To determine the association between socioeconomic level and the presence of obesity, hypertension and type 2 diabetes mellitus in the Peruvian population.Encuesta Nacional Demográfica y de Salud Familiar , Endes) from 2018 to 2020. The outcomes were obesity, hypertension, and type 2 diabetes mellitus. The exposure variables were two indicators of socioeconomic status: educational level  and wealth index (in tertiles). Models were created using Poisson regression, reporting prevalence ratios (PR) and 95% confidence intervals (95%CI).Secondary analysis of data from the National Demographic and Family Health Survey  years, and 55.5% were women. The prevalence of obesity was 26.0% (95%CI: 25.4–26.6); of hypertension, 24.9% (95%CI: 24.3–25.5); and of type 2 diabetes mellitus, 4.8% (95%CI: 4.5–5.1). In multivariate model, and compared with those with a low wealth index, those with a high wealth index had a higher prevalence of obesity , hypertension  and type 2 diabetes mellitus . On the other hand, higher educational level was only associated with a reduction in the prevalence of obesity .There is a differential association between the wealth index, educational level and markers of noncommunicable diseases. There is evidence of a positive association between wealth index and obesity, hypertension and type 2 diabetes mellitus, whereas educational level was only negatively associated with obesity. In Latin America, the situation is not different, and despite the existing heterogeneity, cases of obesity and type 2 diabetes mellitus have increased 4 .Chronic noncommunicable diseases, including obesity, hypertension and type 2 diabetes mellitus, are on the rise, especially in low- and middle-income countries 5 . The prevalence of type 2 diabetes mellitus reaches 7% at the national level 6 , with heterogeneous results between regions. Many noncommunicable diseases are determined by the interaction of genetic and metabolic factors, as well as risk factors, such as poor diet, low levels of physical activity, and aging 7 . All these behavioral changes have been associated with the epidemiological and nutritional transition that include social and economic growth, urbanization, globalization of technologies and food production, implying changes in the causes of morbidity and mortality in the population 8 .In Peru, approximately 22.3% of the population suffers from obesity, and almost 14% have high blood pressure according to the results of the 2019 National Demographic and Health Survey 9 . This indicator can be used as a surrogate to assess the distribution of certain health risk factors, and to give an idea of the transition phase in which a given population is ongoing 10 . For example, the increase in the prevalence of obesity has been unequal when evaluated according to socioeconomic strata in different Latin American contexts 11 . On the other hand, although there are several indicators to determine socioeconomic status, two of them are the most commonly used, including educational level and wealth index 9 .Socioeconomic level is a total measure combining the economic and sociological part of a person’s attaintment Thus, the present study aimed to evaluate the association at the population level between socioeconomic level, assessed using educational level and the wealth index, and the prevalence of cardiovascular risk factors .12 . The Endes is a population-based survey with national and regional representativeness, which is conducted annually by the National Institute of Statistics and Informatics  in the 25 regions of Peru, and includes variables on poverty, fertility, violence and health. Since 2014, a specific module for noncommunicable diseases was included, and information from 2018 to 2020 was used for the present analysis.A secondary analysis was conducted using information from the National Demographic and Family Health Survey (Endes) The selection criteria for participants were to be aged ≥ 20 years, and able to consent to their participation in the study. Pregnant women were excluded from the present analysis.12 .The Endes sampling followed a two-stage random approach. In rural areas, the primary sampling units are clusters of 500 to 2,000 subjects, while the secondary sampling units are households within clusters. In urban areas, the primary sampling units are blocks or groups of blocks with more than 2,000 subjects, and an average of 140 households, and the secondary sampling units are households as in rural areas 13 program was used, assuming a distribution of approximately one-third of the population in each socioeconomic level, and a difference in the prevalence of the event of interest of at least 5%  between groups to be compared. At a 95% confidence level, a power greater than 99% was obtained to find the associations of interest.To calculate the statistical power of this study, the OpenEpi 2 according to international guidelines 14 . The presence of hypertension was characterized by a systolic blood pressure ≥ 140 mm Hg or diastolic ≥ 90 mm Hg or as a self-reported prior diagnosis according to the JNC-7. Finally, and given that the Endes does not collect blood samples for fasting blood glucose, the presence of type 2 diabetes mellitus was defined according to a self-reported previous physician diagnosis.Three were the outcomes of interest: obesity, arterial hypertension, and type 2 diabetes mellitus. Obesity was defined as a body mass index (BMI) ≥ 30 kg/m 15 . All these indicators were weighted, constructing a numerical wealth index that was subsequently categorized into tertiles  for this analysis.The exposure variable of interest was the socioeconomic level, evaluated based on two indicators: educational level and wealth index. In the case of educational level, the years of education, reported by the participant, were used and then categorized into < 7 years (compatible with completed primary school), between 7 and 11 years (compatible with completed secondary school), and ≥ 12 years (compatible with higher education). On the other hand, the wealth index is a composite measure of the standard of living in a household. It is calculated in a simple way with data collected on the respondent’s household assets and services . This procedure is based on DHS Program techniques that are almost common to all countries participating in such program versus female), age , geographic setting , altitude, defined based on meters above sea level (masl) of the individual’s area of residence, and further categorized , and the year in which the Endes was conducted. This last variable was introduced to account for variations in results due to the effect of the Covid-19 pandemic.Other variables were also evaluated as potential confounders of the associations of interest. These included: sex  was used for statistical analysis, and the complex sampling of the study was taken into consideration for all estimates. Initially, the population characteristics were described according to socioeconomic level , and the outcomes of interest . A comparison of missing values by variable and year of the Endes was also performed, because a greater loss of data in anthropometric markers was expected in 2020 due to the Covid-19 pandemic. For comparisons, the Chi-squared test was applied according to study design with the Rao-Scott second-order correction for categorical variables. In addition, the prevalence of these outcomes was estimated and reported with their respective 95% confidence intervals (95%CI).Finally, in order to evaluate the association between socioeconomic level  and the outcome variables, crude and adjusted models were created using Poisson regression, with robust variance, reporting the prevalence ratio (PR) and their corresponding 95% confidence intervals (95%CI). The multivariate models were adjusted for sex, age, geographical area, altitude and year of the Endes. The variance inflation factor (VIF) was used to determine the presence of collinearity due to the multiple variables included in the model; however, all values were less than 5.Universidad Científica del Sur complying with the Declaration of Helsinki (code: 726-2019-PRE15).The Endes database is publicly accessible. The INEI staff ensured the voluntary and informed participation of respondents through informed consent. The research protocol of this study was reviewed and approved by the Ethics Committee of the A total of 109,363 available records came from the 2018, 2019, and 2020 Endes. However, 10,517 (9.6%) records were excluded because they did not meet the inclusion criteria . Thus, a total of 98,846 records were analyzed, with a mean age of 45.3 years (SD: 16.0) and 52,259 (55.5%) females. Of note, a large number of missing data in the measurement of body mass index and blood pressure was found in 2020, especially due restrictions imposed by the Covid-19 pandemic.According to the results in The prevalence of obesity was 26.0% (95%CI: 25.4–26.6), and this prevalence was higher in women than in men (p < 0.001), in those between 40 and 59 years of age (p < 0.001), in urban residents (p < 0.001), in those living below 500 masl (p < 0.001), in those with 7–11 years of education (p < 0.001), and in those with higher wealth index (p < 0.001)   .On the other hand, the prevalence of hypertension was 24.9% (95%CI: 24.3–25.5), with higher frequency in males (p < 0.001), in those of older age (p < 0.001), in residents of