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

The DONALD study is an ongoing, prospectively designed open cohort study conducted in Dortmund, Germany. Since 1985, data on diet, growth, and developmental and metabolic factors are collected continuously from infancy (age 3 months) to adulthood. Approximately 30–35 healthy infants from Dortmund and surrounding communities, whose mothers and/or fathers have a sufficient level of the German language, are recruited every year via personal contacts, maternity wards, or pediatric clinics. The examination schedule includes quarter-yearly examinations in infancy, half-yearly examinations in the second year of life, and annual examinations thereafter until young adulthood. Among others, examinations include anthropometric measurements, lifestyle questionnaires, and a 3-day food record. Chronotype assessment in the DONALD study started in 2014 for participants from 9 years onwards by use of the Munich Chronotype Questionnaire (MCTQ) [17, 18]. Detailed information regarding the study design can be found elsewhere [19].

Until July 2020, n = 652 study participants completed the MCTQ (N = 1.237 Q). Questionnaires collected during the first 2 weeks after the time change in Germany from standard winter to summer time or vice versa [10] (N = 95) and questionnaires with missing values (N = 38) were excluded. The sample was further reduced due to missing information on in parallel collected dietary data leaving a total number of n = 259 participants (N = 461 questionnaires). Of those, (n = 196 out of 172 families) were adolescent (aged 9–16 years) [20] and provided N = 401 questionnaires (Appendix Fig. 3).

Dietary intake in the DONALD study is assessed by use of 3-day weighed dietary records. The participants are free to choose the consecutive days, meaning that week and weekend days can be recorded. All foods and beverages consumed, as well as leftovers, are weighed and recorded over three consecutive days by the parents or by the older participants themselves with the use of regularly calibrated electronic food scales [initially Soehnle Digita 8000 (Leifheit AG, Nassau,Germany), now WEDO digi 2000 (Werner Dorsch GmbH, Muenster/Dieburg, Germany)]. When exact weighing is not possible, household measures (e.g., spoons and cups) are allowed for semi-quantitative recording. Information on recipes and on the types and brands of food items consumed is also requested. Additionally, participants record the time of every eating occasion. Energy and macronutrient intakes were calculated using the continuously updated in-house nutrient database LEBTAB [21], which is based on German standard food composition tables. Energy and nutrient contents of commercial food products are calculated by recipe simulation using labeled nutrient contents and ingredients. Macronutrients were considered as percentages of total energy intake (TEI). Subsequently, the individual means of TEI and macronutrient intakes were calculated from the three record days. The validity of dietary recording was previously evaluated by Bokhof et al. [22].

The MCTQ [17, 18] includes questions regarding sleep and wake times during the week and weekend. The individual chronotype (continuously in h:min) was calculated as the Midpoint of Sleep, i.e., the half-way point between sleep-onset and sleep-end on free days (MSF) [17]. If applicable, the MSF is corrected for “oversleep” on free days to account for sleep-debt accumulated over the week (MSFsc) [18].

Boys and girls are different in their individual level of lateness; girls tend to be earlier in comparison to boys [23]. To account for age and sex differences in chronotype, we derived MSFsc residuals for all observations independent of age and sex and ranked them by the group of two (PROC RANK in SAS®). Afterward, we calculated median MSFsc hours stratified by sex for adolescents with earlier and later chronotype (Fig. 1).

In a first step, we calculated the sum of energy intake per eating occasion, e.g., all foods and beverages consumed within a 30-min time period were summarized into one eating occasion; and all eating occasions < 10 kcal were added to the previous eating occasion [10]. From this, we derived the time of the day at which the greatest amount of energy (> 20% of total daily energy intake) was consumed (timing of ‘highest caloric intake’ per day). Then, these individual eating times were averaged and defined as ‘highest caloric intake’ derived from 3-day dietary records.

In a second step, age- and sex-adjusted median eating times, for which we applied the residual method, were derived for adolescents with earlier and later chronotype. The median eating time was defined based on the sample population and served as the reference point for the definition of diet-chrono-alignment. Third, individual diet-chrono-alignment score (DCAS) was calculated as the difference in hours between the chronotype-specific median eating time of ‘highest caloric intake’ and the individual eating time of ‘highest caloric intake’. Hence, earlier eating in comparison to the population median eating time of ‘highest caloric intake’ resulted in positive alignment for adolescents with an earlier chronotype measured in hours, whereas for adolescents with a later chronotype, later eating resulted in positive alignment (Fig. 1).

