Mediterranean diet, screen-time-based sedentary behavior and their interaction effect on adiposity in European adolescents: The HELENA study

Nutrients 2021, 13, 474. https://doi.org/10.3390/nu13020474 www.mdpi.com/journal/nutrients Article Mediterranean Diet, Screen-Time-Based Sedentary Behavior and Their Interaction Effect on Adiposity in European Adolescents: The HELENA Study Miguel Seral-Cortes 1,*, Sergio Sabroso-Lasa 2, Alexandro Bailo-Aysa 3, Marcela Gonzalez-Gross 4,5,6, Dénes Molnár 7, Laura Censi 8, Cristina Molina-Hidalgo 9, Frederic Gottrand 10, Stefaan De Henauw 11, Yannis Manios 12, Christina Mavrogianni 12, Kurt Widhalm 13, Anthony Kafatos 14, Jean Dallongeville 15, Luis A. Moreno 1,4, Luis Mariano Esteban 16, Idoia Labayen 17, Pilar De Miguel-Etayo 1,4 and on behalf of the HELENA Study Group † 1 Growth, Exercise, NUtrition and Development (GENUD) Research Group, Faculty of Health Sciences, Instituto Agroalimentario de Aragón (IA2), Instituto de Investigación Sanitaria Aragón (IIS Aragón), Universidad de Zaragoza, 50009 Zaragoza, Spain; lmor[email protected] (L.A.M.), [email protected] (P.D.M.-E.) 2 Genetic and Molecular Epidemiology Group (GMEG), Spanish National Cancer Research Centre (CNIO), 28029 Madrid, Spain; ssab[email protected] 3 Department of Physiatry and Nursing, Faculty of Health Sciences, Universidad de Zaragoza, 50009 Zaragoza, Spain; abailo[email protected] 4 CIBER Fisiopatología de la Obesidad y Nutrición (CIBERobn), Instituto de Salud Carlos III, 28029 Madrid, Spain; marcela.gonzalez[email protected] 5 ImFine Research Group, Department of Health and Human Performance, Facultad de Ciencias de la Actividad Física y del Deporte-INEF, Universidad Politécnica de Madrid, 28040 Madrid, Spain 6 Institute of Nutritional and Food Sciences, Nutritional Physiology, University of Bonn, 53113 Bonn, Germany 7 Department of Pediatrics, Medical School, University of Pécs, 7623 Pécs, Hungary; molnar.dene[email protected] 8 Department of Applied Science of Nutrition, Council for Agricultural Research and Economics, Research Center for Food and Nutrition, 00198 Rome, Italy; l[email protected] 9 EFFECTS 262 Department of Medical Physiology, School of Medicine, University of Granada, 18071 Granada, Spain; [email protected] 10 CHU Lille, Inserm U1286 INFINITE, University of Lille, F-59000 Lille, France; [email protected] 11 Department of Public Health and Primary Care, Faculty of Medicine and Health Sciences, Ghent University, 9000 Ghent, Belgium; [email protected] 12 Department of Nutrition and Dietetics, School of Health Science & Education, Harokopio University, 176 71 Athens, Greece; [email protected] (Y.M.); [email protected] (C.M.) 13 Division of Clinical Nutrition and Prevention, Department of Paediatrics, Medical University of Vienna, Austria and Austrian Academic Institute for Clinical Nutrition, 1090 Vienna, Austria; [email protected] 14 Faculty of Medicine, University of Crete, 715 00 Crete, Greece; ka[email protected] 15 Department of Epidemiology Public Health, Institut Pasteur de Lille, 59800 Lille, France; jean.dallonge- [email protected] 16 Escuela Politécnica de La Almunia, Universidad de Zaragoza, 50100 Zaragoza, Spain; [email protected] 17 Department of Health Sciences, Public University of Navarra, 31006 Pamplona, Spain; [email protected] † Membership of HELENA study group is provided in Appendix A. * Correspondence: m[email protected] Abstract: Childhood obesity is a worldwide epidemic. Mediterranean diet (MD) is inversely associated with childhood obesity, but the interaction with other environmental factors, such screen time, might influence the health benefits of a high MD adherence in adolescents. The aim of the present study was to assess whether an association between MD and screen time exists in European adolescents. Moreover, we also explored whether sedentary time has a modulatory effect on the Citation: Seral-Cortes, M.; Sabroso-Lasa, S.; Bailo-Aysa, A.; Gonzalez-Gross, M.; Molnár, D.; Censi, L.; Molina-Hidalgo, C.; Gottrand, F.; De Henauw, S.; Manios, Y.; et al. Mediterranean Diet, Screen-time-Based Sedentary Behavior and Their Interaction Effect on Adiposity in European Adolescents: The HELENA Study. Nutrients 2021, 13, 474. https://doi.org/10.3390/nu13020474 Academic Editor: Maria Annunziata Carluccio Received: 31 December 2020 Accepted: 27 January 2021 Published: 30 January 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Nutrients 2021, 13, 474 2 of 16 association between MD and adiposity. Adherence to the MD (24 h recalls), screen time (questionnaire), pubertal development, body mass index (BMI), fat mass index (FMI) and waist circumference (WC) were evaluated in 2053 adolescents (54.7% females), aged 12.5–17.5 years. In females, MD adherence was associated with lower BMI and FMI only when they were exposed to less than 338 min/day of screen time (81.8% of