Advertisement

Associations between food portion sizes, insulin resistance, VO2 max and metabolic syndrome in European adolescents: The HELENA study

Open AccessPublished:May 30, 2022DOI:https://doi.org/10.1016/j.numecd.2022.05.017

      Highlights

      • Portion size is an important determinants of insulin resistance and metabolic syndrome.
      • Intake of obesogenic foods was observed in adolescents with metabolic syndrome.
      • Insulin resistance improves after weight loss and high levels of physical activity.
      • Dietary factors could increase the risk of obesity and diabetes.

      Abstract

      Background and aims

      This study aims to examine the associations of food portion size (PS) with markers of insulin resistance (IR) and clustered of metabolic risk score in European adolescents.

      Methods

      A total of 495 adolescents (53.5% females) from the Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA) study were included. The association between PS from food groups and homeostasis model assessment of insulin resistance (HOMA-IR) index, VO2 max, and metabolic risk score was assessed by multilinear regression analysis adjusting for several confounders. Analysis of covariance (ANCOVA) was used to determine the mean differences of food PS from food groups by HOMA-IR cutoff categories by using maternal education as a covariable.

      Results

      Larger PS from vegetables in both gender and milk, yoghurt, and milk beverages in males were associated with higher VO2 max, while larger PS from margarines and vegetable oils were associated with lower VO2 max (p < 0.05). Males who consumed larger PS from fish and fish products; meat substitutes, nuts, and pulses; cakes, pies, and biscuits; and sugar, honey, jams, and chocolate have a higher metabolic risk score (p < 0.05). Males with lower HOMA-IR cutoff values consumed larger PS from vegetables, milk, yoghurt, and milk beverages (p < 0.05). Females with lower HOMA-IR cutoff values consumed larger PS from breakfast cereals, while those with higher HOMA-IR cutoff values consumed larger PS from butter and animal fats (p = 0.018).

      Conclusion

      The results show that larger PS from dairy products, cereals, and high energy dense foods are a significant determinant of IR and VO2 max, and larger PS from food with higher content of sugar were associated with higher metabolic risk score.

      Keywords

      Abbreviations:

      ANCOVA (Analysis of covariance), BMI (Body mass index), HELENA (Healthy Lifestyle in Europe by Nutrition in Adolescence), HOMA-IR index (Homeostasis model assessment of insulin resistance), Metabolic Syndrome (MS), PA (Physical activity), PS (Portion size), VO2 max (Maximal oxygen uptake)

      1. Introduction

      During the past decades, increasing prevalence of type 2 diabetes mellitus and prediabetic stages such as insulin resistance (IR) or impaired fasting glucose has been reported in children and adolescents [
      • DeBoer M.
      Assessing and managing the metabolic syndrome in children and adolescents.
      ]. This increase seems to parallel the increase in the prevalence of obesity in these age groups [
      • Kao K.
      • Sabin M.
      Type 2 diabetes mellitus in children and adolescents.
      ]. Dietary factors are environmental determinants of both adiposity, IR, and the components of metabolic syndrome (MS) [
      • Zimmermann M.
      • Aeberli I.
      Dietary determinants of subclinical inflammation, dyslipidemia and components of the metabolic syndrome in overweight children: a review.
      ].
      IRs improve after weight loss [
      • Christensen P.
      • Meinert Larsen T.
      • Westerterp-Plantenga M.
      • Macdonald I.
      • Martinez J.A.
      • Handjiev S.
      • et al.
      Men and women respond differently to rapid weight loss: metabolic outcomes of a multi-centre intervention study after a low-energy diet in 2500 overweight, individuals with pre-diabetes (PREVIEW).
      ] and in the presence of high levels of physical activity (PA) [
      • Kim J.
      • Jeon J.
      Role of exercise on insulin sensitivity and beta-cell function: is exercise sufficient for the prevention of youth-onset type 2 diabetes?.
      ]. Diet composition, in particular, carbohydrate type and amount and fat intake may also influence IR [
      • Reaven G.
      Insulin resistance: the link between obesity and cardiovascular disease.
      ]. In children, it has been found that total energy, fat, saturated fat, and protein intakes were significant predictors of fasting insulin and quantitative insulin sensitivity check index (QUICKI), independent of body mass index (BMI), and age [
      • Aeberli I.
      • Spinas G.
      • Lehmann R.
      • l’Allemand D.
      • Molinari L.
      • Zimmermann M.B.
      Diet determines features of the metabolic syndrome in 6- to 14-year-old children.
      ]. Several dietary factors could promote a positive energy balance [
      • Apovian C.
      The causes, prevalence, and treatment of obesity revisited in 2009: what have we learned so far?.
      ] and thereby increase the risk for obesity and diabetes, including the following: excessive portion size (PS), with single large meals often approaching or exceeding individual daily energy requirements; palatability, emphasizing primordial taste preferences for sugar, salt, and fat; high energy density; and high glycemic load [
      • Flieh S.
      • Miguel-Berges M.
      • González-Gil E.
      • Gottrand F.
      • Censi L.
      • Widhalm K.
      • et al.
      The association between portion sizes from high-energy-dense foods and body composition in European adolescents: the HELENA study.
      ]. PS of many foods have been increasing in countries with a well-established industrialized food supply [
      • Young L.
      • Nestle M.
      Expanding portion sizes in the US marketplace: implications for nutrition counseling.
      ].
      Increased consumption of margarine, sweets (candies, lollipops, jellies, and traditional fruit in heavy syrup), and savory snacks (chips, cheese puffs, and not home-made popcorn) was associated with high homeostasis model assessment of IR (HOMA-IR) index value in children and adolescents [
      • Karatzi K.
      • Moschonis G.
      • Barouti A.
      • Lionis C.
      • Chrousos G.P.
      • Manios Y.
      Dietary patterns and breakfast consumption in relation to insulin resistance in children.
      ,
      • Sesé M.
      • Jiménez-Pavón D.
      • Gilbert C.
      • González-Gross M.
      • Gottrand F.
      • de Henauw S.
      • et al.
      Eating behaviour, insulin resistance and cluster of metabolic risk factors in European adolescents.
      ]. Additionally, it was shown previously that sugar intake in the form of sugar-sweetened beverages was associated with IR in adolescents [
      • Kynde I.
      • Johnsen N.
      • Wedderkopp N.
      • Bygbjerg I.B.
      • Helge J.W.
      • Heitmann B.L.
      Intake of total dietary sugar and fibre is associated with insulin resistance among Danish 8-10- and 14-16-year-old girls but not boys. European Youth Heart Studies I and II.
      ]. Data from children indicated that short absorption time that follows the consumption of sugar may impair blood glucose control and may result in hyperinsulinemia and peripheral IR [
      • Harrington S.
      The role of sugar-sweetened beverage consumption in adolescent obesity: a review of the literature.
      ]. Frequent intake of obesogenic foods such as crackers, chips, and cooked ham was observed in adolescents with MS [
      • Pedrozo W.
      • Rascón M.
      • Bonneau G.
      • de Pianesi M.I.
      • Olivera C.C.
      • de Aragón S.J.
      • et al.
      Revista panamericana de salud publica = Pan.
      ]. Moreover, a ‘Western’ dietary pattern was associated with a greater risk for MS, among female adolescents [
      • Ambrosini G.
      • Huang R.
      • Mori T.
      • Hands B.P.
      • O’Sullivan T.A.
      • de Klerk N.H.
      • et al.
      Dietary patterns and markers for the metabolic syndrome in Australian adolescents. Nutrition, metabolism, and cardiovascular diseases.
      ].
      The relationship between food PS and IR and the development of MS in children and adolescents have not been previously examined. Therefore, this study investigates the potential effect of food PS on IR and a quantitative score of metabolic risk in European adolescents.

      2. Methods

      2.1 Study design

      A European multicentre cross-sectional study was performed (2006–2007) in adolescents aged 12.5–17.5 years from 10 cities to assess a Healthy Lifestyle in Europe by Nutrition in Adolescence (HELENA) [
      • Moreno L.
      • González-Gross M.
      • Kersting M.
      • Molnár D.
      • de Henauw S.
      • Beghin L.
      • et al.
      Assessing, understanding and modifying nutritional status, eating habits and physical activity in European adolescents: the HELENA (healthy Lifestyle in Europe by nutrition in adolescence) study.
      ]. The main objective of the HELENA-Cross sectional was to obtain reliable and comparable data from randomly selected European adolescents (n = 3528, 52.3% females) by using wide relevant health and nutrition-related parameters that included the following: dietary intake, food choices and preferences, serum vitamin and mineral status, lipid and glucose metabolism, anthropometric measurements, PA and fitness, and genetic markers [
      • Henauw S.D.
      • Gottrand F.
      • Bourdeaudhuij I.D.
      • Gonzalez-Gross M.
      • Leclercq C.
      • Kafatos A.
      • et al.
      Nutritional status and lifestyles of adolescents from a public health perspective. The HELENA Project—healthy Lifestyle in Europe by nutrition in adolescence. OriginalPaper.
      ]. The inclusion criteria were participants who were free from any acute infection lasting less than 1 week before the inclusion process and were not concurrently involved in another clinical trial [
      • Moreno L.
      • De Henauw S.
      • González-Gross M.
      • Kersting M.
      • Molnár D.
      • Gottrand F.
      • et al.
      Design and implementation of the healthy Lifestyle in Europe by nutrition in adolescence cross-sectional study.
      ]. The exclusion criteria were participants not having information on age, gender, height, and weight; participants who were concurrently involved in another clinical trial, age more than 17.5 years or less than 12.5 years and having any acute infection lasting more than 1 week before the inclusion process [
      • Moreno L.
      • De Henauw S.
      • González-Gross M.
      • Kersting M.
      • Molnár D.
      • Gottrand F.
      • et al.
      Design and implementation of the healthy Lifestyle in Europe by nutrition in adolescence cross-sectional study.
      ]. More details about recruitment and sampling process are described elsewhere [
      • Moreno L.
      • De Henauw S.
      • González-Gross M.
      • Kersting M.
      • Molnár D.
      • Gottrand F.
      • et al.
      Design and implementation of the healthy Lifestyle in Europe by nutrition in adolescence cross-sectional study.
      ].