urban areas (p < 0.001), in those living below 500 masl (p < 0.001), in those with lower educational level (p < 0.001), in those with higher wealth index (p < 0.001), and in those evaluated in the year 2020 (p < 0.001)   .Finally, the prevalence of type 2 diabetes mellitus was 4.8% (95%CI: 4.5–5.1), and was more frequent in older participants (p < 0.001), in residents of urban areas (p < 0.001), in those living below 500 masl (p < 0.001), in those with lower educational level (p < 0.001), those with a higher wealth index (p < 0.001), and in those evaluated in the year 2020 (p = 0.004)   .In multivariate model   , there On the other hand, in multivariate model we only found an association between educational level and obesity: those with the highest educational level were 11% less likely to present obesity  compared to those with the lowest educational level   . This fThis study evidences a differential association between indicators of socioeconomic level and the presence of noncommunicable diseases: wealth index was associated with the presence of obesity, hypertension, and type 2 diabetes mellitus. Thus, those with a medium or high wealth index had a higher prevalence of any of the cardiovascular risks evaluated. However, this was not the case for educational level, as those with higher education had lower prevalence of obesity, but not association was found with the other chronic conditions studied. Finally, 1 in 4 had obesity, 1 in 5 had hypertension, and about 1 in 20 had type 2 diabetes mellitus.17 . Other studies have evaluated the association between socioeconomic level and cardiovascular risk using cross-sectional studies 18 . This association tends to vary depending on the socioeconomic indicator used and the phase of the nutritional and epidemiological transition in which the population is. Thus, in developed countries, cardiovascular risk factors are usually found in people with low socioeconomic status 22 , whereas in developing countries it tends to be variable.A cohort study in Brazil reported that participants with high socioeconomic status had a higher risk of overweight and obesity compared to those with low socioeconomic status, and this risk was higher in those who always remained in the high socioeconomic status 21 . This result is consistent with our findings using nationally representative data. Another study that reviewed the association between socioeconomic status, education and obesity in Peruvian women showed that obesity was more frequent in women with higher socioeconomic status; in addition, there was a lower prevalence of obesity in those with higher educational levels 23 .A previous study in Peru, using data from the CRONICAS Cohort Study, reported that the population with higher income and asset index were more likely to have obesity, whereas those with higher levels of education had a lower prevalence of obesity 24 . The apparently contradictory results, using the wealth index and educational level, could be supported by the nutritional transition that the Peruvian population is undergoing. Changes in dietary and physical activity patterns may be initially driven by the economic development of the country, evident in the last 30 years in Peru. Thus, the increase in the prevalence of obesity, hypertension and type 2 diabetes mellitus is due to the availability of processed and high-energy foods, associated with a sedentary lifestyle that is initially seen in the groups with higher economic income, and then affects those with lower income, with a subsequent reduction in the prevalence of these cardiovascular risk factors in the better educated groups 28 . This seems to be clear for obesity, but is not yet visible for hypertension or type 2 diabetes mellitus.Our results seem to be in agreement with previous studies, in which the wealth index, used as a surrogate of socioeconomic level, has a much clearer association with cardiovascular risk factors than educational level. It expands these results to conditions such as hypertension and type 2 diabetes mellitus that have been reported in international studies 29 Previous results suggest that Peru is in a post-transition epidemiological stage, in which more than 80% of deaths are attributed to non-communicable diseases 30 , which may pose a greater challenge to the health system. The interventions generated should be context-specific, especially where people may not have an appropriate access to the health system , which is where the problem of noncommunicable conditions will be at the end of the transition.Our study could help to define prevention strategies aimed at the population most vulnerable to chronic diseases, usually the most economically vulnerable; however, there is still a higher prevalence of these risk factors in those with a medium-high socioeconomic level. Current evidence suggests that the impact of the transition is being observed more in rural areas than in urban areas, especially in the case of obesity. This paper utilized data from a population-based study with regional and national representativeness to assess the associations of interest. However, there are some limitations that should be highlighted. First, as a cross-sectional study, it only allowed us to determine association and not causality between exposures and outcomes. Although temporality could be an issue, the probability of reverse causality in this analysis is almost negligible. Second, diabetes mellitus was assessed by self-report and not objectively. It is known that only 50% of the population with diabetes are aware of their condition, and getting tested probably depends on the socioeconomic status of the individuals, so this could have an effect on the reported associations. However, our findings show similar results to other studies. Finally, other variables of interest, potential confounders such as diet or physical activity levels, were not included because they are not routinely collected by the Endes.In conclusion, there is a differential association between wealth index, educational level and markers of noncommunicable diseases. There is evidence of positive association between wealth index and obesity, hypertension and type 2 diabetes mellitus, while educational level was only negatively associated with obesity. 1 . En Latinoamérica, la situación no es diferente, y a pesar de la heterogeneidad existente, se han incrementado los casos de obesidad y diabetes mellitus tipo 24 .Las enfermedades crónicas no transmisibles, entre las que se encuentran la obesidad, hipertensión y diabetes mellitus tipo 2, están en aumento, especialmente en países de ingresos bajos y medios5 , mientras que la prevalencia de diabetes mellitus tipo 2 llega al 7% a nivel nacional6 , todas con resultados variables entre regiones. Muchas de las enfermedades no transmisibles se ven determinadas por la interacción de factores genéticos y metabólicos, así como de factores de riesgo, como la mala alimentación, los bajos niveles de actividad física y el envejecimiento7 . Todos estos cambios conductuales han sido asociados a la transición epidemiológica y nutricional que incluyen el crecimiento social y económico, la urbanización, la globalización de las tecnologías y la producción de alimentos, implicando cambios en las causas de morbilidad y mortalidad en la población8 .En Perú se estima que el 22,3% de la población sufre de obesidad y que casi el 14% presenta hipertensión arterial según los resultados de la Encuesta Nacional Demográfica y de Salud del 20199 . Este indicador puede ser usado como un surrogado para evaluar la distribución de ciertos factores de riesgo para la salud y dar una idea de la fase de transición en la que determinada población se encuentra10 . Por ejemplo, el incremento en la prevalencia de obesidad ha sido desigual cuando se evalúa según estratos socioeconómicos en diferentes contextos latinoamericanos11 . De otro lado, aunque existen varios indicadores para determinar el nivel socioeconómico, dos de ellos son los más comúnmente usados, incluyendo nivel educativo e índice de bienestar9 .El nivel socioeconómico es una medida total que combina la parte económica y sociológica de la preparación laboral de una personaDe esta manera, el objetivo del presente estudio fue evaluar la asociación a nivel poblacional entre el nivel socioeconómico, evaluado usando el nivel educativo y el índice de bienestar, y la prevalencia de factores de riesgo cardiovascular .12 . La Endes es un estudio de base poblacional con representatividad nacional y regional, que es realizado anualmente por el Instituto Nacional de Estadística e Informática (INEI) en las 25 regiones del Perú, y que incluye variables de pobreza, fertilidad, violencia y salud. Desde el año 2014 se incluyó un módulo específico para enfermedades no transmisibles, y para el presente análisis se utilizó la información del 2018 al 2020.Se realizó un análisis secundario usando la información de la Encuesta Nacional Demográfica y de Salud Familiar (Endes)Los criterios de selección de los participantes fueron tener edad ≥ 20 años y ser capaces de dar consentimiento para su participación en el estudio. Las mujeres gestantes fueron excluidas del presente análisis.12 .El muestreo de la Endes sigue un diseño aleatorio bietápico. En las áreas rurales, las unidades primarias de muestreo son los conglomerados de entre 500 y 2.000 sujetos, mientras que las unidades secundarias