Prospective association between breakfast consumption frequency and BMI z-score among European school-aged children. The Feel4Diabetes Study.

doi:10.21203/rs.3.rs-4828591/v1

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Prospective association between breakfast consumption frequency and BMI z-score among European school-aged children. The Feel4Diabetes Study.

https://doi.org/10.21203/rs.3.rs-4828591/v1

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Background

The long-term effects of breakfast on childhood z-BMI remain inconclusive.

Objective

To prospectively assess the impact of stable and altered breakfast consumption habits on z-BMI change over two years, in school-aged children across six European countries.

Methods

Data of 6,528 children (8.2 ± 1.0 years, 48.9% male) from the Feel4Diabetes study were used. Children's lifestyle behaviors, including breakfast frequency, were assessed through parent-reported questionnaires. Weight and height were objectively measured and converted to z-BMI. Participants were categorized into trajectories combining breakfast consumption habit (i.e. regular consumption, irregular consumption or omission) at baseline and follow up. Logistic regression analyzed the prospective association between breakfast trajectories and z-BMI increase, with adjustment for demographic and lifestyle variables.

Results

A statistically signidficant effect of breakfast trajectories was observed in z-BMI, both at baseline and follow up. Stable skippers was the only trajectory that showed a significant increase in BMI z-score from baseline to follow up (0.891 ± 1.058 vs. 1.034 ± 1.072 respectively, p < 0.05). BMI z-score at follow up of regular eaters (0.615 ± 0.967) was significantly lower, compared to both irregular eaters (1.229 ± 0.890) and stable skippers (both p < 0.05).Stable regular consumption and transition from omission to regular consumption were independently associated with significantly lower risk of z-BMI gain, compared to stable omission [OR,95%CI: 0.55 (0.31,0.97), p = 0.039 and 0.43 (0.21, 0.88), p = 0.021 respectively].

Conclusion

Stable regular breakfast consumption or transition from omission to regular consumption seem to contribute to superior weight regulation in children and to independently protect against z-BMI gain, compared to stable omission. Further studies are warranted to extend the evidence on the breakfast habit-BMI association among children.

Health sciences/Health care/Paediatrics

Health sciences/Health care/Nutrition

Health sciences/Health care/Weight management

body weight

BMI

breakfast

childhood obesity

diet

skipper

Childhood obesity has emerged as a major public health concern of global importance ¹. Currently, about 390 million children and adolescents are overweight worldwide, with 160 million of them present with obesity ². Over the past decades, the prevalence of childhood overweight and obesity has alarmingly increased in all regions of the world, including Europe. According to the WHO European Regional Obesity Report 2022, one-third of school-aged children in the region are classified as either overweight or obese ³. Evidence suggests that obesity at a young age is associated with increased risk of obesity and obesity-related disorders, such as cardiovascular diseases and type-2 diabetes mellitus (T2DM), during adulthood ⁴, lower quality of life ⁵ and increased morbidity⁶. In this context, it is crucial to prevent obesity during childhood. Even if prevention strategies are multiple ⁷, nutritional management and dietary recommendation appear to be particularly effective in the pediatric population ^8,9. Notably, the modification of unhealthy dietary behaviors, such as skipping breakfast, has been considered of high importance in weight management among children ^9,10.

Breakfast has been frequently stated as the most important meal of the day ¹⁰, with a regular breakfast consumption being linked to a range of health benefits in children ^11–13, including positive weight-related outcomes ^14–16. Among the current body of literature, breakfast meal has been perceived as a potential protective factor against elevated Body Mass Index (BMI) ^17,18, weight gain ¹⁶ and excess adiposity ¹⁵ during childhood. Although less is known on the underlying mechanism by which breakfast consumption may enhance body weight regulation in children, it has been suggested that breakfast omission may adversely affect appetite ratings during the day, leading to the consumption of larger meal portions and energy-dense food in the rest of the day ^19,20. In general terms, breakfast-skipping behaviors have been independently positively related with childhood obesity ^21,22. Despite this, the prevalence of skipping breakfast in pediatric subjects is rising ^18,23 and is globally estimated between 10–30% ²⁴. Accumulating evidence indicates that the decline in daily breakfast consumption may be involved in the escalation of childhood overweight and obesity ^17,25.

Α series of studies have examined the relationship between breakfast habits and children’s weight status. Systematic reviews on this topic indicate that children who eat breakfast on a consistent basis have a reduced risk of becoming overweight or obese ^17,18,26 and have a lower Body Mass Index (BMI), compared with those who skip breakfast ^17,26. Moreover, few meta-analyses ^27–29 report positive association between skipping breakfast and overweight or obesity in children. Nevertheless, the aforementioned studies mainly summarize the results of cross-sectional and retrospective studies and therefore demonstrate only correlations and not necessarily a causal link. The longitudinal relationship between breakfast frequency and weight-related outcomes among children is not well-defined, since prospective studies are scarce and indicate inconsistent results ^30,31. It has been acknowledged that additional prospective studies are required to strengthen the evidence on the directionality and causality of this association ³⁰. Therefore, the objective of the present study was to prospectively assess the association between breakfast consumption frequency and absolute changes in BMI z-score (z-BMI) during childhood, using a 2-year follow-up data from school-aged children in six European countries.