      2.2 Study sample

      Blood samples were obtained from around one-third of patients, following the same randomization criteria as those for the whole sample. Out of 3528 adolescents included in the HELENA study, blood samples were obtained from one-third (1089) of the adolescents as was foreseen in the protocol. A total of 1188 adolescents (33.7%) did not have information for the 24-hr. Also, 1198 adolescents who were considered over-reporters (173) and under-reporters (526), according to the approach of Goldberg et al. [
      • Goldberg G.
      • Black A.
      • Jebb S.
      • Cole T.J.
      • Murgatroyd P.R.
      • Coward W.A.
      • et al.
      Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording.
      ], were excluded. Out of those with valid dietary data, 647 participants were excluded, as they had no data on glucose, insulin, and HOMA-IR index, skinfold thickness, systolic blood pressure, triglycerides, maximal oxygen uptake (VO2 max), maternal education, or PA. Finally, 495 (265 females) adolescents were included in the present analysis.
      The study was performed following the ethical guidelines of the Declaration of Helsinki 1964 (revision of Edinburgh, 2000), the Good Clinical Practice, and the legislation about clinical research in humans in each of the participating countries. The protocol was approved by the Human Research Review Committees of the involved centres [
      ]. Moreover, a written informed consent was obtained from participating adolescents and their parents [
      • Béghin L.
      • Castera M.
      • Manios Y.
      • Gilbert C.C.
      • Kersting M.
      • De Henauw S.
      • et al.
      Quality assurance of ethical issues and regulatory aspects relating to good clinical practices in the HELENA cross-sectional study.
      ].

      2.3 Questionnaires

      The education level of the adolescent's mothers was adapted from the International Standard Classification of Education (ISCED) [

      UNESCO. International Standard Classification of Education. http://www.uis.unesco.org/Education/Documents/isced-2011-en.pdf.

      ] and reported as primary education, lower secondary education, higher secondary education, and higher education/university degree. In this study, the two lowest levels have been merged into one group called lower level of education, in addition to higher level of education. More details have been reported elsewhere [
      • Béghin L.
      • Dauchet L.
      • De Vriendt T.
      • Cuenca-García M.
      • Manios Y.
      • Toti E.
      • et al.
      Influence of parental socio-economic status on diet quality of European adolescents: results from the HELENA study.
      ].

      2.4 Physical examination

      Measurements were taken 3 times by trained researchers in each city. A training session was conducted by the coordinator of HELENA, with the 10 field workers who planned to perform anthropometric measurements. The aim of the training was to familiarize researchers with the exact protocol to be used and to perform the 1st approach to assess the intra-observer technical error. Then, a workshop was organized that aims to assess the intra-observer (2nd time) and inter-observer (1st time) technical error of measurements (TEMs<1) and the reliability (>90%) of anthropometry and BIA measurements. All the anthropometric variables were measured in order, and the same measurements were then repeated two more times [
      • Nagy E.
      • Vicente-Rodriguez G.
      • Manios Y.
      • Béghin L.
      • Iliescu C.
      • Censi L.
      • et al.
      Harmonization process and reliability assessment of anthropometric measurements in a multicenter study in adolescents.
      ].
      Weight was measured with an electronic scale (model 871; SECA, Hamburg, Germany) to the nearest 0.05 kg, and height was measured with a telescopic height measuring instrument (model 225; SECA, Hamburg, Germany) to the nearest 0.1 cm. All measurements were performed in underwear and barefoot [
      • Nagy E.
      • Vicente-Rodriguez G.
      • Manios Y.
      • Béghin L.
      • Iliescu C.
      • Censi L.
      • et al.
      Harmonization process and reliability assessment of anthropometric measurements in a multicenter study in adolescents.
      ]. BMI was calculated as body weight (kg) divided by the height (m) squared (kg/m2). The obesity status was classified using the International Obesity Task Force scale [
      • Cole T.
      • Bellizzi M.
      • Flegal K.
      • Dietz W.
      Establishing a standard definition for Child overweight and obesity worldwide: international Survey.
      ]. Skinfold thickness was measured to the nearest 0.2 mm in triplicate in the right side at biceps, triceps, subscapular, suprailiac, thigh, and medial calf with a Holtain Caliper (Crymmych, Wales, UK). The sum of six skinfold thickness was used as an indicator of total body fat [
      • Slaughter M.
      • Lohman T.
      • Boileau R.
      • Horswill C.A.
      • Stillman R.J.
      • Van Loan M.D.
      • et al.
      Skinfold equations for estimation of body fatness in children and youth.
      ].

      2.5 Physical activity measurement

      Accelerometers (Actigraph MTI, model GT1M, Manufacturing Technology Inc., Fort Walton Beach, FL, USA) were used to obtain an objective measurement of PA. The devices were placed on the lower back of the participants under the clothes using an elastic belt for seven sequent days. Instructions were given to participants when they wake up to wear the instrument and remove it for water-based activities and sleeping [
      • Hagströmer M.
      • Bergman P.
      • De Bourdeaudhuij I.
      • Ortega F.B.
      • Ruiz J.R.
      • Manios Y.
      • et al.
      Concurrent validity of a modified version of the international physical activity questionnaire (IPAQ-A) in European adolescents: the HELENA study.
      ]. Data were downloaded to the computer using manufacturer software and analyzed later by software based on Visual Basic. Time spent in moderate and vigorous physical activity (MVPA) was determined using the cutoff point of 2000 cpm to generate the various indices; the number of days per week was multiplied by minutes per day, to calculate minutes per week for each activity [
      • Ainsworth B.
      • Haskell W.
      • Whitt M.
      • Irwin M.L.
      • Swartz A.M.
      • Strath S.J.
      • et al.
      Compendium of physical activities: an update of activity codes and MET intensities.
      ]. More detailed information has been reported elsewhere [
      • Hagströmer M.
      • Bergman P.
      • De Bourdeaudhuij I.
      • Ortega F.B.
      • Ruiz J.R.
      • Manios Y.
      • et al.
      Concurrent validity of a modified version of the international physical activity questionnaire (IPAQ-A) in European adolescents: the HELENA study.
      ].

      2.6 Blood samples

      Briefly, fasting blood samples were collected by venepuncture at school between 8:00 and 10:00 after a 10-h overnight fast. Whole blood samples for the hemogram were sent directly to the local laboratory of each country to be analyzed. Concentrations of triglycerides, total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c), and glucose were measured in fresh serum enzymatically on the Siemens Dimension RxL Max Integrated Chemistry System (Dade Behring, Schwalbach, Germany) using the manufacturer's reagents and instructions at the University Hospital in Bonn (Germany). TC/HDL-c ratio was calculated. Insulin was measured by a solid-phase two-site chemiluminescent immunometric assay with an Immulite 2000 analyzer (DPC Biermann GmbH, Bad Nauheim, Germany). More details about blood handling procedures have been described elsewhere [
      • González-Gross M.
      • Breidenassel C.
      • Gómez-Martínez S.
      • Ferrari M.
      • Béghin L.
      • Spinneker A.
      • et al.
      Sampling and processing of fresh blood samples within a European multicenter nutritional study: evaluation of biomarker stability during transport and storage.
      ]. The intra-assay coefficients of variation were <3.3%, and the inter-assay coefficients were <3.9% for all parameters.

      2.7 Cardiorespiratory fitness

      Cardiorespiratory fitness was measured by the progressive 20-m shuttle run test [
      • Léger L.
      • Lambert J.
      • Goulet A.
      • Rowan C.
      • Dinelle Y.
      Canadian journal of applied sport sciences.
      ]. This test required participants to run back and forth between two lines set 20 m apart following a running pace determined by audio signals and with an initial speed of 8.5 km h−1 increasing by 0.5 km h-1 every minute (1 min equals 1 stage). The test is finished when the adolescent failed to reach the end lines concurrent with the audio signals on two consecutive occasions, and the final score was computed as the number of stages completed (precision of 0.5 stages). Maximal oxygen uptake (VO2 max) was estimated using the formula described by Léger et al. (1984).

      2.8 Metabolic risk score

      The HOMA-IR index was calculated as fasting insulin [(pmol/l)/6.945] ∗ fasting glucose [(mmol/l)/22.5] [
      • Katz A.
      • Nambi S.
      • Mather K.
      • Baron A.D.
      • Follmann D.A.
      • Sullivan G.
      • et al.
      Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans.
      ]. The cutoff value for HOMA-IR was based on the 90th percentile. A QUICKI was calculated as QUICKI = 1/log insulin (lIU/mL) + log glucose (mg/dl) [
      • Katz A.
      • Nambi S.
      • Mather K.
      • Baron A.D.
      • Follmann D.A.
      • Sullivan G.
      • et al.
      Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans.
      ]. Systolic blood pressure was measured with an automatic oscillometric device (M6, HEM-7001-E, Omron). A continuous score of clustering metabolic risk factors was computed using the following variables: systolic blood pressure, triglycerides, TC/HDL-c ratio, HOMA-IR index, the sum of six skinfolds, and VO2 max. Z-scores were calculated for each risk factor variable by age and gender, and then all individual Z-scores were summed to create a clustered risk score based on the one by Andersen et al. [
      • Andersen L.
      • Harro M.
      • Sardinha L.
      • Froberg K.
      • Ekelund U.
      • Brage S.
      • et al.
      Physical activity and clustered cardiovascular risk in children: a cross-sectional study (The European Youth Heart Study).
      ], that has been used in the previous HELENA study [
      • Sesé M.
      • Jiménez-Pavón D.
      • Gilbert C.
      • González-Gross M.
      • Gottrand F.
      • de Henauw S.
      • et al.
      Eating behaviour, insulin resistance and cluster of metabolic risk factors in European adolescents.
      ].

      2.9 Dietary assessment

      The HELENA Dietary Assessment Tool (HELENA-DIAT) was used to assess adolescents dietary consumption; this software was used as self-administered, computerized 24-h recall, developed and validated originally in Flemish adolescents, and then in the HELENA-CSS [
      • Vereecken C.
      • Covents M.
      • Sichert-Hellert W.
      • Alvira J.M.
      • Le Donne C.
      • De Henauw S.
      • et al.
      Development and evaluation of a self-administered computerized 24-h dietary recall method for adolescents in Europe.
      ]. HELENA-DIAT is based on previous day assessments of the intake from six meal occasions (breakfast, morning snack, lunch, afternoon snack, evening meal, and evening snack). The two nonconsecutive 24-h recalls performed on one convenient weekday and one weekend day. A well-trained dietitian was present to assess the adolescent in case they need any help to complete the diet 24-hr recall.
      A total of 800 photographs were available in the HELENA-DIAT program. The participants were able to select one of the amounts that appeared in a photograph or indicate that they consume less or more than the amount appeared on the computer. In addition, they were able to type the consumed amount for each food item in a text box. Moreover, the participants were able to remove or modify the selected items at any time. Moreover, foods that can be measured with household tools like cups, several portions appeared on the screen, so that the participants can select the consumption amount by clicking directly on the portion. In case some foods usually eaten in combination with other items such as french fries and mayonnaise, a box was shown on the screen to remind them to include this item [
      • Vereecken C.
      • Covents M.
      • Sichert-Hellert W.
      • Alvira J.M.
      • Le Donne C.
      • De Henauw S.
      • et al.
      Development and evaluation of a self-administered computerized 24-h dietary recall method for adolescents in Europe.
      ].