de muestreo son las viviendas dentro de los conglomerados. En las áreas urbanas, las unidades primarias de muestreo son las cuadras o grupos de cuadras con más de 2.000 sujetos y un promedio de 140 viviendas, y las unidades secundarias de muestreo son las viviendas como en las áreas rurales13 , asumiendo una distribución de aproximadamente un tercio de la población en cada nivel socioeconómico, y una diferencia en la prevalencia del evento de interés de al menos 5% (p. ej. 14% de obesidad en el nivel socioeconómico inferior versus 19% en el nivel superior) entre grupos a comparar. Con un nivel de confianza del 95%, se obtuvo una potencia mayor al 99% para encontrar las asociaciones de interés.Para calcular la potencia estadística de este estudio se utilizó el programa OpenEpi2según las guías internacionales14 . La presencia de hipertensión se caracterizó por una presión arterial sistólica ≥ 140 mm Hg o diastólica ≥ 90 mm Hg o como autorreporte de diagnóstico previo de acuerdo al JNC-7. Finalmente, y dado que la Endes no colecta muestras de sangre para glucemia en ayunas, la presencia de diabetes mellitus tipo 2 se definió de acuerdo al autorreporte de diagnóstico previo, hecho por un médico.Tres fueron las variables dependientes de interés: obesidad, hipertensión arterial y diabetes mellitus 2. La obesidad se definió como un índice de masa corporal (IMC) ≥ 30 kg/m15 . Todos estos indicadores fueron ponderados, construyéndose un índice de bienestar numérico que fue posteriormente categorizado en terciles  para este análisis.La variable de exposición de interés fue el nivel socioeconómico, evaluado a partir de dos indicadores: nivel educativo e índice de bienestar. En el caso del nivel educativo se usó los años de educación reportado por el participante y luego categorizado en < 7 años (compatible con primaria completa), entre 7 y 11 años (compatible con secundaria completa), y ≥ 12 años (compatible con educación superior). De otro lado, el índice de bienestar es una medida compuesta del estándar de vida en una vivienda. Se calcula en forma sencilla con datos colectados sobre las posesiones y servicios del hogar del encuestado . Este procedimiento se realiza con base en las técnicas del Programa DHS que es casi común a todos los países que participan en dicho programaversus mujer), edad , ámbito geográfico , altitud, definido con base en metros sobre el nivel del mar (msnm) de la zona de residencia del individuo y posteriormente categorizado , y el año en que se realizó la Endes. Esta última variable fue introducida para tomar en cuenta variaciones de resultados debido al efecto de la pandemia del covid-19 en la realización de la encuesta.Otras variables fueron también evaluadas como potenciales confusores de las asociaciones planteadas. Estas incluyeron: sexo  y se tomó en consideración el muestreo complejo del estudio para todas las estimaciones. Inicialmente, se describió a la población según el nivel socioeconómico (nivel educativo e índice de bienestar) y las variables dependientes de interés . Se realizó además una comparación de los valores faltantes por variable y año de la Endes, debido a que se esperaba una mayor pérdida de datos en los marcadores antropométricos en el 2020 debido a la pandemia del covid-19. Para las comparaciones se aplicó la prueba de chi-cuadrado según diseño del estudio con la corrección de segundo orden de Rao-Scott para variables categóricas. Además, se estimó la prevalencia de dichas variables dependientes reportando los intervalos de confianza al 95% (IC95%).Por último, para evaluar la asociación entre nivel socioeconómico (nivel educativo e índice de bienestar) y las variables resultado se crearon modelos crudos y ajustados utilizando la regresión de Poisson, con varianza robusta, reportándose la razón de prevalencia (RP) y sus IC95% correspondientes. Los modelos multivariables fueron ajustados por sexo, edad, ámbito geográfico, altitud y año de la Endes. Se utilizó el valor de inflación de la varianza (VIF) para determinar la presencia de colinealidad debido a las múltiples variables incluidas en el modelo, sin embargo, todos los valores fueron inferiores a 5.La base de datos de la Endes es de acceso público, dicho estudio aseguró la participación voluntaria e informada de los encuestados mediante el consentimiento informado. El protocolo de investigación de este estudio fue revisado y aprobado por el Comité de Ética de la Universidad Científica del Sur cumpliendo con la Declaración de Helsinki (código: 726-2019-PRE15).Un total de 109.363 registros disponibles proceden de las Endes del 2018, 2019 y 2020. Sin embargo, se excluyeron 10.517  registros por no cumplir los criterios de inclusión . Así, se analizaron un total de 98.846 registros, con una media de edad de 45.3 años  y 52.259  mujeres. De importancia, hubo un gran número de datos faltantes en la medición del índice de masa corporal y de la presión arterial en el 2020, debido especialmente a las restricciones de la pandemia por el covid-19.De acuerdo con los resultados de la La prevalencia de obesidad fue del 26% , y esta prevalencia fue mayor en mujeres que en varones , en aquellos entre 40 y 59 años , en los residentes urbanos , en aquellos que vivían por debajo de los 500 msnm , en aquellos con 7–11 años de educación , y en aquellos con mayor índice de bienestar    .De otro lado, la prevalencia de hipertensión arterial fue del 24,9% , con más frecuencia en los varones , en aquellos de mayor edad , en los residentes de áreas urbanas , en aquellos que vivían por debajo de los 500 msnm , en aquellos con menor nivel educativo , en aquellos con mayor índice de bienestar , y en los evaluados en el año 2020    .Finalmente, la prevalencia de diabetes mellitus tipo 2 fue del 4,8% , y fue más frecuente en los participantes de mayor edad , en los residentes de áreas urbanas , en aquellos que vivían por debajo de los 500 msnm , en aquellos con menor nivel educativo , los que tenían un mayor índice de bienestar , y en los evaluados en el año 2020    .En modelo multivariable   , hubo aDe otro lado, en modelo multivariable solo se encontró asociación entre el nivel educativo y obesidad: aquellos con el mayor nivel educativo tenían el 11% menor probabilidad de presentar obesidad  en comparación con los de menor nivel educativo   . Este hEste estudio evidencia una asociación diferencial entre los indicadores de nivel socioeconómico y la presencia de enfermedades no transmisibles: el índice de bienestar estuvo asociado a la presencia de obesidad, hipertensión y diabetes mellitus tipo 2. Así, aquellos con un índice de bienestar medio o alto tuvieron mayor prevalencia de cualquiera de los riesgos cardiovasculares evaluados. Sin embargo, este no fue el caso para el nivel educativo, ya que aquellos con mayor educación tuvieron menor prevalencia de obesidad, pero no las otras condiciones crónicas estudiadas. Finalmente, 1 de cada 4 presentaron obesidad, 1 de cada 5 presentaron hipertensión, y alrededor de 1 de cada 20 tuvieron diabetes mellitus tipo 2.17 . Otros estudios han evaluado la asociación entre nivel socioeconómico y riesgo cardiovascular usando estudios transversales18 . Dicha asociación suele ser variable dependiendo del indicador socioeconómico usado y la fase de la transición nutricional y epidemiológica en el que la población se encuentra. Así, en los países desarrollados, usualmente los factores de riesgo cardiovascular se encuentran en las personas con bajo nivel socioeconómico22 , mientras que en los países en desarrollo suele ser variable.Un estudio de cohorte en Brasil reportó que los participantes con nivel socioeconómico alto tuvieron mayor riesgo de sobrepeso y obesidad comparados con los de nivel bajo, y ese riego fue mayor en aquellos que siempre se mantuvieron en el nivel alto21 . Este resultado coincide con nuestros hallazgos usando datos representativos del país. Otro estudio que analizó la asociación entre nivel socioeconómico, educación y obesidad en mujeres peruanas mostró que la obesidad fue más frecuente en mujeres con posición socioeconómica más alta; además, hubo una menor prevalencia de obesidad en aquellas con mayor nivel educativo23 .Un estudio previo en Perú, usando la cohorte CRONICAS, reportó que la población de mayores ingreso e índice de activos tuvo más chances de tener obesidad, mientras que aquellos con niveles más altos de educación tuvieron una menor prevalencia de obesidad24 . Los resultados, aparentemente contradictorios y usando el índice de bienestar y el nivel educativo, podrían tener sustento en la transición nutricional que sufre la población peruana. Los cambios en patrones de dieta y de actividad física pueden verse empujados inicialmente por el desarrollo económico del país, el cual ha sido patente en los últimos treinta años en Perú. Así, el aumento en la prevalencia de obesidad, hipertensión y diabetes mellitus tipo 2 se debería a la disponibilidad de alimentos procesados y de alto contenido energético, asociado a un estilo de vida sedentario que se ve inicialmente en los grupos con mayor ingreso económico, para luego afectar a los de menores ingresos con la reducción subsecuente de la prevalencia de estos factores de riesgo cardiovascular en los grupos con mejor educación28 . Esto parece ser claro para obesidad, pero no es visible aún para hipertensión o diabetes mellitus tipo 2.Nuestros resultados parecen estar acordes a estudios