females); MD adherence was also associated with lower WC only when females were exposed to less than 143 min/day of screen time (31.5% of females). No significant MD-screen time interaction was observed in males. In conclusion, screen-time-based sedentary behaviours had a modulatory effect in the association between MD adherence and adiposity in European female adolescents. Keywords: Mediterranean diet; sedentary time; adiposity; adolescents; gender and HELENA 1. Introduction Childhood overweight and obesity’s prevalence has been rising worldwide in recent years [1]. Recent studies showed that overall, European children continue to struggle with high prevalence of obesity despite the effort in terms of prevention programs in previous years [2]. Metabolic syndrome and type 2 Diabetes are more likely to occur in adulthood in those children and adolescents where obesity is gradually establishing [3]. The Mediterranean dietary pattern is inversely associated with childhood obesity [4]. In fact, a high Mediterranean diet (MD) adherence from an early age is related to a lower risk of overweight and obesity development in childhood [5]. However, the interaction with other environmental factors, such as screen time, might influence the health benefits of a high MD adherence in adolescents. Sedentary behaviours, such screen time and physical inactivity were found to increase the risk of overweight and obesity in European adolescents [6,7]. Current recommendations suggest limiting recreational screen time to less than 2 h per day [8]. However, previous studies have shown that more than half of all children exceed screen time recommendations [9–11]. Additionally, increasing sedentary time is associated with unhealthy dietary patterns in European adolescents [12]. Furthermore, there is a growing evidence of transition from the traditional MD pattern into consumption of energy-dense foods, such as in the Western diets, especially in Mediterranean countries [13–16]. Previous data showed an inverse association between MD adherence and sedentary time, including screen time [17,18]. However, to our knowledge, a potential interaction effect between MD and screen time on adiposity remains unknown. Therefore, the aim of the present study is to assess whether an association between MD and sedentary time exists in European adolescents in the Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA) study. Moreover, we intend to explore whether sedentary time has a modulatory effect on the association between MD and adiposity markers. We hypothesize that high levels of screen time may attenuate the protective effect of MD adherence on adiposity parameters. 2. Materials and Methods 2.1. Study Design and Population HELENA is a multicentric and cross-sectional study. Description of the study sampling and recruitment, standardization and harmonization methodology, data collection, analysis strategies and quality control procedures was published elsewhere [19,20]. The HELENA study was designed to obtain reliable and comparable information on adolescents’ nutritional, environmental and health-related influences to prevent risk factors for present and future nutrition-related chronic diseases [21]. Each one of the participating countries involved in the HELENA study approved the protocol by the local Research Ethics Committees and followed the ethical guidelines of the Declaration of Helsinki 1961 Nutrients 2021, 13, 474 3 of 16 (revision of 2000), the Good Clinical Practice and the legislation about clinical research in humans [22]. A written consent was provided to the parents of all individuals participating in the study, which was read and signed. The present study comprises 2047 adolescents (54.7% females), aged 12.5–17.5 years, with valid and specific data on adherence to MD, screen time and adiposity. Description of the selection process is shown in a flow chart (Figure 1). Figure 1. Flow chart of the sample selection process. Abbreviations: FAS, family affluence scale; BMI, body mass index; FMI, fat mass index and WC, waist circumference. 2.2. Physical Examination and Adiposity Measurements Anthropometric measurements were strictly controlled and performed following standard protocols [23]. Body height was measured barefoot with a telescopic stadiometer (SECA 225) to the nearest 0.1 cm. Body weight was measured in underwear and with no footwear to the nearest 0.1 kg with an electronic scale (SECA 861, Hamburg, Germany). Height and weight were measured in triplicate. Body mass index (BMI) was calculated by dividing weight (kg) by the square of height (m) [24]. Waist circumference (WC) measurements were performed with a non-elastic tape (SECA 200) to the nearest 0.1 cm at the midpoint between the lowest rib and the iliac crest. Subscapular and tricipital skinfold thicknesses were measured in triplicate. In order to assess the contribution of fat mass relative to body size, the body fat percentage was calculated using the Slaughter’s equation [25], Nutrients 2021, 13, 474 4 of 16 and then, FMI was calculated as body fat in relation to height squared [FM (kg)/height (m2)] [26]. Pubertal status was evaluated during a medical examination by a physician/paediatrician following the methodology described by Tanner and Whitehouse [27]. Pubertal status was categorized as Tanner stages from no sexual maturation (stage I) to complete sexual maturation (stage V). 