      2.10 Selection of food groups

      Based on the European food groups classification system, about 4179 foods and beverages, in the form of recipes or as individual food, were aggregated into food groups [
      • Vereecken C.
      • Covents M.
      • Sichert-Hellert W.
      • Alvira J.M.
      • Le Donne C.
      • De Henauw S.
      • et al.
      Development and evaluation of a self-administered computerized 24-h dietary recall method for adolescents in Europe.
      ]. In our study, we excluded the foods that were very infrequently consumed from the analysis: products for special nutrition use, soya beverages, and miscellaneous due to their very low consumption (reported by less than 15% of the participants). Furthermore, the daily diet was divided into 11 food groups based on their nutritional composition: (1) water, (2) bread and cereal, (3) grains and potatoes, (4) fruit, (5) vegetables, (6) milk, milk desserts, and yogurt, (7) cheese, (8) meat/fish/eggs/vegetarian substitutes, (9) spread and cooking fat (10) low-nutrient, energy-dense foods (e.g., chocolate, sugar products, biscuits, pies, savoury snacks, creams, and confectionery), and (11) low-nutrient, energy-dense drinks (e.g., carbonated soft drinks, juices, and alcoholic drinks). Milk products and cheese were allocated to different food groups because of the important difference in fat content.

      2.11 Portion size calculation

      PS was calculated by dividing the intake in grams (g) of the items included in the group and reported to be consumed during the 24 h-recall, by the number of eating occasions of these consumed items. In this study, the average amount of PS was calculated from the two days included in the 24 h-recall by each eating occasion. For instance, if an individual consumed 100 g of meat for lunch in the first day and 100 g in the lunch for the second day, then his/her PS at lunch from this food item was 100 g, and if the individual consumed 100 g of meat only in lunch and did not consume meat in any other meal, his/her PS was 100 g. Several studies of food PS effect on overweight in children and adults have used the same methodology [
      • Lioret S.
      • Volatier J.L.
      • Lafay L.
      • Touvier M.
      • Maire B.
      Is food portion size a risk factor of childhood overweight?.
      ,
      • Pereira J.
      • Mendes A.
      • Crispim S.
      • Marchioni D.M.
      • Fisberg R.M.
      Association of overweight with food portion size among adults of são Paulo - Brazil.
      ]. Thus, these data represent per consumer averages, not per capita averages, and it is used to show the average change on the PS for those who consume a certain item. Therefore, to analyze a specific food group only participants who consumed this food group were included in the analysis.

      2.12 Statistical analysis

      Descriptive analysis of mean and standard deviation for general characteristics were presented with Student's t-test for continuous variables. Chi-square test was used to assess the difference of categorical variables between genders. To achieve normality Johnson transformation has been performed for VO2 max, HOMA-IR index, and metabolic risk score. Sensitivity analysis was carried out in order to consider age or pubertal stage in adjustment. Interaction products of gender and markers of body fat in the association between PS from food groups with markers of IR were calculated. Since an interaction effect was observed for gender, all the analyses were performed separately for females and males. Analysis of covariance (ANCOVA) was also used to determine the mean differences and standard deviations of food PS from the studied food groups by HOMA-IR cutoff categories and metabolic risk score median cutoff categories between gender by using maternal education as covariable for all participants.
      The association between PS from food groups as independent variables and HOMA-IR, systolic blood pressure, triglycerides, TC/HDL-c ratio, the sum of six skinfolds and VO2 max, and the metabolic risk score as dependent variables was assessed by multilinear regression analysis. All regression models were adjusted for age, maternal education, PA, total energy intake, BMI, and city as dummy variable. To avoid multiple testing, all analysis were performed individually. The analyses were conducted using IBM-SPSS (v25, SPSS Inc., Chicago, IL, USA), and the level of significance was set to 0.05.

      3. Results

      3.1 General characteristics of study participants

      Sample descriptive characteristics by gender are presented in Table 1. A total of 495 adolescents aged between 12.5 and 17.5 years old were included in this study. More than half (53.5%) of the participants were females. Males had significantly higher mean glucose (p = 0.047), QUICKI (p = 0.010), and VO2 max (p = 0.002), than females.
      Table 1Descriptive characteristics of the study sample.
      General characteristicsAll participants (n = 495)p-value
      Males (n = 230)Females (n = 265)
      Mean (SD)Mean (SD)
      Age14.7 (1.3)14.7 (1.2)0.314
      Maternal education (n, %)a0.914
      Low69 (30.0%)90 (33.9%)
      High161 (70.0%)175 (66.1%)
      BMI (kg/m2)20.6 (3.4)20.8 (3.3)0.789
      BMI categories (n,%)0.090
      Normal weight189 (82.2%)229 (86.4%)
      Overweight30 (13.0%)26 (9.8%)
      Obesity11 (4.8%)10 (3.8%)
      Glucose (mg/dL)91.9 (7.2)88.3 (6.2)0.047
      Triglycerides (mg/dL)65.4 (31.9)70.8 (30.7)0.730
      Total cholesterol (mg/dL)152.4 (25.4)166.2 (27.1)0.198
      Insulin (μlU/mL)8.9 (6.9)10.0 (6.8)0.899
      HOMA-IR indexb2.1 (1.7)2.2 (1.7)0.811
      QUICKI0.4 (0.03)0.3 (0.03)0.010
      SBP (mm Hg)119.2 (13.0)112.2 (11.5)0.102
      VO2max (ml/kg/min)53.2 (7.9)36.5 (6.4)0.002
      Sum of skinfold thickness (mm)22.4 (11.3)30.2 (11.8)0.216
      Waist circumference (cm)b72.9 (8.3)69.9 (7.2)0.164
      Metabolic risk scorec−0.81 (3.4)0.75 (2.7)0.060
      TC/HDL ratio2.9 (0.6)2.9 (0.6)0.680
      All values are mean ± standard deviation, or a percentage. BMI: body mass index; HOMA-IR index: homeostasis model assessment for insulin resistance; SBP: systolic blood pressure; TC/HDL-c: total cholesterol/high-density lipoprotein cholesterol; VO2max: maximal oxygen uptake. Non transformed data are presented in this table, but analyzes were performed with b and c Johnson transformation. Level of significance was set to 0.05.

      3.2 Association between PS from various food groups and HOMA-IR index and metabolic risk score components (systolic blood pressure, triglycerides, TC/HDL-c ratio, sum of six skinfolds, and VO2 max) by gender

      The results are showing no significant association between food PS and HOMA-IR index (Table 2), and all of metabolic risk score components except VO2 max in both genders (Supplementary Tables S1–S4) after adjustment for age, maternal education, PA, total energy intake, BMI, and city.
      Table 2The association between food PS and HOMA-IR index in a selected sample of European adolescents, by gender.
      Food Groups (g/day)HOMA-IR index
      Males (n = 230)Females (n = 265)
      β95% CIP-valueβ95% CIP-value
      LowerUpperLowerUpper
      Bread and Cereals
      Bread and rolls0.011−0.0020.0030.589−0.003−0.0070.0010.102
      Breakfast cereals−0.010−0.0220.0030.212−0.007−0.0140.0010.051
      Grains and potato
      Rice and other grains0.050−0.0040.0090.500−0.012−0.0020.0050.847
      Starch roots, potatoes−0.001−0.0030.0020.512−0.001−0.0020.0010.561
      Pasta0.004−0.0010.0030.5890.0020.0000.0040.087
      Fruits0.051−0.0100.0060.0520.007−0.0050.0030.507
      Vegetables0.0020.0010.0030.0660.002−0.0010.0020.654
      Milk, milk desserts and yogurt
      Milk, yoghurt, and milk beverages0.001−0.0050.0010.0650.002−0.0010.0020.160
      Desserts and puddings milk based−0.007−0.0120.0030.067−0.004−0.0040.0030.164
      Cheese0.001−0.0020.0050.792−0.025−0.0080.0020.614
      Meat/poultry/fish/eggs
      Meat and poultry0.002−0.0030.0020.5460.003−0.0020.0050.253
      Fish and fish products0.013−0.0040.0010.2980.005−0.0010.0090.845
      Eggs−0.015−0.0080.0070.7710.002−0.0030.0020.524
      Meat substitutes, nuts, pulses−0.045−0.0550.0960.105−0.010−0.0090.0180.096
      Spread and cooking fats
      Margarines and vegetable oils0.011−0.0080.0080.9120.006−0.0090.0160.395
      Butter and animal fats0.023−0.0050.0070.1360.019−0.0190.0050.950
      Low nutrient energy- dense food
      Cakes, pies, biscuits0.012−0.0100.0090.1040.001−0.0020.0020.600
      Savoury snacks−0.003−0.0040.0040.5070.006−0.0020.0120.213
      Sugar, honey, jams, chocolate−0.005−0.0020.0060.460−0.004−0.0010.0060.105
      Sauces and creams0.003−0.0080.0050.1360.019−0.0200.0000.248
      Low nutrient energy- dense drinks
      Carbonated soft/isotonic drinks0.001−0.0030.0030.3910.0060.0020.0090.125
      Fruit and vegetables juices0.008−0.0040.0090.2350.003−0.0020.0110.161
      β: regression coefficient. CI: confidence interval; Adjusting for confounders: age, maternal education, PA, total energy intake, BMI and city. Level of significance was set to 0.05.