previos, en que el índice de bienestar, usado como un surrogado del nivel socioeconómico, tiene una más clara asociación con los factores de riesgo cardiovascular que el nivel educativo; y expande estos resultados a condiciones como la hipertensión arterial y diabetes mellitus tipo 2 que han sido reportadas en trabajos internacionales29 . Resultados previos sugieren que el Perú está en una etapa de postransición epidemiológica, en la cual más del 80% de las muertes son atribuidas a enfermedades no transmisibles30 , lo que puede significar un mayor reto al sistema de salud. Las intervenciones generadas deberían ser específicas de cada contexto, especialmente en aquellos sitios donde el sistema de salud puede no tener un acceso apropiado  que es donde el problema de las condiciones no transmisibles terminará alojándose.Nuestro estudio podría ayudar a definir estrategias de prevención dirigidas a la población más vulnerable de tener enfermedades crónicas, usualmente los más vulnerables económicamente; sin embargo, aún existe una mayor prevalencia de estos factores de riesgo en los de nivel socioeconómico medio-alto. La evidencia actual sugiere que el impacto de la transición se está viendo más en las zonas rurales que en las urbanas, especialmente en el caso de obesidadEste trabajo usa información de un estudio de base poblacional con representatividad regional y nacional para evaluar las asociaciones de interés. Sin embargo, hay algunas limitaciones que deben ser resaltadas. Primero, al ser un estudio transversal, solo permitió determinar asociación y no causalidad entre exposiciones y efectos. Aunque la temporalidad podría ser un problema, vemos poco probable que exista causalidad reversa en este análisis. En segundo lugar, diabetes mellitus fue evaluado por autoreporte y no en forma objetiva. Se sabe que solo el 50% de la población con diabetes sabe que tiene el diagnóstico, y realizarse la prueba, probablemente, dependa del nivel socioeconómico de las personas, por lo que esto podría tener un efecto en las asociaciones reportadas. Sin embargo, nuestros hallazgos muestran resultados similares a otros estudios. Finalmente, otras variables de interés, potenciales confusores como la dieta o los niveles de actividad física, no fueron incluidos por no ser colectados en forma rutinaria por la Endes.En conclusión, existe asociación diferencial entre el índice de bienestar, el nivel educativo y los marcadores de enfermedades no transmisibles: hay evidencia de asociación positiva entre el índice de bienestar y obesidad, hipertensión y diabetes mellitus tipo 2, mientras que el nivel educativo solo estuvo asociado de forma negativa a obesidad.


Since CAPES published its revised QUALIS criteria, the Brazilian Medical Association (AMB - Associação Médica Brasileira) has been organizing a series of meetings, held at its headquarters in São Paulo, Brazil, starting in August of 2009, in partnership with the Brazilian Association of Scientific Editors (ABEC - Associação Brasileira de Editores Científicos). These meetings led to the publication of an editorial entitled Classification of journals in the QUALIS System of CAPES – URGENT need of changing the criteria! The editorial was signed by 62 editors of scientific journals and published in full in all of them, and in countless others, primarily in the health sciences, demonstrating that Brazil's periodicals are more and more committed to discussing the problems they share in common.The scientific community remains concerned with the future prospects and direction of Brazilian periodicals.–that the criteria used by CAPES to classify periodicals be revised, adopting the composite impact factor calculated by summing the ISI/JCR and SciELO impact factors;–that a seat on the CAPES Scientific Committee be created for ABEC (the Brazilian Association of Scientific Editors) so that editor can be heard within the process;–that CNPq be requested to create an “Editor's Scholarship” program to support scientific publishing and to be awarded to the editors of journals funded by CNPq. The objective of this project is to improve the quality of these journals by providing their editors with more time to dedicate to their editorial activities;Considering that the criteria have already been set for CAPES' current triennial assessment, the assembled editors decided to draft a second editorial containing a list of suggestions for the next assessment, to be sent to the administration at CAPES. The list of suggestions, which supplement those in the first editorial, is as follows:Additionally, the assembled editors decided to seek support for their criticisms and suggestions from the Brazilian Academy of Sciences (Academia Brasileira de Ciências), from FINEP and from Federal Deputy Eleuses Vieira de Paiva. At a later date the editors will request a detailed breakdown from CNPq of the criteria adopted for, and the results of, the distribution of the resources allocated through the Publishing Support Grants (Editais para Auxílio à Editoração). The editors intend to use these data to construct a database on the annual budgets of Brazilian periodicals, which will be useful for comparative analysis and mutual cooperation. Publication of these two editorials and promotion of discussion is one of our goals in seeking the recognition that Brazilian periodicals both need and deserve.Adagmar AndrioloAlfredo José Afonso BarbosaArnaldo José HernandezAroldo F. CamargosBenedito BarravieraBogdana Victoria KaduncBruno CaramelliCarlos BritesDejair Caitano do NascimentoDomingo M. BraileDov Charles GoldenbergEdmund Chada BaracatEdson MarchioriEduardo de Paula VieiraEros Antônio de AlmeidaFlávia MachadoGeraldo Pereira JotzGianna Mastroianni KirsztajnGilberto CamanhoGustavo GussoIvomar Gomes DuarteIzelda Maria Carvalho CostaJoão Ferreira de Mello JúniorJoel FaintuchJosé Antônio Baddini MartinezJosé Eduardo Ferreira MansoJosé Eulálio Cabral FilhoJosé Heverardo da Costa MontalJosé Luiz Gomes do AmaralJosé Luiz MartinsJurandyr Moreira de AndradeLeonardo SavassiLuiz Augusto CasulariLuiz Eugenio Garcez LemeLuiz Felipe P. MoreiraLuiz Henrique GebrimMarcelo MadeiraMarcelo RibertoMarcus BastosMário Cícero FalcãoMario J. da ConceiçãoMauricio Rocha e SilvaMilton Artur RuizMilton K. ShibataJornal Brasileiro de Patologia e Medicina LaboratorialJornal Brasileiro de Patologia e Medicina LaboratorialRevista Brasileira de Medicina do EsporteRevista FeminaJournal of Venomous Animals and Toxins including Tropical DiseasesSurgical & Cosmetic Dermatology da Soc. Brasileira de DermatologiaRevista da Associação Médica BrasileiraBrazilian Journal of Infectious DiseasesHansenologia InternationalisRevista Brasileira de Cirurgia CardiovascularRevista Brasileira de Cirurgia PlásticaRevista da Associação Médica BrasileiraRevista Radiologia BrasileiraRevista Brasileira de ColoproctologiaRevista da Sociedade Brasileira de Clínica MédicaRevista Brasileira de Terapia IntensivaRevista Brasileira de Cirurgia Cabeça e PescoçoJornal Brasileiro de NefrologiaRevista Brasileira de OrtopediaMedicina Família e ComunidadeRevista de Administração em SaúdeAnais Brasileiros de DermatologiaBrazilian Journal of OtorhinolaryngologyRevista Brasileira de Nutrição ClínicaJornal Brasileiro de PneumologiaRevista do Colégio Brasileiro de CirurgiõesRevista Brasileira de Saúde Materno InfantilRevista da Associação Brasileira de Medicina de TráfegoRevista da Associação Médica BrasileiraArchives of Pediatric SurgeryRevista Brasileira de Ginecologia e ObstetríciaRevista Brasileira de Medicina de Família e ComunidadeBrasilia MédicaGeriatria & GerontologiaArquivos Brasileiros de CardiologiaRevista Brasileira de MastologiaRevista Brasileira de MastologiaRevista Acta FisiátricaJornal Brasileiro de NefrologiaRevista Brasileira de Nutrição ClínicaRevista da Sociedade Brasileira de AnestesiologiaRevista ClinicsRevista Brasileira de Hematologia e HemoterapiaArquivos Brasileiros de NeurocirurgiaMittermayer Barreto SantiagoNelson Adami AndreolloNivaldo AlonsoOsvaldo MalafaiaOlavo Pires de CamargoPaulo Manuel Pêgo FernandesRegina Helena Garcia MartinsRenato Soibelmann ProcianoyRicardo César Pinto AntunesRicardo FullerRicardo Guilherme ViebigRicardo NitriniRogério DedivitisRonaldo DamiãoRosângela MonteiroSergio LianzaSigmar de Mello RodeTarcisio E.P. Barros FilhoWallace ChamonWinston Bonetti YoshidaZuher HandarRevista Brasileira de ReumatologiaArquivos Brasileiros de Cirurgia DigestivaBrazilian Journal of Craniomaxilofacial SurgeryArquivos Brasileiros de Cirurgia DigestivaActa Ortopedica BrasileiraSão Paulo Medical JournalBrazilian Journal of OtorhinolaryngologyJornal de PediatriaRevista da Sociedade Brasileira de CancerologiaRevista Brasileira de ReumatologiaArquivos de GastroenterologiaDementia & NeuropsychologiaRevista Brasileira de Cirurgia Cabeça e PescoçoUrologia ContemporâneaRevista Brasileira de Cirurgia CardiovascularRevista Medicina de ReabilitaçãoBrazilian Oral ResearchActa Ortopedica BrasileiraArquivos Brasileiros de OftalmologiaJornal Vascular BrasileiroRevista Brasileira de Medicina do TrabalhoThis editorial is signed by:


