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The prevalence of plant-based diets, that is, vegetarian (without meat and fish) and vegan (plant foods only) diets, is increasing also among children and adolescents, and energy and nutrient requirements are highest during this age. Hence the question emerges whether and, if so, how much animal-source food should be included in a healthy and sustainable diet. Recent studies, published 2018–2023, mostly showed comparable anthropometrics between omnivore and vegetarian children, however, fat mass was lower in vegetarians or vegans. Results on marker of iron status were inconsistent. Vitamin B12 status was lower without supplementation, but did not differ between groups when supplementation prevalence was high. Blood lipid profile seems to be more favourable in plant-based groups. Bone mineral density was lower in vegetarians and vegans, but differences attenuated after adjustment. Nevertheless, the long-term clinical relevance of these results remains unclear. Energy intake did not differ between groups and was in the recommended range, although protein intake was lower in vegetarians and vegans. Reported calcium intakes did not differ or were lower in vegetarians compared with omnivores, and were lowest in vegans. More favourable intakes in subjects on plant-based diets were found for fibre, sugar, folate, magnesium and iron. All but one study were cross-sectional, and longitudinal studies of both vegetarian and vegan children/adolescents are required to prospectively examine associations of plant-based diets with health. Professional societies should develop country-specific food-based dietary guidelines adapted to the special dietary habits for children on plant-based diets.

PurposeThere is an ongoing debate whether vegetarian (VG) and especially vegan (VN) diets are nutritionally adequate in early childhood. Hence, the Vegetarian and Vegan Children Study (VeChi Diet Study) aimed to assess the food and nutrient intake of VG and VN infants.MethodsThe study examined the diets of 1–3-year-old VG, VN, and omnivorous (OM) children (n = 430). Dietary intake was assessed via a 3-day weighed dietary record and compared between groups using ANCOVA. Lifestyle data were collected using a questionnaire. Here, the results of micronutrient and fatty acid intakes are presented.ResultsMost nutrient intakes (with and without supplements) differed significantly between VN children and the two other groups, with a more favourable overall micronutrient intake in VN, followed by VG children, [e.g., the highest intake of vitamin E (8.3 mg/d vs. VG 7.4 mg/d and OM 5.1 mg/d), vitamin B1 (569 µg/d vs. VG 513 µg/d and OM 481 µg/d), folate (143 µg/d vs. VG 116 µg/d and OM 108 µg/d), magnesium (241 mg/d vs. VG 188 mg/d and OM 164 mg/d), and iron (8.9 mg/d vs. VG 7.3 mg/d and OM 6.0 mg/d)] as well as fat quality [highest intake of polyunsaturated fatty acids (8.7 E% vs. VG 6.9 E% and OM 4.5 E%) and lowest intake of saturated fatty acids (9.1 E% vs. VG 11.9 E% and OM 14.0 E%)]. In contrast, OM children had the highest intake of vitamin B2 (639 µg/d vs. VG 461 µg/d and VN 429 µg/d), calcium (445 mg/d vs. VG 399 mg/d and VN 320 mg/d), iodine (47 µg/d vs. VG 33 µg/d and VN 31 µg/d), and DHA (35.4 mg/d vs. VG 16.6 mg/d and VN 18.4 mg/d). Without supplementation, OM children had the highest average vitamin B12 intake (1.5 µg/d vs. VG 0.6 µg/d and VN 0.2 µg/d), whereas VN children had the highest average vitamin B12 intake with supplementation (73.8 µg/d vs. VG 1.3 µg/d and OM 1.7 µg/d). Without supplementation, none of the groups’ median intakes met the harmonised Average Requirement (h-AR) for vitamin D and iodine. Moreover, VG and VN children did not achieve h-ARs for vitamin B2, vitamin B12, and iron—if a low absorption of iron is anticipated; VN children also did not do so for calcium.ConclusionIn early childhood, VN and VG diets can provide most micronutrients in desirable amounts and a preferable fat quality compared to an OM diet. Special focus should be paid to (potentially) critical nutrients, particularly vitamin D, iodine, and DHA for all children regardless of diet, as well as vitamin B2, vitamin B12, calcium, and iron for VG and VN children.Trail registrationThis study was registered with the German Clinical Trials Register (DRKS00010982) on (September 2, 2016).