      3.3 Association between PS from various food groups and VO2 max stratified by gender

      The result from Table 3 shown that in males, larger PS from vegetables (β = 0.001; p = 0.048), milk, yoghurt, and milk beverages (β = 0.002; p = 0.026) were associated with higher VO2 max; while larger PS from margarines and vegetable oils (β = -0.004; p = 0.025) were associated with lower VO2 max. In females, larger PS from vegetables (β = 0.001; p = 0.007) were associated with higher VO2 max, taking in consideration the adjustment for age, maternal education, PA, total energy intake, BMI, and city.
      Table 3The association between food PS and VO2 max in a selected sample of European adolescents, by gender.
      Food Groups (g/day)VO2 Max
      Males (n = 230)Females (n = 265)
      β95% CIP-valueβ95% CIP-value
      LowerUpperLowerUpper
      Bread and Cereals
      Bread and rolls0.006−0.0050.0040.605−0.002−0.022−0.0010.990
      Breakfast cereals0.002−0.0020.0020.1500.003−0.0030.0100.110
      Grains and potato
      Rice and other grains0.006−0.0010.0030.1040.000−0.0020.0010.376
      Starch roots, potatoes0.022−0.0080.0040.4670.005−0.0050.0150.293
      Pasta0.031−0.0230.0140.4500.021−0.0130.0110.219
      Fruits0.001−0.0020.0050.2320.002−0.0010.0040.154
      Vegetables0.033−0.0020.0150.0380.026−0.0070.0090.012
      Milk, milk desserts and yogurt
      Milk, yoghurt, and milk beverages0.009−0.0010.0080.0060.0000.0000.0010.559
      Desserts and puddings milk based0.004−0.0100.0000.0790.005−0.0040.0030.374
      Cheese0.001−0.0090.0020.0900.002−0.0050.0030.754
      Meat/poultry/fish/eggs
      Meat and poultry0.009−0.0060.0020.1500.030−0.0150.0090.059
      Fish and fish products0.005−0.0130.0030.4200.003−0.0050.0090.400
      Eggs0.001−0.0060.0080.7720.001−0.0010.0030.445
      Meat substitutes, nuts, pulses0.002−0.0030.0070.205−0.001−0.0030.0040.306
      Spread and cooking fats
      Margarines and vegetable oils−0.008−0.0120.0090.0150.003−0.0030.0100.267
      Butter and animal fats0.004−0.0010.0120.5080.002−0.0320.0050.508
      Low nutrient energy- dense food
      Cakes, pies, biscuits0.000−0.0010.0020.692−0.002−0.0040.0000.067
      Savoury snacks0.004−0.0030.0100.288−0.007−0.0140.0010.077
      Sugar, honey, jams, chocolate−0.001−0.0030.0020.512−0.001−0.0020.0010.561
      Sauces and creams0.000−0.0030.0020.687−0.009−0.0030.0020.937
      Low nutrient energy- dense drinks
      Carbonated soft/isotonic drinks0.000−0.0030.0020.6860.0020.0000.0040.087
      Fruit and vegetables juices0.000−0.0010.0010.7010.000−0.0010.0010.617
      β: regression coefficient. CI: confidence interval; Adjusting for confounders: age, maternal education, PA, total energy intake, BMI and city. Level of significance was set to 0.05

      3.4 Association between PS from various food groups and metabolic risk score

      Table 4 illustrates the results of multilinear regression model by gender using metabolic risk score categories as a dependent variable and PS of food as independent variables. The model was adjusted for age, maternal education, PA, total energy intake, BMI, and city. The results indicates that males with larger PS consumption from fish and fish products (β = 0.007; p = 0.015); meat substitutes, nuts, and pulses (β = 0.018; p = 0.032); cakes, pies, and biscuits (β = 0.005; p = 0.010); sugar, honey, jams, and chocolate (β = 0.005; p = 0.009) have higher metabolic risk score. Females with only larger PS from cakes, pies, and biscuits (β = 0.005; p = 0.030) have higher metabolic risk score.
      Table 4The association between food PS and a metabolic risk score in a selected sample of European adolescents, by gender.
      Food Groups (g/day)Metabolic risk score
      Males (n = 230)Females (n = 265)
      Β95% CIP-valueβ95% CIP-value
      LowerUpperLowerUpper
      Bread and Cereals
      Bread and rolls0.000−0.0020.0010.4350.003−0.0020.0010.695
      Breakfast cereals−0.003−0.0020.0070.2090.005−0.0030.0120.210
      Grains and potato
      Rice and other grains−0.001−0.0020.0010.3800.001−0.0010.0030.461
      Starch roots, potatoes−0.001−0.0010.0030.2150.000−0.0020.0010.522
      Pasta−0.006−0.0020.0010.7580.000−0.0020.0010.666
      Fruits0.003−0.0010.0010.9360.000−0.0010.0000.428
      Vegetables0.011−0.0120.0020.0580.001−0.0080.0020.097
      Milk, milk desserts and yogurt
      Milk, yoghurt, and milk beverages−0.0020.0000.0010.0560.002−0.0100.0010.559
      Desserts and puddings milk based−0.001−0.0020.0030.456−0.002−0.0050.0000.077
      Cheese0.002−0.0010.0040.2980.001−0.0030.0040.285
      Meat/poultry/fish/eggs
      Meat and poultry0.000−0.0010.0000.157−0.002−0.0010.0010.959
      Fish and fish products0.005−0.0120.0200.0070.001−0.0030.0020.593
      Eggs0.001−0.0060.0080.7720.001−0.0010.0030.445
      Meat substitutes, nuts, pulses0.0310.0060.0500.0010.004−0.0040.0140.307
      Spread and cooking fats
      Margarines and vegetable oils0.013−0.0050.0020.0500.001−0.0030.0100.267
      Butter and animal fats0.000−0.0090.0080.848−0.002−0.0120.0080.698
      Low nutrient energy-dense food
      Cakes, pies, biscuits0.0040.0010.0230.0360.004−0.0120.0030.020
      Savoury snacks0.015−0.0040.0190.1470.025−0.0020.0120.184
      Sugar, honey, jams, chocolate0.0230.0090.0280.009−0.001−0.0070.0060.728
      Sauces and creams−0.003−0.0020.0020.974−0.006−0.0020.0020.945
      Low nutrient energy- dense drinks
      Carbonated soft/isotonic drinks0.005−0.0010.0090.7630.003−0.0040.0010.254
      Fruit and vegetables juices−0.000−0.0010.0120.1010.002−0.0020.0000.243
      β: regression coefficient. CI: confidence interval; Adjusting for confounders: age, maternal education, PA, total energy intake, BMI and city. Level of significance was set to 0.05.

      3.5 Relationship between PS mean intake from food groups and HOMA-IR cutoff categories by gender

      PS mean intake characteristics were obtained from the different food groups and HOMA-IR cutoff categories using mother's education as covariable (ANCOVA) for all participants (Table 5). The results indicate that males with lower HOMA-IR cutoff consumed higher mean PS from vegetables (p = 0.036) and milk, yoghurt, and milk beverages (p = 0.040). In the same line, females with lower HOMA-IR cutoff consumed higher mean PS from breakfast cereals (p = 0.010). Contrary, females with higher HOMA-IR cutoff, consumed higher mean PS from butter and animal fats (p = 0.018).
      Table 5Mean PS from the main contributing food groups by HOMA-IR cut off categories, in both gender (ANCOVA).
      Food groups PS (g/day)Males (n = 230)Females (n = 265)
      HOMA-IR cut off categorized based on the 90th percentile. SD: Standard deviation. Level of significance was set to 0.05.
      HOMA-IR cut off≤2.5
      HOMA-IR cut off >2.5p-valueHOMA-IR cut off≤2.5HOMA-IR cut off >2.5p-value
      NMean (SD)NMean (SD)NMean (SD)NMean (SD)
      Bread and rolls146134.1 (82.5)42144.2 (74.9)0.228166111.3 (70.7)6793.1 (49.3)0.493
      Breakfast cereals4854.6 (30.5)2170.7 (50.3)0.4796349.9 (28.6)1928.5 (10.8)0.010
      Rice and other grains57192.1 (135.9)15225.2 (117.5)0.81755156.2 (95.7)21128.0 (75.1)0.976
      Starch roots, potatoes76159.2 (89.1)16158.4 (62.4)0.993103142.4 (87.7)45123.8 (74.3)0.244
      Pasta64217.9 (109.9)14218.7 (105.7)0.69873178.1 (73.5)26215.0 (113.5)0.149
      Fruits98244.1 (153.3)26239.8 (153.9)0.778121206.2 (125.5)43194.1 (130.2)0.954
      Vegetables127193.4 (122.6)37136.4 (115.7)0.036149133.6 (112.3)63128.2 (99.7)0.784
      Milk, yoghurt, and milk beverages120434.1 (354.6)34348.9 (217.7)0.040137317.1 (212.9)51279.5 (168.4)0.188
      Desserts and puddings milk based29105.6 (74.1)652.3 (24.8)0.0605589.2 (68.9)1576.3 (45.7)0.693
      Cheese11957.4 (42.9)3177.9 (48.36)0.31412344.3 (34.2)5243.6 (29.9)0.989
      Meat and poultry147237.1 (200.6)37260.6 (225.3)0.265144161.6 (133.5)66178.5 (134.6)0.331
      Fish and fish products30208.5 (172.1)5108.6 (54.3)0.25130156.3 (132.9)13145.9 (118.9)0.439
      Eggs2853.5 (41.5)955.8 (34.1)0.5125251.5 (42.6)1562.4 (50.1)0.715
      Meat substitutes, nuts, pulses2961.5 (57.0)1027.9 (10.9)0.1204082.5 (76.1)1275.9 (56.4)0.962
      Margarines and vegetable oils10227.1 (13.4)2637.4 (30.1)0.1889718.9 (16.4)4319.9 (17.1)0.515
      Butter and animal fats5728.8 (23.5)1422.9 (13.6)0.4965818.9 (13.6)2527.3 (24.9)0.018
      Cakes, pies, biscuits109115.9 (84.8)26106.7 (78.2)0.79813298.5 (78.8)50104.3 (101.1)0.377
      Savoury snacks3862.8 (51.3)655.8 (42.9)0.6714338.2 (25.2)1432.9 (15.5)0.457
      Sugar, honey, jams, chocolate10674.4 (62.4)2668.7 (55.4)0.92013363.2 (50.8)5742.6 (36.5)0.138
      Sauces and creams10767.0 (52.8)2277.9 (49.1)0.49411364.2 (54.1)4473.9 (56.3)0.343
      Carbonated soft/isotonic drinks99529.9 (325.2)26529.9 (416.1)0.824102462.3 (309.4)32419.2 (262.6)0.869
      Fruit and vegetables juices103408.6 (319.7)30351.3 (209.3)0.162105332.2 (241.5)51352.9 (180.1)0.437
      a HOMA-IR cut off categorized based on the 90th percentile. SD: Standard deviation. Level of significance was set to 0.05.