To assess the association of gestational age (GA) and intrauterine growth with body composition at 11 years of age.2), fat-free mass , fat-free mass index  – measured by air displacement plethysmography – and body mass index for age . The exposures of interest were the gestational index (GA) of infants born at less than 33 weeks, from 34 to 36 and from 37 to 41, and intrauterine growth categorized as small (SGA), adequate (AGA) and large (LGA) for gestational age. Analysis of variance was used to compare means and linear regression was used to assess the strength of association. The analyses were adjusted according to variables collected at birth, such as monthly family income, maternal characteristics – education, age, pre-gestational body mass index (BMI), weight gain during pregnancy, smoking during pregnancy, type of delivery, and parity – and adolescent characteristics – skin color and birth weight. For analysis, FM and FMI underwent logarithmic transformation due to data asymmetry. Analysis of data from the 2004 Pelotas birth cohort, whose outcomes were fat mass , fat mass index  than AGA girls. A total of 3,401 adolescents were analyzed, including boys and girls born at less than 33 weeks, with lower FM and FFM means than those born at term. However, in the adjusted analyses, there was no association between GA and any of the outcomes in either sex. LGA boys had a 10.5% higher FMI (p = 0.026) and +0.3 BMI/age Z-score (p = 0.019) as compared to AGA boys, and LGA girls had +0.3 kg/m  GA was not associated with body composition at 11 years of age. However, LGA boys had higher BMI and BMI/age Z-score, and LGA girls had higher FFMI than AGA girls. In Brazil, national surveys have shown, in the last three decades, an increase in the incidence of overweight in children and adolescents between 6 and 19 years, regardless of the macro-region or family income2.The prevalence of overweight in young people has increased in recent decades. In 2016, it was estimated that 340 million children and adolescents aged between 5 and 19 years, worldwide, were overweight or obese” , excess weight and body composition in early life are associated with cardiovascular and metabolic diseases in adulthood3. It is between late childhood and early adolescence that there is a great variation in body composition4, a period considered critical for the beginning of the development of obesity in both sexes5, although the distribution of body fat occurs differently across boys and girls4. In this, in addition to factors related to lifestyle, such as eating habits, physical activity and sedentary behavior, the role of biological factors, such as gestational age (GA), in the occurrence of overweight in childhood and adolescence, has also been investigated8.According to the “Theory of the fetal origin of diseases8and cross-sectional9studies show that the association between GA and body composition, assessed by indirect methods – air displacement plethysmography7and X-ray dual absorption densitometry (Dexa) –9, is still not well established. While some studies have detected lower body fat and fat-free mass (FFM) measurements in preterm infants – compared to full-term infants7–, others have not found an association both in early adolescence, at 11 years9, and at the end of it, at the age of 188.LongitudinalIn addition, the relationship between GA, intrauterine growth and body composition in adolescence is rarely found in the literature. Thus, the aim of this study was to investigate the association of GA and intrauterine growth with body composition in 11-year-old boys and girls in the 2004 Pelotas birth cohort.10. That year, a birth cohort was started that included all newborns from hospital deliveries, which took place between January 1 and December 31, and which followed mothers who lived in the urban area of the municipality and in the Jardim América neighborhood, adjacent to Pelotas. but belonging to the neighboring municipality of Capão do Leão. A total of 4,231 newborns, i.e., 99.2% of all births in the city that year, were included in the study, in addition to standardized interviews conducted with mothers during their hospital stay  to investigate socioeconomic, demographic and behavioral characteristics. The newborns were examined within 24 hours after delivery by the study team, under the supervision of a pediatrician, being followed up at birth, at 3, 12, 24 and 48 months, and at 6 and 11 years of age, with follow-up rates ranging from 87% to 96% – a detailed description of the methodology is provided elsewhere11.The research was developed based on the analysis of data from the birth cohort of Pelotas in 2004, a city with 328,275 inhabitants and located in south BrazilFor the current study, those followed at 11 years of age and who had sufficient information for at least one of the indices analyzed in this research were eligible; twins and those who had some congenital malformation that could interfere with feeding and walking were excluded. Considering this, at 11 years of age, of the 4,231 members of the cohort, 567 were lost or refused to participate in the follow-up, 84 were twins and 81 did not have data on the essential variables for carrying out the research. These losses occurred, for example, in cases in which the adolescent was unable to stand up to check weight and height, which are essential measures for air displacement plethysmography, or when they were unable to stay inside the air chamber. Adding these cases to the 98 deaths that occurred between birth and 11 years of age, there remained the 3,401 children  included in the present analysis.®, Cosmed), performed by specifically trained technicians; a safe, fast and non-invasive method that can be applied in different population groups, such as obese people, children, adults and elderly people12. In order to evaluate it, the participants remained inside the device, a closed chamber, for a few seconds, without moving. To then measure with adequate accuracy and minimize disparities in body volume measurement, it was necessary to eliminate the volume effect of clothing, hair, body surface, and lungs. On this account, participants were provided with sets of rubber caps – such as those for swimming –, shorts and spandex t-shirts, made especially for the study. To predict the volume of thoracic gas, a formula was used based on the participants age, sex and height13and, to estimate body fat and FFM, the Lohman equation was used14.At the 11-year follow-up, body composition was assessed using air displacement plethysmography  in kg/m2 and body mass index (BMI) for age (Z-score). FMI and FFMI were calculated from the ratio, respectively, between FM (kg) and FFM (kg) and height in square meters (m2). BMI/age was calculated as the ratio between weight in kg and height in m2. And age- and sex-specific BMI/age Z-scores were calculated according to growth curves published by the World Health Organization (WHO) in 200615, using the ANTHRO 2007 software.The outcomes of this study were fat mass (FM) in kg, fat mass index (FMI) in kg/m® equipment; height was measured twice, using a Harpenden metal stadiometer  with a maximum height of 2.06 m and an accuracy of 1 mm; finally obtaining the average of these two measurements. In cases where the difference between the two measurements was greater than 1 cm, a third measurement was performed.Weight was assessed using a high-precision scale (0.01 kg), coupled to the BodPod16was used to estimate GA at birth, based on the date of the last menstrual period (LMP) – whenever consistent with weight, length and head circumference at birth, according to the standard curves of these parameters for each week of GA17. If the estimate was unknown or inconsistent, clinical maturity was estimated based on the Dubowitz method18. In this analysis, participants born before 37 weeks of gestation were classified as preterm. Thus, GA was categorized into three groups: those born at less than 33 weeks, which include moderately preterm, very preterm and extremely preterm infants; those at 34 to 36 weeks, late preterm infants; and those at 37 to 41 weeks, term newborns19.The algorithm proposed by the National Center for Health Statistics (NCHS)20 parameters, which include birth weight according to GA and sex and the classification of participants as small for gestational age , adequate for gestational age  and large for gestational age . Birth weight was collected using electronic pediatric scales (Harpenden@) with an accuracy of 10g, checked daily by the research team, and those weighing less than 2,500g were considered low birth weight (LBW).Intrauterine growth was defined according to the of INTERGROWTH-2121. Therefore, family income was obtained by adding the income of people who lived in the same house, in the month before the perinatal interview, and later divided into quintiles 22.The adjustment variables were defined according to a conceptual model created by the authors, based on the literatureained in the perinatal interview, included schooling, collected in complete years with approval and classified into groups from 0 to 4 years, 5 to 8, 9 to 11 and greater than or equal to 12 years; age in complete years, categorized as younger than 20 years old, from 20 to 34 years old, and greater than or equal to 35 years old; smoking, if they smoked at least one cigarette a day, every day, in any trimester of pregnancy; type of delivery, whether normal or cesarean, extracted from hospital records; parity, that is, number of live or dead births, categorized as 0, 1, 2, 3 and greater than or equal to 4; weight at the beginning and end of pregnancy, extracted from the Pregnancy Card, the last record being considered the final weight, or, when not