2.1. Study population

Data were derived from the multicentered Feel4Diabetes (F4D) study, a large population-based cohort implemented between 2016 and 2018 in six European countries (Belgium, Finland, Greece, Spain, Bulgaria and Hungary). The Feel4Diabetes study aimed to develop and implement a school-, community- and family-based intervention to promote behavioral changes and create a more supportive social and physical environment, as part of the broader attempt to prevent diabetes in vulnerable families across Europe. The details of the study design and sample stratification have been previously described ³². Briefly, in each country, primary schools located within low socioeconomic status municipalities served as the entry point to the community, targeting families with children attending the first three grades of compulsory education. All the adult members of these families were invited to go through a first-stage screening for T2DM, while their children underwent anthropometric measurements in the school setting. Children were randomized into an intervention and a control group. The randomization was performed at municipality level, on a 1:1 ratio and thus, the participating schools and families, were allocated to either intervention or control group, based on their municipality of residence. In schools where the intervention took place trained teachers aimed to create a more supportive social and physical environment promoting a healthy and active lifestyle for the children during school hours, such as providing opportunities and acting as role models for healthy behaviours. The activities at school were complemented with simple and easy-to-read newsletters, aiming to inform and actively engage the families in the intervention. The newsletters were culturally adapted and aimed to motivate, improve self-efficacy to apply environmental changes at home, and provide practical tips to adopt a healthier and more active lifestyle. Three measurements were performed in the final trimester of three consecutive academic years (2016–2018) in both groups: baseline, follow-up 1, and follow-up 2. All procedures were approved by the relevant Bioethics Review Board of each country and a written and signed parental consent was obtained. In the present analysis, the final sample consisted of 6,528 children from the ‘all-families’ component, providing complete data on breakfast eating habits and anthropometric indicators during both baseline and two-year follow-up. Children from both the intervention and control group were included in the analysis and models, using group as a potential confounder where appropriate. To better assess unhealthy weight gain in children, participants who were classified as underweight at baseline (n = 763) were excluded from the analyses.

2.2. Data Collection and Measurement

Demographics and lifestyle characteristics: Demographic characteristics and children's lifestyle behaviours were assessed through parent-reported questionnaires. Sociodemographic and lifestyle data, used in the present study, were obtained at baseline only. At first stage, parents were asked to complete a questionnaire on demographics, providing data around their education level and income, as well as their’s child age and sex. Parental education was asked in a scale question ranging from “less than 6 years” to “more than 16 years” of education and was dichotomized for analysis into ≤ 9 and > 9 years of education. Likewise, on the basis of a scale question about families’ livelihoods ranging from “very difficult” to “very easy”, family income was categorized for analysis into “difficult” and “easy”, reflecting the ease in covering the household expenditure. Afterwards, parents were asked to fulfill ‘the family’s energy balance-related (EBRB) behaviours’ questionnaire, through which information on children’s physical activity, screen time and food intake was examined ³³. Children’s physical activity was assessed by adding up the reported number of days on weekdays and weekend days during which children were physically active at a moderate to vigorous level for at least 60 min a day. Sceen time was assessed on a 10-point Likert scale, ranging from “None” to “more than 7 h”, for weekdays and weekend days. The information was categorized into hours per day. Consumption frequency of queried foods and beverages was asked in a scale question ranging from “Less than 1 time per week” to “more than 6 times per day”. The portion size, defined with a household unit, was provided under the question. In this analysis, food and beverages assessed for children were fruits, vegetables, sugar-sweetened beverages and sweets. For each of the four food groups, the aforementioned categorical values were recoded into numerical values expressed in servings per day according to the midpoint method. Anthropometric measurements: Anthropometric data were obtained at baseline (2016) and follow-up (2018). Children underwent anthropometric measurements that were conducted by trained researchers using standard procedures and calibrated equipment. ³⁴. Height was measured to the nearest 0.1 cm and body weight was assessed to the nearest 0.1 kg, using a wall-mounted stadiometer (type SECA 217) and a calibrated digital scale (type SECA 813). Two readings were obtained for each measurement and the mean was used for the analysis. A third measurement was carried out, if the difference between the previous measurements was greater than 0.1 kg for weight and 0.1 cm for height. BMI was computed as weight divided by height squared (kg/m²), and BMI z-scores (z-BMI) were calculated for age and sex, as recommended by the International Obesity Task Force (IOTF) (³⁵).

Breakfast frequency: During both baseline and follow-up 2 measurements, children’s breakfast eating habits were evaluated through a parent-reported validated questionnaire, developed for the Feel4Diabetes-Study ³³. At each time point, data on breakfast consumption frequency were derived from two questions. The first question was ‘How often does your child eat breakfast on weekdays?’ with answer categories including “(a) never; (b) on 1 day ; (c) on 2 days; (d) on 3 days; (e) on 4 days; (f) on 5 days”, while, the second question was ‘How often does your child eat breakfast on weekends?’ with answer choices of “(a) never; (b) on 1 day; (c) on 2 days”. On the basis of the reported number of days on which children consumed breakfast separately for weekdays (coded as 0–5) and weekend days (coded as 0–2), the scores were summed to calculate the total weekly frequency of breakfast consumption (0–7). Afterwards, participants were categorized as regular breakfast eaters, irregular breakfast eaters and breakfast skippers, according to a frequency of breakfast consumption of 6–7, 3–5 and 0–2 days per week respectively. The same approach was taken in order to define the distinct breakfast frequency category of each subject, independently for baseline and follow up. As a next step, to evaluate the changes in breakfast frequency of subjects at these two time points, trajectories of breakfast consumption frequency were defined. Specifically, participants were categorized into nine trajectory groups, using the simultaneous combination of breakfast status at baseline (breakfast skipper, irregular eater or regular eater) and at follow up (breakfast skipper, irregular eater or regular eater). The nine defined trajectories are listed below : 1) ‘skippers-skippers’ (i.e. breakfast skippers at both baseline and follow up); 2) ‘skippers-irregular eaters’ (i.e. breakfast skippers at baseline who turned into irregular eaters at follow up); 3) ‘skippers-regular eaters’ (i.e. breakfast skippers at baseline who turned into regular eaters at follow up); 4) ‘irregular eaters-skippers’ (i.e. irregular breakfast eaters at baseline who turned into skippers at follow up) ; 5) ‘irregular eaters-irregular eaters’ (i.e. irregular breakfast eaters at both baseline and follow-up); 6) ‘irregular eaters-regular eaters’ (i.e. irregular breakfast eaters at baseline who turned into regular eaters at follow up); 7) ‘regular eaters-skippers’ (i.e. regular breakfast eaters at baseline who turned into skippers at follow up) ; 8)‘regular eaters-irregular eaters’ (i.e. regular breakfast eaters at baseline who turned into irregular eaters at follow up) ; 9)‘regular eaters-regular eaters’ (i.e. irregular breakfast eaters at both baseline and follow-up).