      3.6 Relationship between PS mean intake from food groups and metabolic risk score median cutoff categories by gender

      The results indicate that no significant relationship between food PS and metabolic risk score median cutoff categories (Supplementary Table S5).

      4. Discussion

      The main results suggest that there is an association between PS of some food groups and a metabolic risk score in adolescence. Specifically, we identified that larger PS from cakes, pies, biscuits in males and females were associated with higher metabolic risk score. Meanwhile, PS from fish; meat substitutes, nuts, and pulses; and sugar, honey, jams, and chocolate were associated with a higher metabolic risk score in males, considering potential confounders, such as PA, total energy intake, BMI, city, and maternal education.

      4.1 Portion size of specific food groups and metabolic risk score components

      Out of the components of the metabolic risk score, significant results were found only for VO2 max, a marker of cardiorespiratory fitness. We found that larger PS from vegetables in both genders and milk, yoghurt, and milk beverages were associated with higher VO2 max in males, while larger PS from margarines and vegetable oils were associated with lower VO2 max in males. In general, fruit and vegetables are one of the most abundant source of natural flavonoids Quercetin [
      • Harwood M.
      • Danielewska-Nikiel B.
      • Borzelleca J.
      • Flamm G.W.
      • Williams G.M.
      • Lines T.C.
      Critical review of the data related to the safety of quercetin and lack of evidence of in vivo toxicity, including lack of genotoxic/carcinogenic properties.
      ]. Noteworthy, these compounds play an important role as antioxidant and anti-inflammatory activity, in addition to the most prominent role which is the ability to increase mitochondrial biogenesis in both muscle and brain in mice [
      • Davis J.
      • Murphy E.
      • Carmichael M.
      • Davis B.
      Quercetin increases brain and muscle mitochondrial biogenesis and exercise tolerance.
      ]. In adults, low doses of the naturally occurring dietary flavonoid quercetin were associated with a modestly higher VO2 max [
      • Davis J.
      • Carlstedt C.
      • Chen S.
      • Carmichael M.D.
      • Murphy E.A.
      The dietary flavonoid quercetin increases VO(2max) and endurance capacity.
      ]. Similarly, in young adult, it has been observed a significantly higher levels of VO2 max in the vegetarian compared with omnivores [
      • Boutros G.
      • Landry-Duval M.
      • Garzon M.
      • Karelis A.D.
      Is a vegan diet detrimental to endurance and muscle strength?.
      ]. Moreover, it has been found that adolescents with the highest cardiorespiratory fitness are the most active and tend to consume higher fruits and vegetables [
      • Howe A.
      • Skidmore P.
      • Parnell W.
      • Wong J.E.
      • Lubransky A.C.
      • Black K.E.
      Cardiorespiratory fitness is positively associated with a healthy dietary pattern in New Zealand adolescents.
      ].

      4.2 Portion size of specific food groups and metabolic risk score

      Regarding metabolic risk score, we found that larger PS from cakes, pies, biscuits in males and females were associated with higher metabolic risk score. PS from fish and fish products; meat substitutes; nuts, pulses, and sugar; and honey, jams, and chocolate were associated with higher metabolic risk score in males. In a previous study in adolescents, significant differences were found, in the consumption of pretzels, chips, ham, and burgers between the group with MS and those who did not have the syndrome [
      • Pedrozo W.
      • Rascón M.
      • Bonneau G.
      • de Pianesi M.I.
      • Olivera C.C.
      • de Aragón S.J.
      • et al.
      Revista panamericana de salud publica = Pan.
      ].
      In adult men, it has been noticed that seafood consumption was significantly associated with elevated high-sensitivity C-reactive protein levels, after adjustment for age, PA, and BMI [
      • Nanri H.
      • Nakamura K.
      • Hara M.
      • Higaki Y.
      • Imaizumi T.
      • Taguchi N.
      • et al.
      Association between dietary pattern and serum C-reactive protein in Japanese men and women.
      ]. However, contrary to our results, a study found that low-fat meats and fish and fish products consumption was negatively associated with the inflammatory response in adults [
      • Yeo R.
      • Yoon S.
      • Kim O.
      The association between food group consumption patterns and early metabolic syndrome risk in non-diabetic healthy people.
      ]. In this study, higher intake of fish and fish products was associated with higher metabolic risk score. The benefits of increased fish and fish products consumption on health have been associated to its content of omega 3 and PUFA, but fish consumption contributes with considerable amounts of other nutrients that may have an effect on the metabolic score [
      • Carpentier Y.
      • Portois L.
      • Malaisse W.
      n-3 fatty acids and the metabolic syndrome.
      ]. The possible explanation of our results is that processed fish such as fish cakes, fish balls, fish pudding, and fish fingers are made from lean fish filet mixed with other ingredients like: milk and flour; moreover, it has been found that these products contain free or added sugars and saturated and trans-fats that makes them high energy dense food, these fish products represent nearly 40% of the total fish consumption [
      • Christine T.
      • Marianne M.
      • Milada C.
      Lean fish consumption is associated with beneficial changes in the metabolic syndrome components: a 13-year follow-up study from the Norwegian tromsø study.
      ]. The lack of health benefits from processed fish may partly be explained by a reduction of some of the nutrients present during the processing such as deep-fried, fried, boiled, or minced, and therefore, they contain a higher amount of total fat. Additionally, these products previously contained trans-fatty acids that are known to be associated with lowered HDL-level [
      • Yanai H.
      • Katsuyama H.
      • Hamasaki H.
      • Abe S.
      • Tada N.
      • Sako A.
      Effects of dietary fat intake on HDL metabolism.
      ].
      Moreover, an increased frequency of consumption of fruits and vegetables, and dairy products decreased the probability of having MS in adolescents [
      • Kelishadi R.
      • Gouya M.
      • Adeli K.
      • Ardalan G.
      • Gheiratmand R.
      • Majdzadeh R.
      • et al.
      Factors associated with the metabolic syndrome in a national sample of youths: CASPIAN Study. Nutrition, metabolism, and cardiovascular diseases.
      ], while the probability of having MS increased along with the consumption of solid hydrogenated fat, and bread made with white flour in both genders [
      • Kelishadi R.
      • Gouya M.
      • Adeli K.
      • Ardalan G.
      • Gheiratmand R.
      • Majdzadeh R.
      • et al.
      Factors associated with the metabolic syndrome in a national sample of youths: CASPIAN Study. Nutrition, metabolism, and cardiovascular diseases.
      ]. Similarly, a systematic review focusing on European dietary patterns and MS from various age groups concluded that higher intake of meat or meat products, desserts, and sugar-sweetened beverages, which are considered as a good source of saturated fatty acids, salts, and added sugars, have been associated with higher risk of MS [
      • Martínez-González M.
      • Martín-Calvo N.
      The major European dietary patterns and metabolic syndrome.
      ]. In contrast, higher intake from vegetables, fruits, whole cereals, and fish and fish products were associated with a reduced risk of MS [
      • Martínez-González M.
      • Martín-Calvo N.
      The major European dietary patterns and metabolic syndrome.
      ].
      Interestingly, the associations between PS of sugar-rich products such as cakes, pies, and biscuits and sugar, honey, jams, and chocolate were associated with a higher metabolic risk score. It has been confirmed that sucrose and mainly fructose induce MS [
      • Raben A.
      • Vasilaras T.
      • Møller A.
      • Astrup A.
      Sucrose compared with artificial sweeteners: different effects on ad libitum food intake and body weight after 10 wk of supplementation in overweight subjects.
      ]. For example, in young men, serum triacylglycerol increased when receiving a diet supplemented with 200 g sucrose/day; moreover, one-third of these patients developed hyperinsulinemia [
      • Raben A.
      • Vasilaras T.
      • Møller A.
      • Astrup A.
      Sucrose compared with artificial sweeteners: different effects on ad libitum food intake and body weight after 10 wk of supplementation in overweight subjects.
      ]. In addition, consumption of sugar-sweetened beverages has the same result on MS development, which may be explained by less satiety-inducing effects and a high-glycemic-index, which raises the postprandial glucose levels [
      • McKeown N.
      • Meigs J.
      • Liu S.
      • Saltzman E.
      • Wilson P.W.
      • Jacques P.F.
      Carbohydrate nutrition, insulin resistance, and the prevalence of the metabolic syndrome in the Framingham Offspring Cohort.
      ]. The mechanism was associated to the inability of sugar to acutely stimulate insulin and leptin and to inhibit ghrelin that are known to affect the satiety center in the central nervous system [
      • Teff K.
      • Elliott S.
      • Tschöp M.
      • Kieffer T.J.
      • Rader D.
      • Heiman M.
      • et al.
      Dietary fructose reduces circulating insulin and leptin, attenuates postprandial suppression of ghrelin, and increases triglycerides in women.
      ]. Moreover, the sweetness of fructose or sucrose often makes food more palatable, which may stimulate an increase in food intake [
      • Yudkin J.
      Evolutionary and historical changes in dietary carbohydrates.
      ].