available, the mother’s report; weight gain during pregnancy, calculated as the difference between final and initial weights; pre-pregnancy BMI, classified as underweight (≤ 18.49 kg/m2), adequate (18.5–24.9 kg/m2), overweight (25.0–29.9 kg/m2) or obesity (≥ 30 kg/m2)23; and maternal height, measured at home by the research team, in follow-ups performed three months after delivery.Maternal characteristics, obtFinally, information on the adolescent’s sex was collected directly from the birth medical record; and the color of their skin, self-reported by the mother as white, brown, black or other, information obtained in the follow-ups carried out at the age of six. For girls, in the follow-up of 11 years old, they were asked if they had already had menarche. It is also worth mentioning that, as only 182 mothers reported skin color as “other”, we ended up grouping these adolescents in the “brown” category for analysis purposes.Statistical analyses were performed using Stata version 16.0 . After describing the follow-up sample at 11 years of age, the means and 95% confidence intervals (95%CI) of FM, FMI, FFM, FFMI and BMI/age Z-score were described, according to GA and intrauterine growth, separated by sex; to compare the means, analysis of variance (Anova) was used.As 251 girls (15.4%) had already had menarche, analyses were performed to investigate the existence of an interaction between GA and menarche history on outcomes. As there was no statistical evidence of interaction, all girls were included in the analyses, regardless of menarche history. However, there was an interaction (p < 0.20) between GA and sex on the FFMI outcomes (p = 0.145) and BMI/age Z-score (p = 0.092), causing all analyses to be stratified by sex.The strength of association between the independent variables and the outcomes was verified by linear regression, through beta coefficients (β) and 95%CI; FM and FMI data underwent logarithmic transformation, as they present asymmetric distribution; and the beta coefficients of the raw and adjusted analyses are shown in their exponential form. Thus, the adjusted analyzes were hierarchical, the first level comprising maternal education and age and family income; the second level, pre-gestational BMI, smoking during pregnancy, weight gain during pregnancy, type of delivery and parity; and the third, the adolescent’s skin color and birth weight.Current height was included in the fourth level, but as it is used in the construction of the FMI, FFMI and BMI, in the analyses for these outcomes, height was not included as a potential confounder. Likewise, when the exposure of interest was intrauterine growth, birth weight was not included as a confounder in the adjusted models. Therefore, at each level, the p-value was verified, removing the variables with the highest p-value, one by one, from the analysis model. However, the variables associated with the outcome with p-value <0.20 were kept in the analysis to control for possible confounding effects. In the course of this, a significance level of 5% was assumed in the statistical analyses.All follow-ups of the 2004 Pelotas birth cohort were approved by the Research Ethics Committee of the Faculty of Medicine of the Federal University of Pelotas, affiliated with the National Research Ethics Commission (Conep). Therefore, the children’s parents or legal guardians gave written consent at each follow-up and consent was obtained, also in writing, from participants aged at least 11 years .In the analyzed sample , 51.7%  of the participants were boys, with about 45%  born by cesarean section, 13.5% (n = 430) preterm, and 8.5% (n = 280), SGA . Regardi2, respectively) than those born at term (p < 0.001 and p < 0.001). In girls, only FM was different between the GA categories, with the medians of those born at or less than 33 and at 34 to 36 weeks being 7.6 kg and 9.2 kg smaller, also respectively, compared to those born at term (p = 0.024). The BMI/age Z-score was statistically different only among boys, and it was 0.3 point lower among those born at or less than 33 weeks than in those born at term (p < 0.001).2 less, respectively, in the FM and FMI medians, compared to AGA boys. Among SGA girls, the mean BMI Z-score was 0.3 point lower and the FM and FMI medians were 8.9 kg and 4.1 kg/m2 lower, respectively, than among those born at AGA  (= 0.039) .This study analyzed the association of GA and intrauterine growth with body composition in early adolescence, that is, at 11 years of age. After adjusting for confounders, only FMI and BMI/age Z-score were higher among LGA boys compared to AGA ones; and, among girls, only LGA’s FFMI was higher than that of AGA.9. In the 1993 Pelotas birth cohort, for example, no association was observed between GA and FM, FFM – measured by air displacement plethysmography – or BMI, in both sexes at the end of adolescence, at 18 years of age8. Likewise, a case-control study carried out in Spain found no difference in FM and FFM between preterm and full-term infants aged 7 to 11 years, in the control group assessed by DEXA9.The non-association of GA with outcomes is consistent with the findings of other authors8. Other studies, with participants aged between 8 and 12 years old, in the United Kingdom6, and 11 years old, in England7, both carried out by DEXA, also identified lower body fat contents among adolescents born preterm7.On the other hand, in the same cohort, lower values of FM, IMG, FFM and IMLG, measured by air displacement plethysmography, were found in six-year-old boys born between 34 and 36 weeks, compared to full-term boys24, occur mainly in the last trimester of pregnancy. Thus, termination of pregnancy before 37 weeks would lead to a low reserve of energy and nutrients in preterm infants3.The plausibility of the association between preterm birth and lower body fat in childhood and adolescence is based on the fact that the storage of energy – fat and glycogen – of nutrients, in addition to the greater deposition of MG and FFM24. Therefore, as suggested by Fewtrell et al.6, it is possible that the premature interruption of intrauterine fat deposition in preterm infants has a long-term effect on subsequent body fat gain.The mechanisms responsible for the difference in the body composition of children born preterm are likely to be multifactorial, such as factors that influence the handling or availability of nutrients, concomitant illnesses such as infection and lung disease, and hormonal influences, for example, the use of post-natal corticosteroids8and an inversion of the effect of GA on FM in adulthood8, so that preterm infants have more FM than full-term infants as adults. This reversal of the effect throughout life may be related to mediating mechanisms, such as education level, alcohol consumption, physical activity and eating habits25.Although evidence on the association between GA and body composition in adolescence is still scarce, studies show lower FM among preterm infants in childhood4. Pubertal phenomena show variability in terms of age at onset and termination and the speed and magnitude with which they are expressed, which can influence anthropometric and body composition changes that differ in the process of growth and development in adolescence4. During this period, in both sexes, there is a significant increase in FM, but in boys, it is slower, being overcome by the gain in FFM4.The interactions between GA and sex for the FFMI and BMI/age Z-score, observed in our study, possibly result from the fact that sexual maturation occurs differently across boys and girls26. It is possible that the higher FMI, BMI/age Z-score and FMI of LGA infants reflect a metabolic pattern, which may contribute to the regulation of childhood metabolism and the long-term risk of obesity26. In addition, genetic factors, the intrauterine environment and the postnatal environment probably act in combination, having a greater influence than fetal weight gain on the risk of later obesity27.Regarding intrauterine growth, being born LGA is associated with childhood obesity and metabolic dysfunction in adulthood, both important public health problems in low, medium or high income environments12, used to assess body fat (FM and FMI) and FFM (FFM and FFMI). And, in order to facilitate comparability with other studies and the communication of our findings, the BMI/age Z-score was also evaluated.The strengths of the present study are the use of population data, the low rate of losses or refusals, and the use of air displacement plethysmography, a useful method for assessing body composition in adolescents28.However, it should be considered that air displacement plethysmography is among the limitations of the research, given that it analyzes body density and body composition measurements through the use of equations whose assumptions may not be applicable to all of the cohort participants29. Therefore, body composition results at age 11 could potentially be worse if children born at an earlier GA had survived.In addition, possible survival bias must be considered. The greatest loss to follow-up occurred among individuals born with or less than 33 weeks of GA, and body composition results may have been underestimated, since the small sample size in this group (n = 37 boys and n = 33 girls) reduced the tests’ statistical power to find associations. About 20% of newborns with or less than 33 weeks of GA in the 2004 cohort died before reaching one year of age30.Lastly, other factors can interfere with GA, such as elective cesarean sections and errors in the estimation arising from low-quality or late obstetric ultrasounds, which can overestimate GA in our country by up to 1.8 weeksTherefore, there was no association between GA and body composition measures at 11 years of age, in either sex. On the other hand, FMI and the BMI/age Z-score were higher in boys born LGA, and FFMI, in girls born LGA, compared to their peers born LGA. However, the long-term consequences of being born LGA are not yet well established in the literature. 