2.3. Statistical analysis

Descriptive statistics were used to summarize participant’s baseline characteristics, overall and by breakfast frequency category. Continuous variables were expressed as means and one standard deviation of the mean and categorical variables as proportions. Differences between breakfast frequency categories were assessed using the Chi-squared test for categorical variables and one-way ANOVA for normally distributed continuous variables. A series of ANOVA and Bonferroni post-hoc tests were applied to analyze differences in the means of the z-BMI at baseline, the z-BMI at follow up and the absolute two-year change in z-BMI, among the nine defined trajectory groups of breakfast consumption frequency. Within each trajectory group, the mean z-BMI at follow up was compared with that at baseline by using paired t-test. Linear regression analysis was performed to assess the prospective association between change in breakfast frequency and increase in z-BMI during the two-year period of interest. Trajectories of breakfast frequency were used to predict z-BMI increase (any increase versus no increase or a decrease) by logistic regression analysis. Both crude and adjusted models were run setting stable skippers (i.e. trajectory group ‘skippers-skippers’) as a reference group. Adjustments were made for age, sex, assignment to intervention or control group, family income, physical activity, screen time, and the consumption of fruits, vegetables, sugar sweetened beverages and sweets. Results are presented as odds ratios (ORs), with 95% confidence intervals (CIs), for increasing z-BMI over time by each trajectory. Statistical significance was set at a p-value < 0.05.

3.1 Subject Characteristics

Overall, the study sample comprised 6,528 children (3,333 girls and 3,195 boys) with a mean age of 8.2 ± 1.0 years. No statistically significant interaction was observed between group (intervention and control groups) and breakfast trajectories (p = 0.230 for 2-sided Pearson’s Chi-Square test) and thus results are presented into a pooled group. Descriptive characteristics of study participants, stratified according to baseline breakfast status, are presented in Table 1. The majority of children’ s parents had completed compulsory secondary education. Slightly less than one-half of the children resided in households with a difficult family income status. Compared with breakfast skippers and irregular eaters, regular breakfast eaters were more likely to have parents who have both attained compulsory secondary education and earn a higher income. Age and sex distribution were similar on all breakfast categories. At baseline, significant differences among groups were found for all weight-related indicators (body weight, BMI, z-BMI), but not for mean height. Breakfast skippers were heavier and presented with higher BMI and z-BMI, than regular breakfast eaters. Regarding lifestyle parameters regular breakfast eaters were more likely to engage in higher levels of physical activity and to spend a smaller period of time on screen, as compared to their counterparts. Statistically significant differences by breakfast frequency were also observed for daily consumption of fruits, vegetables, sugar-sweetened beverages and sweets. Particularly, regular breakfast eaters displayed significantly higher vegetable intake and lower consumption of sugar-sweetened beverages and sweets than did skippers.

Table 1

Baseline characteristics of study participants (N = 6528), overall and by breakfast status at baseline
Variables	Total sample	Breakfast status at baseline			p-value
	Total sample	Skippers	Irregular Eaters	Regular Eaters
	(n = 6528)	(n = 207)	(n = 135)	(n = 6186)
Demographic factors
Sex, male (%)	48.9	46.9	51.9	48.9	0.665
Age, years (%)	8.2 ± 1.0	8.3 ± 1.0	8.2 ± 1.0	8.2 ± 1.0	0.339
Mother’s education, ≤ 9 years (%)	6.1	20.4	20.6	5.3	< 0.001
Father’s education, ≤ 9 years (%)	14.8	44.6	42.6	13.6	< 0.001
Family income, difficult (%)	45.8	68.0	67.7	44.7	< 0.001
Anthropometric Indicators
Height, cm	130.5 ± 7.9	130.4 ± 7.4	130.4 ± 7.9	130.5 ± 7.9	0.957
Body Weight, kg	29.8 ± 6.8	30.9 ± 8.3^a	30.4 ± 7.1	29.7 ± 6.8	0.030
BMI, kg/m²	17.3 ± 2.6	18.0 ± 3.3^a	17.7 ± 2.7	17.3 ± 2.6	< 0.001
BMI z-score	0.63 ± 0.94	0.82 ± 1.09^a	0.79 ± 0.93	0.62 ± 0.94	0.002
Lifestyle factors
PA (days with ≥ 60 min of MVPA/week)	5.4 ± 1.6	4.7 ± 2.0^a	4.9 ± 2.0^a	5.4 ± 1.6	< 0.001
Screen Time (hours/day)	1.8 ± 1.1	2.2 ± 1.4^a	2.1 ± 1.1^a	1.7 ± 1.0	< 0.001
Fruit consumption (servings/day)	1.4 ± 1.2	1.2 ± 1.3	1.2 ± 1.3	1.4 ± 1.2	0.036
Vegetable consumption (servings/day)	1.1 ± 1.0	0.8 ± 0.9^a,b	1.1 ± 1.3	1.2 ± 1.0	< 0.001
SSB consumption (servings/day)	0.4 ± 0.9	0.5 ± 1.2^a	0.5 ± 1.2	0.3 ± 0.8	0.001
Sweets consumption (servings/day)	0.8 ± 0.9	1.1 ± 1.4^a	0.9 ± 1.3	0.8 ± 0.8	< 0.001
Values are means ± 1 standard deviation or % proportions
^a p < 0.05, vs regular eaters ; ^b p < 0.05, vs irregular eaters ; m ± sd mean ± standard deviation ; BMI Body Mass Index ; PA Physical Activity ; MVPA Moderate-to-Vigorous Physical Activity ; SSBs Sugar-sweetened beverages