      4.3 Portion size of specific food groups and HOMA-IR cutoff

      Our results indicated that males with lower HOMA-IR cutoff were consuming higher mean PS from vegetables, milk, yoghurt, and milk beverages. In the same manner, females with lower HOMA-IR cutoff were consuming higher mean PS from breakfast cereals. In contrast, females with higher HOMA-IR cutoff were consuming higher mean PS from butter and animal fats. These results are in line with other studies which observed that consumption of vegetables, low-fat dairy, cruciferous vegetables, tomato, chicken, and beans and low consumption of butter, red meat, and cereals was associated with low levels of HOMA-IR in adults [
      • Ehrampoush E.
      • Nazari N.
      • younfar R.
      • Ghaemi A.
      • Osati S.
      • Tahamtan S.
      • et al.
      ]. On the contrary, they found that consumption of processed meats, mayonnaise, and solid fats was associated with a higher level of fasting blood glucose, fasting insulin, 2-h insulin, and HOMA-IR index [
      • Ehrampoush E.
      • Nazari N.
      • younfar R.
      • Ghaemi A.
      • Osati S.
      • Tahamtan S.
      • et al.
      ]. A previous HELENA study also found that frequently consumption of nuts, chocolates, burgers, and meat stick in females and frequently consumption of burgers and pizzas in males were directly associated with HOMA-IR index [
      • Sesé M.
      • Jiménez-Pavón D.
      • Gilbert C.
      • González-Gross M.
      • Gottrand F.
      • de Henauw S.
      • et al.
      Eating behaviour, insulin resistance and cluster of metabolic risk factors in European adolescents.
      ]. Moreover, a marked preference for sweet and low fruit consumption and IR were observed in other study in adolescents [
      • Pedrozo W.
      • Rascón M.
      • Bonneau G.
      • de Pianesi M.I.
      • Olivera C.C.
      • de Aragón S.J.
      • et al.
      Revista panamericana de salud publica = Pan.
      ]. However, to the best of our knowledge, there are no studies addressing the relationship between food PS and HOMA-IR index.
      The possible explanation of these results is that vegetables, fruits, and cereals are a good source of antioxidants mainly polyphenols and fibers, in addition to the significant amount of magnesium, calcium, potassium and having a limited amount of sodium, which play an important role in IR in all age groups [
      • Guasch-Ferré M.
      • Merino J.
      • Sun Q.
      • Fitó M.
      • Salas-Salvadó J.
      Dietary polyphenols, Mediterranean diet, prediabetes, and type 2 diabetes: a narrative review of the evidence. Oxidative medicine and cellular longevity.
      ]. For instance, individuals with low levels of serum magnesium have impaired blood sugar levels [
      • Ferguson M.A.
      • Gutin B.
      • Owens S.
      • Litaker M.
      • Tracy R.P.
      • Allison J.
      Fat distribution and hemostatic measures in obese children.
      ]; it has been found that the lack of magnesium can result in disordered transfer of the cellular glucose, which influencing in insulin signaling pathways or even reducing the pancreatic secretion of insulin [
      • Sales C.
      • Pedrosa L.
      • Lima J.
      • Lemos T.M.
      • Colli C.
      Influence of magnesium status and magnesium intake on the blood glucose control in patients with type 2 diabetes.
      ]. Furthermore, a high amount of sodium intake has been associated with IR and MS development in adults [
      • Baudrand R.
      • Campino C.
      • Carvajal C.
      • Olivieri O.
      • Guidi G.
      • Faccini G.
      • et al.
      High sodium intake is associated with increased glucocorticoid production, insulin resistance and metabolic syndrome.
      ].
      Moreover, the type and quality of fats in the diet play an important role in homeostasis and insulin sensitivity [
      • Sears B.
      • Perry M.
      The role of fatty acids in insulin resistance.
      ]. It seems that the fatty acid combination or fat type can affect independently on insulin function and lead to change the cells sensitivity to insulin [
      • Silva Figueiredo P.
      • Carla Inada A.
      • Marcelino G.
      • Maiara Lopes Cardozo C.
      • de Cássia Freitas K.
      • de Cássia Avellaneda Guimarães R.
      • et al.
      Fatty acids consumption: the role metabolic aspects involved in obesity and its associated disorders.
      ]. However, in adolescents with obesity, a positive effect when consumption of omega-3 fatty acids on insulin sensitivity has been identified [
      • Dangardt F.
      • Chen Y.
      • Gronowitz E.
      • Dahlgren J.
      • Friberg P.
      • Strandvik B.
      High physiological omega-3 Fatty Acid supplementation affects muscle Fatty Acid composition and glucose and insulin homeostasis in obese adolescents.
      ]. Consumption of milk and yogurt is another factor that has been examined on IR syndrome; it has been shown that the daily consumption of dairy and calcium cause a reduction of the IR syndrome, cardiovascular disease, blood pressure, and stroke in young adults [
      • Ehrampoush E.
      • Nazari N.
      • younfar R.
      • Ghaemi A.
      • Osati S.
      • Tahamtan S.
      • et al.
      ].
      In this study, no significant relationship between food PS and continuous HOMA-IR index was found, which may be associated with the very restrictive model with various confounders. However, some relevant results when analyzing the results considering the HOMA-IR cutoff values were observed, as it was discussed previously.
      Several studies have attempted to explain the association of dietary indexes on the development of IR and MS, but most of them do not focus on PS of food items. Although the effect sizes in our study were small, further studies are needed to confirm the association between food PS and the metabolic markers.
      The main limitation of this study is the relatively small sample size. Further studies with larger population samples and longitudinal observation are needed. Moreover, the HELENA study was performed some years ago, and it is not useful to describe the current situation. Additionally, the cross-sectional nature of the HELENA study does not allow us to assess the behavior over a period of time and did not provide information in determining the cause–and–effect association. The self-reported questionnaires were used for collecting the food consumption data, and therefore, a social bias should be considered. Moreover, the food groups did not differentiate between type of chocolate (black, with milk, etc) and artificially sweetened products from sugar-sweetened products. However, there are several strengths in this study that need to be mentioned. To our best of knowledge, the present study is the first to investigate the association between the PS of different food groups and both IR and metabolic risk among European adolescents, taking into account potential confounders such as age, total energy intake, BMI, PA, city, and maternal education. To increase the accuracy, highly standardized and validated procedures were used to collect the sample and assess anthropometric measurements. Despite the limitation, these results may suggest useful information to promote proper selection of healthy foods for early prevention of MS and other health concerns.

      Conclusion

      Larger PS from cakes, pies, and biscuits were associated with a higher metabolic risk score, and PS from fish and fish products; meat substitutes; nuts, pulses, and sugar; and honey, jams, and chocolate were associated with a higher metabolic risk score in males. Noteworthy, these results suggests that there are associations between PS of sugar-based foods and metabolic risk already in adolescence. Larger PS from vegetables, cereals, and dairy products enhance the VO2 max and might reduce developing IR. Overall, these findings suggest that intervention studies should focus on the food PS and not only on the potential effect of food habits and energy density in order to prevent IR and metabolic risk in youth.

      Author contributions

      The HELENA study was designed and contributed to get the funding by Moreno. L, Gonzalez-Gross. M, Castillo. M, Molnár.D, Stehle. P, Widhalm. K, Kafatos. A, and Dallongeville.J. The supervision procedure and acquisition of data were done by Moreno. L, and Gonzalez-Gross.M. Field work contribution and data analysis were conducted by Castillo. M, Marcos. A, Gottrand. F, Huybrechts. I and the rest of authors. Molnár.D was responsible for the body composition work package. Flieh. S analyzed the data and wrote the manuscript Miguel-Berges. M, González-Gil. EM and Moreno. L critically revised the manuscript, provided essential comments, and supervised all procedures. All co-author revised the manuscript and provided their essential comments. All authors have read and agreed to the published version of the manuscript.

      Funding

      HELENA study received funding from the European Community Sixth RTD Framework Program (Contract FOODCT-2005-007034). E.M.G.-G. holds a Juan de la Cierva-Formación grant from the Spanish Government (FJCI-2017-34,967).

      Institutional review board statement

      The HELENA study was approved by the ethics commit-tees in all countries, and followed good clinical practice, ethical guidelines of the Declaration of Helsinki 1964 (revision of 2000), and the legislation about clinical research in humans in each one of the countries involved in the study. The ethical approval code from the coordinator centre was 03/2006; date of approval: February 2006, obtained from the Ethical Committee of clinical research in Aragon (CEICA).

      Informed consent statement

      Informed consent was obtained from all subjects involved in the study.

      Data availability statement

      The data presented in this study are available for further scientific analysis on request from the coordinator of the HELENA study to the following. e-mail: [email protected] .

      IARC disclaimer

      Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article, and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer/World Health Organization.

      Declaration of competing interest

      The authors declare no conflict of interest.

      Acknowledgments

      We are grateful for the support provided by school boards, headmasters, teachers, school staff and communities, and the effort of all study nurses, laboratory technicians, and our data managers.

      Appendix A. Supplementary data

      The following is the Supplementary data to this article:

      HELENA Study Group.

