on fewer days per week (all RR ≤ 0.93; P < 0.001). Several of these relationships were modified by age, sex and IMD. CONCLUSIONS:Smoking appears to be associated with eating and dietary behaviours consistent with inhibited food intake, low diet quality and altered food preference. Several of these relationships are moderated by age, sex and socioeconomic status.

Abstract Considerable progress has been made to characterise the metabolic responses to exercise among children and adolescents (‘young people’) over the last 90 years using a variety of techniques and study designs. Yet, the paediatric literature is often bound to ethical and technical constraints, and insights at the muscle cell level are typically reliant on studies conducted around 40–50 years ago. Among young people, exercise is associated with a complex interplay of distinct aerobic and anaerobic metabolic responses. The child–adolescent–adult transition is accompanied by a reduced reliance on fat oxidation and a reduced Fatmax, with a concomitant increased reliance on total and endogenous carbohydrate oxidation and reduced reliance on exogenous carbohydrate oxidation during exercise. These changes are governed by advances in puberty rather than age per se, with possible underlying mechanisms related to glycogen storage limitations, oxidative and glycolytic enzyme activity, lactate accumulation, and muscle fibre recruitment patterns. Growing evidence among young people shows that fat oxidation dominates during exercise targeted at individual Fatmax (typically at a moderate intensity), that is more prolonged in duration and that utilises a larger muscle mass. Conversely, carbohydrate oxidation dominates during high-intensity, short-duration exercise utilising a smaller muscle mass. From a public health perspective, acute moderate- and high-intensity exercise can induce favourable metabolic responses, including reduced postprandial glucose, insulin, and triacylglycerol concentrations in young people. Notwithstanding progress, much knowledge remains to be accumulated with regard to the metabolic responses to exercise among young people, particularly girls, using new minimally invasive technologies to advance current insights.