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Timing of food intake predicts weight loss effectiveness.

ABSTRACT: There is emerging literature demonstrating a relationship between the timing of feeding and weight regulation in animals. However, whether the timing of food intake influences the success of a weight-loss diet in humans is unknown.To evaluate the role of food timing in weight-loss effectiveness in a sample of 420 individuals who followed a 20-week weight-loss treatment.Participants (49.5% female subjects; age (mean ± s.d.): 42 ± 11 years; BMI: 31.4 ± 5.4 kg m(-2)) were grouped in early eaters and late eaters, according to the timing of the main meal (lunch in this Mediterranean population). 51% of the subjects were early eaters and 49% were late eaters (lunch time before and after 1500 hours, respectively), energy intake and expenditure, appetite hormones, CLOCK genotype, sleep duration and chronotype were studied.Late lunch eaters lost less weight and displayed a slower weight-loss rate during the 20 weeks of treatment than early eaters (P=0.002). Surprisingly, energy intake, dietary composition, estimated energy expenditure, appetite hormones and sleep duration was similar between both groups. Nevertheless, late eaters were more evening types, had less energetic breakfasts and skipped breakfast more frequently that early eaters (all; P<0.05). CLOCK rs4580704 single nucleotide polymorphism (SNP) associated with the timing of the main meal (P=0.015) with a higher frequency of minor allele (C) carriers among the late eaters (P=0.041). Neither sleep duration, nor CLOCK SNPs or morning/evening chronotype was independently associated with weight loss (all; P>0.05).Eating late may influence the success of weight-loss therapy. Novel therapeutic strategies should incorporate not only the caloric intake and macronutrient distribution - as is classically done - but also the timing of food.

SUBMITTER: Garaulet M

PROVIDER: S-EPMC3756673 | biostudies-literature | 2013 Apr

REPOSITORIES: biostudies-literature

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Timing of food intake predicts weight loss effectiveness.

Garaulet M M Gómez-Abellán P P Alburquerque-Béjar J J JJ Lee Y-C YC Ordovás J M JM Scheer F A J L FA Scheer F A J L FA

International journal of obesity (2005) 20130129 4

<h4>Background</h4>There is emerging literature demonstrating a relationship between the timing of feeding and weight regulation in animals. However, whether the timing of food intake influences the success of a weight-loss diet in humans is unknown.<h4>Objective</h4>To evaluate the role of food timing in weight-loss effectiveness in a sample of 420 individuals who followed a 20-week weight-loss treatment.<h4>Methods</h4>Participants (49.5% female subjects; age (mean ± s.d.): 42 ± 11 years; BMI: ...[more]

PMID: 23357955

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Project description:Background & aimsWhile environmental factors are presumed to be primary drivers of food timing, preliminary evidence suggests that genetics may be an additional determinant. The aim was to explore the relative contribution of genetics and environmental factors to variation in the timing of food intake in a Spanish twin population. Because chronotype, bedtime and wake time are related to food timing, covariance with food timing was further assessed.MethodsIn this observational study, 53 pairs of adult (mean (SD) = 52 (6.03) years) female twins (28 monozygotic; 25 dizygotic) were recruited from the Murcia Twin Register. Zygosity was determined by DNA-testing. Timing of the three main meals of the day was assessed via 7-day dietary records, and the midpoint of food intake was computed by calculating the midpoint between breakfast and dinner times. Chronotype, bedtime and wake time were self-reported. Heritability of food timing and related traits were estimated by comparing monozygotic and dizygotic twin correlations and fitting genetic structural equation models to measured variables.ResultsWe observed genetic influences for food timing, with highest heritability for the midpoint of food intake (64%) in an overweight/obese population (BMI = 26.01 ± 3.77). Genetic factors contributed to a higher degree to the timing of breakfast (56%) than the timing of lunch (38%) or dinner (n.s.). Similarly, heritability estimates were larger in related behavioral traits earlier on in the day (i.e. wake time, (55%)), than those later on in the day (i.e. bedtime, (38%)). Bivariate analyses revealed a significant genetic overlap between food timing and bedtime and chronotype (rG between 0.78 and 0.91).ConclusionsGenetic influences appear to account for a significant proportion of the variability in food timing, particularly breakfast. Thus, interventions related to food timing may be more effective when targeting afternoon/evening traits, such as lunch or dinner times. Furthermore, our data suggest shared genetic architecture underlying food timing and phenotypically related traits.Clinical trialNCT03059576. https://clinicaltrials.gov/ct2/show/NCT03059576.