2.3. Dietary Intake and Mediterranean Diet Score (MDS) Assessment The HELENA Dietary Assessment Tool (HELENA-DIAT) is a self-administered computerized 24 h dietary recall used to collect all the adolescents’ dietary intake [28,29]. This tool was first validated in Flemish adolescents [29] and then adapted to be implemented in the participating centres of each country [30]. Participants provided twice dietary information through the HELENA-DIAT on 2 non-consecutive days within a space of 2 weeks. Previous authors considered this method as an useful procedure to evaluate the dietary intake in European children and adolescents [31]. The multiple source method (MSM) allowed us to calculate usual dietary intake of each individual, which enables the possibility to correct the dietary information for between and within individuals´ variability [32]. A Mediterranean diet score (MDS) was computed from the sum of 9 single subcomponents that were described elsewhere [33]. In short, vegetables, fruits and nuts, cereals, legumes, fish, dairy products (recommended during growth and development periods [34]) and unsaturated to saturated fat ratio were considered healthy food subgroups of MD, whereas meat products (including processed meat) and alcohol consumption were classified as unhealthy factors. Therefore, a participant consuming a healthy MD-associated food group was designated with 1 point, whereas the unhealthy food subgroups contributed with −1 points. A Mediterranean diet score (MDS), showing the degree of adhesion to the MD for each individual, was developed using a 0–9 point scale, with low values (0–4) indicating poor adherence and high values (5–9) greater adherence, respectively [35,36]. Supplementary Table S1 shows the median intake in g/day by sex of each subgroup from the MDS and the adherence levels to the MD. 2.4. Screen-Time-Based Sedentary Behavior Assessment In order to report the habitual time devoted to screen time among adolescents, a validated self-reported screen-time-based sedentary behaviours questionnaire was used [37]. The time spent in TV viewing, computer games, video games and internet for non-study reasons during both week and weekend days was collected in categories in a scale ranging from 0–240 min per day. The daily mean time for each category was obtained and the final time was calculated summing weekdays and weekend days, obtaining the total screen time in minutes per day (min/day). Lastly, a total sedentary time value was obtained by summing up the time reported in each category. The weighted Cohen’s κ-coefficients were used to assess the test–retest reliability of the screen-time-based questionnaire used in the HELENA study. The most common values observed were moderate, substantial or almost perfect agreement (>0.7). Exceptionally, internet for study reasons showed 0.46 in weekdays and 0.33 in weekends, respectively [38]. Furthermore, a sensitivity analysis was carried out in order to discard potential disparities in the interaction models due to those outlier individuals considered (or not) in the screen time variable. 2.5. Socioeconomic Status The family affluence scale (FAS) is an indicator of material affluence, which ranges from 0 (lowest) to 8 (highest) and further recategorized in low (0–2), medium (3–5) and high (6–8) levels [39]. The scale considers parameters such as car ownership, having an own bedroom, internet availability and computer ownership. This information was assessed through a questionnaire, and it was used as a predictor of the adolescents’ health outcomes [40]. Nutrients 2021, 13, 474 5 of 16 2.6. Statistical Analysis The normality of the variables was assessed with the Shapiro–Wilk non-parametric test. Not all variables followed a normal distribution, so the descriptive sex-specific characteristics are shown as median and interquartile range (IQR) for continuous variables, while categorical variables are shown as absolute and relative frequencies. Moreover, Pearson’s chi-square statistical test was used to obtain comparative sex-related differences for categorical variables; the Mann–Whitney–Wilcoxon test was performed for continuous variables. In order to observe the association between MD and screen time, sex-specific multiple linear regression models were performed. First, a raw simple linear regression model was constructed to observe associations