1. No Brasil, inquéritos nacionais têm mostrado, nas últimas três décadas, o aumento na incidência de excesso de peso em crianças e adolescentes entre 6 e 19 anos, independentemente da macrorregião ou renda familiar2.A prevalência de excesso de peso em jovens tem aumentado nas últimas décadas. Em 2016, foi estimado que 340 milhões de crianças e adolescentes com idades entre 5 e 19 anos, em todo o mundo, estavam acima do peso ou obesasDevelopmental Origins of Health and Disease – DoHaD), o excesso de peso e a composição corporal no início da vida estão associados a doenças cardiovasculares e metabólicas na idade adulta3. É entre o final da infância e início da adolescência que ocorre uma grande variação na composição corporal4, período considerado crítico para o início do desenvolvimento da obesidade em ambos os sexos5, embora a distribuição da gordura no corpo ocorra diferentemente entre meninos e meninas4. Nisso, além dos fatores relacionados ao estilo de vida, como hábitos alimentares, atividade física e comportamento sedentário, o papel de fatores biológicos, como a idade gestacional (IG), na ocorrência de excesso de peso na infância e adolescência, também tem sido investigado8.De acordo com a “Teoria da origem fetal das doenças”  em nascidos a pré-termo – em comparação aos a termo7–, outros não encontraram associação tanto no início da adolescência, aos 11 anos9, quanto no fim dela, aos 18 anos8.Estudos longitudinaisAlém disso, a relação entre IG, crescimento intrauterino e composição do corpo na adolescência é pouco encontrada na literatura. Dessa forma, o objetivo do presente estudo foi investigar a associação da IG e do crescimento intrauterino com a composição corporal em meninos e meninas de 11 anos de idade, na coorte de nascimentos de Pelotas de 2004.10. Nesse ano, foi iniciada uma coorte de nascimentos que incluiu todos os recém-nascidos de partos hospitalares, ocorridos entre primeiro de janeiro e 31 de dezembro, e que acompanhou mães que moravam na área urbana do município e no bairro Jardim América, contíguo a Pelotas mas pertencente ao município vizinho de Capão do Leão. Um total de 4.231 recém-nascidos, ou seja, 99,2% de todos os nascimentos na cidade naquele ano, foram incluídos no estudo, além de entrevistas padronizadas realizadas com as mães durante a estadia no hospital  para investigar características socioeconômicas, demográficas e comportamentais. Os recém-nascidos foram examinados nas primeiras 24 horas após o parto pela equipe do estudo, sob a supervisão de um pediatra, sendo acompanhados ao nascer, aos 3, 12, 24 e 48 meses, e aos 6 e 11 anos de idade, com taxas de acompanhamento variando entre 87% e 96% – uma descrição detalhada da metodologia é fornecida em outra publicação11.A pesquisa se desenvolveu com base na análise de dados da coorte de nascimentos de Pelotas de 2004, cidade com 328.275 habitantes e localizada ao sul do BrasilPara o atual estudo, foram elegíveis aqueles acompanhados aos 11 anos de idade e que tinham informações suficientes para, pelo menos, um dos índices analisados nesta pesquisa; sendo excluídos os gemelares e os que tinham alguma má formação congênita que pudesse interferir na alimentação e deambulação. Considerando isso, aos 11 anos, dos 4.231 membros da coorte, 567 foram perdidos ou recusaram participar do acompanhamento, 84 eram gemelares e 81 não possuíam dados sobre as variáveis essenciais para a realização da pesquisa. Essas perdas ocorreram, por exemplo, por casos em que o adolescente não conseguia ficar em pé para verificação do peso e altura, medidas essenciais para a pletismografia por deslocamento de ar, ou quando não conseguiam ficar dentro da câmera de ar. Somando-se estes casos aos 98 óbitos ocorridos entre o nascimento e os 11 anos de idade, restaram as 3.401 crianças  incluídas na presente análise.®, Cosmed), realizada por técnicos especificamente treinados; um método seguro, rápido e não invasivo, que pode ser aplicado em diferentes grupos populacionais, como obesos, crianças, adultos e idosos12. A fim de avaliá-la, os participantes permaneceram dentro do dispositivo, uma câmara fechada, por alguns segundos, sem se mover. Para, então, medir com precisão adequada e minimizar as disparidades na medição do volume corporal, foi necessário eliminar o efeito do volume de roupas, cabelos, superfície do corpo e pulmões. Em função disso, foram fornecidos aos participantes conjuntos de touca de borracha – como as de natação –, shorts e camisetas de elastano, feitas especialmente para o estudo. Para predição do volume de gás torácico, foi utilizada uma fórmula com base na idade, sexo e altura do participante13 e, para estimar a gordura corporal e a MLG, foi usada a equação de Lohman14.No acompanhamento feito aos 11 anos, a composição corporal foi avaliada por meio de pletismografia por deslocamento de ar  em kg/m2 e índice de massa corporal (IMC) para idade (em escore-Z). O IMG e IMLG foram calculados a partir da razão, respectivamente, entre a MG (kg) e MLG (kg) e a altura em metros quadrados (m2). O IMC/Idade foi calculado pela razão entre o peso em kg e a altura em m2. E os escores-Z de IMC/Idade específicos para sexo e idade, calculados de acordo com as curvas de crescimento publicadas pela Organização Mundial de Saúde (OMS) em 200615, usando o software ANTHRO 2007.Os desfechos deste estudo foram massa gorda (MG) em kg, índice de massa gorda (IMG) em kg/m®; a altura, foi medida duas vezes, utilizando-se estadiômetro de metal Harpenden  com altura máxima de 2,06 m e precisão de 1 mm; obtendo-se enfim a média dessas duas medidas. Nos casos em que a diferença entre as duas aferições foi maior que 1 cm, uma terceira medida era realizada.O peso foi avaliado por meio da balança de alta precisão , acoplada ao equipamento BodPodNational Center for Health Statistics (NCHS)16 foi utilizado para estimar a IG ao nascimento, baseada na data da última menstruação (DUM) – sempre que consistente com o peso, comprimento e perímetro cefálico ao nascer, de acordo com as curvas padrão desses parâmetros para cada semana de IG17. Caso a estimativa fosse desconhecida ou inconsistente, estimou-se a maturidade clínica baseada no método de Dubowitz18. Nessa análise, os participantes nascidos antes das 37 semanas de gestação foram classificados como prematuros. Assim, a IG foi categorizada em três grupos: os que nasceram com menos de 33 semanas, que incluem os moderadamente prematuros, muito prematuros e extremamente prematuros; os de 34 a 36 semanas, os prematuros tardios; e os de 37 a 41semanas os recém-nascidos a termo19.O algoritmo proposto pelo 20, que incluem o peso ao nascer conforme a IG e sexo e a classificação dos participantes em pequenos para a idade gestacional , adequado para a idade gestacional  e grande para a idade gestacional . O peso ao nascer foi coletado usando balanças pediátricas eletrônicas (Harpenden@) com uma precisão de 10g, verificada diariamente pela equipe de pesquisa, sendo considerados de baixo peso ao nascer (BPN) aqueles com menos de 2.500g.O crescimento intrauterino foi definido de acordo com os parâmetros do INTERGROWTH-2121. Sendo assim, a renda familiar foi obtida pela soma das rendas das pessoas que moravam na mesma casa, no mês anterior à entrevista perinatal, e posteriormente dividida em quintis 22.As variáveis de ajuste foram definidas de acordo com um modelo conceitual criado pelos autores, com base na literatura2), adequado , sobrepeso  ou obesidade (≥ 30 kg/m2)23; e a altura materna, medida no domicílio pela equipe de pesquisa, nos acompanhamentos realizados três meses após o parto.Já as características maternas, obtidas na entrevista perinatal, incluíram a escolaridade, coletada em anos completos com aprovação e classificada em grupos de 0 a 4 anos, de 5 a 8, de 9 a 11 e maior ou igual a 12 anos; idade em anos completos, categorizada em menor de 20 anos, de 20 a 34, e maior ou igual a 35 anos; tabagismo, se fumavam pelo menos um cigarro por dia, todos os dias, em qualquer trimestre da gestação; tipo de parto, se normal ou cesariana, extraído dos registros hospitalares; paridade, ou seja, número de nascimentos vivos ou mortos, categorizada em 0, 1, 2, 3 e maior ou igual a 4; peso no início e final da gestação, extraído da Carteira da Gestante, sendo o último registro considerado o peso final, ou, quando não disponível, o relato da mãe; ganho de peso na gestação, calculado pela diferença entre os pesos final e inicial; IMC pré-gestacional, classificado como baixo peso . Após descrevida a amostra do acompanhamento aos 11 anos de idade, foram descritos as médias e os intervalos de confiança de 95% (IC95%) de MG, IMG, MLG, IMLG e escore-Z de IMC/Idade, de acordo com a IG e o crescimento intrauterino, separando-se por sexo; para comparar as médias, usou-se a análise de