In adolescence a higher 'eveningness in energy intake' is associated with higher total daily energy intake

Alignment between timing of 'highest caloric intake' and chronotype in relation to body composition during adolescence: the DONALD Study

PURPOSE

Our aim was to assess alignment in timing of 'highest caloric intake' with individual chronotype and its association with body composition in adolescents.

METHODS

We used repeatedly collected data from n = 196 adolescents (age 9-16 years, providing N = 401 yearly questionnaires) of the DONALD open cohort study. Chronotype was assessed by the Munich Chronotype Questionnaire from which midpoint of sleep (MSFsc) was derived. A sex- and age-specific diet-chrono-alignment score (DCAS) was calculated as the difference in hours between the chronotype-specific median timing of highest caloric intake of the studied population and the individual timing of 'highest caloric intake' or vice versa. Repeated-measures regression models were applied to study cross-sectional and longitudinal associations between the DCAS and body composition, e.g., Fat Mass Index (FMI) or Fat Free Mass Index (FFMI).

RESULTS

DCAS ranged from -6:42 h to + 8:01 h and was not associated with body composition. Among adolescents with a later chronotype (N = 201) a 1 h increase in DCAS (later consumption of 'highest caloric intake' in comparison to the median intake of that group), increased FFMI by 1.92 kg/m2 (95% CI: 0.15, 3.69, p value = 0.04) over a median follow-up of 0.94 year.

CONCLUSION

Alignment of energy intake with individual chronotype appears beneficial for FFMI among those with a late chronotype.

Time of Dietary Energy and Nutrient Intake and Body Mass Index in Children: Compositional Data Analysis from the Childhood Obesity Project (CHOP) Trial

Meal timing is suggested to influence the obesity risk in children. Our aim was to analyse the effect of energy and nutrient distributions at eating occasions (EO), including breakfast, lunch, supper, and snacks, on the BMI z-score (zBMI) during childhood in 729 healthy children. BMI and three-day dietary protocols were obtained at 3, 4, 5, 6, and 8 years of age, and dietary data were analysed as the percentage of the mean total energy intake (TEI; %E). Intakes at EOs were transformed via an isometric log-ratio transformation and added as exposure variables to linear mixed-effects models. Stratified analyses by country and recategorization of EOs by adding intake from snacks to respective meals for further analyses were performed. The exclusion of subjects with less than three observations and the exclusion of subjects who skipped one EO or consumed 5% energy or less at one EO were examined in sensitivity analyses. Around 23% of the children were overweight at a given time point. Overweight and normal-weight children showed different distributions of dietary intakes over the day; overweight children consumed higher intakes at lunch and lower intakes of snacks. However, no significant effects of timing of EOs on zBMI were found in regression analyses.

Changes in chronotype and social jetlag during adolescence and their association with concurrent changes in BMI-SDS and body composition, in the DONALD Study

BACKGROUND/OBJECTIVES

Adolescence is a critical period for both the development of overweight and the transition toward a later chronotype, often accompanied by an increase in social jetlag. This study assessed whether changes in chronotype and social jetlag, are linked to changes in body composition during adolescence.

SUBJECTS/METHODS

We used data from the DONALD open cohort study, collected between 2014 and 2019, from 213 adolescents (9-17 years at baseline, 45% females) having at least two measures of chronotype and anthropometry (N = 572). Chronotype was assessed with the Munich Chronotype Questionnaire and defined as: midpoint of sleep corrected for sleep-debt (MSFsc) accumulated over the week (later MSFsc represents later chronotype). Social jetlag (SJL) defines the difference between midpoint of sleep during week and weekend. Calculations for Fat Free Mass Index (FFMI [kg/m²)]) and Fat Mass Index (FMI) [kg/m²)]) were based on body fat percentage, weight, and height. To analyze the associations, we used linear mixed-effect regression models. Finally, the total cohort was split into three biologically relevant age groups (cut-off set at <12 years, ≥12 to ≤15 years and >15 years).

RESULTS

Median follow-up was 2.1 years. Overall, change toward a later chronotype was significantly related with an increase in FMI (ß: 0.05, 95% CI: 0.01-0.08). A 1 h increase in social jetlag predicted an increase in BMI-SDS of 0.08 SDS units (95% CI: 0.01-0.14) and in FMI of 0.04 kg/m2 (95% CI: 0.003-0.08). Associations were stronger for the age group ≥12 to ≤15 years (p for interaction: <0.001). No relationship was found with FFMI.

CONCLUSIONS

Changes in MSFsc and SJL during adolescence were associated with concurrent changes in BMI-SDS and FMI. The age ≥12 to ≤15 years appears to be a sensitive period in which chronobiological changes were clearly associated with increasing body fatness.

