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2014 7 Silvia Bel Serrat Dieta, estilos de vida y factores de riesgo cardiovascular en niños y adolescentes europeos Departamento Director/es Fisiatría y Enfermería Moreno Aznar, Luis Krogh, Vittorio Mouratidou, Theodora Director/es Tesis Doctoral Autor Repositorio de la Universidad de Zaragoza – Zaguan http://zaguan.unizar.es UNIVERSIDAD DE ZARAGOZA Departamento Director/es Autor Silvia Bel Serrat DIETA, ESTILOS DE VIDA Y FACTORES DE RIESGO CARDIOVASCULAR EN NIÑOS Y ADOLESCENTES EUROPEOS Director/es Fisiatría y Enfermería Moreno Aznar, Luis Krogh, Vittorio Mouratidou, Theodora Tesis Doctoral 2014 Repositorio de la Universidad de Zaragoza – Zaguan http://zaguan.unizar.es UNIVERSIDAD DE ZARAGOZA Departamento Director/es Director/es Tesis Doctoral Autor Repositorio de la Universidad de Zaragoza – Zaguan http://zaguan.unizar.es UNIVERSIDAD DE ZARAGOZA Dieta, estilos de vida y factores de riesgo cardiovascular en niños y adolescentes europeos Diet, lifestyle and cardiovascular disease risk factors in European children and adolescents Departamento de Fisiatría y Enfermería Facultad de Ciencias de la Salud UNIVERSIDAD DE ZARAGOZA SILVIA BEL SERRAT ZARAGOZA, JULIO DE 2013 A Cinta y Antonio, mis padres, y a Gemma A Guille European PhD Thesis, 2013 4 European PhD Thesis, 2013 5 “Hacer lo que te gusta es libertad; que te guste lo que haces, felicidad” (Anónimo) “Doing what you like is freedom, liking what you do is happiness” (Anonymous) European PhD Thesis, 2013 6 European PhD Thesis, 2013 13 Lista de publicaciones [List of publications] La presente Tesis Doctoral es un compendio de trabajos científicos previamente publicados, aceptados para su publicación o sometidos a revisión. Las referencias de los artículos que componen este documento se detallan a continuación: I. Börnhorst C, Bel-Serrat S, Pigeot I, Huybrechts I, Ottevaere C, Sioen I, De Henauw S, Mouratidou T, Mesana MI, Westerterp K, Bammann K, Lissner L, Eiben G, Pala V, Rayson M, Krogh V, Moreno LA. Validity of 24-h recalls in (pre-)school aged children: Comparison of proxy-reported energy intakes with measured energy expenditure. Clin Nutr 2013. doi:pii: S0261-5614(13)00096-4. 10.1016/j.clnu.2013.03.018. II. Bel-Serrat S, Mouratidou T, Pala V, Huybrechts I, Börnhorst C, Fernández-Alvira JM, Hadjigeorgiou C, Eiben G, Hebestreit A, Lissner L, Molnár D, Siani A, Veidebaum T, Krogh V, Moreno LA. Relative validity of the Children's Eating Habits Questionnaire-food frequency section among young European children: the IDEFICS Study. Public Health Nutr 2013:1-11. III. Bel-Serrat S, Mouratidou T, Huybrechts I, Cuenca-García M, Manios Y, Gómez-Martínez S, Molnár D, Kafatos A, Gottrand F, Widhalm K, Sjöström M, Wästlund A, Stehle P, Azzini E, Vyncke K, González-Gross M, Moreno LA. The role of dietary fat on the association between dietary amino acids and serum lipid profile in European adolescents participating in the HELENA Study. Eur J Clin Nutr (submitted). IV. Bel-Serrat S, Mouratidou T, Huybrechts I, Labayen I, Cuenca-García M, Palacios G, Breidenassel C, Molnár D, Roccaldo R, Widhalm K, Gottrand F, Kafatos A, Manios Y, Vyncke K, Sjöstrom M, Libuda L, Gómez-Martínez S, Moreno, LA. Associations between macronutrient intakes and serum lipid profile depend on body fat and sex in European adolescents: the HELENA study. Am J Clin Nutr (submitted). European PhD Thesis, 2013 14 V. Bel-Serrat S, Mouratidou T, Börnhorst C, Peplies J, De Henauw S, Marild S, Molnár D, Siani A, Tornaritis M, Veidebaum T, Krogh V, Moreno LA. Food consumption and cardiovascular risk factors in European children: the IDEFICS study. Pediatr Obes 2013;8(3):225-36. VI. Bel-Serrat S, Mouratidou T, Jiménez-Pavón D, Huybrechts I, Cuenca-García M, Mistura L, Gottrand F, González-Gross M, Dallongeville J, Kafatos A, Manios Y, Stehle P, Kersting M, De Henauw S,Castillo MJ, Hallstrom L, Molnár D, Widhalm K, Marcos A, Moreno LA. Is dairy consumption associated with low cardiovascular diseases risk in European adolescents? Results from the HELENA Study. Pediatr Obes (accepted). VII. Bel-Serrat S, Mouratidou T, Santaliestra-Pasías AM, Iacoviello L, Kourides YA, Marild S, Molnár D, Reisch L, Siani A, Stomfai S, Vanaelst B, Veidebaum T, Pigeot I, Ahrens W, Krogh V, Moreno LA. Clustering of multiple lifestyle behaviours and its association to cardiovascular risk factors in children: the IDEFICS study. Eur J Clin Nutr 2013;67(8):848-854. VIII. Rey-López JP, Bel-Serrat S, Santaliestra-Pasías A, de Moraes AC, Vicente-Rodríguez G, Ruiz JR, Artero EG, Martínez-Gómez D, Gottrand F, De Henauw S, Huybrechts I, Polito A, Molnar D, Manios Y, Moreno LA. Sedentary behaviour and clustered metabolic risk in adolescents: The HELENA study. Nutr Metab Cardiovasc Dis 2012. European PhD Thesis, 2013 15 Contenidos Proyectos de investigación 23 Listado de abreviaturas 25 Resumen general 27 1. Introducción 35 1.1 Valoración de la dieta en niños y adolescentes 35 1.1.1 Cuestionario de frecuencia de consumo de alimentos 39 1.1.2 Recuerdo dietético de 24 horas 40 1.1.3 Registro de alimentos 41 1.1.4 Historia dietética 43 1.1.5 Validación de los métodos de valoración de la dieta 44 1.2. Riesgo cardiovascular en niños y adolescentes 45 1.3 Relación entre riesgo cardiovascular y estilos de vida 50 1.3.1 Dieta 50 1.3.2 Actividad física 53 1.3.3 Comportamientos sedentarios 54 2. Objetivos 57 3. Material y Métodos 61 3.1 Comités de Ética 61 3.2 Muestra y diseño del estudio 62 3.3 Métodos de medida 66 3.3.1 Factores sociodemográficos (Estudio IDEFICS) 66 3.3.2 Gasto energético total (Estudio IDEFICS) 66 3.3.3 Dieta e ingesta de energía (Estudio IDEFICS) 67 3.3.4 Examen físico (Estudio IDEFICS) 69 European PhD Thesis, 2013 16 3.3.5 Actividad física (Estudio IDEFICS) 69 3.3.6 Comportamientos sedentarios (Estudio IDEFICS) 69 3.3.7 Muestras biológicas (Estudio IDEFICS) 70 3.3.8 Factores socio-demográficos (Estudio HELENA) 71 3.3.9 Dieta e ingesta de energía y nutrientes (Estudio HELENA) 71 3.3.10 Examen físico (Estudio HELENA) 72 3.3.11 Maduración sexual (Estudio HELENA) 72 3.3.12 Actividad física (Estudio HELENA) 72 3.3.13 Comportamientos sedentarios (Estudio HELENA) 73 3.3.14 Muestras biológicas (Estudio HELENA) 73 3.3.15 Condición física 74 3.3.16 Indicador de riesgo cardiovascular 74 3.4 Análisis estadísticos: consideraciones generales 75 4. Resultados 77 4.1 Artículo 1: Validity of 24-h recalls in (pre-)school aged children: Comparison of proxy-reported energy intakes with measured energy expenditure. 79 4.2 Artículo 2: Relative validity of the Children's Eating Habits Questionnaire-food frequency section among young European children: the IDEFICS Study. 87 4.3 Artículo 3: The role of dietary fat on the association between dietary amino acids and serum lipid profile in European adolescents participating in the HELENA Study. 101 4.4 Artículo 4: Associations between macronutrient intakes and serum lipid profile depend on body fat in European adolescents: the HELENA study. 125 4.5 Artículo 5: Food consumption and cardiovascular risk factors in European children: the IDEFICS study. 153 4.6 Artículo 6: Is dairy consumption associated with low cardiovascular diseases risk in European adolescents? Results from the HELENA Study. 167 European PhD Thesis, 2013 17 4.7 Artículo 7: Clustering of multiple lifestyle behaviours and its association to cardiovascular risk factors in children: the IDEFICS study. 181 4.8 Artículo 8: Sedentary behaviour and clustered metabolic risk in adolescents: The HELENA study. 191 5. Discusión 201 5.1 Validez de los métodos de valoración de la dieta 201 5.2 Dieta y factores de riesgo cardiovascular 204 5.3 Estilos de vida y factores de riesgo cardiovascular 209 5.4 Implicaciones para la salud pública 211 6. Aportaciones principales de la Tesis Doctoral 217 7. Conclusiones 221 8. Referencias 225 Apéndice 239 Agradecimientos 241 Anexo 245 European PhD Thesis, 2013 18 European PhD Thesis, 2013 19 Contents Research projects 23 List of abbreviations 25 General abstract 31 1. Introduction [Section in Spanish] 35 1.1 Dietary assessment in children and adolescents 35 1.1.1 Food frequency questionnaire 39 1.1.2 24-hour dietary recall 40 1.1.3 Food record 41 1.1.4. Diet history 43 1.1.5 Validation studies of dietary assessment methods 44 1.2 Cardiovascular disease risk in children and adolescents 45 1.3 Association between cardiovascular disease risk and lifestyle behaviours 50 1.3.1 Diet 50 1.3.2 Physical activity 53 1.3.3 Sedentary behaviours 54 2. Objectives 59 3. Materials and Methods [Section in Spanish] 61 3.1 Ethics Committee 61 3.2 Sample and study design 62 3.3 Measurement methods 66 3.3.1 Socio-demographic factors (IDEFICS study) 66 3.3.2 Total energy expenditure (IDEFICS study) 66 3.3.3 Dietary and energy intake (IDEFICS study) 67 3.3.4 Physical examinations (IDEFICS study) 69 European PhD Thesis, 2013 20 3.3.5 Physical activity (IDEFICS study) 69 3.3.6 Sedentary behaviours (IDEFICS study ) 69 3.3.7 Biological samples (IDEFICS study) 70 3.3.8 Socio-demographic factors (HELENA study) 71 3.3.9 Dietary and energy intake (HELENA study) 71 3.3.10 Physical examinations (HELENA study) 72 3.3.11 Sexual maturation (HELENA study) 72 3.3.12 Physical activity (HELENA study) 72 3.3.13 Sedentary behaviours (HELENA study) 73 3.3.14 Biological samples (HELENA study) 73 3.3.15 Physical fitness 74 3.3.16 Clustered cardiovascular disease risk score 74 3.4 Statistical analysis: general considerations 75 4. Results 77 4.1 Paper 1: Validity of 24-h recalls in (pre-)school aged children: Comparison of proxy-reported energy intakes with measured energy expenditure. 79 4.2 Paper 2: Relative validity of the Children's Eating Habits Questionnaire-food frequency section among young European children: the IDEFICS Study. 87 4.3 Paper 3: The role of dietary fat on the association between dietary amino acids and serum lipid profile in European adolescents participating in the HELENA Study. 101 4.4 Paper 4: Associations between macronutrient intakes and serum lipid profile depend on body fat in European adolescents: the HELENA study. 125 4.5 Paper 5: Food consumption and cardiovascular risk factors in European children: the IDEFICS study. 153 4.6 Paper 6: Is dairy consumption associated with low cardiovascular diseases risk in European adolescents? Results from the HELENA Study. 167 European PhD Thesis, 2013 21 4.7 Paper 7: Clustering of multiple lifestyle behaviours and its association to cardiovascular risk factors in children: the IDEFICS study. 181 4.8 Paper 8: Sedentary behaviour and clustered metabolic risk in adolescents: The HELENA study. 191 5. Discussion [Section in Spanish] 201 5.1 Dietary assessment methods validity 201 5.2 Diet and cardiovascular disease risk factors 204 5.3 Lifestyle behaviours and cardiovascular disease risk factors 209 5.4 Public health implications 211 6. Main thesis contributions 219 7. Conclusions 223 8. References 225 Appendix 239 Acknowledgments 241 Annex 245 European PhD Thesis, 2013 22 European PhD Thesis, 2013 29 individuo. Así mismo, también se observó que la asociación entre la ingesta de aminoácidos y la concentración de lípidos plasmáticos era dependiente de la ingesta de grasa del individuo. El diseño transversal utilizado en todos los artículos supone una de las principales limitaciones de la presente Tesis Doctoral puesto que no nos permite determinar relaciones causales. La información sobre la dieta auto-declarada siempre está sujeta a una variedad de errores de medida no intencionados. En el caso del estudio IDEFICS, toda la información acerca de la ingesta de alimentos fue facilitada por los padres, lo cual disminuiría la precisión de los datos obtenidos, principalmente en cuanto a las ingestas que no tienen lugar bajo la supervisión de los padres. En lo que respecta a los recuerdos de alimentos de 24 horas empleados tanto en el estudio IDEFICS como en el HELENA, únicamente se incluyeron dos días de medición no consecutivos. Hubiera sido deseable un incremento de los días de medición de la dieta para lograr una mayor disminución de la variación intra-persona. Otra limitación importante sería que los comportamientos sedentarios y la actividad física en el estudio IDFICS fueron también obtenidos a través de un cuestionario rellenado por los padres, con lo que no se puede descartar el fenómeno de la deseabilidad social y la posible infra o sobredeclaración de los valores respondidos en cuanto a estas variables. En resumen, estos resultados ponen de manifiesto la necesidad de desarrollar métodos de valoración de la dieta capaces de estimarla de forma más precisa. Además, existe evidencia de que tanto la dieta como los comportamientos sedentarios están asociados con el riesgo cardiovascular durante la infancia y la adolescencia, por ello es necesario diseñar estrategias destinadas a prevenir el desarrollo de riesgo cardiovascular a edades tan tempranas promoviendo el consumo de alimentos saludables y la práctica de actividad física, a expensas de la disminución de los niveles de sedentarismo, tanto en niños como en adolescentes. European PhD Thesis, 2013 30 European PhD Thesis, 2013 31 General abstract Childhood and adolescence are not only characterized as being periods of rapid growth and maturation, but also periods during which dietary habits are formed. There is also existing evidence indicating that the onset of atherosclerosis occur at early stages and that is related to lifestyle-related behaviours such as diet, physical activity and/or sedentary behaviours, among others. For these reasons, it is of great importance to accurately assess diet in both children and adolescents to detect diet-disease relationships, as well as to identify associations between cardiovascular disease risk factors and lifestyle in these population groups. The aims of the present Doctoral Thesis are: 1) to examine the validity of two dietary assessment methods to be applied in children, and 2) to assess the association of diet expressed as dietary intakes and food consumption, and other lifestyle behaviours with cardiovascular disease risk factors in children and adolescents in order to enhance the scientific knowledge and identify research gaps in this area. To investigate the validity of two dietary assessment methods, measurements were obtained in children participating in the IDEFICS (Identification and prevention of dietaryand lifestyle induced health effects in children and infants) study. A total of 36 children (4-10 years) from Belgium and Spain were measured to determine the validity of the 24-hour dietary recall method against the doubly labeled water technique. The second validation study included the examination of 44-item semi-quantitative food frequency questionnaire in 2,508 children (2-8 years) from eight European countries (Italy, Estonia, Cyprus, Belgium, Sweden, Germany, Hungary, Spain). To address the second objective related to examining the association of diet and other lifestyle behaviours with cardiovascular disease risk factors, measurements were obtained from European children (n=5,548) participating in the IDEFICS European PhD Thesis, 2013 32 study and from European adolescents (n=511) aged 12.5-17.5 years taking part in the HELENA study (Healthy Lifestyle in Europe by Nutrition in Adolescence) carried out in ten European cities (Athens in Greece, Dortmund in Germany, Ghent in Belgium, Heraklion in Greece, Lille in France, Pécs in Hungary, Rome in Italy, Stockholm in Sweden, Vienna in Austria and Zaragoza in Spain). The findings of this study showed that the food frequency questionnaire’s ability to rank individuals according to their intakes differed by food groups assessed. The second validation study conducted as part of this Thesis showed that the 24-hour dietary recall method provided valid estimates of energy intake at group level but not at the individual level. Regarding cardiovascular disease risk, higher intakes of certain foods such as nuts and seeds and breakfast cereals, among others, in children and dairy among adolescents were inversely associated with cardiovascular disease risk. Aside dietary factors, lifestyle, specifically indicators of sedentary behaviours, was found to be associated with cardiovascular disease risk, i.e. playing videogames for more than four hours at weekends increased the risk of cardiovascular disease in male adolescents by two folds. Furthermore, the large pool of data obtained in the studies enabled the authors to examine the association between clusters of several individual lifestyle behaviours and risk of cardiovascular diseases. For instance, low sedentary behaviours and low sugar sweetened beverages intake were associated with lower risk of cardiovascular disease. In this thesis, the influence of macronutrients on blood lipid profile was also addressed. Findings showed that high intake of carbohydrates and low intake of fat were associated with a worse serum lipid profile. It is noteworthy that such associations are body fat status-dependent. Likewise, the observed association between amino acids intake and serum lipids concentrations is also dependent of the individual’s fat intake. European PhD Thesis, 2013 33 The cross-sectional design of all the manuscripts is one of the main limitations of the present Doctoral Thesis as it does not allow us to draw causal associations. Self-reported dietary data is always subject to a variety of unintentional measurement errors. Indeed, data was proxy-reported within the IDEFICS study, which decreased the accuracy of the reported data, mainly considering those food intakes that take place out of parental supervision. Regarding the 24-hour dietary recalls applied in both the IDEFICS study and the HELENA study, only two measurement days were collected. It would have been desirable to increase the number of recording days in order to reduce the within-person variability. Another important limitation is that, within the IDEFICS study, sedentary behaviours and physical activity were assessed through proxy-reported questionnaires; therefore, the effect of social desirability on the reported data as well as certain degree of underor over-reporting cannot be precluded when considering these data. In conclusion, these findings highlight the need of developing more accurate dietary measurement methods to assess diet more precisely. Besides, there is evidence that both diet and sedentary behaviours are associated with cardiovascular disease risk during childhood and adolescence; for that reason, it is necessary to design strategies aimed to prevent from suffering cardiovascular disease at early stages of life by means of promoting the intake of healthy foods and engagement in physical activity by reducing sedentary behaviours in both children and adolescents. European PhD Thesis, 2013 34 European PhD Thesis, 2013 35 1. Introducción [Introduction] La infancia y la adolescencia se caracterizan por ser periodos de rápido crecimiento y desarrollo, lo cual conlleva un aumento de los requerimientos nutricionales. Aunque la nutrición constituye un pilar fundamental a lo largo de toda la vida, un suministro adecuado de nutrientes durante estas etapas es esencial para alcanzar un crecimiento y desarrollo óptimos (1). Los procesos de crecimiento y maduración física y de desarrollo de la personalidad que tienen lugar durante la infancia y la adolescencia no influyen únicamente en la cantidad y forma de los nutrientes ingeridos, sino también en la actitud que el niño toma ante los alimentos (1). De hecho, durante la infancia y la adolescencia se establecen los hábitos alimentarios, se definen las preferencias y, en general, se forma la base del comportamiento alimentario para toda la vida (1). Esto pone de manifiesto aún más si cabe la importancia de una correcta alimentación durante edades tempranas como la niñez y la adolescencia. 