Adolescents were measured annually by trained nurses according to standard procedures, dressed in underwear and barefoot. Standing height is measured to the nearest 0.1 cm using a digital stadiometer. Weight was measured to the nearest 0.1 kg with an electronic scale (model 753 E; Seca, Hamburg, German). Skinfold thickness was measured on the right side of the body at the biceps, triceps, subscapular, and suprailiac sites to the nearest 0.1 mm with a Holtain caliper (Holtain Ltd., Crymych, UK) [19]. Sex- and age-independent body mass index (BMI kg/m²) standard deviation scores (SDS) were calculated using the German national reference data according to the LMS Method [24]. Percent body fat (%BF) was estimated from two skinfolds (triceps, subscapular) using age-specific Slaughter equations [25], for the subsequent calculation of FMI (fat mass/m²; where fat mass = body weight * BF%/100) and FFMI (fat free mass/m²; where fat free mass = body weight − fat mass). Since the distribution of FMI was skewed, log10-transformed values were used in the analyses.

Parents are interviewed regarding family characteristics (i.e., parental education, smoking, and persons in the household). Every 4 years, maternal body weight and height were measured with the same equipment as for the children on the child’s admission to the study center.

Adolescents’ pubertal status was defined in accordance with the onset and the end of pubertal growth spurt. Age at Take-Off (ATO) is the age of minimal growth velocity. Age at Peak Height Velocity (APHV) is the age of maximal height velocity. ATO and APHV were derived from the parametric Preece and Baines Model 1 [26], details are explained elsewhere [26, 27]. Under- and over-reporting of dietary intake was assigned if TEI was unrealistic in relation to the estimated basal metabolic rate (according to age- and sex-specific equations of Schofield [28]). Based on the pediatric cut-offs by Sichert-Hellert et al. [29] we detected 64 under-reporters (15.9%) and no over-reporting of dietary records.

Linear mixed-effects regression models (PROC MIXED in SAS), including both fixed and random effects accounting for the nested nature of our data (children within families in the random statement) and the lack of independence between repeated observations on the same person. A further advantage is that the inclusion of all measurements is possible also in case of missing data for a specific point in time [30].

Repeated-measures regression models (PROC MIXED) were used to examine change-on-change associations of (a) the cross-sectional and (b) the longitudinal associations between the DCAS at first assessment and (Δ) body composition as well as (c) the respective changes in associations of (Δ) in DCAS and (Δ) BMI(-SDS), (Δ) FMI, and (Δ) FFMI over time [30, 31]. Variables of change were calculated by subtracting the baseline value from value at each year of assessment with 0 difference at first assessment.

Covariates for model adjustment were selected according to known predictors of BMI, body composition, and timing of energy intake [16, 32, 33]. From here, we identified minimally sufficient adjustment sets (msas) using a diagram (Appendix Fig. 4) representing the relationships among the identified variables [34]. Besides age at baseline and time between first and subsequent measurements (basic model) [31], sex, energy intake (g/d), physical activity (high vs. low), age at take-off and age at peak height velocity as puberty markers, smoking in the household, social jetlag, season of record (spring/summer/autumn/winter), number of questionnaires, and underreporting of dietary intake were important covariates.

Model building was driven by the log-likelihood criterion to define the final crude model. The Akaike Information Criterion (AIC) and the Bayesian Information were used to select the correlation structure best describing the correlated nature of the data. Tests for effect modification were performed by the inclusion of interaction terms between DCAS and MSFsc or sex.

To manage missing data, we undertook multiple imputations, using the MI procedure in SAS and explored the pattern of missingness. We generated an imputed database containing five imputed versions to predict missing values for ATO (n = 72, 18%) and physical activity (n = 67, 17%). Final models were tested regarding multicollinearity, heteroscedasticity, and normal distribution of residuals. Finally, we excluded participants who used an alarm clock during the weekend (n = 59, 15%) in a sensitivity analyses and assessed the influence of ‘eveningness in energy intake’ on body compositional measures. ‘Eveningness in energy intake’ was defined as percentage of energy intake in the evening (after 6 pm until 11:15 pm) − percentage of energy intake in the morning (before 11 am starting at 5:15 am) [9].

Studying BMI as the outcome of interest should be accompanied by additional measures on body composition, such as FMI or FFMI, especially during adolescence [35]. However, most previous studies did not consider body compositional measures next to BMI, which limits comparability with previous research. Nevertheless, Eng et al. [36] reported a paradox in their analyses of the National Health and Nutrition Examination Survey (NHANES) 1999–2004, N = 11,072 aged 2–18 years. In children aged 2–11 years, later eating in the time between 4 pm to midnight was associated with increasing overweight defined as a BMI between the 85th and 94th percentile, and obese (≥ 95th percentile) applying Centers for Disease Control and Prevention (CDC's) BMI-for-age and sex-specific growth charts. Similar results as shown by Eng et al. for children below the age of 11 years were presented by Martínez-Lozano et al. [37] for the age 8–12 years. However, the opposite was true in NHANES for adolescents aged 12–18 years. Late-eating adolescents had 16 times lower odds for being overweight (ß: −15.9, p value = 0.01). This difference may partly be attributable to chronobiological changes toward a later chronotype starting around the age of 12 years [38]. In line with this, Munoz et al. [13] reported that also adults aged 18–65 years having a late chronotype and a higher %TEI at dinner were normal weight, whereas evening types who had more %TEI in the morning were more likely to be overweight. Coulthard et al. [39] found no association between eating after 8 pm and excessed weight or increased energy intake in 1.620 UK children aged 4–10 years. Also, no effect was observed in the CHOP trial where they included 729 healthy children to investigate the distribution of eating occasions on zBMI [40]. These findings support our null findings for BMI-SDS.