To apply FFQ, knowledge about portion sizes is relevant. According to increased energy and nutrient requirements, average portion sizes of foods are supposed to increase during growth. We provide empirically derived portion sizes for 4- to 18-year-olds in different age groups to facilitate analyses of FFQ data in children and adolescents. Using data from the dynamic DOrtmund Nutritional and Anthropometric Longitudinally Designed cohort study, quantile regression for smoothing percentiles was used to derive portion sizes as a function of age from which age- and food group-specific portion sizes were calculated as median food group intake (g). Dortmund, Germany. Data from 3-day weighed dietary records (WDR) of 1,325 participants (♀: 653) were analysed. Participants provided in total 9,828 WDR (on average 7·5 per participant) between 1985 and 2022. WDR were grouped into five age groups, whereby each age group covered 3 years of age. In total, 11 955 food items were reported and categorised into sixteen major food groups with seventy-one sub-groups. Portion sizes tended to increase with age, except for - and . Comparing 4- to 6-year-olds to 16- to 18-year-olds, portion size increased between 22·2 % ( : 18 g . 22 g) and 173·3 % ( : 15 g . 41 g). We provide empirically derived portion sizes for children and adolescents. These data are useful to establish dietary assessment methods based on estimates of portion sizes, such as FFQ, for children and adolescents.

Background/objectivesAdolescence 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/methodsWe 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).ResultsMedian 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.ConclusionsChanges 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.

PurposeTo describe age and time trends in dietary intake of Total Dairy (TD) (g/1000 kcal Total Energy Intake) and types of dairy (weight percent of total dairy intake, w%TD) represented as Low Fat Dairy (LFD), High Sugar Dairy (HSD), Fermented Dairy (FD) and Liquid Dairy (LD) among children and adolescents in Germany.MethodsOverall, 10,333 3-day dietary records kept between 1985 and 2019 by 1275 DONALD participants (3.5–18.5 years; boys: 50.8%) were analyzed using polynomial mixed-effects regression models.ResultsTD intake decreased with age (♂: linear trend p < 0.0001; ♀: linear and quadratic trend p < 0.0001), whereas FD (♀: linear, quadratic, cubic trend p ≤ 0.02) increased slightly in girls. HSD (♂: linear, quadratic, cubic trend p ≤ 0.004; ♀: linear, quadratic, cubic trend p ≤ 0.005) and LD (linear, quadratic trend p ≤ 0.0002) decreased with age. In terms of time trends, TD intake decreased in the last three decades, especially since 1995 (quadratic trend for ♂ 0.0007 and ♀ p = 0.004). LFD intake increased until 2010 and decreased thereafter (linear, quadratic, cubic trend p < 0.0001). HSD decreased until 1995, then increased until 2010 and decreased again afterwards (♂: linear, quadratic, cubic trend p ≤ 0.001; ♀: linear, quadratic, cubic trend p ≤ 0.003). While FD intake increased linear (in both ♂ and ♀: p < 0.0001), LD intake decreased (linear, quadratic trend p ≤ 0.03).ConclusionOur results showed changes in dairy consumption patterns among children and adolescents over the past three decades, demonstrating a decrease in TD intake with age and time, and a shift from liquid to solid dairy products with a simultaneous increase in fermented dairy products, while LFD and HSD fluctuated over time. Further evaluations will examine the health significance of these consumption patterns.

