Early meal timing and chronotype are associated with lower BMI, but their impact on appetite is poorly understood. We examined the impact of meal timing and chronotype on appetite and food reward. Forty-four adults were divided into early (EC; Morningness–Eveningness Questionnaire (MEQ) score = 55 ± 5) or late chronotype (LC; MEQ score = 40 ± 6) and assessed for body mass index, habitual energy intake (EI; three-day online dietary record) and eating behavior traits from the Three-Factor Eating Questionnaire (TFEQ). Participants attended the laboratory after ≥3 h fast on two occasions for early (AM; 8–10 a.m.) and late (PM; 4–6 p.m.) counterbalanced testing sessions in a 2 × 2 design. Appetite ratings and food reward (validated diurnal Leeds Food Preference Questionnaire) were measured in response to a standardized test meal. LC was associated with higher BMI (p = 0.01), but not with EI or TFEQ. The composite appetite score was lower in AM than PM (M_Δ= −5 (95% CI −10, −0.2) mm, p = 0.040). Perceived test meal fillingness was higher in AM than PM and EC compared to LC (p ≤ 0.038). Liking and wanting high-fat food were lower in AM than PM (p ≤ 0.004). The late chronotype was associated with greater desire for high-fat food (p = 0.006). To conclude, early meal timing and early chronotype are independently associated with smaller appetite and lower desire for high-fat food.

Introduction: The aim of this study is to investigate the effects of time-restricted eating (TRE) on change in body weight and describe changes in behaviour and metabolism in individuals at high risk of type 2 diabetes.Methods and analysis: The REStricted Eating Time (RESET) study is a randomised controlled parallel-group open-label trial. 100 women and men with (1) overweight (body mass index (BMI)≥25 kg/m²) and prediabetes (glycated haemoglobin 39–47 mmol/mol); or (2) obesity (BMI≥30 kg/m²) will be randomised to a control group (habitual living) or TRE (self-selected 10-hours eating window within the period from 06:00 to 20:00 in a 1:1 ratio. Testing is scheduled at baseline and after 6 weeks (mid-intervention), 3 months (post-intervention) and 6 months (follow-up). The primary outcome is change in body weight after 3 months of intervention. Secondary outcomes include changes in body composition; measures of glucose metabolism including glycaemic variability, hormones and metabolites; subjective and metabolic markers of appetite, food preferences and reward; dietary intake; physical activity, sleep, chronotype; gastric emptying, gastrointestinal transit time and motility; respiratory and glycolytic capacities; the plasma proteome and metabolome; blood pressure, resting heart rate and heart rate variability; and resting energy expenditure and substrate oxidation. Motivation and feasibility will be examined based on interviews at baseline and after 3 months. After the 3-month intervention, a 3-month follow-up period and subsequent testing are scheduled to assess maintenance and longer-term effects.Ethics and dissemination: The study has been approved by the Ethics Committee of the Capital Region of Denmark (H-18059188) and the Danish Data Protection Agency. The study will be conducted in accordance with the Declaration of Helsinki. Results from the study will address whether TRE is effective and feasible in improving health outcomes in individuals at risk of lifestyle-related diseases and can potentially inform the design of feasible health recommendations.Trial registration number: NCT03854656.

Gut-derived hormones have been suggested to play a role in bone homeostasis following food intake, although the associations are highly complex and not fully understood. In a randomized, two-day cross-over study on 14 healthy individuals, we performed postprandial time-course studies to examine the associations of the bone remodeling markers carboxyl-terminal collagen type I crosslinks (CTX) and procollagen type 1 N-terminal propeptide (P1NP) with the gut hormones glucose-dependent insulinotropic polypeptide (GIP), glucagon-like peptide 1 (GLP-1), and peptide YY (PYY) using two different meal types—a standardized mixed meal (498 kcal) or a granola bar (260 kcal). Plasma concentrations of total GIP, total GLP-1, total PYY, CTX, and P1NP were measured up to 240 min after meal intake, and the incremental area under the curve (iAUC) for each marker was calculated. The iAUC of CTX and P1NP were used to assess associations with the iAUC of GIP, GLP-1, and PYY in linear mixed effect models adjusted for meal type. CTX was positively associated with GIP and GLP-1, and it was inversely associated with PYY (all p < 0.001). No associations of P1NP with GIP or GLP-1 and PYY were found. In conclusion, the postprandial responses of the gut hormones GIP, GLP-1, and PYY are associated with the bone resorption marker CTX, supporting a link between gut hormones and bone homeostasis following food intake.

Consumption of traditional foods is decreasing amid a lifestyle transition in Greenland as incidence of type 2 diabetes (T2D) increases. In homozygous carriers of a TBC1D4 variant, conferring postprandial insulin resistance, the risk of T2D is markedly higher. We investigated the effects of traditional marine diets on glucose homoeostasis and cardio-metabolic health in Greenlandic Inuit carriers and non-carriers of the variant in a randomised crossover study consisting of two 4-week dietary interventions: Traditional (marine-based, low-carbohydrate) and Western (high in imported meats and carbohydrates). Oral glucose tolerance test (OGTT, 2-h), 14-d continuous glucose and cardio-metabolic markers were assessed to investigate the effect of diet and genotype. Compared with the Western diet, the Traditional diet reduced mean and maximum daily blood glucose by 0·17 mmol/l (95 % CI 0·05, 0·29; P = 0·006) and 0·26 mmol/l (95 % CI 0·06, 0·46; P = 0·010), respectively, with dose-dependency. Furthermore, it gave rise to a weight loss of 0·5 kg (95 % CI; 0·09, 0·90; P = 0·016) relative to the Western diet and 4 % (95 % CI 1, 9; P = 0·018) lower LDL:HDL-cholesterol, which after adjustment for weight loss appeared to be driven by HDL elevation (0·09 mmol/l (0·03, 0·15), P = 0·006). A diet–gene interaction was indicated on insulin sensitivity in the OGTT (p = 0·093), which reflected a non-significant increase of 1·4 (–0·6, 3·5) mmol/l in carrier 2-h glucose. A Traditional diet marginally improved daily glycaemic control and plasma lipid profile compared with a Westernised diet in Greenlandic Inuit. Possible adverse effects on glucose tolerance in carriers of the TBC1D4 variant warrant further studies.

Eating behaviors are determined by a complex interplay between behavioral and physiologic signaling occurring before, during, and after eating. The aim was to explore how selected behavioral and physiologic variables separately and grouped together predicted intake of 8 different foods. One hundred adults with normal weight performed a food preference task combined with biometric measurements (the Steno Biometric Food Preference Task) in the fasting state. The task measured food reward as well as biometric (eye tracking, electrodermal activity, and facial expressions) responses to images of foods varying in fat content and taste. Energy intake from an ad libitum buffet of the same 8 foods as assessed in the preference task was subsequently assessed. A mixed-effects random forest approach was applied to explore how individual and combined measures of food reward and biometric responses predicted energy intake of the 8 single foods. The performance of the different prediction models was compared with the predictions from a linear model including only an intercept (naïve model) using bootstrap cross-validation. Participants had a median [IQR] intake of 369 kJ [126–472 kJ] per food. Combined or separate measures of food reward or biometric responses did not predict energy intake better than the naïve model. We did not find that the reward or biometric responses to food cues assessed in a clinical setting were useful in predicting energy intake of single foods. However, this study provides a framework in the field of behavioral nutrition for applying machine learning with a focus on individual predictions. This is necessary on the road toward personalized nutrition and provides great potential for handling complex data with multiple variables. This trial was registered at clinicaltrials.gov as NCT03986619.