3.2 Prospective analyses

3.2.1 Change in z-BMI between baseline and follow-up among trajectories of breakfast frequency

Table 2 depicts observed z-BMI mean values at each time-point and the absolute 2-year change in z-BMI for the different trajectories of breakfast frequency. At baseline and follow up, the z-BMI means were found to differ significantly across the trajectories (p < 0.001). Regular breakfast eaters who retained stable breakfast habits over time (trajectory «regular eaters-regular eaters») displayed the lowest z-BMI mean values at both time points. Post hoc tests revealed that stable regular eaters had a significantly lower mean z-BMI than did regular eaters who turned into skippers (trajectory «regular eaters-skippers») or stable irregular eaters (trajectory «irregular eaters-irregular eaters»), both at baseline and at follow-up. Similar results were observed in follow-up comparison between stable regular eaters and stable skippers. Within the biennium, stable breakfast skippers experienced the largest mean z-BMI increase.Conversely, subjects in the ‘skippers-regular eaters’ trajectory group solely displayed a decrease in z-BMI over 2 years, yet the difference was not statistically significant.

Table 2

Comparison of BMI z-scores between baseline (BL) and follow up (FU), according to the change of breakfast status during the follow-up
Trajectories of breakfast consumption frequencies	z-BMI at BL	z-BMI at FU	Change in z-BMI
Skippers-Skippers (n = 76)	0.891 ± 1.058	1.034 ± 1.072^a *	0.160 ± 0.485
Skippers-Irregular eaters (n = 26)	0.622 ± 1.226	0.703 ± 1.210	0.081 ± 0.443
Skippers-Regular eaters (n = 105)	0.819 ± 1.085	0.807 ± 1.126	-0.015 ± 0.423
Irregular eaters-Skippers (n = 21)	0.928 ± 0.846	1.031 ± 0.855	0.088 ± 0.512
Irregular eaters-Irregular eaters (n = 28)	1.196 ± 0.986^a	1.229 ± 0.890^a	0.033 ± 0.432
Irregular eaters-Regular eaters (n = 86)	0.621 ± 0.899	0.626 ± 0.992	0.004 ± 0.466
Regular eaters-Skippers (n = 139)	0.903 ± 0.998^a	0.952 ± 1.038^a	0.040 ± 0.461
Regular eaters-Irregular eaters (n = 125)	0.718 ± 0.951	0.759 ± 0.912	0.035 ± 0.536
Regular eaters-Regular eaters (n = 5922)	0.616 ± 0.935	0.615 ± 0.967	0.001 ± 0.470
Differences between groups, p-value	< 0.001	< 0.001	0.180
Values are means ± 1 standard deviation. Statistically significant findings are indicated in bold. BL: Baseline, FU: Follow up. ^a p < 0.05, vs. ‘regular eaters-regular eaters’ (based on Bonferroni post-hoc tests) p < 0.05, vs.* baseline within each trajectory group (based on paired t-test)

3.2.2 Logistic Regression analysis of breakfast trajectory on z-BMI increase

Table 3 presents the results of the logistic regression analysis evaluating the potential impact of changes in breakfast frequency (i.e. breakfast frequency trajectories) on z-BMI increase overtime. Results are shown as the unadjusted and adjusted odds ratios for any z-BMI gain versus no gain or a decrease for subjects in each trajectory group, according to breakfast consumption change over time (i.e. breakfast trajectories), using stable breakfast skippers (trajectory ‘Skippers-Skippers’) as the reference group. In the adjusted model, subjects in the trajectory groups ‘Regular eaters-Regular eaters’ and ‘Skippers-Regular eaters’ presented with a 55% and 43% less probability of an increase in BMI z-score over time, as compared to “Skippers-Skippers”, implying that stability of breakfast consumption or a positive change over time may confer a protective effect regarding weight gain. Regarding confounders within the adjusted model, sex [OR,95%CI: 0.77 (0.69,0.86), p < 0.001], family income [OR,95%CI: 0.79 (0.70,0.88), p < 0.001] and age [OR,95%CI: 0.93 (0.88,0.99), p = 0.013] were statistically significant, while intervention arm, physical activity, screen time, and the consumption of fruits, vegetables, sugar-sweetened beverages and sweets did not reach statistical significance (all p > 0.05).

Table 3

Odds ratios (ORs) and 95% confidence intervals (CI) for zBMI increase, by breakfast consumption trajectories
Trajectories of breakfast consumption frequencies	Crude Model		Adjusted Model*
Trajectories of breakfast consumption frequencies	OR (95%CI)	p-value	OR (95%CI)	p-value
Skipper - Skipper	1.000 (Ref.)	-	1.000 (Ref.)	-
Skipper - Irregular Eater	0.596 (0.242, 1.465)	0.259	0.660 (0.236, 1.846)	0.429
Skipper - Regular Eater	0.481 (0.262, 0.883)	0.018	0.429 (0.209, 0.879)	0.021
Irregular Eater - Skipper	0.596 (0.221, 1.609)	0.307	0.849 (0.254, 2.836)	0.790
Irregular Eater - Irregular Eater	0.687 (0.286, 1.654)	0.403	0.513 (0.177, 1.484)	0.218
Irregular Eater - Regular Eater	0.555 (0.295, 1.045)	0.068	0.586 (0.282, 1.221)	0.154
Regular Eater - Skipper	0.660 (0.371, 1.174)	0.157	0.754 (0.380, 1.494)	0.418
Regular Eater - Irregular Eater	0.802 (0.445, 1.446)	0.463	0.894 (0.449, 1.780)	0.751
Regular Eater - Regular Eater	0.527 (0.329, 0.844)	0.008	0.552 (0.314, 0.969)	0.039
* Adjusted for age, sex, intervention group, family income, physical activity, screen time, and consumption of fruits, vegetables, sugar swetened beverages and sweets.

