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      Development and validation of prognostic models to estimate body weight loss in overweight and obese people Translated title: Desarrollo y validación de modelos de pronóstico para estimar la pérdida de peso corporal en personas con sobrepeso y obesidad

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          Abstract

          Abstract Background: predicting weight loss outcomes from information collected from subjects before they start a weight management program is an objective strongly pursued by scientists who study energy balance. Objective: to develop and validate two prognostic models for the estimation of final body weight after a six-month intervention period. Material and methods: the present work was developed following the TRIPOD standard to report prognostic multivariable prediction models. A multivariable linear regression analysis was applied to 70 % of participants to identify the most relevant variables and develop the best prognostic model for body weight estimation. Then, 30 % of the remaining sample was used to validate the model. The study involved a 6-month intervention based on 25-30 % caloric restriction and exercise. A total of 239 volunteers who had participated in the PRONAF study, aged 18 to 50 years, with overweight or obesity (body mass index: 25-34.9 kg/m2), were enrolled. Body composition was estimated by dual-energy X-ray absorptiometry (DXA) and by hand-to-foot bioelectrical impedance (BIA) analysis. Results: prognostic models were developed and validated with a high correlation (0.954 and 0.951 for DXA and BIA, respectively), with the paired t-tests showing no significant differences between estimated and measured body weights. The mean difference, standard error, and 95 % confidence interval of the DXA model were 0.067 ± 0.547 (-1.036-1.170), and those of the BIA model were -0.105 ± 0.511 (-1.134-0.924). Conclusions: the models developed in this work make it possible to calculate the final BW of any participant engaged in an intervention like the one employed in this study based only on baseline body composition variables.

          Translated abstract

          Resumen Antecedentes: predecir los resultados de la pérdida de peso a partir de la información recogida de los sujetos antes de que empiecen los programas de control de peso es un objetivo a largo plazo. Objetivo: desarrollar y validar un modelo de pronóstico para la estimación del peso corporal final después de un período de intervención de seis meses. Material y métodos: el presente trabajo se desarrolló siguiendo el estándar TRIPOD para reportar modelos pronósticos de predicción multivariable. El análisis de regresión lineal multivariable se aplicó al 70 % de los participantes para identificar las variables más relevantes y desarrollar el mejor modelo pronóstico para la estimación del peso corporal. Luego, el 30 % restante se utilizó para validar el modelo. Se realizó una intervención de 6 meses basada en la restricción calórica y el ejercicio. Los participantes fueron 239 voluntarios que habían participado en el estudio PRONAF, de 18 a 50 años de edad y con sobrepeso u obesidad (índice de masa corporal: 25-34,9 kg/m2). La composición corporal se evaluó mediante la absorción de rayos X de energía dual y el análisis de la impedancia bioeléctrica de mano a pie. Resultados: los modelos desarrollados se calibraron y validaron con una alta correlación (más de 0,94), no mostrando las pruebas t emparejadas diferencias significativas entre los pesos corporales estimados y los medidos. Conclusiones: los modelos desarrollados en este trabajo permiten calcular el peso corporal final de cualquier participante que participe en una intervención como las empleadas en este estudio, conociendo únicamente sus variables de composición corporal iniciales.

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          Most cited references39

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          Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration

          The TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) Statement includes a 22-item checklist, which aims to improve the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. This explanation and elaboration document describes the rationale; clarifies the meaning of each item; and discusses why transparent reporting is important, with a view to assessing risk of bias and clinical usefulness of the prediction model. Each checklist item of the TRIPOD Statement is explained in detail and accompanied by published examples of good reporting. The document also provides a valuable reference of issues to consider when designing, conducting, and analyzing prediction model studies. To aid the editorial process and help peer reviewers and, ultimately, readers and systematic reviewers of prediction model studies, it is recommended that authors include a completed checklist in their submission. The TRIPOD checklist can also be downloaded from www.tripod-statement.org.
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            Quantification of the effect of energy imbalance on bodyweight.

            Obesity interventions can result in weight loss, but accurate prediction of the bodyweight time course requires properly accounting for dynamic energy imbalances. In this report, we describe a mathematical modelling approach to adult human metabolism that simulates energy expenditure adaptations during weight loss. We also present a web-based simulator for prediction of weight change dynamics. We show that the bodyweight response to a change of energy intake is slow, with half times of about 1 year. Furthermore, adults with greater adiposity have a larger expected weight loss for the same change of energy intake, and to reach their steady-state weight will take longer than it would for those with less initial body fat. Using a population-averaged model, we calculated the energy-balance dynamics corresponding to the development of the US adult obesity epidemic. A small persistent average daily energy imbalance gap between intake and expenditure of about 30 kJ per day underlies the observed average weight gain. However, energy intake must have risen to keep pace with increased expenditure associated with increased weight. The average increase of energy intake needed to sustain the increased weight (the maintenance energy gap) has amounted to about 0·9 MJ per day and quantifies the public health challenge to reverse the obesity epidemic. Copyright © 2011 Elsevier Ltd. All rights reserved.
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              2 years of calorie restriction and cardiometabolic risk (CALERIE): exploratory outcomes of a multicentre, phase 2, randomised controlled trial

              For several cardiometabolic risk factors, values considered within normal range are associated with an increased risk of cardiovascular morbidity and mortality. We aimed to investigate the short-term and long-term effects of calorie restriction with adequate nutrition on these risk factors in healthy, lean, or slightly overweight young and middle-aged individuals.
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                Author and article information

                Journal
                nh
                Nutrición Hospitalaria
                Nutr. Hosp.
                Grupo Arán (Madrid, Madrid, Spain )
                0212-1611
                1699-5198
                June 2021
                : 38
                : 3
                : 511-518
                Affiliations
                [1] Madrid Madrid orgnameUniversidad Politécnica de Madrid orgdiv1Facultad de Ciencias de la Actividad Física y el Deporte-INEF orgdiv2LFE Research Group. Department of Health and Human Performance Spain
                Article
                S0212-16112021000300511 S0212-1611(21)03800300511
                10.20960/nh.03425
                81ab7512-59ff-437e-aef9-e97142b64e56

                This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

                History
                : 02 February 2021
                : 06 November 2020
                Page count
                Figures: 0, Tables: 0, Equations: 0, References: 40, Pages: 8
                Product

                SciELO Spain

                Categories
                Original Papers

                Body composition,DXA,BIA,Intervención dietética,Intervención de ejercicio,Composición corporal,Dietary intervention,Exercise intervention

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