There is a lack of data on associations between modern vegetarian and vegan diets and health among children and adolescents. The aim of the Vechi Youth Study was to cross-sectionally examine anthropometry, dietary intakes and nutritional status in a sample of 149 vegetarian, 115 vegan and 137 omnivore children and adolescents (6–18 years old, mean age: 12.7 ± 3.9 years). Group differences of dietary intake (calculated from three-day dietary records), nutrient biomarker and blood lipid concentrations were assessed using an analysis of covariance, adjusted for sex, age and other covariates. The total energy intake did not differ significantly between groups, but intake of carbohydrates was higher among vegetarians and vegans than among omnivores (p = 0.0002, respectively). The median protein intake exceeded 0.9 g/kg body weight/day in all diet groups and was lowest among vegetarians (p < 0.02). There was no significant difference of haemoglobin, vitamin B2, 25-OH vitamin D3, HDL-C and triglycerides blood concentrations between diet groups. Vegan participants had higher folate concentrations than vegetarian participants (p = 0.0053). Ferritin concentration was significantly higher in omnivores than in vegetarians (p = 0.0134) and vegans (p = 0.0404). Vegetarians had lower concentrations of holotranscobalamin (p = 0.0042) and higher concentrations of methylmalonic acid (p = 0.0253) than omnivores. Vegans had the lowest non-HDL-C and LDL-C concentrations in comparison to vegetarians (p = 0.0053 and p = 0.0041) and omnivores (p = 0.0010 and p = 0.0010). A high prevalence (>30%) of 25-OH vitamin D3 and vitamin B2 concentrations below reference values were found irrespective of the diet group. In conclusion, the Vechi Youth Study did not indicate specific nutritional risks among vegetarian and vegan children and adolescents compared to omnivores.

Young adults with a later chronotype are vulnerable for a discrepancy in sleep rhythm between work- and free days, called social jet lag (SJL). This study analysed (i) chronotype/SJL association with visceral fat/skeletal muscle mass, (ii) the attribution to physical activity behaviour, and (iii) chronotype-specific changes in physical activity behaviour in young adults during the Covid-19 pandemic lockdown. Chronotype and SJL were derived from the Munich-Chrono-Type-Questionnaire in 320 German students (age 18–25 years) from September 2019 to January 2020, 156 of these participated in an online follow-up survey in June 2020. Body composition was assessed by bioimpedance analysis at baseline. Multivariable linear regression analyses were used to relate chronotype/SJL to body composition; the contribution of self-reported physical activity was tested by mediation analysis. At baseline, a later chronotype and a larger SJL were associated with a higher visceral fat mass (P<0.05), this relation was notably mediated by the attention to physical activity (P<0.05). Chronotype (P = 0.02) but not SJL (P = 0.87) was inversely associated with skeletal muscle mass. During the pandemic lockdown, chronotype hardly changed, but SJL was reduced. Timing and physical activity behaviour remained in most participants and changes were unrelated to chronotype (all P>0.07). A later chronotype/higher SJL may increase the risk of a higher visceral fat mass even in this relatively healthy sample, which may be partly due to their physical activity behaviour. Despite a reduction in SJL during the pandemic lockdown, later chronotypes did not change their physical activity behaviour more than earlier chronotypes.

Metabolomics-derived metabolites (henceforth metabolites) may mediate the relationship between modifiable risk factors and clinical biomarkers of metabolic health (henceforth clinical biomarkers). We set out to study the associations of metabolites with clinical biomarkers and a potential mediation effect in a population of young adults. First, we conducted a systematic literature review searching for metabolites associated with 11 clinical biomarkers (inflammation markers, glucose, blood pressure or blood lipids). Second, we replicated the identified associations in a study population of n = 218 (88 males and 130 females, average age of 18 years) participants of the DONALD Study. Sex-stratified linear regression models adjusted for age and BMI and corrected for multiple testing were calculated. Third, we investigated our previously reported metabolites associated with anthropometric and dietary factors mediators in sex-stratified causal mediation analysis. For all steps, both urine and blood metabolites were considered. We found 41 metabolites in the literature associated with clinical biomarkers meeting our inclusion criteria. We were able to replicate an inverse association of betaine with CRP in women, between body mass index and C-reactive protein (CRP) and between body fat and leptin. There was no evidence of mediation by lifestyle-related metabolites after correction for multiple testing. We were only able to partially replicate previous findings in our age group and did not find evidence of mediation. The complex interactions between lifestyle factors, the metabolome, and clinical biomarkers warrant further investigation.