The present study aimed to investigate prospective associations between trajectories of change in breakfast consumption frequency and long-term z-BMI increase in European schoolchildren from the Feel4Diabetes study. Both at baseline and follow up, z-BMI mean values found to differ among breakfast trajectories; nevertheless, absolute changes over time were not statistically significant. Based upon the trends observed in z-BMI changes among trajectory groups, the highest increase in z-BMI mean was verified among stable breakfast skippers, while stable regular eaters experienced the least increase. Skippers who turned into regular eaters solely displayed a relative z-BMI decline. Stable breakfast skippers presented significantly higher mean values both at baseline and follow-up. Comparative analyses between trajectories groups indicated that stable regular eaters continually had a significantly lower z-BMI mean than stable irregular eaters and skippers who turned into regular eaters, whereas they eventually reported lower z-BMI mean than stable skippers upon completion of the two years. Additionally, both stable regular breakfast eaters and breakfast skippers who turned into regular eaters were presented with significantly lower risk of z-BMI gain overtime, as compared to stable breakfast skippers. Overall, the results of the study suggest that breakfast omission over a prolonged period may have detrimental effects on zBMI change during childhood, in contrast to stable regular breakfast consumption and transition from breakfast omission to regular consumption which seem to contribute to the maintenance of a constant zBMI and to exert an independent protective effect against z-BMI increase in children.

Previous studies have provided inconsistent results regarding the longitudinal association between breakfast habit during childhood and weight-related outcomes assessed by BMI or BMI z-score. Comparable to our study, a number of prospective cohort studies reported significant associations pointing out a beneficial impact of breakfast consumption. In three independent studies on a child population, with follow-up ranging from 1 to 8 years, habitual breakfast skipping was significantly related to increases in BMI across the years ^36–38. Similarly, a German study of elementary schoolchildren reported that regular breakfast consumption reduced the odds for percentile-based BMI gain at one-year follow up ³⁹. On the other hand, other cohorts showed no significant associations. In a study of children aged 6–13 years, followed up after one year, no significant differences were observed in z-BMI change between breakfast skippers and their counterparts who consumed breakfast regularly and moderately ⁴⁰. An additional study reported that breakfast skipping compared to daily breakfast intake, in both early (5–6 years) and later (10–12 years) childhood, did not relate to z-BMI at three-year follow up ⁴¹. These inconsistencies may be partially related to the lack of a standardized definition of breakfast omission. The criteria for skipping breakfast varied across studies, in a range from strict definitions like 'never eating breakfast' ^38,40 to more flexible ones such as ‘less than daily breakfast intake’ ⁴¹.

It should be noted that the results described above originated from studies that measured breakfast consumption frequency at baseline only. To date, fewer studies have examined the impact of stable and altered breakfast habits on childhood BMI overtime and findings are also inconsistent. For instance, in a US all-female cohort, frequent breakfast consumption across time was significantly associated with decreased BMI at ten-year follow up, in participants who were initially classified as obese ⁴². A further US cohort reported that long-term daily breakfast consumption throughout childhood (i.e. age 4–11 years) was related to lower z-BMI at age 13, as compared to non-daily consumption ⁴³. These findings are generally in line with results from the present study indicating that stable regular breakfast initiation and maintenance play a role in the prevention of zBMI gain during childhood, as compared to stable breakfast omission. Still, other prospective studies have reported different findings. Two independent studies among South American children found that both persistence and change in breakfast consumption habits across the years were not associated with change in z-BMI ^44,45. Given the small number of studies and the inconsistency of results, further research is required to extend the evidence base on the relationship between longstanding breakfast habits and zBMI in children.

In the present study, breakfast trajectories corresponding to stable regular consumption and changeover from omission to regular consumption were associated with lower odds of elevated z-BMI over a 2-year period, compared to stable skipping. These results were derived after adjustment not only for basic demographic characteristics, but also for lifestyle confounders related to dietary quality, sedentary behaviors, and physical activity. Thus, our findings suggest that breakfast consumption habits during school-age period may have an independent, long-term impact on children’s weight status. This assumption is supported by previous studies also indicating that lifestyle factors seem not to be involved in the longitudinal association between breakfast habit and childhood BMI ^13,31,46. Even if the underlying mechanism is still unknown, regular breakfast consumption promotes weight control in children possibly through regulating appetite and satiety responses, as well as subsequent energy intake and glycaemic control ⁴⁷. Furthermore, there is evidence that regular breakfast eaters have greater thermogenesis than skippers ⁴⁸. However, it should not be disregarded that certain studies have considered favorable dietary profiles and enhanced activity among regular breakfast consumers as some of the reasons for the association between breakfast habits and weight regulation in children ^49,50. Differences in methods used to measure and quantify lifestyle behaviors could partially explain the mixed findings. In this context, the aforementioned results from the present study should be interpreted with caution.

Major strengths of the current study arise from the strengths of the Feel4Diabetes study, including a large and ethnically diverse sample size, its longitudinal design, as well as the application of highly validated and standardized procedures across all study centers. Anthropometric indicators were objectively measured by trained staff, minimizing the possibility of measurement error. Furthermore, the longitudinal nature of the study design enabled measurements of both breakfast consumption frequency and z-BMI at multiple time points, allowing for the examination of potential longitudinal associations between twofold changes overtime. In addition, our study included a range of important potential confounders of the association between breakfast habit and childhood z-BMI, so as to reduce possible confounding. Nevertheless, the presents study is not exempt of limitations. Firstly, data on children’s lifestyle characteristics, dietary habits and breakfast frequency were assessed through self-administered parent-reported questionnaires. Hence, even if the reliability of questionnaires was tested before the study, reporting bias could be possible. Finally, causal relationships cannot be established owing to the study’s observational nature. Future experimental studies are required to determine the causality in the breakfast habit-BMI association among children.