variância (Anova).Como 251 meninas  já haviam tido a menarca, foram realizadas análises para investigar a existência de interação entre a IG e o histórico de menarca sobre os desfechos. Como não houve evidência estatística de interação, todas as meninas foram incluídas nas análises, independentemente do histórico de menarca. Entretanto, houve interação  entre IG e sexo sobre os desfechos IMLG  e escore-Z de IMC/Idade , fazendo com que todas as análises fossem estratificadas por sexo.A força de associação entre as variáveis independentes e os desfechos foi verificada por regressão linear, através dos coeficientes beta (β) e IC95%; os dados de MG e IMG sofreram transformação logarítmica, por apresentarem distribuição assimétrica; e os coeficientes beta das análises bruta e ajustada são mostrados em sua forma exponencial. Assim, as análises ajustadas foram hierarquizadas, sendo o primeiro nível composto pelas escolaridade e idade maternas e pela renda familiar; o segundo nível, por IMC pré-gestacional, tabagismo na gravidez, ganho de peso na gestação, tipo de parto e paridade; e o terceiro, pela cor da pele do adolescente e peso ao nascer.A altura atual foi incluída no quarto nível, mas como ela é utilizada na construção do IMG, IMLG e IMC, nas análises para esses desfechos, a altura não foi incluída como um potencial fator de confusão. Da mesma forma, quando a exposição de interesse era o crescimento intrauterino, o peso ao nascer não foi incluído como fator de confusão nos modelos ajustados. Logo, em cada nível, o valor de p foi verificado, sendo removidas as variáveis com o maior valor de p, uma a uma, do modelo de análise. No entanto, foram mantidas na análise, para controle de possíveis efeitos de confusão, as variáveis associadas ao desfecho com valor de p < 0,20. Ao decorrer disso, foi assumido um nível de significância igual a 5% nas análises estatísticas.Todos os acompanhamentos da coorte de nascimentos de Pelotas de 2004 foram aprovados pelo Comitê de Ética em Pesquisa da Faculdade de Medicina da Universidade Federal de Pelotas, afiliado à Comissão Nacional de Ética em Pesquisa (CONEP). Sendo assim, os pais ou responsáveis legais pelas crianças deram consentimento por escrito em cada acompanhamento e foi obtido, também por escrito, o assentimento dos participantes com pelo menos 11 anos de idade (ofício número 889.753).Na amostra analisada (n = 3.401), 51,7% (n = 1.757) dos participantes eram meninos, sendo cerca de 45% (n = 1.523) nascidos por cesariana, 13,5% (n = 430), pré-termo, e 8,5% (n = 280), PIG . Em rela2 menores, respectivamente, do que os nascidos a termo . Nas meninas, apenas a MG foi diferente entre as categorias de IG, sendo as medianas das nascidas com ou menos de 33 e com 34 a 36 semanas, 7,6 kg e 9,2 kg menores, também respectivamente, em comparação às nascidas a termo . O escore-Z de IMC/Idade foi estatisticamente diferente somente entre os meninos, sendo 0,3 ponto menor entre os nascidos com ou menos de 33 semanas, em relação aos nascidos a termo .A 2, respectivamente, nas medianas de MG e IMG, em comparação aos AIG. Entre as meninas PIG, a média de escore-Z de IMC foi 0,3 ponto menor e as medianas de MG e IMG, 8,9 kg e 4,1 kg/m2 menores, respectivamente, do que entre as nascidas AIG   .Este estudo analisou a associação da IG e do crescimento intrauterino com a composição corporal no início da adolescência, ou seja, aos 11 anos de idade. Após ajustes para fatores de confusão, apenas o IMG e escore-Z de IMC/Idade foram maiores entre meninos nascidos GIG, em comparação aos AIG; e, entre as meninas, apenas o IMLG das nascidas GIG foi mais elevado do que o das AIG.9. Na coorte de nascimentos de Pelotas de 1993, por exemplo, não foi observada associação entre IG e MG, MLG – mensurados pela pletismografia de deslocamento de ar – ou IMC, em ambos os sexos ao final da adolescência, aos 18 anos de idade8. Da mesma forma, um estudo de caso-controle, realizado na Espanha, não encontrou diferença nas MG e MLG entre os nascidos pré-termo e a termo de sete a 11 anos, grupo controle avaliado pelo Dexa9.A não associação verificada da IG com os desfechos é consistente com os achados de outros autores8. Outros estudos, com participantes de 8 a 12 anos de idade, no Reino Unido6, e de 11 anos, na Inglaterra7, ambos realizados pelo Dexa, também identificaram menores conteúdos de gordura corporal entre adolescentes nascidos pré-termo7.Em contrapartida, na mesma coorte, foram verificados menores valores de MG, IMG, MLG e IMLG, medidos por pletismografia por deslocamento de ar, nos meninos de seis anos de idade nascidos entre 34 e 36 semanas, isso em comparação aos a termo24, ocorrem principalmente no último trimestre da gestação. Desse modo, a interrupção da gestação antes das 37 semanas levaria a uma baixa reserva de energia e nutrientes nos nascidos prematuros3.A plausibilidade da associação entre nascimento pré-termo e menor gordura corporal na infância e adolescência baseia-se no fato de que o armazenamento de energia – gordura e glicogênio –, de nutrientes, além da maior deposição de MG e MLG24. Logo, como sugerido por Fewtrell et al.6, é possível que a interrupção prematura da deposição de gordura intraútero nos nascidos pré-termo tenha efeito de longo prazo no ganho subsequente de gordura corporal.Os mecanismos responsáveis pela diferença na composição do corpo de crianças nascidas prematuras são provavelmente multifatoriais, como fatores que influenciam o manuseio ou a disponibilidade de nutrientes, doenças concomitantes, como infecção e doenças pulmonares, e influências hormonais, por exemplo, o uso de corticosteroides pós-natais8 e uma inversão do efeito da IG sobre a MG na idade adulta8, de modo que os nascidos prematuros apresentam mais MG em relação aos nascidos a termo quando adultos. Essa inversão de efeito ao longo da vida pode estar relacionada a mecanismos mediadores, como o nível de escolaridade, o consumo de álcool, atividade física e hábitos alimentares25.Embora a evidência sobre a associação entre IG e composição do corpo na adolescência ainda seja escassa, estudos mostram menor MG entre os pré-termo na infância4. Os fenômenos puberais apresentam variabilidade quanto às idades de início e término e à velocidade e magnitude com que se expressam, o que pode influenciar as mudanças antropométricas e de composição corporal que se diferenciam no processo de crescimento e desenvolvimento da adolescência4. Nesse período, em ambos os sexos ocorre significativo aumento da MG, mas, nos meninos, ele é mais lento, sendo superado pelo ganho em MLG4.As interações entre IG e sexo para o IMLG e escore-Z de IMC/Idade, observadas em nosso estudo, possivelmente decorrem do fato de que a maturação sexual ocorre de forma diferente entre meninos e meninas26. É possível que os maiores IMG, escore-Z de IMC/Idade e IMLG dos nascidos GIG reflitam um padrão metabólico, podendo contribuir para a regulação do metabolismo na infância e o risco de obesidade a longo prazo26. Além disso, fatores genéticos, o ambiente intraútero e o ambiente pós-natal provavelmente atuam em combinação, tendo maior influência do que o ganho de peso fetal, no risco de obesidade posterior27.Em relação ao crescimento intrauterino, nascer GIG está associado à obesidade infantil e disfunção metabólica na idade adulta, ambos importantes problemas de saúde pública, em ambientes de baixa, média ou alta renda12, utilizado na avaliação da gordura corporal (MG e IMG) e da MLG (MLG e IMLG). E, visando facilitar a comparabilidade com outros estudos e a comunicação de nossos achados, também foi avaliado o escore-Z de IMC/Idade.Os pontos fortes do presente estudo estão no uso de dados populacionais, na baixa taxa de perdas ou recusas e no uso de pletismografia por deslocamento de ar, um método proveitoso para a avaliação da composição corporal em adolescentes28.Contudo, deve-se considerar que a pletismografia por deslocamento de ar está entre as limitações da pesquisa, haja vista que ela analisa a densidade corporal e as medidas de composição corporal por meio do uso de equações cujos pressupostos podem não ser aplicáveis a todos os participantes da coorte29. Logo, resultados da composição corporal aos 11 anos poderiam ser potencialmente piores, se crianças nascidas em IG mais precoces tivessem sobrevivido.Além disso, o possível viés de sobrevivência deve ser considerado. A maior perda de acompanhamento ocorreu entre os indivíduos nascidos com ou menos de 33 semanas de IG, podendo estar subestimados os resultados de composição corporal, visto que o pequeno tamanho amostral nesse grupo (n = 37 meninos e n = 33 meninas) reduziu o poder estatístico dos testes para encontrar associações. Cerca de 20% dos recém-nascidos com ou menos de 33 semanas de IG da coorte de 2004 morreram antes de completar um ano de idade30.Em último lugar, outros fatores podem interferir na IG, como cesarianas eletivas e erros na estimativa oriunda de ultrassons obstétricos de baixa qualidade ou realizados tardiamente, os quais podem superestimar a IG em nosso meio em até 1,8 semanaPortanto, não houve associação entre a IG e as medidas de composição corporal aos 11 anos, em ambos os sexos. Já o IMG e o escore-Z de IMC/Idade foram maiores nos meninos nascidos GIG, e o IMLG, nas meninas nascidas GIG, em comparação a seus pares nascidos AIG. Contudo, as consequências a longo prazo por se nascer GIG ainda não estão bem estabelecidas na literatura.