Background: Obesity continues to be one of the largest public health concerns in our nation. The role of eating patterns as a means for weight management has been studied extensively. However, the role of breakfast in weight management is still poorly understood. The purpose of this study was to understand the role of breakfast in weight management by observing the relationships of energy intake and macronutrient composition, specifically protein and fiber, with weight status during early morning and late morning eating occasions. Methods: Data from two multiple pass 24h dietary recalls from NHANES 2005-2010 were used. N= 4542 non-pregnant, non-lactating participants aged 20-65 y who did not perform shift work and who had a BMI between 18.5 and 60 kg/m2 were included. Individuals with missing data for any of the variables were excluded. Data were analyzed with SPSS software version 21. Each of the 2 days was divided into four time periods: time period 1 defined as the first intake of the day occurring between 12:00 a.m. and 4:59 a.m., time period 2 defined as the first intake occurring between 5:00 a.m. and 8:59 a.m., time period 3 defined as the first intake occurring between 9:00 a.m. and 11:30 a.m., and time period 4 defined as the first intake occurring after 11:30 a.m. Time period 2 was designated as “early morning intake” and time period 3 was designated as “late morning intake”. The other two time periods were designated as energy intake eaten the rest of the day. Energy (kcal), protein (g), and fiber (g) intakes were then calculated for the whole day and for each time period. For early morning and late morning intake, energy, protein and fiber were also divided into 5 categories. Those reporting no intake (0 kcals) made up the first category and quartiles were calculated for those reporting energy intakes of ≥ 0.1 kcal. Modified quartiles for the late morning period using the quartile cutoffs for the early morning time period were also calculated. Similarly, those reporting no intake (0 grams) made up the first category for protein and fiber and quartiles were calculated for those reporting protein or fiber intakes of ≥ 0.01 g. Estimated energy requirements (EER) were determined using the prediction equations developed by the Institute of Medicine (IOM 2005). To determine energy intake reporting plausibility, reported energy intake as a percent of EER was calculated. Standard classifications were used for weight status based on BMI. Descriptive statistics (median and 95% confidence interval) were computed for all variables. Multinomial logistic regression analysis was performed to determine associations between morning energy intake, protein, and fiber categories and risk for overweight (OW) and obesity (OB) for both early morning and late morning time periods. For the energy intake categories, Model 1 was controlled for race/ethnicity, age, gender, poverty-income ratio (PIR), smoking status, alcohol consumption, physical activity, self-reported chronic disease, daily eating frequency, and the two day morning eating pattern. Model 2 was controlled for all of the covariates in Model 1 plus energy intake before and after morning eating. Model 3 was controlled for all of the covariates in Model 2 plus energy intake reporting plausibility. For the protein and fiber categories, Model 1, 2, and 3 controlled for the same covariates as the energy intake categories and also controlled for reported energy intake during the early or late morning eating occasions. A p-value of <0.05 was considered statistically significant. Results: For the energy intake categories during the early morning, compared to no morning intake, Model 1 showed a lower risk for OB in Q2, but no other relationships were seen in any of the other quartiles. Similar results were seen in Model 2 where a lower risk for OB in Q2 was present. In Model 3, however, (controlled for energy intake reporting plausibility) the relationship between energy intake in Q2 and a lower risk for OB disappeared and a higher risk for OW and OB became apparent in Q4. For the late morning analysis, Models 1 and 2 were similar in that there was no association between morning energy intake category and weight status, but for Model 3 there was a higher risk for OW and OB in Q2-Q4. When we used the modified late morning quartile cutoffs in the analysis to eliminate potential bias due to the different quartile cutoffs for the early and late morning eating occasions, the higher risk for OW and OB was still present in Q2-Q4 and the ORs were attenuated compared to when the original late morning cutoffs were used. In terms of composition, compared to no morning intake, there were no significant associations seen between early or late morning protein consumption and weight status in any of the models. Additionally, for the early morning analysis of fiber, Models 1 and 2 did not show an association between morning fiber intake category and weight status, but for Model 3 there was a lower risk for OB in Q4. For the late morning analysis, Model 1 showed a higher risk for OW in Q2, but no other relationships were seen in any of the other quartiles. Similar results were seen in Model 2 where a higher risk for OB in Q2 was present. In Model 3, however, this relationship disappeared and no other associations were seen in any of the other quartiles. Conclusion: In comparison to having no morning intake (i.e., “skipping”) there was an elevated risk for OW and OB when consuming higher amounts of energy during the early morning and moderate to high amounts of energy during the late morning. The risk for OW and OB was higher in the late morning compared to the early morning eating occasions, in part, but not entirely, because of the higher amounts of energy consumed during the later morning in comparison to the early morning. Therefore, higher energy in both early morning and late morning increase the risk for OW and OB. Furthermore, later timing may increase the risk for OW and OB, independent of energy intake the rest of the day, since individuals who ate later also had higher energy intakes in the later morning compared to the early morning. In addition, compared to no morning intake of fiber, having a very high fiber intake in the early morning, but not the late morning, may decrease the risk for OB independent of energy intake and fiber intake the rest of the day. These associations may not be apparent unless energy intake reporting plausibility is taken into account.