In conclusion, our findings suggest that stable breakfast omission exert negative impact on body weight regulation during childhood, as opposed to stable regular breakfast consumption and transition from breakfast omission to regular consumption, which seem to contribute to long-term weight control and to exert an independent protective effect against z-BMI increase among children. Further studies with repeat measures of breakfast behaviors are warranted to strengthen the evidence on the efficacy of breakfast frequency on body weight regulation in the pediatric population.

Acknowledgments

The authors would like to thank the members of the Feel4Diabetes-study group: Coordinator: Yannis Manios, Steering Committee: Yannis Manios, Greet Cardon, Jaana Lindström, Peter Schwarz, Konstantinos Makrilakis, Lieven Annemans, Winne Ko, Harokopio University (Greece): Yannis Manios, Kalliopi Karatzi, Odysseas Androutsos, George Moschonis, Spyridon Kanellakis, Christina Mavrogianni, Konstantina Tsoutsoulopoulou, Christina Katsarou, Eva Karaglani, Irini Qira, Efstathios Skoufas, Konstantina Maragkopoulou, Antigone Tsiafitsa, Irini Sotiropoulou, Michalis Tsolakos, Effie Argyri, Mary Nikolaou, Eleni-Anna Vampouli, Christina Filippou, Kyriaki Apergi, Amalia Filippou, Gatsiou Katerina, Efstratios Dimitriadis, FinnishInstitute for Health and Welfare (Finland): Jaana Lindström, Tiina Laatikainen, Katja Wikström, Jemina Kivelä, Päivi Valve, Esko Levälahti, Eeva Virtanen, Tiina Pennanen, Seija Olli, Karoliina Nelimarkka, Ghent University (Belgium), Department of Movement and Sports Sciences: Greet Cardon, Vicky Van Stappen, Nele Huys, Department of Public Health: Lieven Annemans, Ruben Willems, Department of Endocrinology and Metabolic Diseases: Samyah Shadid, Technische Universität Dresden (Germany): Peter Schwarz, Patrick Timpel, University of Athens (Greece), Konstantinos Makrilakis, Stavros Liatis, George Dafoulas, Christina-Paulina Lambrinou, Angeliki Giannopoulou, International Diabetes Federation European Region (Belgium): Winne Ko, Ernest Karuranga, Universidad De Zaragoza (Spain): Luis Moreno, Fernando Civeira, Gloria Bueno, Pilar De Miguel-Etayo, Esther Mª Gonzalez-Gil, María L. Miguel-Berges, Natalia Giménez-Legarre; Paloma Flores-Barrantes, Alelí M. Ayala-Marín, Miguel Seral-Cortés, Lucia Baila-Rueda, Ana Cenarro, Estíbaliz Jarauta, Rocío Mateo-Gallego, Medical University of Varna (Bulgaria): Violeta Iotova, Tsvetalina Tankova, Natalia Usheva, Kaloyan Tsochev, Nevena Chakarova, Sonya Galcheva, Rumyana Dimova, Yana Bocheva, Zhaneta Radkova, Vanya Marinova, Yuliya Bazdarska, Tanya Stefanova, University of Debrecen (Hungary): Imre Rurik, Timea Ungvari, Zoltán Jancsó, Anna Nánási, László Kolozsvári, Csilla Semánova, Éva Bíró, Emese Antal, Sándorné Radó: Extensive Life Oy (Finland): Remberto Martinez, Marcos Tong.

Ethical standards

This study was conducted conforming to the guidelines laid down in the Declaration of Helsinki and all procedures involving human subjects were granted approval by the human bioethics committees of all of the participating tertiary institutions [Medical Ethics Committee of the Ghent University Hospital, Belgium (Ethical Approval (EA) No.: B670201524237; 21/04/15); Ethics Committee of the Medical University of Varna, Bulgaria (EA No.: 52/10-3-2016; 10/03/16) and the Municipalities of Sofia and Varna, as well as the Ministry of Education and Science local representatives; Hospital district of Southwest Finland ethical committee (EA: No.: 174/1801/2015; 13/03/15); Bioethics Committee of Harokopio University, Greece and the Greek Ministry of Education; (EA No.: 46/3-4-2015; 03/04/15); National Committee for Scientific Research in Medicine, Hungary (EA No.: 20095/2016/EKU; 29/03/16); Clinical Research Ethics Committee and the Department of Consumers´ Health of the Government of Aragón, Spain (EA No.: CP03/2016; 08/04/15)]. All participants gave signed informed consent before inclusion into the study.

Funding

The Feel4Diabetes study has received funding from the European Union’s Horizon 2020 research and innovation program [Grant Agreement: n° 643708]. The content of this article reflects only the authors’ views and the European Community is not liable for any use that may be made of the information contained therein. The funding body had no role in the design of this study and collection, analysis, and interpretation of the data, and in writing this manuscript.

Conflict of Interest

The authors declare that they have no conflict of interest to disclose.

Data Availability Statement

The original data of this work are available upon reasonable request to the corresponding author.