Predictors of vegetable consumption in children and adolescents: analyses of the UK National Diet and Nutrition Survey (2008-2017)

Children's vegetable consumption is generally below national recommendations in the UK. This study examined predictors of vegetable intake by children aged 1·5-18 years using counts and portion sizes derived from 4-d UK National Diet and Nutrition Survey food diaries. Data from 6548 children were examined using linear and logit multilevel models. Specifically, we examined whether demographic variables predicted vegetable consumption, whether environmental context influenced portion sizes of vegetables consumed and which food groups predicted the presence (or absence) of vegetables at an eating occasion (EO). Larger average daily intake of vegetables (g) was predicted by age, ethnicity, equivalised income, variety of vegetables eaten and average energy intake per d (R2 0·549). At a single EO, vegetables were consumed in larger portion sizes at home, with family members and at evening mealtimes (Conditional R2 0·308). Within EO, certain configurations of food groups such as carbohydrates and protein predicted higher odds of vegetables being present (OR 12·85, 95 % CI 9·42, 17·54), whereas foods high in fats, sugars and salt predicted a lower likelihood of vegetable presence (OR 0·03, 95 % CI 0·02, 0·04). Vegetables were rarely eaten alone without other food groups. These findings demonstrate that only one portion of vegetables was eaten per d (median) and this was consumed at a single EO, therefore falling below recommendations. Future research should investigate ways to encourage vegetable intake at times when vegetables are not regularly eaten, such as for breakfast and as snacks, whilst considering which other, potentially competing, foods are presented alongside vegetables.

Eveningness in Energy Intake among Adolescents with Implication on Anthropometric Indicators of Nutritional Status: The CRO-PALS Longitudinal Study

Shifting of energy intake towards a later time in the day is associated with an increased risk of obesity in adults. However, there is a lack of data for adolescents. The aim of this study was to investigate adolescents eveningness in energy intake (EV) and its association with anthropometric indicators of nutritional status. This investigation was based on results from the Croatian physical activity in adolescence longitudinal study (CRO-PALS). The cohort included 607 adolescents (50.25% females and 49.75% males) who were assessed at the age of 15/16 and 18/19. A single multi-pass 24-h recall was used as a dietary assessment method, while anthropometric indicators of nutritional status included body mass index (BMI), waist to hip ratio (WHR) and the sum of four skinfolds. The School Health Action, Planning and Evaluation System (SHAPES) questionnaire was used to assess active daily energy expenditure and sedentary behaviors. EV was significantly higher at 18/19 years compared to 15/16 years in whole population (p < 0.01), among male adolescents (p < 0.01), but not among female adolescents (p > 0.05). Although a significant correlation between EV and WHR was found in females at the age of 15/16 (p < 0.01), the results of this study suggest that EV has no or a minor effect on anthropometric indicators of nutritional status in adolescence.

Associations of Sleep with Food Cravings, Diet, and Obesity in Adolescence

Background: Sleep and dietary intake/quality can contribute to excess weight gain, but food cravings may influence these relationships. This cross-sectional study examined the relationship of adolescents’ sleep characteristics with dietary intake/quality and obesity and whether food cravings mediated these relationships. Methods: Sleep measures were calculated based on 24-h accelerometry, and height and weight were directly measured to calculate body mass index (BMI) z-scores. Food cravings were assessed by the Food Craving Inventory (FCI). Dietary intake and quality were calculated based on dietary recalls. Multivariable linear regression was used to examine the associations among sleep, food cravings, dietary intake/quality, and obesity, adjusting for confounders. Results: In total, 256 adolescents (ages 10–16 years) had complete data; 42% were non-White and 45% were boys. Sleep efficiency was inversely associated with sweet cravings and FCI-28. Sleep duration, meeting the sleep duration guidelines, and fruit/vegetable cravings were each positively associated with dietary quality. Sleep duration was negatively associated with BMI z-score. Mediation models were not performed as no sleep parameter was associated with both cravings and dietary intake/quality or BMI z-score. Conclusions: Associations existed among poor sleep, quantity and quality, with more frequent food cravings and worse dietary quality. Sleep may underlie adolescent obesogenic behaviors.

Chronotype: Implications for Epidemiologic Studies on Chrono-Nutrition and Cardiometabolic Health

Chrono-nutrition is an emerging research field in nutritional epidemiology that encompasses 3 dimensions of eating behavior: timing, frequency, and regularity. To date, few studies have investigated how an individual's circadian typology, i.e., one's chronotype, affects the association between chrono-nutrition and cardiometabolic health. This review sets the directions for future research by providing a narrative overview of recent epidemiologic research on chronotype, its determinants, and its association with dietary intake and cardiometabolic health. Limited research was found on the association between chronotype and dietary intake in infants, children, and older adults. Moreover, most of the evidence in adolescents and adults was restricted to cross-sectional surveys with few longitudinal cohorts simultaneously collecting data on chronotype and dietary intake. There was a gap in the research concerning the association between chronotype and the 3 dimensions of chrono-nutrition. Whether chronotype modifies the association between diet and cardiometabolic health outcomes remains to be elucidated. In conclusion, further research is required to understand the interplay between chronotype, chrono-nutrition, and cardiometabolic health outcomes.