1.1 Valoración de la dieta en niños y adolescentes Valorar la ingesta de alimentos en niños y adolescentes de forma precisa es un factor primordial para conocer el grado de adecuación nutricional de su dieta (2) así como para llevar a cabo investigación de origen clínico y epidemiológico para detectar asociaciones reales entre la dieta y la salud (3). Sin embargo, se ha observado que la obtención de datos dietéticos fiables y precisos en este grupo de población supone una gran dificultad (3). De hecho, valorar la dieta en niños y adolescentes se considera mucho más complicado que hacerlo en adultos puesto que tienden a tener una alimentación altamente variable día a día y, además, sus hábitos alimentarios pueden cambiar muy rápidamente (4). Además, los niños más pequeños tienen una reducida capacidad para recordar, para estimar el tamaño de las porciones y para cooperar en el proceso de valoración de la dieta (4). Se considera que a partir de los 8 años los niños ya son capaces de decir lo que comen puesto que ya han European PhD Thesis, 2013 36 alcanzado el nivel de desarrollo necesario para ser conscientes de su ingesta de alimentos (4). Mientras tanto, es en los padres, madres y/o tutores sobre quienes recae la difícil tarea de informar sobre la dieta de los niños (4). Se ha mostrado que los padres suelen dar información precisa de la ingesta de alimentos de sus hijos cuando ésta tiene lugar en casa (5-8), sin embargo, a menudo no saben qué es lo que el niño consume fuera de casa, por lo que la información que puedan dar en este sentido no es del todo fiable (8). En algunas ocasiones se acude a otros cuidadores, como por ejemplo maestros o cuidadores de guardería, para obtener datos de la ingesta que tiene lugar en el colegio o guardería, pero, en este caso, los niveles de motivación e interés pueden variar ampliamente (3), con lo que también variará la calidad de la información proporcionada. Los adolescentes, por su lado, son completamente capaces de dar información sobre su dieta; sin embargo, a menudo muestran poco interés en dar datos precisos sobre la misma (4). La Tabla 1 muestra aspectos de la valoración de la dieta tanto en niños como en adolescentes que tienen que tener en cuenta los encuestadores a la hora de valorarla. Tabla 1. Aspectos sobre el encuestado-encuestador en la valoración de la dieta en niños y adolescentes (9). Infancia Adolescencia Habilidades cognitivas - Baja habilidad para leer y escribir - Capacidad cognitiva completa - Limitado concepto del tiempo - Memoria limitada - Conocimiento limitado de los alimentos y de la preparación de los mismos - Conocimiento extenso de los alimentos, pero ¿cuál es su conocimiento acerca de su preparación? - La información sobre la dieta debe ser facilitada por los padres y madres - Tienen la responsabilidad de dar ellos mismos la información sobre su dieta Hábitos alimentarios - Hábitos alimentarios que cambian rápidamente, pero patrones de alimentación (más) estructurados - Hábitos alimentarios que cambian rápidamente y patrones de alimentación desestructurados - Mayor frecuencia de ingestas en casa - Mayor frecuencia de ingestas fuera de casa - Bajo la supervisión de adultos - Menor supervisión por parte de adultos - Importante influencia de los padres - Importante influencia de los amigos/as Psicológicos - Los alimentos satisfacen el hambre - Los alimentos son una forma de auto-expresión European PhD Thesis, 2013 37 A pesar de los retos metodológicos que existen, se asume que los métodos de valoración de la dieta disponibles actualmente y que han sido diseñados para su uso en adultos son apropiados para recopilar datos en poblaciones pediátricas (9). Actualmente se dispone de cuatro métodos de valoración de la dieta: el registro de alimentos, el recuerdo dietético de 24 horas, el cuestionario de frecuencia de consumo de alimentos (CFCA) y la historia dietética. A la hora de elegir un instrumento dietético, independientemente del grupo de población al que va dirigido, deben de tenerse en cuenta los objetivos del estudio, el número y características de la población a estudio así como los recursos de los que se dispone (9). Debido a que todos los métodos dietéticos están sujetos a limitaciones (4), es necesario tener una idea muy clara desde un inicio sobre cuál es la información que se quiere medir para no cometer ningún error en la elección del método dietético y así obtener la información deseada. Por esta razón, a menudo se tienden a utilizar de forma combinada para maximizar las ventajas de cada instrumento (4) y compensar sus limitaciones con el uso de otro instrumento distinto. Una combinación bastante frecuente en estudios transversales es la utilización de los recuerdos de 24 horas junto con los CFCA. En la Tabla 2 se pueden observar las ventajas y desventajas de los instrumentos de valoración de la dieta. European PhD Thesis, 2013 38 European PhD Thesis, 2013 45 registradas de forma discreta por observadores entrenados o por el uso de marcadores biológicos, entre ellos el agua doblemente marcada y la excreción de nitrógeno por la orina (4). El estudio de validación definitivo para un CFCA consistiría en observar la dieta habitual del individuo durante un largo periodo de tiempo de forma no intrusiva; sin embargo, todavía no se han llevado a cabo estudios de estas características (4). Por ello, el método más aceptado para examinar la concordancia de las respuesta de frecuencia de consumo de alimentos y la dieta habitual es la utilización de registros de alimentos y recuerdos dietéticos múltiples durante un periodo determinado como indicadores de la dieta habitual (4). Este método se ha utilizado en diversos estudios para evaluar la validez de los CFCAs (27). 1.2 Riesgo cardiovascular en niños y adolescentes La Organización Mundial de la Salud (OMS) ha determinado que las enfermedades cardiovasculares (ECV) son la causa principal de muerte a nivel mundial, es decir, un mayor número de personas mueren anualmente a causa de ECV que de ninguna otra causa (28). De hecho, se estima que en el año 2008 un total de 17,3 millones de personas murieron debido a ECV, lo que representa un 30% de las muertes globales y se prevé que estas cifras alcanzarán los 23,3 millones de personas para el año 2030, por lo que las ECV seguirán siendo la causa de muerte más destacada (28). Además, los países de bajo y medio nivel socioeconómico están afectados de forma desproporcionada puesto que alrededor del 80% de las muertes por ECV tienen lugar en estos países y ocurren de forma equitativa tanto en hombres como en mujeres (28). La obesidad, la hipertensión, la diabetes y la dislipemia son conocidos factores de riesgo cardiovascular (28). Su aparición conjunta en un mismo individuo se denomina “Síndrome Metabólico” (SM). Este término se usa generalmente para indicar una situación European PhD Thesis, 2013 46 clínica en la que co-ocurren trastornos metabólicos y cardiovasculares que son factores de riesgo para el desarrollo de diabetes mellitus tipo 2 y de ECV (29). Diversos estudios han puesto de manifiesto que cuatro factores: obesidad (especialmente la obesidad central), intolerancia a la glucosa, dislipidemia aterogénica (niveles altos de triglicéridos (TG) y niveles bajos de lipoproteínas de alta densidad (HDL-c)) e hipertensión co-ocurren en algunos individuos en un mayor grado de lo esperado sólo por casualidad (29). Además, parece ser que la prevalencia así como las interacciones entres estos componentes varían según el sexo, la edad y el grupo étnico (29). Por otro lado, existen diversos problemas relacionados con la definición del SM: 1) todos los componentes del SM son variables continuas, lo que implica la necesidad de definir unos puntos de corte; sin embargo no hay todavía un consenso sobre cuales deben de ser los umbrales para establecer el diagnóstico de cada componente; 2) todas estas variables están interrelacionadas, pero la pato-fisiología de su relación todavía no se conoce de forma completa; y 3) la inclusión de la resistencia a la insulina o diabetes como componente de diagnóstico todavía es una cuestión que genera controversia, aunque desde el punto de vista fisiopatológico parece ser el factor central (30). Los dos criterios más utilizados para el diagnóstico de SM en adultos son los establecidos por la OMS (31) y el National Cholesterol Education Program’s Adult Treatment Panel III (NCEPT-ATP III) (32), sin embargo, existen algunas diferencias entre ellos. La definición de la OMS requiere la evaluación de la resistencia a la insulina o del trastorno del metabolismo de la glucosa. Por otro lado, la definición de la NCEPT-ATP III no exige la medida de la resistencia a la insulina, lo que facilita su utilización en estudios epidemiológicos (33). Las ECV se han convertido también en un problema pediátrico puesto que se ha observado que las primeras manifestaciones de arteriosclerosis podrían darse de forma temprana durante la infancia (34). Además, también se ha observado tanto en niños como en adolescentes la aparición conjunta de factores de riesgo metabólico y cardiovasculares mencionados previamente como la obesidad central, la tensión arterial elevada, la resistencia European PhD Thesis, 2013 47 a la insulina, TG elevados y niveles bajos de HDL-c (35), pero, lo más preocupante, es que estos trastornos metabólicos tienden a continuar desde la infancia a la edad adulta (36). Los cambios que tienen lugar durante el crecimiento y el desarrollo hacen que la identificación de criterios apropiados así como de puntos en corte para diagnosticar el SM en niños y adolescentes sea mucho más complicada aún si cabe que en el caso de los adultos (37). De hecho, actualmente no existe una definición del SM en niños y adolescentes, por lo que muchos de los estudios proponen el uso de la definición de la NCEPT-ATP III modificada, pero todavía no existe un consenso a la hora de establecer los umbrales para cada componente (38). En la revisión sistemática de Moraes et al. (33) la prevalencia de SM en adolescentes fue mayor en aquellos estudios que usaron los criterios de la OMS que en aquellos que usaron la definición dada por la NCEPT-ATP III. Sin embargo, no solo el uso de una definición u otra puede llevar a diferencias en la estimación de la prevalencia, sino que con el uso de la misma definición también pueden observarse diferencias en dicha estimación (38). No obstante, lo que realmente importa es que independientemente de la definición utilizada, Olza et al. (39) diagnosticaron el SM tanto en niños como en adolescentes obesos, aunque es importante destacar que la frecuencia fue mayor en estos últimos. La Tabla 3 muestra los criterios que se han usado hasta ahora para diagnosticar el SM en niños y adolescentes. European PhD Thesis, 2013 48 Tabla 3. Criterios para el diagnóstico del SM en poblaciones pediátricas (39). Estudio Población (sexo, edad y grupo étnico) Exceso de adiposidad Presión arterial Lípidos Glucosa (insulina) Cook et al., 2003 (40) Chicos y chicas 12-19 años Americanos blancos, de color y mexicanos CC ≥ percentil 90 Presión sistólica o diastólica ≥ percentil 90 TG ≥ 110 mg/dl HDL-c ≤ 40 mg/dl ≥ 110 mg/dl de Ferranti et al., 2004 (41) Chicos y chicas 12-19 años Americanos blancos no hispánicos, de color y mexicanos CC > percentil 75 Presión sistólica > percentil 90 TG ≥ 100 mg/dl HDL-c < 50 mg/dl ≥ 110 mg/dl Weiss et al., 2003 (30) Chicos y chicas 4-20 años Blancos, de color e hispánicos IMC > percentil 97 o z-score > 2 Presión sistólica o diastólica ≥ percentil 95 TG > percentil 95 y HDL-c < percentil 5 según edad, sexo y raza POTG > 140 y < 200 mg/dl a las 2 horas Cruz et al., 2003 (42) Chicos y chicas 8-13 años Hispánicos (americanos mexicanos, americanos de la zona central, o mezclados) CC ≥ percentil 90 Presión sistólica o diastólica ≥ percentil 90 TG ≥ percentil 90 y HDL-c ≤ percentil 10 según edad, sexo y raza POTG: glucosa a los 120 minutos ≥ 140 mg/dl y < 200 mg/dl Viner et al., 2003 (43) Chicos y chicas 2-18 años Blancos, de color, surasiáticos, y otros o etnias mixtas IMC > percentil 95 Presión sistólica ≥ percentil 95 Cualquiera de los siguientes: TG elevados (≥ 150 mg/dl), bajo HDL-c (< 35 mg/dl) o colesterol total elevado (≥ percentil 95) (Cualquiera de los siguientes: hiperinsulinemia en ayunas [pre- ≥ 15 mU/l, medio- (estadíos 2-4) ≥ 30 mU/l y pospuberal ≥ 20 mU/l ]}, glucosa en ayunas alterada (≥ 110 mg/dl)) o tolerancia a la glucosa alterada: glucosa a los 120 minutos ≥ 140 mg/dl Ford et al., 2003 (44) Chicos y chicas 12-17 años CC ≥ percentil 90 Presión sistólica o diastólica ≥ percentil 90 TG ≥ 110 mg/dl HDL-c ≤ 40 mg/dl ≥ 110 mg/dl IDF, 2007 a(45) ≥ 10 a < 16 años CC ≥ percentil 90 Presión sistólica ≥ 130 o presión diastólica ≥ 85 mm Hg TG ≥ 150 mg/dl HDL-c < 40 mg/dl ≥ 110 mg/dl CC, circunferencia de la cintura; HDL-c, lipoproteína de alta densidad; IMC, índice de masa corporal; TG, triglicéridos; POTG, prueba oral de tolerancia a la glucosa. a Esta definición considera a la circunferencia de la cintura una condición sine qua non para el síndrome metabólico. Debido a que todavía no se ha llegado a un acuerdo común para establecer una definición de SM en poblaciones pediátricas puesto que los puntos de corte para cada uno de los factores de riesgo cardiovascular varían en función de la edad, el género y el estadío European PhD Thesis, 2013 49 puberal (39), diversos estudios epidemiológicos focalizados en factores de riesgo cardiometabólicos en niños y adolescentes han utilizado diferentes estrategias para calcular un indicador continuo del SM (46). Tanto las variables incluidas en el indicador como el enfoque estadístico varían de forma considerable (46). A la hora de interpretar el indicador de SM, se considera que los valores bajos son indicativos de un mejor perfil mientras que valores elevados indicarían un perfil peor. Según Andersen et al. (47) la agrupación de los factores de riesgo cardiovascular en un solo indicador sería una mejor medida de la salud cardiovascular en poblaciones pediátricas que los factores de riesgo por sí solos. Además, el uso de este tipo de indicadores compensaría las fluctuaciones que se dan día a día en cada uno de los factores de riesgo de forma individual (48). Por otro lado, está incrementando la evidencia que apoya el uso de un indicador continuo en lugar de un enfoque dicotómico puesto que la capacidad para mostrar asociaciones entre factores de exposición y variables dicotómicas del SM a través de regresión logística limitaría la fortaleza de la asociación (46). El hecho de que el riesgo cardiovascular es una función progresiva de varios factores de riesgo del SM, y que aumenta con el número de factores de riesgo del SM, respalda la importancia de un indicador de riesgo cardiometabólico, puesto que considera a todos los componentes de forma independiente a la combinación de dos o tres de ellos, y asume que todos los factores de riesgo son igual de importantes y de responsables para definir el riesgo de SM o cardiovascular (46). Sin embargo, el uso de un indicador de riesgo continuo no está exento de limitaciones puesto que es específico de la muestra en la que se usa y no se puede comparar con otros estudios a no ser que las características sociodemográficas, la distribución de los datos, las medidas de tendencia central y la variabilidad sean similares en las dos muestras (46). European PhD Thesis, 2013 50 1.3 Relación entre el riesgo cardiovascular y los estilos de vida Según la OMS, una proporción importante de las ECV podrían ser prevenidas con la modificación de sus factores de riesgo, principalmente a través de una dieta saludable y el control del peso, la actividad física, el uso limitado del alcohol y el tabaco, y de mantener la presión arterial y los lípidos plasmáticos dentro de los niveles normales (28). Los factores de riesgo relacionados con el estilo de vida son responsables de alrededor del 80% de las ECV, incluyendo las cerebrovasculares (28). Los efectos de una dieta no saludable y de la inactividad física se ponen de manifiesto con un incremento de la tensión arterial, aumento de la glucosa en sangre, lípidos plasmáticos elevados y sobrepeso y obesidad (28). Son estos factores “intermedios” los que indican un riesgo incrementado de desarrollar ECV como infartos, fallo cardiaco y otras complicaciones (28). Se ha observado que el dejar de fumar, la reducción del contenido de sal de la dieta, el consumo de frutas y vegetales, la actividad física regular y el evitar el uso dañino del alcohol reducen el riesgo cardiovascular en adultos (28). El riesgo cardiovascular también puede reducirse con la prevención o el tratamiento de los factores de riesgo “intermedios” (28). Sin embargo, si los estilos de vida, como la dieta y la actividad física, influyen o no en la expresión del SM o de los factores de riesgo cardiovascular en niños y adolescentes no se sabe todavía con certeza (49). 1.3.1 Dieta Diversos estudios han puesto de manifiesto que la ingesta elevada de grasa y de azúcares añadidos junto con baja ingesta de fibra son factores de riesgo para el síndrome metabólico en adultos; sin embargo hay poca información disponible con respecto a la asociación entre dieta y riesgo cardiovascular o síndrome metabólico en poblaciones infantiles (50). Dado que las ECV parecen iniciarse durante la infancia (34) y que los patrones alimentarios que se forman durante la infancia tienden a persistir durante la adolescencia European PhD Thesis, 2013 51 (51) y hasta la edad adulta; las intervenciones dietéticas para prevenir la obesidad y los factores de riesgo de enfermedad crónica deberían estar dirigidas a la edad infantil (52). Algunos estudios han investigado la asociación entre la dieta y los factores de riesgo cardiovascular en niños y adolescentes. Williams y Strobino (53) observaron que tras el seguimiento durante 4 años de niños neoyorquinos, la ingesta de energía estaba directamente relacionada con los niveles plasmáticos de colesterol total. Sin embargo, la ingesta de grasa monoinsaturada y de fibra dietética fueron variables protectoras del colesterol total. Así mismo, la ingesta de sacarosa mostró una asociación inversa con los niveles de HDL-c. La ingesta de hidratos de carbono se relacionó de forma adversa con la circunferencia de la cintura, los niveles de TG y glucosa en una muestra de niños norteamericanos de entre 7 y 12 años (49). En niños de origen latino con sobrepeso (50), se observó que la ingesta de fibra era significativamente mayor en aquellos participantes con ninguna característica del SM (5,2 gramos de fibra/día) comparados con aquellos con más de 3 características propias del SM (4,1 gramos de fibra/día). En el caso de los adolescentes, ingestas elevadas de fibra fueron relacionadas con el SM; sin embargo no se halló ninguna relación entre una baja ingesta de grasa o colesterol y el SM (54). A pesar del gran interés y trascendencia que tienen los resultados mencionados anteriormente, cabe destacar que los nutrientes son consumidos habitualmente en conjunto a través de los alimentos que conforman nuestra dieta, y que, a la hora de dar recomendaciones a la población general, es mucho más sencillo hacerlo en términos de consumo de alimentos que de nutrientes. Por ello, cobran mayor interés aquellos estudios focalizados en la ingesta de alimentos y su relación con el SM o los factores de riesgo cardiovascular. La ingesta de bebidas azucaradas se relacionó directamente con mayores valores del índice HOMA (Homeostatic Model Assessment, medida de la resistencia de la insulina), de la tensión arterial sistólica, de la circunferencia de la cintura y del IMC y con valores más bajos de las concentraciones de HDL-c (55). En niños mejicanos de entre 9 a 13 años de edad, también se European PhD Thesis, 2013 52 observó una asociación directa entre la ingesta de bebidas azucaradas y los niveles plasmáticos de glucosa y de presión arterial diastólica (56). En este mismo estudio, las ingestas elevadas de pan blanco y de grasa añadidas estaban asociadas con mayores concentraciones de insulina y de TG, respectivamente. En el estudio llevado a cabo por Kelishadi et al. (57) en una población joven de entre 6 y 18 años, el riesgo de SM se incrementaba conforme aumentaba el consumo de grasa hidrogenada y de pan hecho con harina refinada. Por el contrario, cuanto mayor era la frecuencia de consumo de frutas, vegetales y productos lácteos, menor era el riesgo de padecer SM. Tabla 4. Resumen de los estudios que han valorado la asociación entre ingesta de alimentos y síndrome metabólico o factores de riesgo cardiovascular en niños y adolescentes. Estudio Población (edad, género, país) Tamaño de la muestra Diseño del estudio Resultados Kelishadi et al., 2008 (57) 6-18 años Chicos y chicas Irán 4.811 sujetos Estudio transversal ↑ pan de harina recinada  ↑ SM ↑ fruta y vegetales  ↓ SM ↑ leche, queso, yogur  ↓ SM Pan & Prat, 2008 (58) 12-19 años Chicos y chicas de diferente etnias Estados Unidos 4.450 sujetos Estudio transversal ↑ fruta  ↓ SM ↑ Ifndice de Ingesta Saludable a  ↓ SM Bremer et al., 2009 (55) 12-19 años Chicos y chicas de diferentes etnias Estados Unidos 6.967 sujetos Estudio transversal ↑ bebidas azucaradas  ↑ HOMA, ↑ TAS, ↑ CC, ↑ percentil de IMC, HDL-c Perichart-Perera et al., 2010 (56) 9-13 años Chicos y chicas Méjico 228 sujetos Estudio transversal ↑ bebidas azucaradas  ↑ TAD, ↑ glucosa ↑ pan blanco  ↑ insulina ↑ grasas añadidas  ↑ TG ↑ lagcteos de alto contenido graso  ↑ TAD, ↑ HDL-c ↑ fruta  ↑ glucosa ↑ aceites vegetales  ↓ glucosa ↑ carne  ↓ glucosa Ambrosini et al., 2013 (59) 13-17 años Chicos y chicas Australia 1.433 sujetos Estudio longitudinal ↑ bebidas azucaradas  ↑ obesidad, ↑ TG, ↓ HDL-c, ↑ riesgo cardio-metabólico CC, circunferencia de la cintura; HOMA, homeostatic model assessment; HDL-c, lipoproteína de alta densidad; IMC, índice de masa corporal; SM, síndrome metabólico; TAD, tensión arterial diastólica; TAS, tensión arterial sistólica; TG, triglicéridos. a Índice de Ingesta Saludable (Healthy Eating Index): incluye la ingesta de granos, vegetales, fruta, leche, carne/alternativas a la carne, grasa, grasa saturada, colesterol y sodio. European PhD Thesis, 2013 53 1.3.2 Actividad física Es un hecho aceptado que la práctica regular de actividad física supone una medida preventiva efectiva para una gran variedad de factores de riesgo de enfermedad. Sin embargo, la transición que tiene lugar desde la infancia a la edad adulta marca una disminución muy llamativa en la práctica de actividad física que es dependiente de la edad (60-63). Las recomendaciones de la Academia Americana de Pediatría (AAP) (64) establecen que los niños y los adolescentes deben de realizar una hora de ejercicio físico al día de intensidad moderada-intensa y no deben ver la televisión más de dos horas/día. Sin embargo, estas recomendaciones están muy lejos de la realidad y el aumento de la inactividad se está convirtiendo en un hecho especialmente importante en poblaciones pediátricas (65). Datos obtenidos a través del “Canadian Health Measures Survey” (66) ponen de manifiesto que solamente el 7% de los niños y adolescentes de entre 6 y 19 años participan en al menos 60 minutos de actividad física moderada e intensa al día, lo cual alcanzaría las recomendaciones de varios países (67-70) y de la OMS (71). Pan et al. (58) observaron una menor prevalencia de SM en aquellos adolescentes con mayor práctica de actividad física. Los mismos resultados fueron mostrados por Brage et al. (72) en una muestra de niños daneses de entre 8 y 10 años. Con respecto a los factores de riesgo individuales, niveles elevados de actividad física en adolescentes norteamericanos se han asociado con una disminución de los valores del índice HOMA, de las concentraciones de LDL-C y TG, así como un aumento en la concentración de HDL-c (55). La actividad física total diaria llevada a cabo por niños de entre 7 y 12 años también se relacionó con una mayor concentración de HLC-c (49). Además, en un estudio llevado a cabo por Elekund et al. (73) en niños de 9 y 10 años y en adolescentes de 15 y 16 años, la actividad física fue inversamente relacionada con las concentraciones de insulina, glucosa, y TG y con los valores de tensión sistólica y diastólica, así como con el indicador de riesgo cardio-metabólico de forma European PhD Thesis, 2013 54 independiente de la adiposidad y de la variable “ver la televisión”, como medida del comportamiento sedentario. Resultados similares fueron mostrados por Sardinha et al. (74) al observar que la actividad física estaba asociada a la resistencia a la insulina independientemente de la masa grasa central y total en niños portugueses de entre 9 y 10 años. 1.3.3 Comportamientos sedentarios No es sólo motivo de alarma la disminución de la práctica de actividad física de la población, sino también el incremento de los comportamientos sedentarios. De hecho, se ha observado que algunos comportamientos sedentarios como ver la televisión o jugar a los videojuegos, entre otros, también están notablemente elevados durante la infancia y la adolescencia (75, 76). La actividad física y los comportamientos sedentarios son dos comportamientos totalmente contrarios; sin embargo, el hecho de que unos sean más sostenibles en el tiempo que otros (por ejemplo, los comportamientos sedentarios son más fáciles de ser sostenidos que la actividad física), hace que a menudo sean analizados de forma conjunta como comportamientos que co-ocurren en lugar de como acciones independientes entre sí (77). Aparte de las recomendaciones de la AAP para la práctica de actividad física, este organismo también aconseja a los niños y adolescentes no ver la televisión más de dos horas/día (64). Fuentes diversas han señalado que tanto los niños como los adolescentes pasan la mayor parte del tiempo realizando actividades sedentarias (66, 78-83). Los jóvenes canadienses pasan una media de 8.6 horas al día, o el 62% del tiempo que están despiertos siendo sedentarios (66). Tendencias similares se han observado en los jóvenes norteamericanos quienes pasan un media de entre 6 y 8 horas por día siendo sedentarios (7883). European PhD Thesis, 2013 61 3. Materiales y Métodos [Materials and Methods] La presente Tesis Doctoral está basada en datos procedentes de los estudios IDEFICS (Artículos I, II, IV y VI) y HELENA (Artículos III, V, VII y VIII). 