Di Cesare M, Sorić M, Bovet P, Miranda JJ, Bhutta Z, Stevens GA et al. The epidemiological burden of obesity in childhood: a worldwide epidemic requiring urgent action. BMC Med 2019; 17. doi:10.1186/s12916-019-1449-8.
World Health Organization. Obesity and overweight. Who.int. 2024.
Europe WHOrganizationRO for. WHO European Regional Obesity Report 2022. World Health Organization. Regional Office for Europe: Copenhagen, 2022https://iris.who.int/handle/10665/353747.
Reilly JJ, Kelly J. Long-term impact of overweight and obesity in childhood and adolescence on morbidity and premature mortality in adulthood: systematic review. Int J Obes (Lond) 2011; 35: 891–898.
van de Pas KGH, de Krom MAP, Winkens B, van Dielen FMH, Vreugdenhil ACE. Health-related quality of life in children and adolescents with overweight, obesity, and severe obesity: A cross-sectional study. Obes Facts 2023; 16: 282–292.
Smith JD, Fu E, Kobayashi MA. Prevention and management of childhood obesity and its psychological and health comorbidities. Annu Rev Clin Psychol 2020; 16: 351–378.
Zolotarjova J, ten Velde G, Vreugdenhil ACE. Effects of multidisciplinary interventions on weight loss and health outcomes in children and adolescents with morbid obesity. Obes Rev 2018; 19: 931–946.
Coles N, Birken C, Hamilton J. Emerging treatments for severe obesity in children and adolescents. BMJ 2016; 354: i4116.
Kim J, Lim H. Nutritional management in childhood obesity. J Obes Metab Syndr 2019; 28: 225–235.
O’Neil CE, Byrd-Bredbenner C, Hayes D, Jana L, Klinger SE, Stephenson-Martin S. The role of breakfast in health: Definition and criteria for a quality breakfast. J Acad Nutr Diet 2014; 114: S8–S26.
Sandercock GRH, Voss C, Dye L. Associations between habitual school-day breakfast consumption, body mass index, physical activity and cardiorespiratory fitness in English schoolchildren. Eur J Clin Nutr 2010; 64: 1086–1092.
Ptomey LT, Steger FL, Schubert MM, Lee J, Willis EA, Sullivan DK et al. Breakfast intake and composition is associated with superior academic achievement in elementary schoolchildren. J Am Coll Nutr 2016; 35: 326–333.
Smith KJ, Gall SL, McNaughton SA, Blizzard L, Dwyer T, Venn AJ. Skipping breakfast: longitudinal associations with cardiometabolic risk factors in the Childhood Determinants of Adult Health Study. Am J Clin Nutr 2010; 92: 1316–1325.
Horikawa C, Kodama S, Yachi Y, Heianza Y, Hirasawa R, Ibe Y et al. Skipping breakfast and prevalence of overweight and obesity in Asian and Pacific regions: A meta-analysis. Prev Med 2011; 53: 260–267.
Blondin SA, Anzman-Frasca S, Djang HC, Economos CD. Breakfast consumption and adiposity among children and adolescents: an updated review of the literature. Pediatr Obes 2016; 11: 333–348.
Wang K, Niu Y, Lu Z, Duo B, Effah CY, Guan L. The effect of breakfast on childhood obesity: a systematic review and meta-analysis. Front Nutr 2023; 10. doi:10.3389/fnut.2023.1222536.
Szajewska H, Ruszczyński M. Systematic review demonstrating that breakfast consumption influences body weight outcomes in children and adolescents in Europe. Crit Rev Food Sci Nutr 2010; 50: 113–119.
Rampersaud GC, Pereira MA, Girard BL, Adams J, Metzl JD. Breakfast habits, nutritional status, body weight, and academic performance in children and adolescents. J Am Diet Assoc 2005; 105: 743–760.
Betts JA, Richardson JD, Chowdhury EA, Holman GD, Tsintzas K, Thompson D. The causal role of breakfast in energy balance and health: a randomized controlled trial in lean adults. Am J Clin Nutr 2014; 100: 539–547.
Vereecken C, Group the HE& DF, Dupuy M, Rasmussen M, Kelly C, Nansel TR et al. Breakfast consumption and its socio-demographic and lifestyle correlates in schoolchildren in 41 countries participating in the HBSC study. Int J Public Health 2009; 54: 180–190.
Utter J, Scragg R, Schaaf D, Fitzgerald E, Wilson N. Correlates of body mass index among a nationally representative sample of New Zealand children. Int J Pediatr Obes 2007; 2: 104–113.
Patro B, Szajewska H. Meal patterns and childhood obesity. Curr Opin Clin Nutr Metab Care 2010; 13: 300–304.
Delva J, O’Malley PM, Johnston LD. Racial/ethnic and socioeconomic status differences in overweight and health-related behaviors among American students: National trends 1986–2003. J Adolesc Health 2006; 39: 536–545.
Monzani A, Ricotti R, Caputo M, Solito A, Archero F, Bellone S et al. A systematic review of the association of skipping breakfast with weight and cardiometabolic risk factors in children and adolescents. What should we better investigate in the future? Nutrients 2019; 11: 387.
Siega-Riz AM, Popkin BM, Carson T. Trends in breakfast consumption for children in the United States from 1965–1991. Am J Clin Nutr 1998; 67: 748S-756S.
de la Hunty A, Gibson S, Ashwell M. Does regular breakfast cereal consumption help children and adolescents stay slimmer? A systematic review and meta-analysis. Obes Facts 2013; 6: 70–85.
Horikawa C, Kodama S, Yachi Y, Heianza Y, Hirasawa R, Ibe Y et al. Skipping breakfast and prevalence of overweight and obesity in Asian and Pacific regions: A meta-analysis. Prev Med 2011; 53: 260–267.
Ardeshirlarijani E, Namazi N, Jabbari M, Zeinali M, Gerami H, Jalili RB et al. The link between breakfast skipping and overweigh/obesity in children and adolescents: a meta-analysis of observational studies. J Diabetes Metab Disord 2019; 18: 657–664.
Wang K, Niu Y, Lu Z, Duo B, Effah CY, Guan L. The effect of breakfast on childhood obesity: a systematic review and meta-analysis. Front Nutr 2023; 10. doi:10.3389/fnut.2023.1222536.