Project description:Purpose: To investigate graded levels of low protien dites effects on hypothalamic gene expression pattern, RNA-seq was performed on hypothalamus of mice treated with different levels of low protein and normal protein under both 60% fat and 20% fat conditions. Methods: There were 8 diets in total, the fat content of first 4 diets were fixed at 60% fat level by energy and protein was varied from 1% to 20% (1%, 2.5%, 5% and 20% respectively) by energy (D14121903, D14121904, D14071601,D14071604), another 4 kinds of diets contained the same graded levels of protein content as the first 4 diets but fat contents were fixed at 20% by energy (D14121905, D14121906, D14071607, D14071610). The remaining energy was compensated of carbohydrate which included corn starch and maltodextrose. Casein was used as the protein source in all diets. To mimic the typical western diet a mix of cocoa butter, menhaden oil, sunflower oil, palm oil and coconut oil was used as the fat source and was designed to generate a 47.5: 36.8: 15.8 proportion of saturated, mono-unsaturated and polyunsaturated fats and 14.7: 1 proportion of n-6 and n-3 fatty acids, the proportions of different fat compositions didn’t change under two different 60% and 20% fat conditions. Sucrose and cellulose were fixed at 5% level by energy and standard vitamin and mineral mix were also added to all diets as well (Hu et al., 2018). The two series were isocaloric within each series. Diets can be ordered direct from research diets (https://researchdiets.com) using the diet codes provided. The hypothalamus of 6 individuals were collected for each diet group, 3 of which were pooled together as one sample, resulting in each group having 2 pooled samples of 3 hypothalamus. Hypothalamic RNA was extracted using Trizol method described in star methods in paper and then sent to Beijing Genomic Institute (BGI) for RNA sequencing. All RNA sequencing was measured by using the BGI-seq 500 sequencer. To determine the sequencing quality of each sample, FASTQ raw data files were measured by using fastQC (www.bioinformatics.bbsrc.ac.uk/projects/fastqc/) quality control tool, after confirmed sequencing quality of each sample, raw reads were aligned to the Mus musculus reference genome (GRCm38) using HISAT2-2.1.0 (Kim et al., 2015; Pertea et al., 2016) and Samtools-1.2 modules (Li et al., 2009), and then featureCounts (Liao et al., 2014) tool in Subread-5.0 (Liao et al., 2013) was used to get counts for each sample from BAM files which was obtained from alignment step. To exclude the low counts, only genes with counts per million (CPM) value > 1 were included in the further analysis. Counts were normalized by the trimmed means of M values (TMM normalization) (Robinson and Oshlack, 2010) method in the edgeR (Anders et al., 2013; Lund et al., 2012; McCarthy et al., 2012; Robinson et al., 2010; Robinson and Oshlack, 2010; Robinson and Smyth, 2007) package. To investigate different dietary protein contents under fixed two levels fat content effects on gene expression levels Generalized Linear Modelling (GLM) function in R-3.5.3 edgeR package was used, The GLM model used in this study were: ~P+F+P:F, which means regression against protein (P) and fat contents (F) of diets plus their interaction (P:F). If the interaction effect was not significant (p > 0.05), the interaction was not included in the analysis and a revised model (~P+F) was utilized (Hu et al., 2018). To explore significantly correlated genes with dietary protein content respectively under 60% fat and 20% fat conditions, Pearson correlation analysis was performed for normalized log counts of all genes by using Pearson correlation method in R-3.5.3. Then selected the significantly correlated genes with dietary protein content (GLM: p < 0.05) respectively in the GLM and Pearson correlation (Pearson: p < 0.05 for 60% fat and 20% fat conditions) analysis were loaded into the Ingenuity pathway analysis (IPA) program (Ingenuity Systems; http://www.ingenuity.com/) to observe the significantly affected pathways. Results: The most affected pathways by the dietary low protein included the mTOR signaling pathway, the hunger signaling pathway, eIF2a signaling pathway and regulation of eIF4 and p70S6K signaling pathway. In the hunger signal pathway, we found there were strong correlations for four key hunger signaling pathway genes Pomc, Cart, Npy and Agrp with the protein content in the diet across different protein content groups (from 1% protein to 20% protein group) with Pomc and Cart expression significantly reduced as protein level declined while Npy and Agrp expression increased as protein levels decreased.

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Timing of food intake predicts weight loss effectiveness.

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Timing of food intake predicts weight loss effectiveness.

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