PurposeTo analyse the association between intake of total dairy (TD) and types of dairy [liquid dairy (LD), solid dairy (SD), low-fat dairy (LFD), high-fat dairy (HFD), high sugar dairy (HSD), low-sugar dairy (LSD), not fermented dairy (NFD), as well as fermented dairy (FD)] and long-term changes in body weight status and composition among children and adolescents in Germany.MethodsIn total, 9999 3-day dietary records collected between 1985 and 2019 by 1126 participants (3.5–18.5 years; boys: 50.8%) of the Dortmund Nutritional and Anthropometric Longitudinally Designed (DONALD) study were analysed. Polynomial mixed-effects regression models were used to examine whether changes (median follow-up: 9 years) in the intake of TD and dairy types (in 100 g/1000 kcal total energy intake) were associated with changes in body-mass-index-standard-deviation-score (BMI-SDS); fat mass index (FMI); fat-free mass index (FFMI) over time.ResultsAn individual increase in TD intake was slightly but significantly associated with an increase in BMI-SDS (β = 0.0092; p = 0.0371), FMI (β = 0.022; p = 0.0162), and FFMI (β = 0.0156; p = 0.0417) after adjustment for potential confounder. Analyses for LD (BMI-SDS: β = 0.0139; p = 0.0052; FMI: β = 0.0258; p = 0.0125; FFMI: β = 0.0239; p = 0.0052) and LSD intake (BMI-SDS: β = 0.0132; p = 0.0041, FMI: β = 0.02; p = 0.0316, FFMI: β = 0.0183; p = 0.0189) showed similar results to TD. Both processing method and fat content showed no association with body composition in our analyses.ConclusionIncreases in TD, LD, and LSD intake showed small but significant increases in BMI and concomitant increases in fat mass and lean mass. However, the observed changes were too small to expect biological or physiological meaningful effects. Overall, our results showed that policies to promote dairy intake in childhood are to be welcomed, as no negative effects on body composition are expected, while the intake of important nutrients for growth is ensured. The type of dairy does not seem to matter.

Background Reports on body mass index (BMI) trajectories from childhood into late adolescence, their determinants, and subsequent cardiometabolic risk markers, particularly among European populations have been few. Moreover, sex-specific investigation is necessary considering the sex difference in BMI, and the sex-specific association between BMI and some cardiometabolic risk markers. Methods Using a sample from the DOrtmund Nutritional and Anthropometric Longitudinally Designed study, we explored sex-specific trajectories of the BMI standard deviation score (SDS) from 4 to 18 years of age in 354 males and 335 females by latent (class) growth models. The determinants of trajectory were assessed by logistic regression. We identified cardiometabolic risk markers that were highly associated with BMI SDS trajectory by random forest regression, and finally we used generalized linear models to investigate differences in the identified cardiometabolic risk markers between pairs of trajectories. Results We observed four: ‘low-normal weight’, ‘mid-normal weight’, ‘high-normal weight’, and ‘overweight’, and three: ‘‘low-normal weight’, ‘mid-normal weight’, and ‘high-normal weight’ trajectories in males and females, respectively. Higher maternal prepregnancy BMI was associated with the ‘overweight’ trajectory, and with ‘high-normal weight’ trajectory in both sexes. In addition, employed mothers and first-born status were associated with ‘high-normal weight’ trajectory in females. BMI SDS trajectory was associated with high-density lipoprotein-cholesterol and interleukin-18 (IL-18) in males, and diastolic blood pressure and interleukin-6 (IL-6) in females. However, only males following the ‘overweight’ trajectory had significantly higher IL-18 when compared to their ‘low-normal weight’ counterpart. Conclusions We identified sex-specific distinct trajectories of BMI SDS from childhood into late adolescence, higher maternal prepregnancy BMI as a common determinant of the ‘high-normal weight’ and ‘overweight’ trajectories, and ‘overweight’ trajectory being associated with elevated IL-18 in late adolescence–young adulthood. This study emphasizes the role of maternal prepregnancy BMI in overweight, and highlights IL-18 as a cardiometabolic signature of overweight across life.