between MD and screen time. Then, an initial multiple linear regression model was performed considering energy intake, socioeconomic status and Tanner stage as confounders. A step-by-step algorithm was applied to select the significant variables in a multivariate model to shortlist the independent variables significantly associated with the adiposity parameters in the final model. Furthermore, a new multiple regression analysis was created to assess the association between adiposity parameters and MD, adding the screen time interaction effect, the MD effect alone and the abovementioned confounders. Finally, as we observed extreme values for screen time in the highest end of the distribution, we performed a sensitivity analysis, excluding the outliers in the top end of the distribution. Level of significance was set at p < 0.05. RStudio Version 1.2.5001 (RStudio Team (2015). RStudio: Integrated Development for R. RStudio, Inc., Boston, MA, USA, URL http://www.rstudio.com/) was used to perform all statistical analyses. 3. Results 3.1. Descriptive Characteristics of the Study Sample Table 1 shows the main characteristics of the HELENA participants included in the present study. Summarizing, males had higher weight, height and WC (p ≤ 0.001), and lower FMI than females (all p < 0.001). Moreover, males were more exposed to screen time (p ≤ 0.001) and had higher energy intake (p ≤ 0.001) than females, although females were in more advanced pubertal stages than males (p ≤ 0.001). Finally, there were no significant differences regarding FAS and MDS. Table 1. Demographics and behavioural characteristics of the Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA) participants displayed by sex. Total Male Female p n = 2047 n = 925 n = 1122 Age (years) 14.7 (13.7–15.7) 14.8 (13.7–15.7) 14.7 (13.7–15.7) 0.590 Height (cm) 165.9 (159.3–172.0) 170.1 (163.9–177.1) 162.4 (157.7–167.0) <0.001 Weight (kg) 58.4 (50.3–64.3) 61.5 (52.3–68.9) 55.8 (49.0–61.2) <0.001 BMI (kg/m2) 21.1 (18.7–22.8) 21.1 (18.6–22.8) 21.1 (18.8–22.8) 0.282 WC (cm) 71.8 (66.2–75.8) 73.8 (67.8–78.3) 70.2 (65.0–74.4) <0.001 FMI (kg/m2) 5.15 (3.1–6.3) 4.5 (2.4–5.3) 5.7 (4.0–6.8) <0.001 Pubertal stage [n (%)] <0.001 I 7 (0.3%) 7 (0.8%) 0 (0%) II 134 (6.5%) 84 (9.1%) 50 (4.5%) Nutrients 2021, 13, 474 6 of 16 III 502 (24.5%) 226 (24.4%) 276 (24.6%) IV 881 (43.0%) 381 (41.2%) 500 (44.6%) V 523 (25.5%) 227 (24.5%) 296 (26.4%) FAS [n (%)] 0.112 Low 199 (9.7%) 76 (8.2%) 123 (11.0%) Medium 1133 (55.3%) 519 (56.1%) 614 (54.7%) High 715 (35.0%) 330 (35.7%) 385 (34.3%) MDS * (points) 4 (3–5) 4 (3–5) 4 (3–5) 0.071 Energy intake (kcal/day) 2180.1 (1634.9–2569.7) 2517.9 (1921.0–2984.3) 1901.6 (1492.4–2244.9) <0.001 Screen time (min/day) 256.2 (139.3–330.0) 288.1 (171.4–367.4) 229.9 (126.4–300.0) <0.001 Median values (p25–p75) expressed. Abbreviations: BMI, body mass index; WC, waist circumference; FMI, fat mass index; FAS, family affluence scale; MDS, Mediterranean diet score. * Mediterranean diet score resulting from the sum of 9 food subgroups compliance. Score ranging from 0–9 points. Significant values (p < 0.05) expressed in bold font. The screen time distribution (min/day) between HELENA participants is shown in Figure 2. Figure 2. Distribution of screen time (% displayed) among HELENA participants by sex. 3.2. Association between MD Adherence and Screen-Time-Based Sedentary Behaviors The associations between MD and screen time are shown in Table 2. The univariate model (Model I) showed that MD and screen time were inversely associated in both males and females (p < 0.001). These relationships were maintained in the initial multivariate model (p < 0.001) and after adjusting by confounders (Model II; p < 0.001). Table 2. Multiple linear regression models showing the associations of the Mediterranean diet score (MDS) with screen time. Screen Time (min/day) Model I a Model II b β p β p R2 Male 0.029 Nutrients 2021, 13, 474 7 of 16 MDS (point) −12.535 <0.001 −12.402 <0.001 Energy Intake (kcal/day) - - 0.026 <0.001 Female 0.016 MDS (point) −12.402 <0.001 −12.402 <0.001 Energy Intake (kcal/day) - - - - a Model I, unadjusted model, studies the association between screen time and MDS. b Model II presents the variables statistically significant in relation to sedentary time as follows: an initial model was constructed between screen time and MD considering Tanner stage, FAS categories and energy intake as covariates. Furthermore, a step-by-step algorithm was applied to discard non-significant associations. Only statistically significant variables are shown in the present table. Significant values (p<0.05) expressed in bold font. 3.3. Interaction between MD Adherence and Screen-Time-Based Sedentary Behaviors on Adiposity The interaction effects between MD and screen time on adiposity parameters by sex group are displayed in Table 3. In males, the screen-time–MD interaction