3.1. Comités de Ética Estudio IDEFICS (Artículos I, II, V, VII) El protocolo del estudio se desarrolló según la normativa española y siguiendo las consignas éticas establecidas por la Declaración de Helsinki en 1975 (revisión de Edimburgo en 2000). Dicho protocolo fue aprobado por el Comité de Ética de cada centro en el que se llevó a cabo estudio. En el caso de Zaragoza, éste fue aprobado por el Comité Ético de Investigación Clínica de Aragón (CEICA). Finalmente, los padres de los niños que participaron en el estudio entregaron un consentimiento firmado para participar en el mismo. Estudio HELENA (Artículos III, IV, VI, VIII) El protocolo del estudio se desarrolló según la normativa española y siguiendo las consignas éticas establecidas por la Declaración de Helsinki en 1975 (revisión de Edimburgo en 2000). Dicho protocolo fue aprobado por el Comité de Ética de cada centro en el que se llevó a cabo estudio. En el caso de Zaragoza, éste fue aprobado por el Comité Ético de Investigación Clínica de Aragón (CEICA). Además, tanto adolescentes como padres entregaron un consentimiento firmado para participar en el estudio. European PhD Thesis, 2013 62 3.2. Muestra y diseño del estudio Estudio IDEFICS (Artículos I, II, V, VII) El estudio IDEFICS es un estudio de cohortes, prospectivo y multicéntrico que se llevó a cabo en ocho países europeos (Italia, Estonia, Chipre, Bélgica, Suecia, Alemania, Hungría y España). En primer lugar, se seleccionaron una zona intervención y una zona control en cada país que fueran comparables en cuanto a sus características sociodemográficas, socioeconómicas y de infraestructura. El contacto con los participantes se realizó a través de las escuelas y las guarderías, para así facilitar su participación en el estudio y la posterior implementación y seguimientos de las actividades relacionadas con la intervención. El tamaño de muestra se estableció inicialmente en 16.000 niños (2.000 por país) distribuidos equitativamente por género, curso escolar y región. Para que un sujeto fuera válido debía haber completado el cuestionario de padres y tener medidas completas de la altura y del peso. La muestra final comprendió a 16.224 niños de entre 2 y 9 años. Para los artículos que se incluyen en esta Tesis Doctoral se han utilizado los datos obtenidos durante el estudio transversal que se llevó a cabo durante el curso académico 2007-2008. La muestra utilizada en el artículo I se obtuvo a través de un estudio de validación específico que se llevó a cabo en el seno del estudio IDEFICS en tres países únicamente: España, Bélgica y Suecia, por ello la muestra es de 36 niños de entre 4 y 10 años en los que se valoró el gasto total energético a través de la técnica del agua doblemente marcada. Al tratarse el artículo II de un estudio de validación de un cuestionario dietético, únicamente se incluyeron a aquellos niños cuyos padres habían completado un CFCA y dos recuerdos dietéticos de 24-horas, con lo que la muestra final fue de 2.508 niños. La muestra en la que se basan los artículos V y VII es de 5.448 y 4.619 niños, respectivamente, puesto que se recogieron muestras de sangre en un 79.7% de los niños que participaron en el estudio IDEFICS. Las diferencias en el tamaño de muestra entre el artículo European PhD Thesis, 2013 63 V y el VII es debida a la presencia de valores perdidos en alguna de las variables de análisis y/o criterios de inclusión específicos establecidos en cada artículo. La metodología completa del estudio ya ha sido previamente publicada de forma más detallada (84). Los aspectos más relevantes en relación con la Tesis se describen a continuación. European PhD Thesis, 2013 64 Estudio HELENA (Artículos III, IV, VI, VIII) El estudio HELENA es un estudio transversal y multicéntrico que se llevó a cabo en diez ciudades europeas: Dortmund (Alemania), Viena (Austria), Gante (Bélgica), Lille (Francia), Atena y Heraklion (Grecia), Pécs (Hungría), Roma (Italia), Estocolmo (Suecia) y Zaragoza (España) durante los años 2006-2007. Para la selección de la muestra se llevó a cabo un muestreo aleatorio por conglomerados para conseguir una muestra de 3.000 adolescentes de 12,5 a 17,5 años, estratificados según la localización geográfica, la edad y el nivel socioeconómico. Fueron invitados a participar todos los alumnos de entre una selección de clases de entre todas las escuelas presentes en las 10 ciudades europeas, todas ellas mayores de 100.000 habitantes. Los criterios de inclusión del estudio HELENA fueron los siguientes: los participantes no debían de estar participando simultáneamente en otro estudio clínico; haber estado enfermo durante la semana anterior a la toma de medidas; tener entre 12,5 a 17,5 años; haber firmado el consentimiento informado, tener medidas del peso y de la altura y haber completado al menos el 75% del resto de pruebas. Finalmente, la muestra comprendió un total de 3.528 adolescentes de entre 12,5 a 17,5 años. European PhD Thesis, 2013 65 Figura 2. Esquema sobre el proceso de muestreo utilizado en en estudio HELENA (85). Según el protocolo del estudio HELENA, se recogieron muestras de sangre en 1/3 de la muestra del estudio, elegida al azar (n=1.089). Tras la obtención de dichas muestras, se analizaron diversos parámetros bioquímicos: glucosa, insulina, TG, colesterol total, HDL-c, lipoproteína de baja densidad (LDL-c), apolipoproteína A1 y apolipoproteína B. Por otro lado, la dieta fue registrada mediante dos recuerdos dietéticos de 24-horas. Lamentablemente, debido a que los datos de ingesta dietética obtenidos en Pécs y en Heraklion eran incompletos, tuvieron que ser excluidos de los análisis. Por ello, la muestra en la que se basan los artículos III, IV y VI es de 511 adolescentes (si bien puede existir alguna variación debido a la presencia de valores perdidos en alguna de las variables de análisis y/o criterios de inclusión establecidos en cada artículo). La muestra del artículo VIII fue mayor (n=769) debido a que no se incluyeron variables relacionadas con la dieta. Las características generales del estudio ya han sido publicadas previamente con detalle (85). Los aspectos más relevantes en relación con la Tesis se describen a continuación. European PhD Thesis, 2013 66 3.3. Métodos de medida Estudio IDEFICS (Artículos I, II, V, VII) 3.3.1 Factores sociodemográficos (Artículos V, VII) Para el análisis de los factores socio-demográficos se recogieron datos sobre la edad, el género, la educación de los padres y el trabajo de los padres. Para evitar las diferencias que existen en los países a la hora de establecer el nivel socioeconómico, se usó la Clasificación Internacional Normalizada de la Educación (ISCED) (86). Se tuvo en cuenta el mayor nivel educativo alcanzado por cualquiera de los dos padres. 3.3.2 Gasto energético total (Artículo I) El GET fue medido durante un periodo de 9 días (desde el Día -1 al Día 8) por medio de la técnica del agua doblemente marcada siguiendo el protocolo de Maastricht (87). La Figura 1 muestra el esquema que se siguió para realizar las mediciones. Cada niño recibió una dosis única de agua doblemente marcada basada en su peso (ml de agua doblemente marcada/kg) compuesta por 250-300 partes por millón (ppm) de Oxígeno-18 y por 125-150 ppm de Deuterio. Se obtuvieron diversas muestras de orina de cada niño. La primera se obtuvo unos minutos antes (máximo 15 minutos) de la ingesta de la dosis de agua doblemente marcada (orina de referencia) y el resto se recogieron en los Días 1, 4 y 8. Cada muestra de orina fue recogida a la misma hora del día (± 1 hora) en todos los niños en función de sus horarios y de sus necesidades fisiológicas. Tras su recogida, las muestras fueron congeladas a -20 ºC y mandadas al laboratorio central situado en Maastricht para su análisis con espectrometría de masas. El GET fue calculado de acuerdo a la fórmula de Schoeller (88). European PhD Thesis, 2013 67 Figura 1. Esquema del protocolo de medida del estudio de validación de IDEFICS (89). 3.3.3 Dieta e ingesta de energía (Artículos I, II, V, VII) 1. Recuerdo dietético de 24-horas: la ingesta de alimentos y de energía fue estimada a partir de un recuerdo dietético de 24-horas electrónico llamado SACINA (Self Administered Children and Infants Nutrition Assessment), basado en un software previamente diseñado para su uso en adolescentes llamado HELENA-DIAT (Dietary Assessment Tool) (90, 91). Se trata de un programa estructurado de acuerdo a 6 comidas (desayuno, almuerzo, comida, merienda, cena y recena) incorporadas dentro de una serie de preguntas realizadas en orden cronológico sobre actividades diarias (90-93). Las entrevistas fueron completadas por los padres con la ayuda del personal del estudio y cada entrevista tenía una duración aproximada de 20-30 minutos y debían de registrar todos los alimentos y bebidas que su hijo/a había consumido durante el día anterior. Las ingestas que tenían lugar en el colegio, se registraban por el personal del estudio mediante observación directa. El tamaño de las porciones se estimó principalmente mediante fotos de tamaños de porciones, porciones estándar, tamaños European PhD Thesis, 2013 68 de envasado habituales y alimentos troceados o en lonchas para así disminuir el error relacionado con la estimación de las cantidades. La ingesta de energía se obtuvo a partir de tablas de composición de alimentos (TCA) propias de cada país debido a la ausencia de una TCA válida para ser usada en toda Europa. Para minimizar el error, se adoptaron directrices y procedimientos comunes para armonizar las bases de datos de nutrientes de distintos los países. Tanto en el artículo I como en el II, únicamente se incluyeron aquellos niños con dos recuerdos dietéticos de 24-horas recogidos en días no consecutivos y distribuidos a lo largo de la semana para recoger información de días de entre semana y de fin de semana. Las ingestas de alimentos y de energía se obtuvieron mediante el cálculo de la ingesta media de los dos días. 2. Cuestionario de frecuencia de consumo de alimentos: se utilizó un CFCA, llamado Children’s Eating Habits Questionnaire-food frequency section (CEHQ-FFQ), que fue diseñado como un instrumento de medida de los comportamientos alimentarios de los niños relacionados con el riesgo de sobrepeso, obesidad y con la salud en general. Los padres eran los encargados de rellenar el cuestionario en casa y debían aportar información sobre el número de veces que su hijo/a había consumido los grupos de alimentos incluidos en el cuestionario durante una semana típica del mes anterior. El CEHQ-FFQ consistía en 14 grupos de alimentos: vegetales, frutas, bebidas, cereales de desayuno, leche, yogur, pescado, huevo, carnes y productos cárnicos, productos a base de soja y/o sustitutivos de la carne, queso, productos para untar (mermelada, miel, mantequilla, etc.), cereales (pan, pasta, arroz, etc.) y aperitivos o snacks (frutos secos, dulces, pasteles, chocolate, palomitas de maíz, ganchitos, etc.). Para facilitar las repuestas a los padres, se adoptó una escala utilizada en un estudio previo (94) y que incluía las siguientes categorías de consumo: “nunca/menos de una vez por semana”, European PhD Thesis, 2013 69 “1-3 veces por semana”, “4-6 veces por semana”, “1 vez al día”, “2 veces al día”, “3 veces al día”, “4 o más veces al día” y “no lo sé”. El tamaño de las porciones no fue estimado. 3.3.4 Examen físico (Artículos I, V, VII) Todas las medias fueron tomadas por personal del estudio entrenado previamente para ello. El peso (kg) se midió en ropa interior con una báscula electrónica (TANITA BC 420 SMA) y la altura (cm) se midió sin zapatos mediante un estadiómetro. Los pliegues cutáneos se midieron por duplicado a través de un lipómetro (Holtain Ltd., Croswell, UK). La tensión arterial se midió a través de un esfingomanómetro electrónico (Welch Allyn 4200B-E2) en el brazo derecho del niños. Se tomaron dos medidas en un intervalo de 2 minutos, y en el caso en el que hubiera una diferencia mayor al 5% entre las dos medidas, se tomaba una tercera medida. 3.3.5 Actividad física (Artículos V, VII) La información sobre actividad física (AF) fue obtenida mediante un cuestionario autoadministrado a los padres con las siguientes preguntas: “¿Su hijo/a es miembro de algún club deportivo?”. A lo que los padres debían de responder “sí” o “no”. En el caso de que hubieran contestado “sí”, se les preguntaba “¿Cuánto tiempo (en horas) pasa a la semana haciendo ejercicio en un club deportivo?”. La respuesta era abierta para que pudieran determinar el número de horas y minutos invertidos en la actividad. 3.3.6 Comportamientos sedentarios (Artículos V, VII) A través de un cuestionario, se les preguntó a los padres: “¿Cuánto tiempo suele ver su hijo/a la televisión/vídeos/DVDs por día?”. Las respuestas estaban divididas para los días de entre semana y los de fin de semana e incluían 5 categorías: “nada en absoluto”, “<30 minutos al día”, “<1 hora al día”, “1-2 horas al día”, “2-3 horas al día”, y “>3 horas al día”. La media de horas al día de televisión/videos/DVDs vistas a la semana se calcularon de la siguiente manera: [(horas/día entre semana × 5) + (horas/día en fin de semana × 2)]/7. European PhD Thesis, 2013 70 3.3.7 Muestras biológicas (Artículos V, VII) Las muestras de sangre se obtuvieron después de 8 horas de ayuno. Las concentraciones sanguíneas de TG, colesterol total, HDL-c y glucosa fueron determinadas in situ, unos minutos después de que la extracción tuviera lugar con un aparato denomindado Cholestech LDX (Cholestech LDX analyzer, Cholestech Corp., Hayward, CA, USA). Por otro lado, las concentraciones de insulina se valoraron en el laboratorio central situado en Dortmund (Alemania) a través de inmnunoensayo de luminiscencia (Immulite 2000, Siemens, Eschborn, Germany). La resistencia a la insulina se definió a través del índice HOMA (95) y se calculó a través de una fórmula estándar usando los niveles en ayunas de glucosa y de insulina plasmática: HOMA= [insulina (μUI/ml) × glucosa (mg/dl)]/405. European PhD Thesis, 2013 77 4. Resultados Los resultados y discusión de la presente Tesis Doctoral se muestran en forma de artículos científicos. 4. Results The results and discussion of this Doctoral Thesis are shown as research manuscripts. European PhD Thesis, 2013 78 European PhD Thesis, 2013 79 Artículo I [Paper I]: Validity of 24-h recalls in (pre-)school aged children: Comparison of proxy-reported energy intakes with measured energy expenditure Börnhorst C, Bel-Serrat S, Pigeot I, Huybrechts I, Ottavaere C, Sioen I, De Henauw S, Mouratidou T, Mesana MI, Westerterp K, Bammann K, Lissner L, Eiben G, Pala V, Rayson M, Krogh V, Moreno LA, on behalf of the IDEFICS consortium Clin Nutr 2013. doi:pii: S0261-5614(13)00096-4. 10.1016/j.clnu.2013.03.018 European PhD Thesis, 2013 80 Original article Validity of 24-h recalls in (pre-)school aged children: Comparison of proxyreported energy intakes with measured energy expenditure q , qq C. Börnhorst a , k , S. Bel-Serrat b , k , I. Pigeot a , I. Huybrechts c , d , C. Ottavaere c , I. Sioen c , S. De Henauw c , e , T. Mouratidou b , M.I. Mesana b , K. Westerterp f , K. Bammann a , g , L. Lissner h , G. Eiben h ,V.Pala i , M. Rayson j , V. Krogh i , L.A. Moreno b , * , on behalf of the IDEFICS consortium a BIPS eInstitute for Epidemiology and Prevention Research, Bremen, Germany b GENUD (Growth, Exercise, Nutrition and Development) Research Group, Faculty of Health Sciences, University of Zaragoza, Zaragoza, Spain c Department of Public Health, Ghent University, Ghent, Belgium d Dietary Exposure Assessment Groups, International Agency for Research on Cancer, Lyon, France e University College Ghent, Department of Nutrition and Dietetics, Faculty of Health Care “Vesalius”, Ghent, Belgium f Department of Human Biology, Maastricht University, The Netherlands g Institute for Public Health and Nursing Research, University of Bremen, Bremen, Germany h Department of Public Health and Community Medicine, University of Gothenburg, Gothenburg, Sweden i Department of Preventive and Predictive Medicine, Nutritional Epidemiology Unit, Fondazione IRCSS Istituto Nazionale dei Tumori, Milan, Italy j BioTel Ltd. Clifton, Clifton, Bristol, United Kingdom article info Article history: Received 26 July 2012 Accepted 24 March 2013 Keywords: Child Doubly labeled water Energy expenditure Energy intake summary Background & aims: Little is known about the validity of repeated 24-h dietary recalls (24-HDR) as a measure of total energy intake (EI) in young children. This study aimed to evaluate the validity of proxyreported EI by comparison with total energy expenditure (TEE) measured by the doubly labeled water (DLW) technique. Methods: The agreement between EI and TEE was investigated in 36 (47.2% boys) children aged 4e10 years from Belgium and Spain using subgroup analyses and BlandeAltman plots. Low-energy-reporters (LER), adequate-energy-reporters (AER) and high-energy-reporters (HER) were defined from the ratio of EI over TEE by application of ageand sex-specific cut-off values. Results: There was good agreement between means of EI (1500 kcal/day) and TEE (1523 kcal/day) at group level though in single children, i.e. at the individual level, large differences were observed. Almost perfect agreement between EI and TEE was observed in thin/normal weight children (EI: 1511 kcal/day; TEE: 1513 kcal/day). Even in overweight/obese children the mean difference between EI and TEE was only 86 kcal/day. Among the participants, 28 (78%) were classified as AER, five (14%) as HER and three (8%) as LER. Conclusion: Two proxy-reported 24-HDRs were found to be a valid instrument to assess EI on group level but not on the individual level. Ó2013 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved. 1. Introduction Dietary intake has been recognized to be related not only to normal growth, but also to the development and progression of chronic diseases that start early in life. 1 However, accurate assessment of dietary information is problematic - especially among children. 2e5 These lack the ability to report their own intake, so that data in children younger than seven years mainly rely on proxy-reports. 6 Furthermore, children’s diets tend to be highly variable from day-to-day and their food habits change rapidly during childhood which makes dietary assessment a challenging issue. 7 Abbreviations: EI, energy intake; TEE, total energy expenditure; DLW, doubly labelled water; 24-HDR, 24-h dietary recall; LER, low-energy-reporters; HER, highenergy-reporters; AER, adequate energy reporters. q 1. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie (GMDS) und der Deutschen Gesellschaft für Epidemiologie (DGEpi). Mainz (Germany), 2011. qq 2. 11th European Nutrition Conference (FENS). Madrid (Spain), 2011. *Corresponding author. Faculty of Health Sciences, University of Zaragoza, C/ Domingo Miral, s/n, 50009 Zaragoza, Spain. Tel.: þ34 976 76 10 00x4457; fax: þ34 976 76 17 52. E-mail address: [email protected] (L.A. Moreno). k C. Börnhorst and S. Bel-Serrat contributed equally to this manuscript. Contents lists available at SciVerse ScienceDirect Clinical Nutrition journal homepage: http://www.elsevier.com/locate/clnu 0261-5614/$ esee front matter Ó2013 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved. http://dx.doi.org/10.1016/j.clnu.2013.03.018 Clinical Nutrition xxx (2013) 1e6 Please cite this article in press as: Börnhorst C, et al., Validity of 24-h recalls in (pre-)school aged children: Comparison of proxy-reported energy intakes with measured energy expenditure, Clinical Nutrition (2013), http://dx.doi.org/10.1016/j.clnu.2013.03.018 The validity of a dietary assessment method can be evaluated by comparing reported energy intake (EI) to measured total energy expenditure (TEE) 5 because TEE and EI can be assumed to be equal for individuals in energy balance. 3 This assumption is also justified in children as energy cost of growth and development during childhood is very small, i.e. 1e2%. 4 Doubly labeled water (DLW) is considered the “gold-standard”method to assess TEE. 2 Validation studies with DLW in children and adolescents have shown self-reported (partly with parental assistance) 24-h dietary recalls (24-HDR) to be a valid measure of EI at least on group level, though misreporting of EI is a common problem especially in overweight/obese study populations. 8 Results concerning misreporting in children and adolescents vary widely among studies (underreporting from 19% to 41%; overreporting from 7% to 11% of reported EI) 9 and yet it is unknown whether the study participants’ weight status is predictive for misreporting in data relying on proxy-reports as well. Moreover overweight/obese study subjects may be more likely than thin/normal weight subjects to be on an energy-restricted diet, i.e. to actually eat less than physiologically required (EI <TEE; undereating) which complicates the evaluation of reported EI. Apart from total EI, nutrient intakes such as fat, sugar or micronutrients are commonly misreported which was shown to result in flawed associations between food/nutrient intakes and body weight. 10 To date, little is known about the validity of 24-HDR data obtained by parents acting as surrogate reporters. Therefore the present study aims to investigate the validity of 24-HDR data in four-to-ten-year-old children by comparison of proxy-reported EI with objectively measured TEE. 2. Materials and methods 2.1. Sample The present validation study was conducted within the framework of the IDEFICS (“Identification and prevention of dietaryand lifestyleinduced health effects in children and infants”) study from October 2008 to July 2009. It is based on a convenience sample of four-to-ten-year-old children from three different centres (Belgium, Sweden, Spain). 11 The burden for participating children and their parents was deemed to be too high to justify a random sample. Belgium and Spain recruited children through schools, newspapers or by asking colleagues or friends. In contrast, a subgroup of ten obese Swedish children was recruited from an obesity clinic which underwent the same protocol. Although these childrenwereweight stable at the time of the validation study, they were excluded from the main analysis presented here as they were selected from a clinical setting and hence this would have limited the generalisability of the results. The Swedish children were considered in a subgroup analysis only. Furthermore, only children with complete information on age, sex, height, weight, at least two 24-HDR as well as DLW measurements were included in the present study resulting in 36 children (six from Belgium, thirty from Spain). The study was approved by the appropriate local ethics committees in each centre and written informed consent was obtained from the parents before participation. 2.2. Total energy expenditure measurement TEE was measured over a nine-day period (from Day 1toDay 8) by means of DLW following the Maastricht protocol. 12 The measurement schedule is summarised in Fig. 1. Each child was given a single oral dose of DLW based on his/her body weight (ml DLW/kg), increasing background levels of Oxygen-18 with 250e300 parts per million (ppm) and background levels of Deuterium with 125e150 ppm. Urine samples were obtained from each child several minutes (maximum 15 min) before ingesting the DLW dose (baseline urine), and on Days 1, 4, and 8 after the DLW dose. Each urine sample was collected at the same time of the day (1 h) in all children depending on their timetable and physiological needs. Parents were asked to record the collection time for each urine sample. After collection of urine samples, 2 ml from each urine sample were transferred into two individual glass vials and kept frozen at 20  C at each study centre. Urine samples were sent as one batch directly from all centres to the central laboratory at the end of data collection. Samples were analysed by isotope ratio mass spectrometry with an analytic precision of 0.2 ppm for 2 H isotope and 0.4 ppm for 18 O isotope. All analytical tests were performed in the Human Biology Department, University of Maastricht (The Netherlands). The value of 0.85 was used as an estimate of the respiratory quotient, based on the consumption of a standard Western diet, 13 and TEE was calculated according to Schoeller et al. 13 2.3. Energy intake measurement During the DLW measurement period at least two 24-HDR per child were recorded. EI was assessed using a computerised 24-HDR called SACINA (Self Administered Children and Infants Nutrition Assessment) that is based on the previously designed software YANA-C developed and validated for Flemish adolescents. 14 Within the framework of the IDEFICS study, this 24-HDR was adapted for assessment of proxy-reported dietary intakes in young children. 15 The SACINA is structured according to six meal occasions (breakfast, morning snack, lunch, afternoon snack, dinner, evening snack) embedded within chronological questions related to daily activities aiming to help proxies, mainly the parents, to recall their child’s intakes of the previous day. 14 Portion sizes were assessed by photos of serving sizes, standard portions, customary packing size and foods in pieces or slices that were displayed on the screen. For every meal, parents were asked to select all food items their child had eaten at the specific occasion and, for each item, the respondents typed the amount consumed or modified the standard serving size with two command buttons (more/less). The 24-HDR interviews were completed by proxies under supervision of fieldwork personnel. On weekdays, school meals were additionally assessed either through parents or by means of direct observation (N¼18). In the latter case, data was documented by survey personnel using pre-defined recording sheets specifically designed for that purpose. Based on these sheets, the observer indicated the amount eaten by the child. Pre-defined portion sizes ranged from “nothing”,“¼ portion”,“½ portion”,“1 portion”,“1½ portions”,“2 portions”up to “more than 2 portions”. Different standard measures like hands-full, slices, table spoons, etc. were given depending on the regarded food item. School meal data Fig. 1. Measurement schedule of the IDEFICS validation study. 11 C. Börnhorst et al. / Clinical Nutrition xxx (2013) 1e62 Please cite this article in press as: Börnhorst C, et al., Validity of 24-h recalls in (pre-)school aged children: Comparison of proxy-reported energy intakes with measured energy expenditure, Clinical Nutrition (2013), http://dx.doi.org/10.1016/j.clnu.2013.03.018 were merged with the parentally reported 24-HDR data to enhance completeness of dietary intakes. Although up to three repeated 24-HDR were carried out on non-consecutive days, only two recalls per child were used in the current analyses to achieve an equal number of 24-HDR per child. In general, the first and second recall day was used. If both recalls were assessed on a weekday but the third one on a weekend day, the weekend day was chosen to increase the number of children having one weekday and one weekend day. The remaining weekday was selected randomly in such cases. Energy and nutrient intakes were obtained using countryspecific Food Composition Tables (FCTs) 15 due to the lack of a panEuropean FCT. Common guidelines and procedures to prevent and minimizebiaswereadoptedto harmonise nutrient databases across countries. Nutrient values were also harmonized using the documentation of the country-specific food components. In detail, the FCTs used were “Centre d’Ensenyament Superior de Nutrició I Dietètica (CESNID). Tablas de composición de alimentos del CESNID, 2nd edn. Madrid: McGraw-Hill Interamericana, 2004”in Spain, “NUBEL. Belgian Food Composition Table, 4th edn. Brussels: Ministry of Public Health, 2004”in Belgium and “Swedish National Food Agency(SNFA).SwedishFoodDatabase(availableat http://www.slv. se/en-gb/Group1/Food-and-Nutrition/The-Food-Database/)”in Sw eden. EI (kcal/day) and macronutrient intakes (carbohydrates, protein, fat in g/day) were calculated as mean of the two 24-HDR for each child. Macronutrient intakes were additionally expressed as percentage of total EI derived from carbohydrates, proteins and fat. 2.4. Anthropometric measurements Height was measured to the nearest 0.1 cm with a calibrated stadiometer (Telescopic height measuring instruments SECA 225, Birmingham, UK). Body weight was measured in light underwear on a calibrated scale accurate to 0.1 kg on the first and last day of the measurement period (TANITA BC 420 SMA digital weighing scale, Tanita Europe GmBH, Sindelfingen, Germany). Body mass index (BMI) was calculated by dividing body mass in kg measured at Day 1 (if missing, the measurement of the last day was used) by the squared body height in meters. BMI was categorised according to the International Obesity Task Force (IOTF) criteria 16 and BMI zscores were calculated according to Cole et al. 17 For most analyses, thin/normal weight and overweight/obese children were each combined into one group. 2.5. Statistical analysis Analyses were done for all children as well as stratified by study centre, sex, weight status (thin/normal weight vs. overweight/ obese) and age group (4 to <6 years vs. 6 to <10 years). Only one of the mentioned variables was considered at the same time due to the small number of children per strata. The BlandeAltman plot 18 was used to assess the agreement between EI and TEE. This method calculates a bias as mean difference between the reference method (TEE) and the reported value (EI). Limits of agreement are calculated as bias 2 SD of this observed bias. In the following, the term ‘bias’always refers to the difference between EI and TEE unless another specification is given. Following the approach of Sjoberg et al. 19 the ratio of EI over TEE was used to differentiate adequate-energy-reports (AER) from lowenergy-reports (LER) and high-energy-reports (HER). Participants were classified as AER, LER or HER based on 95% confidence limits (CL) of the expected ratio EI over TEE, which equals 1.00 under the assumption of energy balance. CLs were calculated according to the following formula 20 : 95% CLl;u¼1:96$ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ðCVEIÞ2 dþðCVTEEÞ2 s: Child-specific reference values (boys: 22.5%, girls 21.3%) based on previous literature were used for the coefficient of variation for daily EI (CV EI ). 21 The number of days (d) was set to two and for the coefficient of variation of TEE (CV TEE ) the value 8.2% was chosen. 20 Children were defined as AER, LER and HER according to the cutoff values calculated by insertion of the reference values in the above formula (LER: boys: EI/TEE<0.65, girls: EI/TEE<0.66; AER: boys: 0.65 EI/TEE<1.35, girls: 0.66 EI/TEE<1.34; HER: boys: EI/ TEE1.35, girls EI/TEE1.34). Macronutrient intakes as well as TEE, EI and study participants’ characteristics were compared between groups of AER, LER and HER. All analyses were performed using the statistical software package SAS (version 9.1; SAS Institute, Cary, NC, USA). 3. Results Descriptive analyses are presented inTable 1. In total, nine out of the 36 children were overweight or obese. Mean ages differed only Table 1 Main characteristics of the study population by age group, sex and study centre (mean, SD and total numbers). All 4e<6 years 6e<10 years Boys Girls Belgium Spain NN N NNN N Boys 17 6 11 ee215 Girls 19 8 11 ee415 4e<6 years 14 ee 683 11 6e<10 years 22 ee 11 11 3 19 Thin a 41 3 3 1 1 3 Normal weight a 23 11 12 13 10 4 19 Overweight a 51 4 1 4 1 4 Obese a 41 3 0 4 0 4 All 36 14 22 17 19 6 30 Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Mean SD Age 6.7 1.4 5.3 0.5 7.7 0.8 6.8 1.4 6.7 1.3 6.2 1.6 6.9 1.3 Weight (kg) 24.4 5.0 20.9 2.8 26.6 4.9 22.8 3.6 25.8 5.7 22.7 4.7 24.7 5.1 Height (cm) 121.1 7.8 114.0 4.0 125.6 6.1 121.4 7.9 120.9 8.0 120.8 11.8 121.2 7.1 BMI z-score b 0.2 1.2 0.1 0.9 0.4 1.3 0.1 0.9 0.4 1.3 0.2 1.2 0.3 1.6 SD: standard deviation. Due to the small sample size the authors abstained from the presentation of percentages in the subgroups (sex, age, weight status, study centre). a Cut-offs according to IOTF criteria. 16 Prevalence of overweight not including obesity. b Body mass index z-score according to Cole et al. 17 C. Börnhorst et al. / Clinical Nutrition xxx (2013) 1e63 Please cite this article in press as: Börnhorst C, et al., Validity of 24-h recalls in (pre-)school aged children: Comparison of proxy-reported energy intakes with measured energy expenditure, Clinical Nutrition (2013), http://dx.doi.org/10.1016/j.clnu.2013.03.018 slightly between study centres and the sex distribution was almost balanced across the whole study group (17 boys,19 girls) as well as across study centres. As expected, TEE was higher in older children, higher in boys compared to girls and higher in overweight/obese compared to thin/normal weight children (Table 2) though differences in TEE were only small between the weight groups (thin/normal weight: 1513 kcal/day; overweight/obese: 1554 kcal/day). EI was again higher in older children as well as in boys but slightly lower in overweight/obese children compared to thin/normal weight children. Mean EI was lower compared to TEE by up to 5% (6e<10 years) and higher by up to 2% (4e<6 years) depending on the study group addressed. Regarding the total study group, the ratio of EI over TEE was 1.00 indicating that reported EI and measured TEE matched almost exactly (EI: 1500 kcal/day; TEE: 1523 kcal/day). The greatest difference between EI and TEE was found in children younger than six years (mean difference: 113 kcal/day), followed by the group of overweight/obese children (mean difference: 86 kcal/day). The BlandeAltman plot (Fig. 2) revealed only moderate agreement between EI and TEE reflecting in particular the high day-today variation in dietary intakes. Individual differences between EI and TEE varied widely, ranging from 836 up to þ953 kcal/day. The largest bias was observed in case of very low mean values of EI and TEE (mainly negative bias, EI <TEE), as well as in case of very high mean values of EI and TEE (mainly positive bias, EI >TEE). The cut-off technique identified five HER (14%) and three LER (8%) (Table 3). Mean TEE was highest in the group of LER whereas, as expected, EI was lowest (Table 4). Fat intakes expressed as percentage of total EI were slightly lower in LER compared to AER and highest in HER whereas the opposite was found for percentages of EI from carbohydrates. Percentages of EI from proteins were lowest in AER. Intakes in g per day were lowest in LER and highest in HER for all macronutrients and water. In the subgroup analysis of the exclusively obese Swedish children that had previously been treated in an obesity clinic (not included in the tables), a mean difference between EI and TEE of 455 kcal/day was observed and seven out of the ten Swedish children were classified as LER. 4. Discussion The present study aimed to evaluate the accuracy of EI estimated from two repeated 24-HDR using objective measurements of TEE obtained by the gold-standard technique DLW. 2 Results revealed good agreement betweenEI and TEE for the total study group as well as in subgroups of age, sex, study centre and weight status; Mean EI was lowercompared to mean TEE by up to 5% and higher by up to 2% depending on the study group addressed. Considering the whole sample, the mean ratio of reported EI over TEE equalled one which meansthatbothvaluesagreedalmostexactlyongrouplevel.Previous studies in children found underreporting of EI relative toTEE ranging from19%to41% andoverreportingrangingfrom7%to11%. 9 However, findings strongly vary depending on the participants’age and on the dietary assessment method used. 22e24 The good agreement between EI and TEE in our study may be explained by the additional assessment of school meals which may have reduced reporting errors caused by meals not under parental control. Furthermore, the display of pictures with increasing portion sizes on the screen may have improvedthe estimationofportionsizes.However, alsorestrictingon a convenience study sample may have contributed tothese results as it is likely that the participants were highly-motivated. Even in overweight/obese children, a group that has repeatedly been reported to be strongly influenced by misreporting, 8,25 only a Table 2 Mean energy expenditure (TEE), energy intake (EI), ratio of EI over TEE and difference between EI and TEE (bias) by age group, sex, study centre and weight status. NTEE a (kcal/day) EI a (kcal/day) Ratio EI over TEE Bias b (kcal/day) Mean SD Mean SD Mean SD Mean SD 4e<6 years 14 1386 217 1273 352 0.95 0.35 113 460 6e<10 years 22 1610 186 1645 465 1.02 0.27 35 431 Boys 17 1574 219 1535 488 0.99 0.34 39 485 Girls 19 1477 226 1469 440 1.00 0.28 8 412 Belgium 6 1491 226 1434 290 0.96 0.11 57 154 Spain 30 1530 228 1513 487 1.00 0.33 16 481 Thin/normal weight 27 1513 231 1511 501 1.01 0.33 2 471 Overweight/obese 9 1554 214 1468 313 0.96 0.23 86 356 All 36 1523 225 1500 458 1.00 0.30 23 442 a TEE: total energy expenditure; EI: energy intake. b Mean difference between reported energy intake (means of two 24-HDR per child) and total energy expenditure (DLW measurements). Difference -1500 -1200 -900 -600 -300 0 300 600 900 1200 Mean (kcal/da y ) 1000 1200 1400 1600 1800 2000 2200 2400 2600 All Fig. 2. BlandeAltman plot agreement between measured energy expenditure (TEE; kcal/day) and reported energy intake (EI; kcal/day). The mean of EI and TEE is plotted on the x-axis, the difference of both values (bias) on the y-axis accordingly. The solid line indicates the line of total agreement (zero differences between EI and TEE). Mean difference and upper/lower limits of agreement (mean difference 2 SD) are superimposed by broken line. Table 3 Number of low-energy-reports (LER), adequate reports (AER) and high-energyreports (HER) by age group, sex, study centre and weight status. Low-energyreports b Adequate reports c High-energyreports d NNN Total study group 3 (8%) 28 (78%) 5 (14%) 4e<6 years 2 10 2 6e<10 years 1 18 3 Boys 2 13 2 Girls 1 15 3 Belgium 0 6 0 Spain 3 22 5 Thin a 130 Normal weight a 2174 Overweight a 041 Obese a 040 Due to the small sample size the authors abstained from the presentation of percentages in the subgroups. a Cut-offs according to IOTF criteria. 23 Prevalence of overweight not including obesity. b Low-energy-reporters defined as: EI/TEE<0.65 (boys), EI/TEE<0.66 (girls). c Adequate reports defined as: 0.65 EI/TEE<1.35 (boys), 0.66 EI/TEE<1.34 (girls). d High-energy-reports defined as: EI/TEE1.35 (boys), EI/TEE1.34 (girls). C. Börnhorst et al. / Clinical Nutrition xxx (2013) 1e64 Please cite this article in press as: Börnhorst C, et al., Validity of 24-h recalls in (pre-)school aged children: Comparison of proxy-reported energy intakes with measured energy expenditure, Clinical Nutrition (2013), http://dx.doi.org/10.1016/j.clnu.2013.03.018 mean difference of 84 kcal/day was observed between reported EI and measured TEE in our data. Forrestal 8 stated in a review that higher weight, obesity and BMI had consistently been associated with low-energy-reporting at all ages. In proxy-reports, misreporting (comprising lowand high-energy-reporting) may to a large extent be explained by meals not under parental control and difficulties in estimation of portion sizes, but also by intentional misreporting due to social desirability bias. The lower percentages of EI from fat but higher percentages of EI from carbohydrates in LER may indicate selective omission of certain foods high in fat and protein, although the exact nature of these differences cannot be determined with available methodologies. No study was found investigating socially desirable answer behaviour in proxy-reports in that age group. Social desirability was shown to be a predictor of misreporting among adults, 26 so it can be hypothesised that parents feeling ashamed for their child’s unhealthy/energy-dense diet may intentionally misreport in proxy-reports as well. Inconsistently with literature, children classified as LER were either thin or normal weight in our data. It can be assumed that these lowenergy-reports rather reflect exceptional days (e.g. child was ill) or reporting errors caused by meals not under parental control than intentional misreporting. In addition, by use of a cut-off approach, underreporting cannot be distinguished from a hypocaloric diet (undereating). Thus, participants classified as LER may not be underreporting but following a specific diet. In these children, EI values may actually be lower than TEE so that the proxy-report may still be valid. However, when investigating dietedisease associations these cases might still bias the true dietedisease association as their current diet (exceptional days) might not be the cause of their current health or weight status. To the authors’knowledge, child-specific cut-off values to classify 24-HDR in different reporting groups were only applied in a study comparing EI to estimated basal metabolic rates 22 making it difficult to compare proportions of LER, AER and HER to other studies. Differences between EI and TEE were large at the individual level (regarding single children’s values), whereas on group level (mean EI vs. meanTEE) good agreement was observed. Forexample in thin/normal weight children means of EI and TEE (EI: 1511 kcal/ day; TEE: 1513 kcal/day) almost coincide although individual differences between EI and TEE strongly differ from zero (836 kcal/ day up to þ953 kcal/day). This may be explained by random errors including day-to-day variation that cancel out on group level. These results agree with other studies reporting biases at the individual level, but better agreement on group level. 