      Coordinator: Luis A. Moreno.
      Core Group members: Luis A. Moreno, Fréderic Gottrand, Stefaan De Henauw, Marcela González-Gross, Chantal Gilbert.
      Steering Committee: Anthony Kafatos (President), Luis A. Moreno, Christian Libersa, Stefaan De Henauw, Sara Castelló, Fréderic Gottrand, Mathilde Kersting, Michael Sjöstrom, Dénes Molnár, Marcela González-Gross, Jean Dallongeville, Chantal Gilbert, Gunnar Hall, Lea Maes, Luca Scalfi.
      Project Manager: Pilar Meléndez.
      1. Universidad de Zaragoza (Spain):
      Luis A. Moreno, José A. Casajús, Jesús Fleta, Gerardo Rodríguez, Concepción Tomás, María I. Mesana, Germán Vicente-Rodríguez, Adoración Villarroya, Carlos M. Gil, Ignacio Ara, Juan Fernández Alvira, Gloria Bueno, Olga Bueno, Juan F. León, Jesús Ma Garagorri, Idoia Labayen, Iris Iglesia, Silvia Bel, Luis A. Gracia Marco, Theodora Mouratidou, Alba Santaliestra-Pasías, Iris Iglesia, Esther González-Gil, Pilar De Miguel-Etayo, Mary Miguel-Berges, Isabel Iguacel, Azahara Rupérez.
      2. Consejo Superior de Investigaciones Científicas (Spain):
      Ascensión Marcos, Julia Wärnberg, Esther Nova, Sonia Gómez, Ligia Esperanza Díaz, Javier Romeo, Ana Veses, Belén Zapatera, Tamara Pozo, David Martínez.
      3. Université de Lille 2 (France):
      Laurent Beghin, Christian Libersa, Frédéric Gottrand, Catalina Iliescu, Juliana Von Berlepsch.
      4. Research Institute of Child Nutrition Dortmund, Rheinische Friedrich–Wilhelms–Universität Bonn (Germany):
      Mathilde Kersting, Wolfgang Sichert-Hellert, Ellen Koeppen.
      5. Pécsi Tudományegyetem (University of Pécs) (Hungary):
      Dénes Molnar, Eva Erhardt, Katalin Csernus, Katalin Török, Szilvia Bokor, Mrs. Angster, Enikö Nagy, Orsolya Kovács, Judit Répasi.
      6. University of Crete School of Medicine (Greece):
      Anthony Kafatos, Caroline Codrington, María Plada, Angeliki Papadaki, Katerina Sarri, Anna Viskadourou, Christos Hatzis, Michael Kiriakakis, George Tsibinos, Constantine Vardavas, Manolis Sbokos, Eva Protoyeraki, Maria Fasoulaki.
      7. Institut für Ernährungs-und Lebensmittelwissenschaften–Ernährungphysiologie. Rheinische Friedrich Wilhelms Universität (Germany):
      Peter Stehle, Klaus Pietrzik, Marcela González-Gross, Christina Breidenassel, Andre Spinneker, Jasmin Al-Tahan, Miriam Segoviano, Anke Berchtold, Christine Bierschbach, Erika Blatzheim, Adelheid Schuch, Petra Pickert.
      8. University of Granada (Spain):
      Manuel J. Castillo, Ángel Gutiérrez, Francisco B Ortega, Jonatan R Ruiz, Enrique G Artero, Vanesa España, David Jiménez-Pavón, Palma Chillón, Cristóbal Sánchez-Muñoz, Magdalena Cuenca.
      9. Council for Agricultural Research and Economics, Research Centre for Food and Nutrition (Italy) (former INRAN):
      Davide Arcella, Elena Azzini, Emma Barrison, Noemi Bevilacqua, Pasquale Buonocore, Giovina Catasta, Laura Censi, Donatella Ciarapica, Paola D'Acapito, Marika Ferrari, Myriam Galfo, Cinzia Le Donne, Catherine Leclercq, Giuseppe Maiani, Beatrice Mauro, Lorenza Mistura, Antonella Pasquali, Raffaela Piccinelli, Angela Polito, Romana Roccaldo, Raffaella Spada, Stefania Sette, Maria Zaccaria.
      10. University of Napoli “Federico II” Dept of Food Science (Italy):
      Luca Scalfi, Paola Vitaglione, Concetta Montagnese.
      11. Ghent University (Belgium):
      Ilse De Bourdeaudhuij, Stefaan De Henauw, Tineke De Vriendt, Lea Maes, Christophe Matthys, Carine Vereecken, Mieke de Maeyer, Charlene Ottevaere, Inge Huybrechts.
      12. Medical University of Vienna (Austria):
      Kurt Widhalm, Katharina Phillipp, Sabine Dietrich, Birgit Kubelka, Marion Boriss-Riedl.
      13. Harokopio University (Greece):
      Yannis Manios, Eva Grammatikaki, Zoi Bouloubasi, Tina Louisa Cook, Sofia Eleutheriou, Orsalia Consta, George Moschonis, Ioanna Katsaroli, George Kraniou, Stalo Papoutsou, Despoina Keke, Ioanna Petraki, Elena Bellou, Sofia Tanagra, Kostalenia Kallianoti, Dionysia Argyropoulou, Stamatoula Tsikrika, Christos Karaiskos.
      14. Institut Pasteur de Lille (France):
      Jean Dallongeville, Aline Meirhaeghe.
      15. Karolinska Institutet (Sweden):
      Michael Sjöstrom, Jonatan R Ruiz, Francisco B. Ortega, María Hagströmer, Anita Hurtig Wennlöf, Lena Hallström, Emma Patterson, Lydia Kwak, Julia Wärnberg, Nico Rizzo.
      16. Asociación de Investigación de la Industria Agroalimentaria (Spain):
      Jackie Sánchez-Molero, Sara Castelló, Elena Picó, Maite Navarro, Blanca Viadel, José Enrique Carreres, Gema Merino, Rosa Sanjuán, María Lorente, María José Sánchez.
      17. Campden BRI (United Kingdom):
      Chantal Gilbert, Sarah THOMA-IRs, Elaine Allchurch, Peter Burgess.
      18. SIK - Institutet foer Livsmedel och Bioteknik (Sweden):
      Gunnar Hall, Annika Astrom, Anna Sverkén, Agneta Broberg.
      19. Meurice Recherche & Development asbl (Belgium):
      Annick Masson, Claire Lehoux, Pascal Brabant, Philippe Pate, Laurence Fontaine.
      20. Campden & Chorleywood Food Development Institute (Hungary):
      Andras Sebok, Tunde Kuti, Adrienn Hegyi.
      21. Productos Aditivos SA (Spain):
      Cristina Maldonado, Ana Llorente.
      22. Cárnicas Serrano SL (Spain):
      Emilio García.
      23. Cederroth International AB (Sweden):
      Holger von Fircks, Marianne Lilja Hallberg, Maria Messerer.
      24. Lantmännen Food R&D (Sweden):
      Mats Larsson, Helena Fredriksson, Viola Adamsson, Ingmar Börjesson.
      25. European Food Information Council (Belgium):
      Laura Fernández, Laura Smillie, Josephine Wills.
      26. Universidad Politécnica de Madrid (Spain):
      Marcela González-Gross, Raquel Pedrero-Chamizo, Agustín Meléndez, Jara Valtueña, David Jiménez-Pavón, Ulrike Albers, Pedro J. Benito, Juan José Gómez Lorente, David Cañada, Alejandro Urzanqui, Rosa María Torres, Paloma Navarro.

      References

        • DeBoer M.
        Assessing and managing the metabolic syndrome in children and adolescents.
        Nutrients. 2019; 11
        • Kao K.
        • Sabin M.
        Type 2 diabetes mellitus in children and adolescents.
        Aust Fam Physician. 2016; 45
        • Zimmermann M.
        • Aeberli I.
        Dietary determinants of subclinical inflammation, dyslipidemia and components of the metabolic syndrome in overweight children: a review.
        Int J Obes. 2008; 32 (2005)
        • Christensen P.
        • Meinert Larsen T.
        • Westerterp-Plantenga M.
        • Macdonald I.
        • Martinez J.A.
        • Handjiev S.
        • et al.
        Men and women respond differently to rapid weight loss: metabolic outcomes of a multi-centre intervention study after a low-energy diet in 2500 overweight, individuals with pre-diabetes (PREVIEW).
        Diabetes Obes Metabol. 2018; 20
        • Kim J.
        • Jeon J.
        Role of exercise on insulin sensitivity and beta-cell function: is exercise sufficient for the prevention of youth-onset type 2 diabetes?.
        Ann Pediat Endocrinol Metabol. 2020; 25
        • Reaven G.
        Insulin resistance: the link between obesity and cardiovascular disease.
        Med Clin. 2011; 95
        • Aeberli I.
        • Spinas G.
        • Lehmann R.
        • l’Allemand D.
        • Molinari L.
        • Zimmermann M.B.
        Diet determines features of the metabolic syndrome in 6- to 14-year-old children.
        Int J Vitam Nutrit Res Int Zeitsch Vitam Ernahrungsforschung J Int Vitaminol Nutri. 2009; 79
        • Apovian C.
        The causes, prevalence, and treatment of obesity revisited in 2009: what have we learned so far?.
        Am J Clin Nutr. 2010; 91
        • Flieh S.
        • Miguel-Berges M.
        • González-Gil E.
        • Gottrand F.
        • Censi L.
        • Widhalm K.
        • et al.
        The association between portion sizes from high-energy-dense foods and body composition in European adolescents: the HELENA study.
        Nutrients. 2021; 13
        • Young L.
        • Nestle M.
        Expanding portion sizes in the US marketplace: implications for nutrition counseling.
        J Am Diet Assoc. 2003; 103
        • Karatzi K.
        • Moschonis G.
        • Barouti A.
        • Lionis C.
        • Chrousos G.P.
        • Manios Y.
        Dietary patterns and breakfast consumption in relation to insulin resistance in children.
        Healthy Growth Study Public Health Nutr. 2014; 17
        • Sesé M.
        • Jiménez-Pavón D.
        • Gilbert C.
        • González-Gross M.
        • Gottrand F.
        • de Henauw S.
        • et al.
        Eating behaviour, insulin resistance and cluster of metabolic risk factors in European adolescents.
        HELENA study. 2012; 59 (Appetite)
        • Kynde I.
        • Johnsen N.
        • Wedderkopp N.
        • Bygbjerg I.B.
        • Helge J.W.
        • Heitmann B.L.
        Intake of total dietary sugar and fibre is associated with insulin resistance among Danish 8-10- and 14-16-year-old girls but not boys. European Youth Heart Studies I and II.
        Publ Health Nutr. 2010; 13
        • Harrington S.
        The role of sugar-sweetened beverage consumption in adolescent obesity: a review of the literature.
        J Sch Nurs Offic Publ Nat Assoc School Nurses. 2008; 24
        • Pedrozo W.
        • Rascón M.
        • Bonneau G.
        • de Pianesi M.I.
        • Olivera C.C.
        • de Aragón S.J.
        • et al.
        Revista panamericana de salud publica = Pan.
        Am J Publ Health. 2008; 24
        • Ambrosini G.
        • Huang R.
        • Mori T.
        • Hands B.P.
        • O’Sullivan T.A.
        • de Klerk N.H.
        • et al.
        Dietary patterns and markers for the metabolic syndrome in Australian adolescents. Nutrition, metabolism, and cardiovascular diseases.
        Nutr Metabol Cardiovasc Dis. 2010; 20
        • Moreno L.
        • González-Gross M.
        • Kersting M.
        • Molnár D.
        • de Henauw S.
        • Beghin L.
        • et al.
        Assessing, understanding and modifying nutritional status, eating habits and physical activity in European adolescents: the HELENA (healthy Lifestyle in Europe by nutrition in adolescence) study.
        Publ Health Nutr. 2008; 11
        • Henauw S.D.
        • Gottrand F.
        • Bourdeaudhuij I.D.
        • Gonzalez-Gross M.
        • Leclercq C.
        • Kafatos A.
        • et al.
        Nutritional status and lifestyles of adolescents from a public health perspective. The HELENA Project—healthy Lifestyle in Europe by nutrition in adolescence. OriginalPaper.
        J Public Health. 2007; 15: 187-197
        • Moreno L.
        • De Henauw S.
        • González-Gross M.
        • Kersting M.
        • Molnár D.
        • Gottrand F.
        • et al.
        Design and implementation of the healthy Lifestyle in Europe by nutrition in adolescence cross-sectional study.
        Int J Obes. 2008; 32 (2005)
        • Goldberg G.
        • Black A.
        • Jebb S.
        • Cole T.J.
        • Murgatroyd P.R.
        • Coward W.A.
        • et al.
        Critical evaluation of energy intake data using fundamental principles of energy physiology: 1. Derivation of cut-off limits to identify under-recording.
        Eur J Clin Nutr. 1991; 45
      1. The world medical association-declaration of Helsinki. Initiated, 2000 (1964:17.C)
        • Béghin L.
        • Castera M.
        • Manios Y.
        • Gilbert C.C.
        • Kersting M.
        • De Henauw S.
        • et al.
        Quality assurance of ethical issues and regulatory aspects relating to good clinical practices in the HELENA cross-sectional study.
        Int J Obes. 2008; 32 (2005)
      2. UNESCO. International Standard Classification of Education. http://www.uis.unesco.org/Education/Documents/isced-2011-en.pdf.