Results of the CRO-PALS Longitudinal Study including 607 adolescents showed no associations between ‘eveningness in energy intake’, based on a single 24-h recall and BMI or waist–hip-ratio [41]. This is in line with our results on ‘eveningness in energy intake’ and BMI-SDS. The association between ‘eveningness in energy intake’ and FFMI supported our results of the main analyses. The weaker association of our sensitivity analyses for FFMI may suggest that not solely the amount of energy intake eaten earlier or later during the day, but especially the balance with the underlying chronotype, hence the internal clock, may be of particular interest regarding beneficial longitudinal developments in body composition. The importance of dietary intake timing as an independent risk factor besides the amount of food intake is supported by an intervention trial based on 110 participants aged 18–22 years in which timing of food consumption relative to clock hour and endogenous circadian time was assessed in relation to body composition [42].

The timing of food intake is important in harmonizing the central and peripheral clocks. A desynchrony between both may lead to metabolic distortion and unbeneficial body compositional developments [43]. Earlier, we have shown in DONALD that children, while they age, do change the amount of energy intake to later times of the day [9]. Such an observation requires a supportive family environment enabling later eating times. In line with this, Morgan et al. [44] reported earlier on the benefits in autonomous decisions by emerging adults regarding eating regulation styles by parents on body fat. The adaptation toward later eating hours in adolescents who naturally develop toward later chronotypes may contribute to a better alignment between their internal and external clock. The benefits of such an alignment has been elaborated on in the other studies [13, 43] and is supported by our findings.

To summarize, a large body of evidence suggests ‘later’ eating as being unbeneficial for metabolic health [42]. Especially, in adults. However, this does not seem to be applicable for everybody [13, 43]. In the current study, we could show that the best time for food consumption depends on a person’s individual chronotype. Not considering chronotype in the analyses may partly explain the large controversy we find in the current literature [45, 46]. Another possible reason for the discrepancies in results between ‘later’ energy intake and body composition could be related to the definition of eating time. For instance, ‘late’ energy intake 2 h before bedtime [12] or closer to melatonin onset (i.e., biological night) [42] represents more extreme ‘lateness’ and shows positive associations with overweight in adult later chronotypes.

A major advantage of the DONALD Study is the longitudinal study design, enabling the examination of longitudinal associations. Other studies had to rely on cross-sectional data mainly [39]. Further strengths are the dietary assessment method, avoiding recall bias, and the assessment of chronotype using the validated MCTQ [18, 47] which was applied previously among adolescents in the other studies [23, 48]. In addition, the consideration of sleep/wake cycles which are used for chronotype development were shown earlier to be a practical approach for the definition of circadian timing of food intake [12, 42].

Generalizability of our results may be limited, since the DONALD study is characterized by a homogeneous sample of German participants, represented by a high socioeconomic status (SES) [19]. Also, females were slightly underrepresented in the current analyses. Regarding body compositional measures, we would like to add that despite high SES of the DONALD population, the prevalence of overweight (15.5%) is very well comparable to the overall German population (15.4%) [49]. Another disadvantage is the small sample size. However, by generating the DCAS, we saved an additional level of stratification by early and late timing of ‘highest caloric intake’.

This is the first analysis performed in adolescence showing longer term consequences of circadian alignment with energy intake timing. Supporting adolescents in following their individual clock appears to be most beneficial for a healthy body compositional development [35]. General practitioners should prepare and explain parents the natural occurring changes in chronobiology of their children they can expect during adolescence and enable parents to accept changes in timing of sleep-onset [18] as well as food intake [9]. Of note, it is not the later chronotype that increase the risk for future metabolic disease development, but the attached individual behavior [43]. The application of our approach to a larger and more heterogeneous sample of adolescence, that may also include participants not enabled by their parents to follow their internal clock, would enhance this field of research.

In conclusion, changes in chronobiology and eating time accompany the phase of the transition from childhood to adulthood. Chronotype as well as eating time advance naturally to later times of the day during adolescence. The alignment between dietary intake timing and chronotype appears to encourage favorable developments in body composition in adolescents with a later chronotype.