23,27e29 The large difference between reported EI and measured TEE (mean difference: 455 kcal/day) that was observed in the Swedish subgroup may be a consequence of previous treatment in the obesity clinic resulting in changes in lifestyle behaviours including eating habits and leisure time activities. Also intentional or unintentional underreporting of energy intake by the parents of these obese children may have contributed to these findings. In adults, a combination of methods (two non-consecutive 24HDR interviews in combination with a food frequency questionnaire (FFQ)) to measure dietary intake has been recommended for use in monitoring and epidemiological surveys (EFCOVAL & IDAMES project: 2 EC 6th FP). 30 However, recommendations for measuring dietary intake among children are still lacking but it can be hypothesised that additional FFQ information may help to improve the estimation of individual intakes in childhood populations as well. 4.1. Limitations and strengths The relatively small sample size and correspondingly low power is a limitation and was also the reason to abstain from statistical testing for differences between groups. However, the sample size is consistent with numbers in other validation studies using the costintense doubly labeled water technique. Furthermore, only approximate agreement between EI and TEE can be expected when assessing only two 24-HDR per child due to the large day-to-day variability in EI. Moreover, the cut-off technique aims only to identify under-/overestimations resulting in physiologically implausible EI 24 and does not allow distinction between various degrees of misreporting or to differentiate between underreporting and undereating. As a convenience sample was chosen, a selection bias of highly motivated parents and children cannot be precluded. To the authors’knowledge, this is among the first studies documenting accuracy of parental reporting using objective measurements of TEE. A strength is the assessment of EI and TEE within the same time frame, 9 increasing the reliability of the measurements. In addition, the assessment of school meals enhanced the completeness of the 24-HDR and the display of pictures with increasing portion sizes in the SACINA program simplified the estimation of portion sizes for the respondents. 4.2. Conclusions In summary, good agreement between proxy-reported EI and measured TEE was observed on group level eeven in overweight/ Table 4 Mean energy expenditure (TEE), energy intake (EI), difference between EI and TEE (bias) and macronutrient intakes (expressed as g/day and % of total EI/day) by reporting group (low-energy-report, adequate report and high-energy-report). Low-energy-reports c Adequate-energy-reports d High-energy-reports e Total study group Mean SD Mean SD Mean SD Mean SD TEE a (kcal/day) 1595 183.9 1544 219.0 1364 252.9 1523 225.0 EI a (kcal/day) 862 236.4 1460 367.9 2107 336.9 1500 457.6 Bias b (kcal/day) 733 142.7 84 282.2 743 150.6 22.9 441.8 Carbohydrate intake (g/day) 89.4 14.1 152.8 50.7 187.1 39.3 152.3 51.8 %EI from carbohydrates 43.3 6.9 42.2 7.4 35.9 3.7 41.4 7.2 Fat intake (g/day) 37.5 13.3 65.0 19.9 99.4 17.7 67.5 24.1 %EI from fat 37.8 3.9 39.4 7.3 41.8 5.0 39.6 6.8 Protein intake (g/day) 41.9 16.7 66.3 17.3 116.8 34.6 71.3 27.8 %EI from proteins 19.3 3.4 18.7 3.4 22.5 5.7 19.3 3.9 Water intake (g/day) 824.8 386.4 1176 398.5 1733 587.7 1224 472.4 a TEE, total energy expenditure; EI, energy intake. b Mean difference between reported energy intake (means of two 24-HDR per child) and total energy expenditure (DLW measurements). c Low-energy-reporters defined as: EI/TEE<0.65 (boys), EI/TEE<0.66 (girls). d Adequate reports defined as: 0.65 EI/TEE<1.35 (boys), 0.66 EI/TEE<1.34 (girls). e High-energy-reports defined as: EI/TEE1.35 (boys), EI/TEE1.34 (girls). C. Börnhorst et al. / Clinical Nutrition xxx (2013) 1e65 Please cite this article in press as: Börnhorst C, et al., Validity of 24-h recalls in (pre-)school aged children: Comparison of proxy-reported energy intakes with measured energy expenditure, Clinical Nutrition (2013), http://dx.doi.org/10.1016/j.clnu.2013.03.018 obese children ewhereas individual differences between both values varied widely. Two proxy-reported 24-HDR including visual aids and school meal reporting are a valid measure for EI on group level but not sufficient to evaluate individual intakes. Future research is required to improve the precision of proxy-reported EI for individual children. Ethical statement The study was approved by the appropriate ethics committees in each centre, specifically by the “Comité Ético de Investigación Clínica de Aragón (CEICA)”in Spain, the Ethical Committee of the Ghent University Hospital in Belgium, and the “Regionala Etikprövningsnämnden i Göteborg”in Sweden. Conflict of interest All the authors declare that there are no conflicts of interest. Acknowledgements This manuscript represents original work that has not been published previously and is currently not considered by another journal. The authors confirm that the manuscript will not be published elsewhere in the same form, in English or in any other language, if it is accepted by Clinical Nutrition. Each author has seen and approved the contents of the submitted manuscript. All authors contributed to conception and design, acquisition of data, analysis or interpretation of data. Final approval of the version published was given by all the authors. In detail: C. Börnhorst and S. Bel-Serrat drafted the manuscript; IP, KB, IH, SDH, GE, LL, KW and LAM conceived the study and participated in its design and coordination; IH, IP and LAM helped drafting the manuscript and interpreting the data; all the authors revised the article critically for important intellectual content. This work was done as part of the IDEFICS Study (www.idefics. eu) and is published on behalf of its European Consortium. We gratefully acknowledge the financial support of the European Community within the Sixth RTD Framework Programme Contract No. 016181 (FOOD). The information in this document reflects the author’s view and is provided as is. SBS was funded by a grant from the Aragón’s Regional Government (Diputación General de Aragón, DGA). IS was financially supported by the Research Foundation - Flanders (Grant n  : 1.2.683.11.N.00). References 1. Moreno LA, Rodriguez G. Dietary risk factors for development of childhood obesity. Curr Opin Clin Nutr Metab Care 2007;10(3):336e41. 2. Schoeller DA. Validation of habitual energy intake. Public Health Nutr 2002;5(6A):883e8. 3. Schoeller DA. Measurement of energy expenditure in free-living humans by using doubly labeled water. J Nutr 1988;118(11):1278e89. 4. Kuzawa CW. Adipose tissue in human infancy and childhood: an evolutionary perspective. Am J Phys Anthropol 1998;(Suppl. 27):177e209. 5. Livingstone MB, Black AE. Markers of the validity of reported energy intake. J Nutr 2003;133(Suppl. 3):895Se920S. 6. Livingstone MB, Robson PJ. Measurement of dietary intake in children. Proc Nutr Soc 2000;59(2):279e93. 7. Willet W. Nutritional epidemiology. 1st ed. New York, NY: Oxford University Press; 1990. 8. Forrestal SG. Energy intake misreporting among children and adolescents: a literature review. Matern Child Nutr 2011;7(2):112e27. 9. Burrows TL, Martin RJ, Collins CE. A systematic review of the validity of dietary assessment methods in children when compared with the method of doubly labeled water. J Am Diet Assoc 2010;110(10):1501e10. 10. Huang TT, Roberts SB, Howarth NC, McCrory MA. Effect of screening out implausible energy intake reports on relationships between diet and BMI. Obes Res 2005;13(7):1205e17. 11. Bammann K, Sioen I, Huybrechts I, Casajus JA, Vicente-Rodriguez G, Cuthill R, et al. The IDEFICS validation study on field methods for assessing physical activity and body composition in children: design and data collection. Int J Obes 2011;35(Suppl. 1):S79e87. 12. Westerterp KR, Wouters L, van Marken Lichtenbelt WD. The Maastricht protocol for the measurement of body composition and energy expenditure with labeled water. Obes Res 1995;3(Suppl. 1):49e57. 13. Schoeller DA, Ravussin E, Schutz Y, Acheson KJ, Baertschi P, Jequier E. Energy expenditure by doubly labeled water: validation in humans and proposed calculation. Am J Physiol 1986;250(5 Pt 2):R823e30. 14. Vereecken CA, Covents M, Matthys C, Maes L. Young adolescents’nutrition assessment on computer (YANA-C). Eur J Clin Nutr 2005;59(5):658e67. 15. Hebestreit A, Eiben G, Brünings-Kuppe C, Huybrechts I. Computer based 24 hour dietary recall: the SACINA program. In: Bammann K, Ahrens W, editors. Measurement tools for a health survey on Nutrition, physical activity and lifestyle in children: the European idefics study. 1st ed. Berlin, Germany: Springer; 2012. In Press. 16. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ 2000;320(7244):1240e3. 17. Cole TJ, Freeman JV, Preece MA. Body mass index reference curves for the UK, 1990. Arch Dis Child 1995;73(1):25e9. 18. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 1986;1(8476):307e10. 19. Sjoberg A, Slinde F, Arvidsson D, Ellegard L, Gramatkovski E, Hallberg L, et al. Energy intake in Swedish adolescents: validation of diet history with doubly labelled water. Eur J Clin Nutr 2003;57(12):1643e52. 20. Black AE, Cole TJ. Withinand between-subject variation in energy expenditure measured by the doubly-labelled water technique: implications for validating reported dietary energy intake. Eur J Clin Nutr 2000;54(5):386e94. 21. Nelson M, Black AE, Morris JA, Cole TJ. Betweenand within-subject variation in nutrient intake from infancy to old age: estimating the number of days required to rank dietary intakes with desired precision. Am J Clin Nutr 1989;50(1):155e67. 22. Sichert-Hellert W, Kersting M, Schoch G. Underreporting of energy intake in 1 to 18 year old German children and adolescents. Z Ernahrungswiss 1998;37(3): 242e51. 23. Montgomery C, Reilly JJ, Jackson DM, Kelly LA, Slater C, Paton JY, et al. Validation of energy intake by 24-hour multiple pass recall: comparison with total energy expenditure in children aged 5-7 years. Br J Nutr 2005;93(5):671e6. 24. Haraldsdottir J, Sandstrom B. Detection of underestimated energy intake in young adults. Int J Epidemiol 1994;23(3):577e82. 25. Fisher JO, Johnson RK, Lindquist C, Birch LL, Goran MI. Influence of body composition on the accuracy of reported energy intake in children. Obes Res 2000;8(8):597e603. 26. Scagliusi FB, Ferriolli E, Pfrimer K, Laureano C, Cunha CS, Gualano B, et al. Characteristics of women who frequently under report their energy intake: a doubly labelled water study. Eur J Clin Nutr 2009;63(10):1192e9. 27. Reilly JJ, Montgomery C, Jackson D, MacRitchie J, Armstrong J. Energy intake by multiple pass 24 h recall and total energy expenditure: a comparison in a representative sample of 3-4-year-olds. Br J Nutr 2001;86(5):601e5. 28. Johnson RK, Driscoll P, Goran MI. Comparison of multiple-pass 24-hour recall estimates of energy intake with total energy expenditure determined by the doubly labeled water method in young children. J Am Diet Assoc 1996;96(11): 1140e4. 29. O’Connor J, Ball EJ, Steinbeck KS, Davies PS, Wishart C, Gaskin KJ, et al. Comparison of total energy expenditure and energy intake in children aged 6e9y. Am J Clin Nutr 2001;74(5):643e9. 30. de Boer EJ, Slimani N, van ’t Veer P, Boeing H, Feinberg M, Leclercq C, et al. The European food consumption validation project: conclusions and recommendations. Eur J Clin Nutr 2011;65(Suppl. 1):S102e7. C. Börnhorst et al. / Clinical Nutrition xxx (2013) 1e66 Please cite this article in press as: Börnhorst C, et al., Validity of 24-h recalls in (pre-)school aged children: Comparison of proxy-reported energy intakes with measured energy expenditure, Clinical Nutrition (2013), http://dx.doi.org/10.1016/j.clnu.2013.03.018 (mean 531 %). Extreme misclassification was about or even lower than 12 % for all food groups in both younger and older children; the highest values were observed for white bread (12 %) in young children and cheese (11 %) in older children. Mean k w was 0?20 for 2–,6-year-old children and 0?17 for 6–9-year-old children. The k w values showed an acceptable agreement for fruit, milk, cold cuts, cheese and white bread, whereas low agreement (,0?20) was seen for vegetables, meat and sweets in both age groups. Results changed when examining the adapted food groups, since the proportion of correct classification ranged from 38 % for wholemeal bread to 49 % for sweetened milk in younger children (mean 540 %) and from 32% for wholemeal bread to 52 % for sweetened milk in older children (mean 538 %). The mean proportion of individuals classified into the opposite tertile, however, was 22 % in both age groups, varying from 10 % and 6 % for sweetened milk to 29 % and 28 % for soft drinks in younger and older children, respectively. Mean k w for the adapted food groups was 0?20 for children aged 2–,6 years and 0?17 for those aged 6–9 years. Poor agreement was found except for sweetened milk, which showed acceptable agreement in both younger and older children (k w 50?30 and 0?36, respectively). Among younger children, breakfast cereals and butter & margarine also showed acceptable agreement (.0?20). Following exclusion of the Hungarian data, de-attenuated correlation coefficients were slightly higher compared with the crude coefficients, with an average of 0?31 in 2–,6-year-old children and 0?28 in 6–9-year-old children (Supplementary Materials, Tables 3 and 4). Average variance ratio increased to 0?64. Supplementary Materials, Tables 5 and 6 show the results of the cross-classification analysis excluding the Hungarian data. The mean percentage of correctly classified subjects increased to 35 % in younger children and to 32 % in older children. Mean extreme misclassification was considerably lower for both younger (5 %) and older children (7 %). The k w values Public Health Nutrition Table 2 Food group intakes (daily number of portions) from the CEHQ-FFQ and 24-HDR: younger children aged 2–,6 years from eight European countries participating in the IDEFICS Study (2007–2008) CEHQ-FFQ 24-HDR (SACINA) Food group (portions/d) nMean Median SD Mean Median SD Mean DPvalue Vegetables 983 0?57 0?29 0?47 0?91 1?00 0?68 20?34 0?000* Fried potatoes 976 0?11 0?00 0?19 0?09 0?00 0?22 0?02 0?101 Raw vegetables 979 0?67 0?29 0?66 0?55 0?50 0?70 0?12 0?000* Fruit 971 1?07 1?00 0?76 0?87 0?50 0?79 0?20 0?000* Sweetened fruit 914 0?19 0?00 0?46 0?04 0?00 0?14 0?15 0?648 Water 955 3?06 4?29 1?51 1?72 1?50 1?12 1?34 0?000* Fruit juices 979 1?08 0?71 1?57 0?60 0?50 0?76 0?48 0?000* Soft drinks 978 0?37 0?00 0?81 0?53 0?50 0?72 20?16 0?000* Light soft drinks 959 0?15 0?00 0?61 0?01 0?00 0?09 0?14 0?000* Sweetened breakfast cereals 976 0?33 0?29 0?42 0?17 0?00 0?33 0?16 0?000* Breakfast cereals 936 0?20 0?00 0?46 0?17 0?00 0?43 0?03 0?000* Milk 940 0?88 0?71 0?93 0?79 0?50 0?77 0?09 0?000* Sweetened milk 969 0?70 0?29 0?79 0?41 0?00 0?62 0?29 0?000* Yoghurt 940 0?20 0?00 0?39 0?07 0?00 0?23 0?13 0?000* Sweetened yoghurt 971 0?51 0?25 0?50 0?30 0?00 0?45 0?21 0?000* Fish 941 0?13 0?00 0?18 0?07 0?00 0?20 0?06 0?000* Fried fish 956 0?14 0?00 0?24 0?07 0?00 0?21 0?07 0?000* Cold cuts 982 0?60 0?29 0?54 0?88 1?00 0?72 20?28 0?000* Meat 989 0?70 0?57 0?59 0?71 0?50 0?56 20?01 0?047* Fried eggs 976 0?15 0?00 0?20 0?07 0?00 0?20 0?08 0?000* Eggs 959 0?11 0?00 0?15 0?07 0?00 0?19 0?04 0?000* Mayonnaise 967 0?08 0?00 0?18 0?03 0?00 0?12 0?05 0?000* Cheese 986 0?83 0?61 0?70 0?47 0?50 0?56 0?36 0?000* Jam & honey 966 0?26 0?29 0?36 0?16 0?00 0?34 0?10 0?000* Chocolate/nut-based spread 977 0?26 0?00 0?39 0?14 0?00 0?31 0?12 0?000* Butter & margarine 966 0?61 0?29 0?74 0?42 0?00 0?61 0?19 0?000* Ketchup 971 0?21 0?29 0?10 0?11 0?00 0?25 0?10 0?000* White bread 978 1?07 1?00 0?94 1?37 1?50 0?84 20?30 0?000* Wholemeal bread 960 0?47 0?29 0?65 0?39 0?00 0?61 0?08 0?000* Pasta & rice 974 0?43 0?29 0?35 0?66 0?50 0?50 20?23 0?000* Milled cereal 959 0?07 0?00 0?18 0?00 0?00 0?02 0?07 0?674 Pizza 963 0?06 0?00 0?13 0?09 0?00 0?23 20?03 0?000* Fast food 982 0?26 0?00 0?41 0?04 0?00 0?16 0?22 0?239 Nuts 978 0?16 0?00 0?25 0?04 0?00 0?18 0?12 0?000* Snacks 988 0?23 0?29 0?34 0?09 0?00 0?24 0?14 0?001* Sweets 992 1?17 1?00 0?95 1?39 1?50 0?97 20?22 0?000* CEHQ-FFQ, Children’s Eating Habits Questionnaire–food frequency section; 24-HDR, 24 h dietary recall; SACINA, Self Administered Children and Infants Nutrition Assessment; D, difference. *P,0?05. FFQ validity 5 showed acceptable agreement except for sweets and fruit, for which it was poor (,0?20) and moderate (.0?40), respectively, in both younger and older children. Vegetables, milk and meat also showed poor agreement among 6–9-year-old children. Regarding the adapted food groups, the mean proportion of individuals classified into the same tertile increased in both age groups compared with the non-adapted food groups. Average misclassification of individuals into the opposite tertiles decreased, being 16 % and 20 %, respectively, for 2–,6-year-old and 6–9-year-old children. Higher mean k w values were obtained: 0?20 in younger children and 0?14 in older children. Figure 1 (Supplementary Materials) illustrates findings of the Bland–Altman analysis representative of the observed trends. For most food groups (vegetables, raw vegetables, breakfast cereals, sweetened milk, cold cuts, meat, cheese, butter and sweets), a systematic increase in difference between the two methods with increasing intake was observed indicating worse agreement at higher intakes. For fruit, fruit juices, soft drinks, milk, white bread and wholemeal bread, however, a double interpretation is possible. When considering intakes within the LOA only, it was observed that the agreement between methods was similar regardless of the average intake. On the other hand, beyond the LOA, it seemed that when mean intake increased the bias also increased up to a certain value, after which it started decreasing. Discussion The aim of the present study was to evaluate the ability of the CEHQ-FFQ in estimating age group-specific proxyreported intakes of obesity-related foods compared with two 24-HDR (SACINA). To the authors’ knowledge, the present study is the largest one carried out in children in which relative validity has been evaluated through food group intakes. Results showed wide differences in relative Public Health Nutrition Table 3 Food group intakes (daily number of portions) from the CEHQ-FFQ and the 24-HDR: older children aged 6–9 years from eight European countries participating in the IDEFICS Study (2007–2008) CEHQ-FFQ 24-HDR (SACINA) Food group (portions/d) nMean Median SD Mean Median SD Mean DPvalue Vegetables 1499 0?52 0?29 0?43 0?86 1?00 0?69 20?34 0?000* Fried potatoes 1485 0?15 0?00 0?20 0?09 0?00 0?23 0?06 0?000* Raw vegetables 1501 0?65 0?29 0?62 0?56 0?50 0?72 0?09 0?000* Fruit 1498 0?97 1?00 0?81 0?76 0?50 0?77 0?21 0?000* Sweetened fruit 1377 0?23 0?00 0?56 0?03 0?00 0?12 0?20 0?000* Water 1455 3?05 4?29 1?54 1?86 2?00 1?10 1?19 0?000* Fruit juices 1484 1?01 0?71 1?20 0?57 0?50 0?74 0?44 0?000* Soft drinks 1480 0?48 0?00 0?96 0?61 0?50 0?76 20?13 0?000* Light soft drinks 1472 0?15 0?00 0?56 0?01 0?00 0?13 0?14 0?000* Sweetened breakfast cereals 1495 0?44 0?29 0?45 0?19 0?00 0?36 0?25 0?000* Breakfast cereals 1411 0?13 0?00 0?29 0?12 0?00 0?30 0?01 0?086 Milk 1422 0?76 0?71 0?83 0?66 0?50 0?64 0?10 0?020* Sweetened milk 1464 0?64 0?50 0?71 0?32 0?00 0?53 0?32 0?000* Yoghurt 1426 0?19 0?00 0?37 0?05 0?00 0?18 0?14 0?000* Sweetened yoghurt 1485 0?45 0?29 0?48 0?23 0?00 0?41 0?22 0?000* Fish 1432 0?11 0?00 0?17 0?06 0?00 0?19 0?05 0?000* Fried fish 1446 0?12 0?00 0?16 0?05 0?00 0?17 0?07 0?000* Cold cuts 1500 0?67 0?71 0?60 0?99 1?00 0?76 20?32 0?000* Meat 1505 0?73 0?57 0?54 0?81 0?50 0?62 20?08 0?002* Fried eggs 1481 0?16 0?00 0?18 0?07 0?00 0?18 0?09 0?000* Eggs 1482 0?10 0?00 0?17 0?04 0?00 0?15 0?06 0?000* Mayonnaise 1475 0?09 0?00 0?20 0?04 0?00 0?17 0?05 0?000* Cheese 1510 0?89 0?71 0?83 0?46 0?50 0?54 0?43 0?000* Jam & honey 1486 0?26 0?29 0?37 0?16 0?00 0?34 0?10 0?000* Chocolate/nut-based spread 1490 0?27 0?29 0?37 0?13 0?00 0?29 0?14 0?000* Butter & margarine 1490 0?64 0?29 0?71 0?46 0?00 0?64 0?18 0?000* Ketchup 1490 0?25 0?29 0?34 0?07 0?00 0?22 0?18 0?000* White bread 1503 1?27 1?00 1?01 1?61 1?50 0?92 20?34 0?000* Wholemeal bread 1467 0?40 0?29 0?63 0?24 0?00 0?51 0?16 0?000* Pasta & rice 1489 0?37 0?29 0?30 0?64 0?50 0?48 20?27 0?000* Milled cereal 1471 0?06 0?00 0?20 0?00 0?00 0?01 0?06 0?000* Pizza 1477 0?07 0?00 0?17 0?06 0?00 0?18 0?01 0?057 Fast food 1500 0?39 0?29 0?52 0?03 0?00 0?14 0?36 0?000* Nuts 1488 0?14 0?00 0?26 0?04 0?00 0?17 0?10 0?000* Snacks 1505 0?24 0?29 0?35 0?11 0?00 0?25 0?13 0?000* Sweets 1511 1?07 0?86 0?93 1?38 1?00 0?94 20?31 0?000* CEHQ-FFQ, Children’s Eating Habits Questionnaire–food frequency section; 24-HDR, 24 h dietary recall; SACINA, Self Administered Children and Infants Nutrition Assessment; D, difference. *P,0?05. 