Wijtzes AI, Jansen W, Bouthoorn SH, van Lenthe FJ, Franco OH, Hofman A et al. Meal-skipping behaviors and body fat in 6-year-old children. J Pediatr 2016; 168: 118–125.e2.
Tin SPP, Ho SY, Mak KH, Wan KL, Lam TH. Breakfast skipping and change in body mass index in young children. Int J Obes (Lond) 2011; 35: 899–906.
Manios Y, Androutsos O, Lambrinou C-P, Cardon G, Lindstrom J, Annemans L et al. A school- and community-based intervention to promote healthy lifestyle and prevent type 2 diabetes in vulnerable families across Europe: design and implementation of the Feel4Diabetes-study. Public Health Nutr 2018; 21: 3281–3290.
Anastasiou CA, group F, Fappa E, Zachari K, Mavrogianni C, Van Stappen V et al. Development and reliability of questionnaires for the assessment of diet and physical activity behaviors in a multi-country sample in Europe the Feel4Diabetes Study. BMC Endocr Disord 2020; 20. doi:10.1186/s12902-019-0469-x.
Androutsos O, group on behalf of the F, Anastasiou C, Lambrinou C-P, Mavrogianni C, Cardon G et al. Intra- and inter- observer reliability of anthropometric measurements and blood pressure in primary schoolchildren and adults: the Feel4Diabetes-study. BMC Endocr Disord 2020; 20. doi:10.1186/s12902-020-0501-1.
Cole TJ, Lobstein T. Extended international (IOTF) body mass index cut-offs for thinness, overweight and obesity. Pediatr Obes 2012; 7: 284–294.
Kelly Y, Patalay P, Montgomery S, Sacker A. BMI development and early adolescent psychosocial well-being: UK Millennium Cohort Study. Pediatrics 2016; 138. doi:10.1542/peds.2016-0967.
Traub M, Boat” the RG “Join the H, Lauer R, Kesztyüs T, Wartha O, Steinacker JM et al. Skipping breakfast, overconsumption of soft drinks and screen media: longitudinal analysis of the combined influence on weight development in primary schoolchildren. BMC Public Health 2018; 18. doi:10.1186/s12889-018-5262-7.
Tin SPP, Ho SY, Mak KH, Wan KL, Lam TH. Location of breakfast consumption predicts body mass index change in young Hong Kong children. Int J Obes (Lond) 2012; 36: 925–930.
Drenowatz C, Kobel S, Kettner S, Kesztyüs D, Wirt T, Dreyhaupt J et al. Correlates of weight gain in German children attending elementary school. Prev Med 2013; 57: 310–314.
Shang X, Li Y, Xu H, Zhang Q, Hu X, Liu A et al. Healthy breakfast habits and changes in obesity-related cardiometabolic markers in children: a longitudinal analysis. Eur J Clin Nutr 2020; 74: 1685–1697.
MacFarlane A, Cleland V, Crawford D, Campbell K, Timperio A. Longitudinal examination of the family food environment and weight status among children. Int J Pediatr Obes 2009; 4: 343–352.
Albertson AM, Franko DL, Thompson D, Eldridge AL, Holschuh N, Affenito SG et al. Longitudinal patterns of breakfast eating in black and white adolescent girls. Obesity (Silver Spring) 2007; 15: 2282–2292.
Gingras V, Rifas-Shiman SL, Taveras EM, Oken E, Hivert M-F. Dietary behaviors throughout childhood are associated with adiposity and estimated insulin resistance in early adolescence: a longitudinal study. Int J Behav Nutr Phys Act 2018; 15. doi:10.1186/s12966-018-0759-0.
Hassan BK, Cunha DB, da Veiga GV, Pereira RA, Hoffman DJ, Sichieri R. Breakfast consumption, family breakfast, and adiposity trajectory in adolescence—the adolescent nutritional assessment longitudinal cohort study. J Acad Nutr Diet 2019; 119: 944–956.
Haerens L, Vereecken C, Maes L, De Bourdeaudhuij I. Relationship of physical activity and dietary habits with body mass index in the transition from childhood to adolescence: a 4-year longitudinal study. Public Health Nutr 2010; 13: 1722–1728.
Wijtzes AI, Jansen W, Bouthoorn SH, van Lenthe FJ, Franco OH, Hofman A et al. Meal-skipping behaviors and body fat in 6-year-old children. J Pediatr 2016; 168: 118–125.e2.
Ardeshirlarijani E, Namazi N, Jabbari M, Zeinali M, Gerami H, Jalili RB et al. The link between breakfast skipping and overweigh/obesity in children and adolescents: a meta-analysis of observational studies. J Diabetes Metab Disord 2019; 18: 657–664.
Horikawa C, Kodama S, Yachi Y, Heianza Y, Hirasawa R, Ibe Y et al. Skipping breakfast and prevalence of overweight and obesity in Asian and Pacific regions: A meta-analysis. Prev Med 2011; 53: 260–267.
Rampersaud GC, Pereira MA, Girard BL, Adams J, Metzl JD. Breakfast habits, nutritional status, body weight, and academic performance in children and adolescents. J Am Diet Assoc 2005; 105: 743–760.
Tee ES, Nurliyana AR, Norimah AK, Hjbj M, Tan SY, Appukutty M et al. Breakfast consumption among Malaysian primary and secondary school children and relationship with body weight status - Findings from the MyBreakfast Study. Asia Pac J Clin Nutr 2018; 27. doi:10.6133/apjcn.062017.12.

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Prospective association between breakfast consumption frequency and BMI z-score among European school-aged children. The Feel4Diabetes Study.

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Version 1

Abstract

Background

Objective

Methods

Results

Conclusion

1. INTRODUCTION

2. MATERIALS & METHODS

2.1. Study population

2.2. Data Collection and Measurement

2.3. Statistical analysis

3. RESULTS

3.1 Subject Characteristics

3.2 Prospective analyses

3.2.1 Change in z-BMI between baseline and follow-up among trajectories of breakfast frequency

3.2.2 Logistic Regression analysis of breakfast trajectory on z-BMI increase

4. DISCUSSION

Declarations

References

Additional Declarations

Status:

Version 1