was not significantly associated to any adiposity index. However, in females, there were significant interaction effects between the screen time and MD on BMI (p < 0.05), WC (p < 0.01) and FMI (p < 0.05) (Table 3). Table 3. Multiple linear regression models of screen time and Mediterranean diet score (MDS) interaction and covariates to predict body mass index, waist circumference and fat mass index displayed by sex. Males (p-Values) Females (p-Values) BMI (kg/m2) WC (cm) FMI (kg/m2) BMI (kg/m2) WC (cm) FMI (kg/m2) Covariates Pubertal Stage II * 0.502 0.139 0.048 - - - III 0.547 0.209 0.010 0.026 0.012 0.126 IV 0.534 0.936 0.047 <0.001 <0.001 <0.001 V 0.638 0.866 0.008 <0.001 <0.001 <0.001 FAS Medium 0.048 0.604 0.045 <0.001 0.138 <0.001 High 0.024 0.695 0.019 <0.001 0.058 <0.001 Energy Intake (kcal/day) <0.001 <0.001 <0.001 <0.001 <0.001 <0.001 Studied Variables MDS (point) 0.049 0.260 0.198 0.043 0.160 0.042 Screen time: MDS 0.062 0.617 0.122 0.025 0.002 0.022 Abbreviations: BMI, body mass index; WC, waist circumference; FMI, fat mass index; FAS, family affluence scale; MDS Mediterranean diet score. * Tanner II p-values were not considered to be estimated in the female group, as a statistically small number of females were present in this stage for the current analysis. Significant values (p<0.05) expressed in bold font. In order to interpret the modulation effect of screen time on the relationship between MD and adiposity indices in females, a set of figures is displayed in a matrix panel relating to each adiposity index (Figure 3). A number of lines were drawn to represent the MD and adiposity variables modulated by the distribution of the screen time. Most participants were located in lower and central parts of the distribution, corresponding to 50–350 min/day (74.5% of the total population); considering this distribution, the impact of outliers was carefully considered. Despite the observed screen time habits, those individuals represented by a negative slope could benefit from the protective role of MD in relation to adiposity indices when the MD adherence is high. Therefore, a high MD adherence was Nutrients 2021, 13, 474 8 of 16 associated with lower BMI only in those females being exposed to screen time less than 338 min/day (81.8% of the total females). Moreover, high MD adherence was associated with lower WC only in those females being exposed to screen time less than 143 min/day (31.5% of total females). Finally, a high MD adherence was associated with lower FMI only in those females being exposed to screen time less than 338 min/day (81.8% of total females). Nutrients 2021, 13, 474 9 of 16 Figure 3. Matrix panel of interaction models on a BMI (body mass index), b WC (waist circumference), c FMI (fat mass index); and MD (Mediterranean diet) according to screen time modulation in females. In order to design the representation of the distribution in HELENA adolescents, different lines were traced as reference points to observe the slope of the studied population according to the sedentary time. A positive gradient represents the MD acting as risk factor, while a negative gradient shows the MD acting as protective factor. 3.4. Sensitivity Analysis for Screen-Time-Based Sedentary Behaviors Plausible Data For the significant interaction models (females), sensitivity analysis was performed considering screen time outliers (highest value: 856 min/day) vs. interaction models not considering outliers (highest value: 632 min/day) on adiposity indices. Minimal differences were observed in the two interaction models of each adiposity parameter (β < 0.001 vs. β < 0.001 in BMI, WC and FMI). 4. Discussion The main findings of the present study are the observed inverse association between MD adherence and screen time, and the joint interaction effect between both factors on adiposity in European female adolescents. Thus, the benefits associated with a high MD adherence were only observed in those females with lower screen time. On the other hand, no MD–screen-time interaction effect was observed in males. In line with our findings, previous studies reported an inverse association between MD adherence and sedentary time among youth. In a population of Mediterranean European adolescents, self-reported inactivity [41] was inversely related to MD adherence in both sex groups [42]. In non-Mediterranean European adolescents, a low MD adherence was associated with higher sedentary time, although their sedentary time assessment only considered sitting time during weekdays [43]. To our knowledge, no other studies have examined screen time as modulatory factor in the MD effect on adiposity indices. However, a combined effect of different lifestyle patterns in cluster studies assessing their relationship to adiposity has been reported in similar age populations [44–47]. 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