6 S Bel-Serrat et al. validity across the different food groups, emphasizing the importance of validating dietary assessment methods in terms of food groups rather than nutrients. It should also be considered that comparison of findings among validation studies is compromised by differences among the type of FFQ administered, sample size, food groups examined, unit of estimates, use of reference method, recall period or number of recorded days (35) . As expected, the CEHQ-FFQ gave higher mean intakes as opposed to the 24-HDR, a tendency also observed in previous studies carried out in adults and/or children (14,22,35–37) . Our findings suggest that episodically consumed food groups such as milled cereal, light soft drinks, fast food and sweetened fruit tended to be over-reported by the CEHQFFQ in this population group. This can partly be explained by the difficulty of the 24-HDR to capture infrequently consumed products, especially in children with highly varying diets and rapidly changing food habits (8) . More specifically, the low crude correlations observed increased slightly following correction for attenuation effect in the 24-HDR. Correlations tended to be stronger for foods with higher frequency of consumption, again indicating current problems in the assessment of episodically consumed foods. Respectively for younger and older children, fifteen and ten out of the thirty-six foods groups had correlation coefficients within the range of 0?3–0?8 as shown by others (9,14,22,36,38) . Coefficients for fruit (younger children), water, fish, cheese or white bread were comparable to or even higher (raw vegetables, sweetened milk (younger children), chocolate/ nut-based spread, wholemeal bread and pasta & rice) than those found in a validation study conducted with Belgian adolescents (39) . Similarly, low coefficients for cooked vegetables (0?17 in younger children and 0?13 in older children) and for fried potatoes in older children (0?14) were comparable to those of an American validation study in 8–9-year-old students (40) . Correlations from food frequency instruments have generally been shown to be lower in child and adolescent populations than among adults (8) . Such observations Public Health Nutrition Table 4 Pearson correlation coefficients between food group intakes (daily number of portions) from the CEHQ-FFQ and the 24-HDR: younger children aged 2–,6 years from eight European countries participating in the IDEFICS Study (2007–2008) Food group Pearson correlation coefficient Variance ratio De-attenuated correlation coefficient Vegetables 0?14 0?80 0?17 Fried potatoes 0?05 0?92 0?06 Raw vegetables 0?33 0?58 0?37 Fruit 0?36 0?53 0?40 Sweetened fruit 20?01 0?95 20?01 Water 20?41 0?25 20?44 Fruit juices 0?32 0?50 0?36 Soft drinks 0?14 0?42 0?15 Light soft drinks 0?17 0?87 0?20 Sweetened breakfast cereals 0?28 0?66 0?32 Breakfast cereals 0?41 0?33 0?44 Milk 0?32 0?33 0?35 Sweetened milk 0?45 0?30 0?48 Yoghurt 0?20 0?63 0?23 Sweetened yoghurt 0?35 0?54 0?39 Fish 0?24 0?72 0?28 Fried fish 0?12 0?86 0?14 Cold cuts 0?27 0?53 0?30 Meat 0?06 0?79 0?07 Fried eggs 0?17 0?81 0?20 Eggs 0?13 0?95 0?16 Mayonnaise 0?11 0?88 0?13 Cheese 0?25 0?52 0?28 Jam & honey 0?29 0?54 0?33 Chocolate/nut-based spread 0?30 0?49 0?33 Butter & margarine 0?35 0?49 0?39 Ketchup 0?22 0?80 0?26 White bread 0?26 0?60 0?30 Wholemeal bread 0?35 0?34 0?38 Pasta & rice 0?24 0?78 0?28 Milled cereal 20?01 1?00 20?01 Pizza 0?11 0?85 0?13 Fast food 0?11 0?71 0?13 Nuts 0?18 0?71 0?21 Snacks 0?11 0?83 0?13 Sweets 0?17 0?57 0?19 CEHQ-FFQ, Children’s Eating Habits Questionnaire–food frequency section; 24-HDR, 24 h dietary recall. FFQ validity 7 could be partly attributed to the effect of proxy reporting, as proxies are conditioned by their ability to accurately recall their children’s food intake (41) . Additionally, parents as proxies seem to be reliable reporters in the home setting (41) but the opposite is true for food intake out of home (41) . This limits parents’ suitability as the sole informants of their children’s intake. Findings from the cross-classification analyses varied by food group and at times demonstrated the rather limited ability of the questionnaire to discriminate between quartiles of food groups. A third of the participants were allocated into the same category by both methods and on average only 7 % and 8 % of younger and older children, respectively, were likely to be classified into the opposite quartile. Although among the adapted food groups the proportion of misclassified individuals increased, higher agreement between the methods was found in terms of classification. Percentage agreement and misclassification were within the ranges reported by other authors (14,21,38) for the non-adapted food groups. However, the degree of misclassification observed among the adapted groups was remarkably higher compared with previous studies (14,21,38) . Findings from the k w analysis also confirmed fair agreement between the CEHQ-FFQ and 24-HDR. In general, no great differences were observed by age group in terms of correlation coefficients and agreement between the CEHQ-FFQ and 24-HDR, since values were similar for most of the food groups. It is noteworthy, however, that correlation coefficients for some highly consumed food groups – i.e. fruit, breakfast cereals, milk, sweetened milk and yoghurt – were considerably higher in younger children compared with those obtained among their older peers. Similarly, k w values were also higher for milk, white bread, sweetened milk and butter & margarine in 2–,6-year-old children. This can be explained by the fact that younger children are less likely to be unsupervised during in-home and out-of-home eating than older children (1,41) . Consequently, parents become more reliable reporters and more capable of reporting their children’s intake in an accurate way. Public Health Nutrition Table 5 Pearson correlation coefficients between food group intakes (daily number of portions) from the CEHQ-FFQ and the 24-HDR: older children aged 6–9 years from eight European countries participating in the IDEFICS Study (2007–2008) Food group Pearson correlation coefficient Variance ratio De-attenuated correlation coefficient Vegetables 0?11 0?80 0?13 Fried potatoes 0?12 0?79 0?14 Raw vegetables 0?36 0?55 0?41 Fruit 0?30 0?51 0?34 Sweetened fruit 20?02 0?93 20?02 Water 20?42 0?24 20?44 Fruit juices 0?28 0?48 0?31 Soft drinks 0?21 0?43 0?23 Light soft drinks 0?08 0?47 0?09 Sweetened breakfast cereals 0?23 0?56 0?26 Breakfast cereals 0?18 0?47 0?20 Milk 0?24 0?39 0?26 Sweetened milk 0?33 0?38 0?36 Yoghurt 0?10 0?62 0?11 Sweetened yoghurt 0?32 0?46 0?35 Fish 0?25 0?71 0?29 Fried fish 0?12 0?89 0?14 Cold cuts 0?26 0?56 0?29 Meat 0?15 0?82 0?18 Fried eggs 0?10 0?92 0?12 Eggs 0?08 0?97 0?10 Mayonnaise 0?18 0?77 0?21 Cheese 0?24 0?59 0?27 Jam & honey 0?32 0?53 0?36 Chocolate/nut-based spread 0?31 0?54 0?35 Butter & margarine 0?40 0?47 0?44 Ketchup 0?20 0?85 0?24 White bread 0?23 0?47 0?26 Wholemeal bread 0?35 0?31 0?38 Pasta & rice 0?18 0?76 0?21 Milled cereal 20?01 1?00 20?01 Pizza 0?10 0?80 0?12 Fast food 0?12 0?91 0?14 Nuts 0?14 0?63 0?16 Snacks 0?10 0?84 0?12 Sweets 0?18 0?58 0?20 CEHQ-FFQ, Children’s Eating Habits Questionnaire–food frequency section; 24-HDR, 24 h dietary recall. 8 S Bel-Serrat et al. The lack of agreement between methods of assessment observed in children has often been attributed to a number of factors (41) , including the use of proxy reporting as discussed earlier, the nature of the diet of young age groups and the lack of a gold standard for directly assessing the validity or relative validity of FFQ, among others (13) . Moreover, FFQ validity is highly conditioned by the reference method, which is also subject to instrumentspecific limitations. In addition, proxies reported the 24-HDR, who tend to under-report intake (8) . It should be noted that the European Food Consumption Survey Method (EFCOSUM) has recommended the use of two or more non-consecutive 24 h recalls as the best method to assess food consumption in individuals aged 10 years and above in different European countries (42) . Dietary information is affected by high day-to-day variability in children’s diets (8) , which could explain the lack of agreement between methods. This influence could be minimized by an increase in the number of recording days, but long recording periods reduce the accuracy of recording owing to increasing fatigue and boredom, potential alterations of dietary habits and increasing likelihood of drop-outs (43) . Additionally, the large sample size included in the present study makes up for the small number of replicates to keep the same precision of the corrected correlation coefficient (13) . Moreover, the fact that portion sizes were not assessed in the CEHQ-FFQ might also affect the agreement between both methods; i.e. overestimation of foods consumed in small quantities and underestimation of those consumed in higher quantities. Considering the increased respondent burden however, no attempts were done to capture portion sizes in the current study (13) . Our sample differed from the IDEFICS whole sample in terms of baseline characteristics, which means that these results might not be generalized to all participating children. However, no differences were found for BMI which is considered to be an indicator of misreporting (44) . As stated before, Hungary collected the 24-HDR information differently from the other survey centres and this is considered as one of the study limitations influencing the generalizability of its results. Our findings suggest that when Hungarian data were excluded, the strength of the associations between the CEHQ-FFQ and the 24-HDR increased. In fact, the number of food groups showing moderate correlation coefficients increased and the number of slight correlations decreased. Furthermore, when cross-classification analyses were applied, without considering Hungarian data the degree of agreement in both non-adapted and adapted food groups increased. Indeed, the proportions of correctly classified individuals as well as k w values improved towards higher values, whereas the percentages of grossly misclassified individuals decreased. To our knowledge, the present study is the first one performed in a large sample of European children of Public Health Nutrition Table 6 Cross-classification by quartile of food group intakes from the CEHQ-FFQ and the 24-HDR: younger children aged 2–,6 years from eight European countries participating in the IDEFICS Study (2007–2008) CEHQ-FFQ v. two 24-HDR Food group Correctly classified (%) Grossly misclassified (%) k w Vegetables 30?23?60?13 Fruit 35?54?70?34 Milk 38?67?80?36 Cold cuts 32?04?80?27 Meat 27?010?00?10 Cheese 33?710?50?30 White bread 35?812?30?29 Sweets 25?99?90?17 Adapted food groupsRaw vegetables 42?122?60?14 Fruit juices 41?217?70?17 Soft drinks 42?029?20?10 Breakfast cereals 45?919?40?26 Sweetened milk 49?310?10?30 Butter & margarine 47?016?20?24 Wholemeal bread 38?218?60?12 CEHQ-FFQ, Children’s Eating Habits Questionnaire–food frequency section; 24-HDR, 24 h dietary recall; k w , weighted kappa statistic. For fried potatoes, sweetened fruit, water, light soft drinks, sweetened breakfast cereals, yoghurt, sweetened yoghurt, fish, fried fish, fried eggs, eggs, mayonnaise, jam & honey, chocolate/nut-based spread, ketchup, pasta & rice, milled cereal, pizza, fast food, nuts and snacks, ranking into quartiles or tertiles was not possible since .25 % of the participants did not consume these foods on each recall day. -Within that food groups, zero consumers were considered as one group and tertiles were constructed for the remaining participants. Table 7 Cross-classification by quartile of food group intakes from the CEHQ-FFQ and the 24-HDR: older children aged 6–9 years from eight European countries participating in the IDEFICS Study (2007–2008) CEHQ-FFQ v. two 24-HDR Food group Correctly classified (%) Grossly misclassified (%) k w Vegetables 28?73?90?10 Fruit 34?57?30?31 Milk 33?37?40?24 Cold cuts 33?94?70?26 Meat 27?77?00?14 Cheese 32?611?50?31 White bread 29?37?50?23 Sweets 30?410?20?18 Adapted food groupsRaw vegetables 39?621?00?15 Fruit juices 41?918?20?16 Soft drinks 38?828?00?10 Sweetened milk 52?55?80?36 Butter & margarine 38?012?30?10 Wholemeal bread 32?317?00?05 CEHQ-FFQ, Children’s Eating Habits Questionnaire–food frequency section; 24-HDR, 24 h dietary recall; k w , weighted kappa statistic. For fried potatoes, sweetened fruit, water, light soft drinks, sweetened breakfast cereals, breakfast cereals, yoghurt, sweetened yoghurt, fish, fried fish, fried eggs, eggs, mayonnaise, jam & honey, chocolate/nut-based spread, ketchup, pasta & rice, milled cereal, pizza, fast food, nuts and snacks, ranking into quartiles or tertiles was not possible since .25% of the participants did not consume these foods on each recall day. -Within that food groups, zero consumers were considered as one group and tertiles were constructed for the remaining participants. FFQ validity 9 (pre)school age in which proxy-reported data obtained from an FFQ were compared with those from two 24-HDR. Another important strength of the study is standardized procedures followed during the data collection of the IDEFICS fieldwork (17) . High-quality control procedures were applied during the different stages of the project, including checks for plausibility already implemented in the database and performed during data entry. In addition, the reference method used in the study was previously validated with the doubly labelled water method considered as the ‘gold standard’ method for this purpose. Furthermore, portions/d were used instead of g/d offering newer approaches and insights into validation studies using FFQ despite associated limitations. Conclusions Findings of the present study suggest that the strength of association estimates assessed by the CEHQ-FFQ and the 24-HDR varied by food group intakes and by age group. In addition, the ability of the CEHQ-FFQ to rank children according to intakes of food groups was lower than expected but in line with other studies. Overall, these results suggest low agreement for the majority of food groups examined by a proxy-estimated FFQ and two 24-HDR in a large sample of 2–9-year-old European children. However, one should consider that both instruments are subject to measurement errors affecting the strength of the association. In that sense, the CEHQ-FFQ could provide acceptable food estimates at group level. It is of great importance to detect true diet–disease relationships with the aim to develop public health strategies to prevent children from suffering chronic diseases. For that reason, validation studies are indispensable to test the validity and appropriateness of dietary assessment methods used within epidemiological surveys to accurately assess food intake. Acknowledgements Sources of funding: This work was done as part of the IDEFICS Study and is published on behalf of its European Consortium (www.idefics.eu). The work received financial support from the European Community within the Sixth RTD Framework Programme Contract No. 016181 (FOOD). The information in this document reflects the authors’ views and is provided as is. S.B.-S. was funded by a grant from the Aragon Regional Government (Diputacio ´n General de Arago ´n, DGA). Conflicts of interest: The authors reported no conflicts of interest. Authors’ contributions: The authors contributed as follows: L.A.M., V.K., D.M., A.S. and T.V. planned and directed the study; S.B.-S., J.M.F.-A., C.B. and G.E. conducted the research; S.B.-S. wrote the manuscript and performed statistical analyses; T.M., L.A.M., I.H., C.B., V.P. and V.K. participated in data interpretation; S.B.-S., T.M., L.A.M., V.P., I.H., C.B., J.M.F.-A., C.H., G.E., A.H., L.L., A.S., V.K., D.M. and T.V. critically discussed and reviewed the manuscript. All authors read and approved the final manuscript. 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Montreal: UNESCO Institute for Statistics; available at http://www.uis.unesco.org/Education/Pages/ international-standard-classification-of-education.aspx Public Health Nutrition FFQ validity 11 European PhD Thesis, 2013 100 European PhD Thesis, 2013 101 Artículo III [Paper III]: The role of dietary fat on the association between dietary amino acids and serum lipid profile in European adolescents participating in the HELENA Study Bel-Serrat S, Mouratidou T, Huybrechts I, Cuenca-García M, Manios Y, Gómez-Martínez S, Molnár D, Kafatos A, Gottrand F, Widhalm K, Sjöström M, Wästlund A, Stehle P, Azzini E, Vyncke K, González-Gross M, Moreno LA Eur J Clin Nutr (submitted). European PhD Thesis, 2013 102 European PhD Thesis, 2013 109 laboratories. Serum TG, TC, HDL-c, and LDL-c were measured on a Dimension RxL clinical chemistry system (Dade Behring, Schwalbach, Germany) using enzymatic methods. Apo B and Apo A1 were measured in an immunochemical reaction with a BN II analyzer (Dade Behring, Schwalbach, Germany). The TC/HDL-c and the Apo B/Apo A1 ratio were computed. Statistical analysis. The normality of all variables was checked and non-normally distributed variables (TG, TC, HDL-c, LDL-c, TC/HDL-c ratio, Apo B/Apo A1 ratio and AA intake) were log-transformed prior to the analysis. Gender differences were tested by means of the independent samples T-test for normally distributed variables and the Mann-Whitney U test for non-normally distributed variables. In case of categorical variables, the Chi-squared test was applied. The association between AA intakes (independent variables) and plasma lipids concentrations (dependent variables) was examined separately by gender by performing multilevel linear regression analysis. Study center was included as random intercept. Age, maternal education, sum of four skinfolds, MVPA, sedentary behaviors and total daily energy intake were entered as covariates in model 1. Model 2 included covariates from model 1 plus total fat intake. The statistical software packages Stata version 11.0 (Stata Corp., college Station, TX, USA) and Predictive Analytics SoftWare (PASW, version 18; SPSS Inc., Chicago, IL, USA) were used to perform the analyses. Statistical significance was set at p<0.05. Results Descriptive data is provided in Table 1 and Table 2. Boys were significantly taller and weighed more than their female peers (p<0.001) but no significant differences in terms of BMI were observed. Additionally, girls showed significantly higher concentrations of TG, TC, HDLc, LDL-c, Apo A1 and Apo B than boys (p<0.001), whereas AA intake was significantly higher in adolescent males (p<0.001). Results obtained by multilevel linear regression analysis are displayed in Tables 3-5. In girls and considering model 1, alanine, arginine, asparaginic acid, cysteine, glycine, European PhD Thesis, 2013 110 histidine, lysine, threonine and valine were inversely associated with TC (Table 3). A negative association was also observed between alanine, isoleucine, leucine, methionine, serine, tryptophan, tyrosine and valine and TC/HDL-c ratio in girls (Table 3). Intakes of all AAs were inversely associated with TG in girls (Table 4). A negative association was also observed between alanine and arginine intakes and LDL-c (Table 4) and between alanine intake and Apo B/Apo A1 ratio (Table 5). These associations were no longer significant after adjustment for total fat intake (model 2). In boys, serine and tryptophan were inversely associated with TC/HDL-c ratio (Table 3) in model 1. An inverse association was also observed between alanine, arginine, asparaginic acid, glycine, histidine, lysine and serine intakes and TG (Table 4). Similarly to girls, these associations did not persist in model 2 when total fat intake was considered as a confounder. No associations were observed between AA intake and HDL-c and Apo A1 in any of both genders (data not shown). Discussion This study examined the relationship between AA intake and serum lipid profile in European adolescents participating in the HELENA study. Our initial analysis suggested an inverse association of AA intake with TG, TC, LDL-c, TC/HDL-c ratio, Apo B and Apo B/Apo A1 ratio in girls and with TG and TC/HDL-c ratio in boys; these associations did not remain significant following adjustments for the effect of total fat intake. To the authors’ knowledge, this is the first study addressing this topic in adolescents. Apart from their structural function in proteins, AAs