        • Béghin L.
        • Dauchet L.
        • De Vriendt T.
        • Cuenca-García M.
        • Manios Y.
        • Toti E.
        • et al.
        Influence of parental socio-economic status on diet quality of European adolescents: results from the HELENA study.
        Br J Nutr. 2014; 111
        • Nagy E.
        • Vicente-Rodriguez G.
        • Manios Y.
        • Béghin L.
        • Iliescu C.
        • Censi L.
        • et al.
        Harmonization process and reliability assessment of anthropometric measurements in a multicenter study in adolescents.
        Int J Obes. 2008; 32 (2005)
        • Cole T.
        • Bellizzi M.
        • Flegal K.
        • Dietz W.
        Establishing a standard definition for Child overweight and obesity worldwide: international Survey.
        BMJ (Clinical research ed). 2000; 320
        • Slaughter M.
        • Lohman T.
        • Boileau R.
        • Horswill C.A.
        • Stillman R.J.
        • Van Loan M.D.
        • et al.
        Skinfold equations for estimation of body fatness in children and youth.
        Hum Biol. 1988; 60
        • Hagströmer M.
        • Bergman P.
        • De Bourdeaudhuij I.
        • Ortega F.B.
        • Ruiz J.R.
        • Manios Y.
        • et al.
        Concurrent validity of a modified version of the international physical activity questionnaire (IPAQ-A) in European adolescents: the HELENA study.
        Int J Obes. 2005; 32
        • Ainsworth B.
        • Haskell W.
        • Whitt M.
        • Irwin M.L.
        • Swartz A.M.
        • Strath S.J.
        • et al.
        Compendium of physical activities: an update of activity codes and MET intensities.
        Med Sci Sports Exerc. 2000; 32
        • González-Gross M.
        • Breidenassel C.
        • Gómez-Martínez S.
        • Ferrari M.
        • Béghin L.
        • Spinneker A.
        • et al.
        Sampling and processing of fresh blood samples within a European multicenter nutritional study: evaluation of biomarker stability during transport and storage.
        Int J Obes. 2008; 32 (2005)
        • Léger L.
        • Lambert J.
        • Goulet A.
        • Rowan C.
        • Dinelle Y.
        Canadian journal of applied sport sciences.
        J Can Sci Appl Sport. 1984; 9
        • Katz A.
        • Nambi S.
        • Mather K.
        • Baron A.D.
        • Follmann D.A.
        • Sullivan G.
        • et al.
        Quantitative insulin sensitivity check index: a simple, accurate method for assessing insulin sensitivity in humans.
        J Clin Endocrinol Metab. 2000; 85
        • Andersen L.
        • Harro M.
        • Sardinha L.
        • Froberg K.
        • Ekelund U.
        • Brage S.
        • et al.
        Physical activity and clustered cardiovascular risk in children: a cross-sectional study (The European Youth Heart Study).
        Lancet (London, England). 2006; 368
        • Vereecken C.
        • Covents M.
        • Sichert-Hellert W.
        • Alvira J.M.
        • Le Donne C.
        • De Henauw S.
        • et al.
        Development and evaluation of a self-administered computerized 24-h dietary recall method for adolescents in Europe.
        Int J Obes. 2008; 32 (2005)
        • Lioret S.
        • Volatier J.L.
        • Lafay L.
        • Touvier M.
        • Maire B.
        Is food portion size a risk factor of childhood overweight?.
        Eur J Clin Nutr. 2009; 63: 382-391
        • Pereira J.
        • Mendes A.
        • Crispim S.
        • Marchioni D.M.
        • Fisberg R.M.
        Association of overweight with food portion size among adults of são Paulo - Brazil.
        PLoS One. 2016; 11
        • Harwood M.
        • Danielewska-Nikiel B.
        • Borzelleca J.
        • Flamm G.W.
        • Williams G.M.
        • Lines T.C.
        Critical review of the data related to the safety of quercetin and lack of evidence of in vivo toxicity, including lack of genotoxic/carcinogenic properties.
        Food Chem Toxicol Int J Publ Br Ind Biol Res Assoc. 2007; 45
        • Davis J.
        • Murphy E.
        • Carmichael M.
        • Davis B.
        Quercetin increases brain and muscle mitochondrial biogenesis and exercise tolerance.
        Am J Physiol Regul Integr Comp Physiol. 2009; 296
        • Davis J.
        • Carlstedt C.
        • Chen S.
        • Carmichael M.D.
        • Murphy E.A.
        The dietary flavonoid quercetin increases VO(2max) and endurance capacity.
        Int J Sport Nutr Exerc Metabol. 2010; 20
        • Boutros G.
        • Landry-Duval M.
        • Garzon M.
        • Karelis A.D.
        Is a vegan diet detrimental to endurance and muscle strength?.
        Eur J Clin Nutr. 2020; 74
        • Howe A.
        • Skidmore P.
        • Parnell W.
        • Wong J.E.
        • Lubransky A.C.
        • Black K.E.
        Cardiorespiratory fitness is positively associated with a healthy dietary pattern in New Zealand adolescents.
        Publ Health Nutr. 2016; 19
        • Nanri H.
        • Nakamura K.
        • Hara M.
        • Higaki Y.
        • Imaizumi T.
        • Taguchi N.
        • et al.
        Association between dietary pattern and serum C-reactive protein in Japanese men and women.
        J Epidemiol. 2011; 21
        • Yeo R.
        • Yoon S.
        • Kim O.
        The association between food group consumption patterns and early metabolic syndrome risk in non-diabetic healthy people.
        Clin Nutr Res. 2017; 6
        • Carpentier Y.
        • Portois L.
        • Malaisse W.
        n-3 fatty acids and the metabolic syndrome.
        Am J Clin Nutr. 2006; 83
        • Christine T.
        • Marianne M.
        • Milada C.
        Lean fish consumption is associated with beneficial changes in the metabolic syndrome components: a 13-year follow-up study from the Norwegian tromsø study.
        Nutrients. 2017; 9 (in en): 247
        • Yanai H.
        • Katsuyama H.
        • Hamasaki H.
        • Abe S.
        • Tada N.
        • Sako A.
        Effects of dietary fat intake on HDL metabolism.
        J Clin Med Res. 2015; 7
        • Kelishadi R.
        • Gouya M.
        • Adeli K.
        • Ardalan G.
        • Gheiratmand R.
        • Majdzadeh R.
        • et al.
        Factors associated with the metabolic syndrome in a national sample of youths: CASPIAN Study. Nutrition, metabolism, and cardiovascular diseases.
        Nutr Metabol Cardiovasc Dis. 2008; 18
        • Martínez-González M.
        • Martín-Calvo N.
        The major European dietary patterns and metabolic syndrome.
        Rev Endocr Metab Disord. 2013; 14
        • Raben A.
        • Vasilaras T.
        • Møller A.
        • Astrup A.
        Sucrose compared with artificial sweeteners: different effects on ad libitum food intake and body weight after 10 wk of supplementation in overweight subjects.
        Am J Clin Nutr. 2002; 76
        • McKeown N.
        • Meigs J.
        • Liu S.
        • Saltzman E.
        • Wilson P.W.
        • Jacques P.F.
        Carbohydrate nutrition, insulin resistance, and the prevalence of the metabolic syndrome in the Framingham Offspring Cohort.
        Diabetes Care. 2004; 27
        • Teff K.
        • Elliott S.
        • Tschöp M.
        • Kieffer T.J.
        • Rader D.
        • Heiman M.
        • et al.
        Dietary fructose reduces circulating insulin and leptin, attenuates postprandial suppression of ghrelin, and increases triglycerides in women.
        J Clin Endocrinol Metab. 2004; 89
        • Yudkin J.
        Evolutionary and historical changes in dietary carbohydrates.
        Am J Clin Nutr. 1967; 20
        • Ehrampoush E.
        • Nazari N.
        • younfar R.
        • Ghaemi A.
        • Osati S.
        • Tahamtan S.
        • et al.
        Association between dietary patterns with insulin resistance in an Iranian population. vol. 36. Clinical nutrition ESPEN, 2020
        • Guasch-Ferré M.
        • Merino J.
        • Sun Q.
        • Fitó M.
        • Salas-Salvadó J.
        Dietary polyphenols, Mediterranean diet, prediabetes, and type 2 diabetes: a narrative review of the evidence. Oxidative medicine and cellular longevity.
        Epub, 2017
        • Ferguson M.A.
        • Gutin B.
        • Owens S.
        • Litaker M.
        • Tracy R.P.
        • Allison J.
        Fat distribution and hemostatic measures in obese children.
        Am J Clin Nutr. 1998; 67: 1136-1140
        • Sales C.
        • Pedrosa L.
        • Lima J.
        • Lemos T.M.
        • Colli C.
        Influence of magnesium status and magnesium intake on the blood glucose control in patients with type 2 diabetes.
        Clin Nutr (Edinb). 2011; 30
        • Baudrand R.
        • Campino C.
        • Carvajal C.
        • Olivieri O.
        • Guidi G.
        • Faccini G.
        • et al.
        High sodium intake is associated with increased glucocorticoid production, insulin resistance and metabolic syndrome.
        Clin Endocrinol. 2014; 80
        • Sears B.
        • Perry M.
        The role of fatty acids in insulin resistance.
        Lipids Health Dis. 2015; 14
        • Silva Figueiredo P.
        • Carla Inada A.
        • Marcelino G.
        • Maiara Lopes Cardozo C.
        • de Cássia Freitas K.
        • de Cássia Avellaneda Guimarães R.
        • et al.
        Fatty acids consumption: the role metabolic aspects involved in obesity and its associated disorders.
        Nutrients. 2017; 9
        • Dangardt F.
        • Chen Y.
        • Gronowitz E.
        • Dahlgren J.
        • Friberg P.
        • Strandvik B.
        High physiological omega-3 Fatty Acid supplementation affects muscle Fatty Acid composition and glucose and insulin homeostasis in obese adolescents.
        Journal of nutrition and metabolism. 2012;