Those who practice poor meal timing habits such as irregular day-to-day eating, eating late at night, and have a short overnight fast are more at risk for weight gain, reduced weight loss with weight loss attempts, and increased risk for developing and/or worsening health conditions such as type 2 diabetes mellitus and cardiovascular disease and/or risk factors for said conditions. Recent studies have identified possible factors that influence meal timing, one of which is hunger. Hunger is defined as a physiologic need to eat, and can be triggered by a rise in the hormone ghrelin. Hunger in general, or greater hunger at certain times of day, may lead to poor meal timing and/or difficulty adhering to meal timing recommendations made in behavioral interventions. The goal of our study was to determine if the overall hunger level and time of onset of greatest hunger were associated with poor meal timing cross-sectionally and lower adherence to meal timing recommendations. The meal timing behaviors we examined were eating late at night, having a longer overnight fast, and an earlier ingestive period midpoint based on published evidence suggesting these are important for weight control. We hypothesized that a greater overall hunger level and later onset of greatest hunger would be inversely associated with poor meal timing cross-sectionally and a lower adherence to potential meal timing recommendations to be applied to future interventions. Our cross-sectional study was a secondary analysis of data from a previous study on diet and energy regulation in 116 healthy adults (mean BMI 24.3 kg/m2; SD 3.8, mean age 29.4 years; SD 11.9). Both continuous and categorical meal timing outcomes were examined. The continuous outcomes were eating late at night (defined as eating past 20:00 h), length of overnight fast (defined as the length of time between the last meal consumed before bedtime and first eating occasion after waking), and timing of the largest meal, which we measured using the midpoint of the ingestive period. Categorical outcomes, which had cutoff values determined based on evidence from published research, were: not eating after 20:00, achieving an overnight fast of ≥13 hours, and having the midpoint of the ingestive period before 15:00. Associations of hunger variables with continuous meal timing outcomes were examined in three separate models using analysis of covariance, with hunger variables as the independent variables and the meal timing patterns as the dependent variables. Associations of hunger variables with categorical (bivariate) outcomes had the same independent variables but were examined with logistic regression analysis. Covariates included in both continuous and categorical models were age, sex, race, physical activity level, weighted average bedtime on weekdays and weekends, dietary restraint score, dietary disinhibition score, sleep duration, and sleep quality. After inclusion of all covariates, a higher hunger score was associated with having an overnight fast lasting ≥13 hours (p=0.026), suggesting that participants were able to achieve a longer overnight fast despite being hungrier. There was no significant association between hunger variables and eating late at night or midpoint of ingestive period (p>0.05), although the p-value was marginally non-significant with eating late at night (p=0.080). Time of greatest hunger was not associated with any of the meal timing variables (p>0.05). As previous studies have shown that a longer overnight fast improves weight loss, a possible application of our findings, namely the length of overnight fast, is for individuals who aim to achieve an overnight fast of ≥13 hours to lose weight by consuming a greater proportion energy in the morning/afternoon as opposed to dinner/later at night.. This suggestion is based on previous studies showing eating a larger breakfast decreases feelings of hunger at night. Additionally, including more protein and fiber in the diet can increase satiety at any time of day. Future studies are needed to examine relationships between hunger score and a longer overnight fast, in larger, more diverse populations and with randomized controlled designs, as our study was cross-sectional and was unable to determine causality.

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