are precursors of many essential biological compounds (27) and are involved in a large amount of metabolic pathways in the human organism. Previous studies have focused on the association of AA with diverse CVD risk factors such as obesity, insulin resistance or blood pressure (7, 8, 10, 28) among others, but the available literature on the association between AA intake and serum lipid profile in humans is scarce and remains unclear. For instance, diet supplementation with essential AAs European PhD Thesis, 2013 111 has been shown to lower plasma TG, TC and very low density lipoprotein cholesterol (VLDL-c) concentrations in elderly people (11). Similarly, Hurson et al. (12) also observed a decrease in TC and LDL-c in elderly people supplemented with arginine. Our results are in agreement with these previous findings (only in model 1). In these studies, however, data were not adjusted for potential confounders such as total fat intake and addressed older population. The mechanisms underlying the effect of AA supplementation on serum lipid profile are not known (11). Sulfur AA, i.e. cysteine and methionine, have been recognized as potent modulators of lipid metabolism (29) by increasing plasma HDL-c and lowering VLDL-c (3). Although we did not observe such associations, an inverse association was observed in female adolescents between cysteine and methionine intake and Apo B in model 1, the main protein constituent of LDL-c (30), shown to play a beneficial role on CVD (3). We have also hypothesized that the observed results could be due to an indirect association, i.e. AA intake might be associated with one factor, in this instance body fat, which in turn is associated with serum lipids. Indeed, it is known that obesity enhances metabolic syndrome features such as dyslipemia by increasing LDL-c and TG and lowering HDL-c plasma concentrations (31). This suggests that a decrease on fat mass induced by AA consumption could result in a better lipid profile. Qin et al. (8) observed inverse associations between intakes of BCAA, including leucine, isoleucine and valine, and prevalence of overweight status among apparently healthy middle-aged adults in those from East Asian, i.e. China and Japan, and Western, i.e. UK and USA, countries and with prevalence of obesity in adults from Western countries. Arginine and lysine are also inversely associated with fat mass among 6-year-old (32) and 8-10-year-old (7) European girls. Mechanisms that explain the beneficial role of dietary arginine are not completely known but it seems, however, that arginine alters the balance of energy intake and expenditure in favour of fat loss or reduced growth of white adipose tissue by stimulating mitochondrial biogenesis signaling and brown adipose tissue development (33). Furthermore, the somatotropic effects of arginine and lysine European PhD Thesis, 2013 112 (34) may result in a decrease in serum levels of TG and fat mass (7). In concordance with this, our described associations were found to be significant mainly in girls. This might be explained by gender-differences in body composition e.g. greater percentage of body fat in girls compared to their male peers (35, 36) also seen in our findings, i.e. adolescent females had significantly higher (p<0.001) sum of four skinfolds than boys. Protein-rich diets have been tested in many occasions in order to identify the role of high protein intake in the body. In a cross-over study carried out in adults, Appel et al. (37) found that following the administration of a healthy diet, rich in protein and low in saturated fat, TG concentrations significantly decreased compared to a carbohydrate-rich diet and a diet rich in unsaturated fat. This fact led authors to suggest that protein could have a direct lowering effect on TG, beyond that of replacing carbohydrates, which usually increase serum TG (11). A review carried out by Clifton (38) suggested that high protein diets were associated with greater weight loss, lower plasma TG and blood pressure and, in times, increased lean mass compared to high carbohydrate diets. The Food and Nutrition Board report (39) states that high levels of protein intake may have detrimental effects but no documented adverse effects of high rich protein diets exists. Fat intake, specifically saturated fat, has been observed to be positively associated with CVD by increasing plasma LDL-c and TC concentrations (14); indeed, adolescent boys participating in a dietary intervention focused on decreasing fat intake showed a more favourable lipid profile than those in the control group (40). A recent meta-analysis of prospective epidemiologic studies, however, has shown no significant evidence to conclude that that dietary saturated fat is associated with an increased risk of CVD (14). On the other hand, it has been reported that the saturated fat content of red meat, which is one of the richest sources of protein and, consequently of AA, may be partly responsible for its positive association with CVD risk (41). Our results showed that the initial association observed between AA intakes and blood lipids in model 1 disappeared after adjustment for total fat European PhD Thesis, 2013 113 intake (model 2). This leads authors to hypothesize that the positive role that AAs might play on blood lipids is neutralized when AA, or proteins, are consumed together with fat, mainly saturated fat. High protein intake, however, is not necessarily related to high red meat intake but to other protein-rich foods like fish, poultry, milk and dairy products, nuts, egg or plantbased products among others. Indeed, poultry and dairy intake have been shown to have neutral effects on CVD risk (42). Furthermore, Mangravite et al. (15) reported that on carbohydrate-restricted subjects the source of dietary protein may modify the effects of saturated fat on atherogenic lipoproteins and proposed that the relationship between saturated fat and CVD risk may vary according to the dietary context in which saturated fat is consumed. A limitation of our study is its cross-sectional design that does not allow the determination of any causal associations. Diet was assessed by means of two selfadministered, computer-assisted, non-consecutive 24-HDR. Like any other self-reporting method, our self-administered 24-HDR is also prone to measurement error (43). An increase in the number of recording days would have been advisable to compensate for day-to-day variability (44); however, this method has been shown to be an appropriate measure to collect detailed dietary data in adolescents (22, 45). Furthermore, dietary information was corrected for within-person variability (24). The fact that AA intake might be estimated less accurately than other nutrients is another limitation of the study. It is noteworthy to mention that blood samples were collected following a specific methodology and transport system to a centralized laboratory in order to assure samples viability and stability (26) as study strengths. Additionally, fieldworkers were trained and a manual of operation was developed to guarantee good clinical practice (26). In conclusion, our findings suggest that the association between AA intakes and serum lipid profile does not persist when dietary fat is considered. Whether AA intakes could be associated with a healthier lipid profile in adolescents and, therefore, with CVD risk remains European PhD Thesis, 2013 114 unclear, at least based on the evidence from this sample of European adolescents. Furthermore, our findings add new evidence to the lack of studies addressing the association of AA intakes with plasma lipids concentration during adolescence, and emphasize the importance of considering dietary fat as a possible confounder of this relationship. More prospective and intervention studies are needed to confirm our findings and to obtain robust conclusions on the effect of AA intakes on plasma lipid concentrations and, consequently, on CVD risk in adolescents. European PhD Thesis, 2013 115 European PhD Thesis, 2013 116 European PhD Thesis, 2013 117 European PhD Thesis, 2013 118 European PhD Thesis, 2013 125 Artículo IV [Paper IV]: Associations between macronutrient intakes and serum lipid profile depend on body fat in European adolescents: the HELENA study Bel-Serrat S, Mouratidou T, Huybrechts I, Labayen I, Cuenca-García M, Palacios G, Breidenassel C, Molnár D, Roccaldo R, Widhalm K, Gottrand F, Kafatos A, Manios Y, Vyncke K, Sjöstrom M, Libuda L, Gómez-Martínez S, Moreno, LA. Submitted to Am J Clin Nutr. European PhD Thesis, 2013 126 European PhD Thesis, 2013 127 Abstract Objectives: To investigate the relationships between macronutrient intakes and serum lipid profile in European adolescents from eight European cities participating in the HELENA crosssectional study (2006-2007), and to assess the role of body fat-related variables on these associations. Methods: Weight, height, waist circumference, skinfolds thicknesses, total cholesterol (TC), high density lipoprotein-cholesterol (HDL-c), low density lipoprotein-cholesterol (LDL-c), triglycerides, apolipoprotein B and A1 were measured in 454 adolescents (44% boys) aged 12.5-17.5 years. TC/HDL-c, apolipoprotein B/apolipoprotein A1 and waist-to-height ratios were calculated. Macronutrient intakes (g/1000 kcal/day) were assessed by two 24-hour dietary recalls. Associations were evaluated by multilevel analysis clustered by high and low body fat indicators (z-BMI, sum of four skinfolds and waist-to-height ratio) and adjusted for a wide range of confounders. Results: An inverse association was observed between protein intake and triglycerides (β=- 0.242, p<0.05). Carbohydrate intake was also inversely associated with HDL-c (β=-0.189, p<0.001) and apolipoprotein A1 (β=-0,177, p<0.01) whereas a positive association was observed with TC/HDL-c ratio (β=0.153, p<0.05). Inverse associations were found between fat intake and triglycerides (β=-0.319, p<0.001), LDL-c (β=-0.131, p<0.05), apolipoprotein B (β=-0.068, p<0.05), TC/HDL-c (β=-0.118, p<0.01) and apolipoprotein B/apolipoprotein A1 (β=-0.117, p<0.05) ratios; a positive association, however, was observed with HDL-c (β=0.086, p<0.05). Associations between macronutrients and serum lipids differed between high/low body fat, i.e. protein and fat intake were inversely associated with triglycerides whereas a positive association was observed between carbohydrates intake and triglycerides only in those adolescents with higher adiposity levels. European PhD Thesis, 2013 128 Conclusions: Our results showed that observed associations of the three macronutrients intakes with serum lipid profile which varied according to adiposity levels. As serum lipids and excess body fat are major markers of cardiovascular diseases, these findings should be considered when developing strategies to prevent cardiovascular disease risk among adolescents. Keywords: protein intake, carbohydrates intake, fat intake, serum lipids, body fat, adolescents. European PhD Thesis, 2013 129 Introduction High levels of low density lipoprotein-cholesterol (LDL-c) in childhood and the onset of atherosclerosis in early life (1) could result into adult dyslipidemias (2) which are important public health threats. Therefore, evidence regarding factors influencing lipid profiles is necessary for public health protection and promotion. Dietary modifications that lower atherogenic lipids and lipoproteins are effective in the prevention and treatment of cardiovascular diseases (CVD) risk (3). Nonetheless, the optimal dietary pattern(s) to restrain atherosclerosis progression is still to be identified (4). For instance, high intake of total carbohydrates is suggested to lower high density lipoprotein cholesterol (HDL-c) and to increase triglycerides (TG) in adults (5), and a protein-rich diet low in saturated fat significantly decrease LDL-c, TG and total cholesterol (TC) concentrations in comparison to a carbohydrate-rich diet and a diet rich in unsaturated fat (6). However, findings about the role of dietary fat, mainly those of saturated fat, on CVD risk are still controversial (7) because of individual variability in serum lipid response to changes in dietary saturated fat and cholesterol (3). Additionally, obesity is said to strongly affect serum lipid response to diet (3), making obese individuals less responsive to dietary interventions aimed to improve serum lipid profile. Adolescence is a key period in life because of the growth spurt and sexual maturation that take place; therefore, having a healthy diet is essential to achieve optimal development and to prevent the appearance of chronic diseases later in life (8). Furthermore, associations between macronutrient intakes and serum lipids have mainly been investigated among adults and there is a lack of literature addressing this topic in adolescents. Therefore, the aims of this study were 1) to investigate the relationship between macronutrient intakes and serum lipid profile in European adolescents and 2) to assess the role of body fat-related variables on these associations. European PhD Thesis, 2013 130 Materials and Methods The present study sample was derived from the cross-sectional multi-centre HELENA (Healthy Lifestyle in Europe by Nutrition in Adolescence) study (n=3,528) carried out in adolescents (12.5-17.5 years) between 2006 and 2007 in ten European cities (Athens and Heraklion in Greece, Dortmund in Germany, Ghent in Belgium, Lille in France, Pecs in Hungary, Rome in Italy, Stockholm in Sweden, Vienna in Austria and Zaragoza in Spain). General HELENA procedures, characteristics and inclusion criteria can be found elsewhere (9, 10). Adolescents and their parents gave written informed consent, and the study was performed following the ethical guidelines of the Declaration of Helsinki 1964. Ethical approval was obtained by the local Ethical Committee at each study centre (11). Participants with complete data on TG, TC, HDL-c, LDL-c, apolipoprotein A1 (Apo A1), apolipoprotein B (Apo B) and two 24-hour dietary recalls (24-HDR) were included (n=454; 44.0% boys). Decreases from the original sample size are partially explained by the fact that blood samples were randomly drawn only in one third of the HELENA participants and partially from the fact that Heraklion and Pecs were excluded from 24-HDR analyses due to logistical reasons; therefore, eight out of the ten study centres were included in the 24-HDR analyses resulting in a sample size decrease. Excluded participants (n=3,074) were significantly (p<0.05) older, heavier and had higher mean body mass index (BMI) than those included in this study (data not shown). Macronutrient intakes. Dietary intakes were assessed by a self-administered computerbased tool called HELENA-DIAT (Dietary Assessment Tool), based on the previously developed software Young Adolescents’ Nutrition Assessment on Computer (YANA-C) shown to be appropriate in assessing dietary information of European adolescents (12, 13). The software consists of a single structured 24-HDR according to six meal occasions. Adolescents were asked to recall all food and drinks consumed the previous day. Two non-consecutive 24HDR within a time span of two weeks were obtained from each participant during school time European PhD Thesis, 2013 131 and assisted by fieldworkers. Therefore, no information on Fridays and Saturdays was collected. The German Food Code and Nutrition Date Base (Bundeslebensmittelschlüssel, BLS Version II.3.1) (14)(99) was used to calculate energy and nutrient intakes. Usual food and nutrient intakes were estimated by the multiple source method (MSM) in order to account for within-person variability (15). Energy intake was estimated in kilocalories per day (kcal/d) and macronutrients intake (fat, protein and carbohydrates) in grams/day (g/d). Subsequently, macronutrients intakes were divided by energy intake and were expressed as g/1000 kcal to account for total energy intake (16). Additionally, monosaturated-to-saturated fat ratio (M/S), polyunsaturated-to-saturated fat (P/S) ratio, and Cholesterol-Saturated Fat Index (CSI = [(1.01 × g of saturated fat) + (0.05 × mg of cholesterol)]) (17) were computed. Diet Quality Index for Adolescents. A previously validated Diet Quality Index for Adolescents (DQI-A) (18) was used to adjust for all dietary factors simultaneously. The technical aspects regarding the development of the DQI-A are published elsewhere (18). Briefly, the DQI-A was calculated on food intake data in order to assess adolescents adherence to food-based dietary guidelines. Therefore, daily diet was divided into nine recommended food groups: 1) water, 2) bread and cereals, 3) grains and potatoes, 4) vegetables, 5) fruit, 6) milk products, 7) cheese, 8) meat, fish, eggs and substitutes, and 9) fat and oils. The DQI-A consisted of three components: quality (optimal food quality choices within a food group), diversity (degree of variation of the diet) and equilibrium (difference between the adequacy and the excess to the recommendations). Furthermore, a meal index was also taken into consideration since the number of meals consumed per day is related to a healthier diet. The DQI-A was obtained by computing the mean of the four components (dietary quality, diversity, equilibrium and meal index), resulting in scores ranging from -33 to +100. Higher scores reflected a higher diet quality. European PhD Thesis, 2013 132 Physical examinations. All anthropometric measures were taken following a standardized protocol described elsewhere (19). Weight and height were measured in underwear and barefoot using an electronic scale (Type SECA 861) and a stadiometer (Type SECA 225). BMI was calculated as body weight in kilograms divided by the square of height in meters and was categorized according to Cole et al. (20, 21). Skinfolds thicknesses were measured with a Holtain Calliper (Crymmych, UK) in triplicate on the left side at biceps, triceps, subscapular and suprailiac sites. Waist circumference was taken at the midpoint between the lowest rib and the iliac crest with an anthropometric tape (SECA 200). The waistto-height ratio (WHeR) was calculated. Blood sampling. Blood sampling procedures have been described elsewhere (22). Briefly, blood samples were drawn after an overnight fast and analyzed in centralized laboratories. Serum TG, TC, HDL-c, TG, and LDL-c were measured on a Dimension RxL clinical chemistry system (Dade Behring, Schwalbach, Germany) using enzymatic methods. Apo B and Apo A1 were measured in an immunochemical reaction with a BN II analyzer (Dade Behring, Schwalbach, Germany). The TC/HDL-c and the Apo B/Apo A1 ratios were computed. Education. Maternal education was used as a proxy of socioeconomic status and was assessed via questionnaire according to the following four categories: 1) lower education, 2) lower secondary education, 3) higher secondary education, and 4) higher education/university degree. Sedentary behaviours. Average time engaged in two sedentary behaviours (TV viewing and playing with videogames) was estimated by means of a self-administered questionnaire previously found to demonstrate good reliability (23). Physical activity (PA). PA was objectively measured by uni-axial accelerometers during 7 consecutive days (Actigraph MTI, model GT1M, Manufacturing Technology Inc., Fort Walton Beach, FL, USA) (24). At least three days of recording, with a minimum of 8 hours registration per day, was set as an inclusion criterion. The time sampling interval was set at 15 seconds. European PhD Thesis, 2013 133 The time spent at moderate-to-vigorous PA (MVPA) (>3 metabolic equivalents) was calculated on the basis of the following cut-off point: ≥ 2000 counts per minute for moderateto-vigorous PA (24, 25). Statistical analysis. The normality of all variables was checked and non-normally distributed variables (TG, TC, HDL-c, LDL-c, TC/HDL-c ratio, Apo B/Apo A1 ratio, fat intake and P/S ratio) were log-transformed prior to the analysis. Normality for CSI was reached by comprising to the power of two. M/S ratio was converted as 1/(M/S). Differences across groups were tested by means of the independent samples T-test for normally distributed variables and the Mann-Whitney U test for non-normally distributed variables. Chi-squared test was applied for categorical variables. Multilevel linear regression analyses were performed to investigate the association between macronutrients intakes (independent variables) and plasma lipids concentrations (dependent variables). As no interaction by gender was found the analyses were conducted with boys and girls combined. Study centre was included as random intercept. Gender, age, maternal education, sum of four skinfolds, MVPA, sedentary behaviors, and DQI-A were entered as covariates. Since serum lipid profile has previously been associated with body fat in the HELENA adolescents (26), participants were categorized into low and high body fat content according to three body fat indicators, i.e. z-score of BMI (z-BMI), sum of four skinfolds and WHeR. These cut-offs were calculated specifically by gender and by 1-year groups (12.5-13.49, 13.5-14.49, 14.5-15.49, 15.5-16.49, and 16.5-17.5) based on the median of each subgroup. Multilevel linear regression analyses were performed separately for each group of low/high body fat indicator and by tertiles of protein, carbohydrates and fat intake adjusted for potential confounders, i.e. gender, age, maternal education, sum of four skinfolds, MVPA, sedentary behaviors, and DQI-A. Study center was entered as random intercept. Collinearity tests showed no collinearity among covariates. The statistical software packages Stata version 12.0 (Stata Corp., college Station, TX, European PhD Thesis, 2013 134 USA) and Predictive Analytics SoftWare (PASW, version 18; SPSS Inc., Chicago, IL, USA) were used to perform the analyses. Statistical significance was set at p<0.05. Results Main characteristics of the study sample are shown in Table 1. Table 2 displays means and medians of dietary intake and blood lipids levels according to high/low body fat-related variables, i.e. above or below genderand age-specific median-based cut-offs of z-BMI, sum of four skinfolds and WHeR. Adolescents in the high z-BMI group had significantly higher protein intake, TG serum concentrations and TC/HDL-c values and lower total energy intake and HDL-c concentrations than those with low z-BMI (p<0.05). Adolescents with high sum of skinfolds also showed significantly lower HDL-c serum levels and higher fat intake compared to their peers in the low sum of skinfolds group (p<0.05). Those in the high WHeR group showed significantly higher protein intake, M/S ratio, TG, TC/HDL-c and Apo B/Apo A1 ratios and lower total energy intake than adolescents in the low WHeR group. Associations between macronutrient intakes (g/1000 kcal/day)and serum lipid profile are shown in Table 3. An inverse association was observed between protein intake and TG. Carbohydrates intake was also inversely associated with HDL-c and Apo A1 whereas a positive association was observed for TC/HDL-c ratio. Inverse associations were found between fat intake and TG, LDL-c, Apo B, TC/HDL-c and Apo B/Apo A1 ratios; a positive association, however, was observed with HDL-c. CSI was also positively associated with HDLc. Table 4 shows the associations of macronutrients intake and blood lipid profile across adiposity status categories. We observed that protein intake was inversely associated with TG in the high z-BMI group and in the high WHeR group (p<0.05). Carbohydrates intake showed an inverse association with HDL-c in all body fat groups (p<0.05 in low z-BMI and low sum of skinfolds; p<0.01 in high z-BMI and high sum of skinfolds, and p<0.001 in high WHeR), except in those with low WHeR. Carbohydrates intake was also inversely associated with Apo A1 in European PhD Thesis, 2013 141 European PhD Thesis, 2013 142 European PhD Thesis, 2013 143 European PhD Thesis, 2013 144 European PhD Thesis, 2013 145 European PhD Thesis, 2013 146 Figure 1A Tertiles of fat intake (g/1000 kcal) Tertile 1 Tertile 2 Tertile 3 Triglycerides (mg/dL) 50 55 60 65 70 75 80 Low sum skinfolds High sum skinfolds * * Figure 1B Tertiles of fat intake (g/1000 kcal) Tertile 1 Tertile 2 Tertile 3 Total cholesterol (mg/dL) 150 155 160 165 170 Low sum skinfolds High sum skinfolds Figure 1C Tertiles of fat intake (g/1000 kcal) Tertile 1 Tertile 2 Tertile 3 HDL-c (mg/dL) 50 55 60 Low sum skinfolds High sum skinfolds Figure 1D Tertiles of fat intake (g/1000 kcal) Tertile 1 Tertile 2 Tertile 3 LDL-c (mg/dL) 84 86 88 90 92 94 96 98 Low sumskinfolds High sum skinfolds Figure 1. Mean (SE) triglycerides (1A), total cholesterol (1B), HDL-c (1C) and LDL-c (1D) by tertiles of fat intake for high and low sum of skinfolds after adjustment for covariates: age, sex, study centre, socioeconomic status, moderate-to-vigorous physical activity, sedentary behaviours and diet quality index for adolescents. *p<0.05 across tertiles of fat intake after Bonferroni correction for post-hoc multiple comparisons. Unlogged values for easier interpretability. Median fat intake: Tertile 1: 32.1 g/1000 kcal (low sum skinfolds), 33.6 g/1000 kcal (high sum skinfolds) Tertile 2: 40.4 g/1000 kcal (low sum skinfolds), 42.2 g/1000 kcal (high sum skinfolds) Tertile 3: 49.7 g/1000 kcal (low sum skinfolds), 51.4 g/1000 kcal (high sum skinfolds) HDL-c, high density lipoprotein-cholesterol; LDL-c, low density lipoprotein-cholesterol. European PhD Thesis, 2013 147 Figure 2A Tertiles of fat intake (g/1000 kcal) Tertile 1 Tertile 2 Tertile 3 Triglycerides (mg/dL) 50 55 60 65 70 75 80 Low WHeR High WHeR * * Figure 2B Tertiles of fat intake (g/1000 kcal) Tertile 1 Tertile 2 Tertile 3 Total cholesterol (mg/dL) 150 155 160 165 170 Low WHeR High WHeR * * Figure 2C Tertiles of fat intake (g/1000 kcal) Tertile 1 Tertile 2 Tertile 3 HDL-c (mg/dL) 50 55 60 Low WHeR High WHeR Figure 2D Tertiles of fat intake (g/1000 kcal) Tertile 1 Tertile 2 Tertile 3 LDL-c (mg/dL) 84 86 88 90 92 94 96 98 Low WHeR High WHeR Figure 2. Mean (SE) triglycerides (2A), total cholesterol (2B), HDL-c (2C) and LDL-c (2D) by tertiles of fat intake for high and low waist-to-height ratio after adjustment for covariates: age, sex, study centre, socioeconomic status, moderate-to-vigorous physical activity, sedentary behaviours and diet quality index for adolescents. *p<0.05 across tertiles of fat intake after Bonferroni correction for post-hoc multiple comparisons. Unlogged values for easier interpretability. Median fat intake: Tertile 1: 32.1 g/1000 kcal (low WHeR), 33.6 g/1000 kcal (high WHeR) Tertile 2: 40.8 g/1000 kcal (low WHeR), 41.9 g/1000 kcal (high WHeR) Tertile 3: 50.0 g/1000 kcal (low WHeR), 51.4 g/1000 kcal (high WHeR) European PhD Thesis, 2013 148 HDL-c, high density lipoprotein-cholesterol; LDL-c, low density lipoprotein-cholesterol; WHeR, waist-to-height ratio. European PhD Thesis, 2013 149 References 1. Saland JM. Update on the metabolic syndrome in children. Curr Opin Pediatr 2007; 19(2): 183-191. 2. Nicklas TA, von Duvillard SP, Berenson GS. Tracking of serum lipids and lipoproteins from childhood to dyslipidemia in adults: the Bogalusa Heart Study. Int J Sports Med 2002; 23 Suppl 1: S39-43. 3. Flock MR, Green MH, Kris-Etherton PM. Effects of adiposity on plasma lipid response to reductions in dietary saturated fatty acids and cholesterol. Adv Nutr 2011; 2(3): 261-274. 4. Mozaffarian D, Rimm EB, Herrington DM. Dietary fats, carbohydrate, and progression of coronary atherosclerosis in postmenopausal women. Am J Clin Nutr 2004; 80(5): 11751184. 5. Ma Y, Li Y, Chiriboga DE, Olendzki BC, Hebert JR, Li W, et al. Association between carbohydrate intake and serum lipids. J Am Coll Nutr 2006; 25(2): 155-163. 6. Appel LJ, Sacks FM, Carey VJ, Obarzanek E, Swain JF, Miller ER, 3rd, et al. Effects of protein, monounsaturated fat, and carbohydrate intake on blood pressure and serum lipids: results of the OmniHeart randomized trial. JAMA 2005; 294(19): 2455-2464. 7. Siri-Tarino PW, Sun Q, Hu FB, Krauss RM. Meta-analysis of prospective cohort studies evaluating the association of saturated fat with cardiovascular disease. Am J Clin Nutr 2010; 91(3): 535-546. 8. Bertheke Post G, de Vente W, Kemper HC, Twisk JW. Longitudinal trends in and tracking of energy and nutrient intake over 20 years in a Dutch cohort of men and women between 13 and 33 years of age: The Amsterdam growth and health longitudinal study. Br J Nutr 2001; 85(3): 375-385. 9. Moreno LA, De Henauw S, Gonzalez-Gross M, Kersting M, Molnar D, Gottrand F, et al. Design and implementation of the Healthy Lifestyle in Europe by Nutrition in Adolescence Cross-Sectional Study. Int J Obes (Lond) 2008; 32 Suppl 5: S4-11. 10. Moreno LA, Gonzalez-Gross M, Kersting M, Molnar 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. Public Health Nutr 2008; 11(3): 288-299. 11. Beghin L, Castera M, Manios Y, Gilbert CC, 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 (Lond) 2008; 32 Suppl 5: S12-18. European PhD Thesis, 2013 150 12. Vereecken CA, Covents M, Matthys C, Maes L. Young adolescents' nutrition assessment on computer (YANA-C). Eur J Clin Nutr 2005; 59(5): 658-667. 13. Vereecken CA, Covents M, Sichert-Hellert W, Alvira JM, 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 (Lond) 2008; 32 Suppl 5: S26-34. 14. Dehne LI, Klemm C, Henseler G, Hermann-Kunz E. The German Food Code and Nutrient Data Base (BLS II.2). Eur J Epidemiol 1999; 15(4): 355-359. 15. Harttig U, Haubrock J, Knuppel S, Boeing H. The MSM program: web-based statistics package for estimating usual dietary intake using the Multiple Source Method. Eur J Clin Nutr 2011; 65 Suppl 1: S87-91. 16. Willett WC, Howe GR, Kushi LH. Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr 1997; 65(4 Suppl): 1220S-1228S; discussion 1229S-1231S. 17. Connor SL, Gustafson JR, Artaud-Wild SM, Classick-Kohn CJ, Connor WE. The cholesterol-saturated fat index for coronary prevention: background, use, and a comprehensive table of foods. J Am Diet Assoc 1989; 89(6): 807-816. 18. Vyncke K, Cruz Fernandez E, Fajo-Pascual M, Cuenca-Garcia M, De Keyzer W, GonzalezGross M, et al. Validation of the Diet Quality Index for Adolescents by comparison with biomarkers, nutrient and food intakes: the HELENA study. Br J Nutr 2013; 109(11): 20672078. 19. Nagy E, Vicente-Rodriguez G, Manios Y, Beghin L, Iliescu C, Censi L, et al. Harmonization process and reliability assessment of anthropometric measurements in a multicenter study in adolescents. Int J Obes (Lond) 2008; 32 Suppl 5: S58-65. 20. Cole TJ, Bellizzi MC, Flegal KM, Dietz WH. Establishing a standard definition for child overweight and obesity worldwide: international survey. BMJ 2000; 320(7244): 1240-1243. 21. Cole TJ, Flegal KM, Nicholls D, Jackson AA. Body mass index cut offs to define thinness in children and adolescents: international survey. BMJ 2007; 335(7612): 194. 22. Gonzalez-Gross M, Breidenassel C, Gomez-Martinez S, Ferrari M, Beghin 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 (Lond) 2008; 32 Suppl 5: S66-75. 23. Rey-Lopez JP, Ruiz JR, Ortega FB, Verloigne M, Vicente-Rodriguez G, Gracia-Marco L, et al. Reliability and validity of a screen time-based sedentary behaviour questionnaire for adolescents: The HELENA study. Eur J Public Health 2012; 22(3): 373-377. Cuestionario de frecuencia de consumo de alimentos (CEHQ-FFQ) En el último mes, ¿con qué frecuencia ha consumido su hijo/a Ios siguientes alimentos y bebidas? Por favor, limítese a las cuatro últimas semanas y excluya las comidas del colegio o guardería. Nunca/ menos de una vez por semana 1 - 3 veces por semana 4 – 6 veces por semana 1 vez al día 2 veces al día 3 veces al día 4 o más veces al día No lo sé Vegetales Verduras, patatas y legumbres cocinadas (también combinadas en el mismo plato)  1  2  3  4  5  6  7  8 Patatas fritas, croquetas de patata  1  2  3  4  5  6  7  8 Vegetales crudos (mezclados en la ensalada, zanahoria, pepino, lechuga, tomate, etc.)  1  2  3  4  5  6  7  8 Frutas Frutas frescas (también licuadas) sin azúcar añadido  1  2  3  4  5  6  7  8 Frutas frescas (también licuadas) con azúcar añadido  1  2  3  4  5  6  7  8 Bebidas Agua  1  2  3  4  5  6  7  8 Zumos de frutas (zumo de naranja, manzana, melocotón, piña,etc.)  1  2  3  4  5  6  7  8 Bebidas edulcoradas incluyendo bebidas deportivas, té en lata o embotellado, refrescos, etc.  1  2  3  4  5  6  7  8 Coca-cola light o bebidas refrescantes sin azúcar  1  2  3  4  5  6  7  8 Nunca/ menos de una vez por semana 1 - 3 veces por semana 4 – 6 veces por semana 1 vez al día 2 veces al día 3 veces al día 4 o más veces al día No lo sé Cereales de desayuno Cereales de desayuno azucarados o que se les ha añadido azúcar y muesli azucarado (ej. Corn flakes, crispies, etc.)  1  2  3  4  5  6  7  8 Papillas, copos de avena, cereales no azucarados, muesli natural  1  2  3  4  5  6  7  8 Leche Leche no azucarada  1  2  3  4  5  6  7  8 Leche azucarada (ej. con azúcar, chocolate, cola-cao, miel, etc.)  1  2  3  4  5  6  7  8 Qué tipo de leche consume su hijo/a habitualmente: Entera  Semi-desnatada /desnatada  Yogur Yogur natural o kéfir sin azúcar  1  2  3  4  5  6  7  8 Yogur azucarado y bebidas lácteas fermentadas (ej. Actimel®, LC1®, etc.)  1  2  3  4  5  6  7  8 Qué tipo de yogur consume su hijo/a habitualmente: Entero  Semi-desnatado /desnatado  Nunca/ menos de una vez por semana 1 - 3 veces por semana 4 – 6 veces por semana 1 vez al día 2 veces al día 3 veces al día 4 o más veces al día No lo sé Pescado Pescado fresco o congelado, sin freír  1  2  3  4  5  6  7  8 Pescado frito y varitas de pescado  1  2  3  4  5  6  7  8 Carne y productos cárnicos Productos loncheados y conservados, o listos para cocinar (ej. fiambres, embutidos, jamón, hamburguesas etc.)  1  2  3  4  5  6  7  8 Huevos Huevos fritos o huevos revueltos  1  2  3  4  5  6  7  8 Huevos duros o escalfados  1  2  3  4  5  6  7  8 Mayonesa y productos derivados de la mayonesa (ej. Ligeresa, salsa rosa, etc.)  1  2  3  4  5  6  7  8 Productos sustitutivos de la carne y productos de soja Tofu, tempé, leche de soja, yogures de soja, etc.)  1  2  3  4  5  6  7  8 Queso Queso en lonchas o para untar (ej. Philadelphia, tranchetes. etc.)  1  2  3  4  5  6  7  8 Queso rallado  1  2  3  4  5  6  7  8 Productos para untar Mermelada, miel  1  2  3  4  5  6  7  8 Nocilla o crema de avellanas para untar  1  2  3  4  5  6  7  8 Nunca/ menos de una vez por semana 1 - 3 veces por semana 4 – 6 veces por semana 1 vez al día 2 veces al día 3 veces al día 4 o más veces al día No lo sé Mantequilla, margarina en pan  1  2  3  4  5  6  7  8 Productos bajos en grasa en pan (ej. Mermelada, etc.)  1  2  3  4  5  6  7  8 Ketchup  1  2  3  4  5  6  7  8 Productos hechos a base de cereales Pan blanco, panecillos blancos, biscotes blancos  1  2  3  4  5  6  7  8 Pan integral , panecillos integrales, biscotes integrales  1  2  3  4  5  6  7  8 Pasta, fideos, arroz  1  2  3  4  5  6  7  8 Cuscús, bulgur, etc.  1  2  3  4  5  6  7  8 Pizza como plato principal  1  2  3  4  5  6  7  8 Sandwiches (rellenos con queso, carne, vegetales, etc)  1  2  3  4  5  6  7  8 Aperitivos Frutos secos y semillas y frutas secas (ej. Pipas, cacahuetes, pasas etc.)  1  2  3  4  5  6  7  8 patatas fritas, aperitivos de maíz, palomitas de maíz, etc (ej. Cheetos, Lay’s, risketos, etc.)  1  2  3  4  5  6  7  8 tortas o bollos, pasteles (ej. Tarta de manzana, crepes, palmeras de hojaldre, etc.)  1  2  3  4  5  6  7  8 chocolate, barritas de chocolate (Mars, Lions, Kit Kat, etc.)  1  2  3  4  5  6  7  8 Nunca/ menos de una vez por semana 1 - 3 veces por semana 4 – 6 veces por semana 1 vez al día 2 veces al día 3 veces al día 4 o más veces al día No lo sé caramelos, chucherías, gominolas, etc.)  1  2  3  4  5  6  7  8 galletas, pasteles envasados, tartas (ej.Donuts, bollycao, cañas de chocolate, etc.)  1  2  3  4  5  6  7  8 Helados, polos, sorbetes de fruta(ej. Mágnum, calippo etc.)  1  2  3  4  5  6  7  8 Cuestionario sobre actividad física y comportamientos sedentarios Información socio-demográfica 66. ¿Cuál es el nivel más alto de educación escolar que usted y su cónyuge/pareja tienen? Por favor, marcar solamente uno por persona. Yo Cónyuge/pareja Primaria /EGB  1  1 Secundaria /ESO  2  2 Formación profesional  3  3 Ciclos formativos de grado superior  4  4 Bachillerato/ BUP/COU  5  5 Sin graduación (todavía)  8  8 Otros/desconocido  9  9 67. ¿Cuál es el nivel más alto de cualificación profesional que usted y su cónyuge/pareja tienen? Por favor, marcar solamente uno por persona. Yo Cónyuge/pareja Formación profesional  1  1 Ciclos formativos de grado superior  2  2 Diplomatura universitaria/ingeniería técnica  3  3 Licenciatura/ingeniería superior  4  4 Doctorado  5  5 No formado (todavía)  8  8 Desconocido/otros  9  9 68. ¿Cuál de los siguientes enunciados describe mejor su estado ocupacional actual y el de su cónyuge/pareja? Por favor, marcar solamente uno por persona. Yo Cónyuge/pareja Trabajo a tiempo completo (30 horas o más a la semana)  1  1 Trabajo a tiempo parcial (menos de 30 horas a la semana)  2  2 Estudio o voy a la universidad  3  3 No tengo trabajo remunerado  4  4 Retirado ( también jubilación anticipada)  5  5 Baja temporal de la empresa (ej. baja por maternidad o paternidad)  6  6 En el paro, desde hace menos de un año  7  7 En el paro, desde hace un año o más  8  8 En asistencia pública (asistencia social)  9  9 Otro, por favor especifique: _______________________________________  10  10 69. ¿En que posición laboral están actualmente ocupados usted y su cónyuge/pareja? Si usted o su cónyuge/pareja ya no están ocupados o actualmente no están ocupados, por favor, indique la última posición laboral. Yo Cónyuge/pareja Obrero Obrero no cualificado  1  1 Obrero semi-cualificado  2  2 Obrero cualificado, artesano  3  3 Maestro artesano, capataz  4  4 Patrón o autónomo (incluyendo la ayuda de miembros de la familia) Agricultor y/o ganadero autónomo  1  1 Autónomo, trabajador por cuenta propia  2  2 Patrón con hasta 9 empleados  3  3 Patrón con 10 o más empleados  4  4 Ayudo a algún miembro de la familia  5  5 Empleado Empleado (ej. dependiente, recepcionista, oficinista)  1  1 Empleado cualificado (ej. auxiliar contable, auxiliar dental)  2  2 Empleado altamente cualificado o con funciones de gestión (ej. científico, jefe de departamento)  3  3 Empleado con extensas funciones ejecutivas (ej. director, director general, junta directiva)  4  4 Funcionario público Categoría A  1  1 Categoría B  2  2 Categoría C  3  3 Categoría D  4  4 Categoría E  5  5 No trabajo  6  6 70. ¿Cuáles son los ingresos mensuales familiares, es decir, el beneficio neto que usted (en total) percibe a parte de impuestos y de retenciones? Cuando decimos familiares nos referimos a todos aquellos que están residiendo en el mismo hogar que el niño/a seleccionado y que también participan en los gastos. Por favor, incluya también ingresos procedentes de alquileres o arrendamientos, pensiones, subvenciones para los niños, pensiones alimenticias, etc. hasta 800 €  1 800 € hasta 1050 €  2 1050 € hasta 1300 €  3 1300 € hasta 1550 €  4 1550 € hasta 1900 €  5 1900 € hasta 2500 €  6 2500 € hasta 3000 €  7 3500 € hasta 4000 €  8 Por encima de 4000 €  9 Estudio HELENA Cuestionario de sedentarismo 1. Cuántas horas al día pasas ... viendo la televisión j ugando con juegos en el ordenador j ugando con la videoconsola navegando en internet por razones que no están relacionadas con el estudio navegando en internet por motivos de estudio estudiando sin utilizar internet ninguna menos de media hora de dos a tres horas de tres a cuatro horas cuatro o más horas 2. cuántas horas duermes normalmente por la noche ... durante los días de semana: durante el fin de semana: , , horas por noche horas por noche 4. Cuando estás comiendo con tu familia ¿coméis delante de la televisión? todos los días en cada comida todos los días en 1 0 2 comidas no todos los dias pero mas de 2 comidas a la semana no más de 1 0 2 comidas a la semana escasas veces nunca un día de colegio: un día de fin de semana: un día de colegio: un día de fin de semana: un día de colegio: un día de fin de semana: un día de colegio: un día de fin de semana: un día de colegio: un día de fin de semana: un día de colegio: un día de fin de semana: 3. si realizas alguna actividad académica o de ocio complementaria al colegio (idiomas, ajedrez clases de repaso, clases the música) aparte del tiempo de estudio personal ¿cuántas horas supone a la semana? ,horas por semana de una a dos horas de media a una hora 7788399663 5. tienes en casa ... televisión: ordenador: videoconsola: no si, 1 si, 2 si, 3 o más no si, 1 si, 2 si, 3 o más no si, 1 si, 2 si, 3 o más tienes en tu habitación ... televisión: ordenador: videoconsola: no si no si no si tiene tu hermano / hermana en su habitación ... no si no tengo hermanos viviendo en casa televisión: ordenador: videoconsola: no si no tengo hermanos viviendo en casa no si no tengo hermanos viviendo en casa 6. sin contar las comidas principales, cuántas veces ... bebes algo mientras ves la televisión comes algo mientras ves la televisión nunca menos de una vez a la semana ¿qué comes y bebes mientras ves la televisión? (marca todas las respuestas oportunas de esta lista) agua leche o productos derivados zumo de frutas refrescos light refrescos azucarados infusiones café cerveza otras bebidas: ............................... snack salado (patatas) bollería bocadillo fruta frutos secos productos lácteos caramelos, chocolates y chocolatinas otros: ............................... normalmente no como nada normalmente no bebo nada 7. sin contar las comidas principales, cuántas veces ... bebes algo mientras juegas con videojuegos: ¿qué comes y bebes mientras juegas con videojuegos? (marca todas las respuestas oportunas de esta lista) 8. sin contar las comidas principales, cuántas veces ... bebes algo mientras navegas en internet: comes algo mientras navegas en internet: ¿qué comes y bebes mientras navegas en internet? (marca todas las respuestas oportunas de esta lista) comes algo mientras juegas con videojuegos: agua leche o productos derivados zumo de frutas refrescos light refrescos azucarados infusiones café cerveza otras bebidas: ............................... snack salado (patatas) bollería bocadillo fruta frutos secos productos lácteos caramelos, chocolates y chocolatinas otros: ............................... normalmente no como nada normalmente no bebo nada agua leche o productos derivados zumo de frutas refrescos light refrescos azucarados infusiones café cerveza otras bebidas: ............................... snack salado (patatas) bollería bocadillo fruta frutos secos productos lácteos caramelos, chocolates y chocolatinas otros: ............................... normalmente no como nada normalmente no bebo nada menos de una vez a la semana nunca menos de una vez a la semana nunca 1-2 días po r semana 1-2 días por semana 1-2 días por semana 3-4 días po r semana 3-4 días por semana 3-4 días por semana (casi) todos los días (casi) todos los días (casi) todos los días varias veces al día varias veces al día varias veces al día 0340399660