Emmanuel Disse1, Séverine Ledoux2, Cécile Bétry3, Cyrielle Caussy4, Christine Maitrepierre5, Muriel Coupaye2, Martine Laville4, Chantal Simon4. 1. Centre Intégré de l'Obésité Rhône-Alpes, Fédération Hospitalo-Universitaire DO-iT, Department of Endocrinology and Nutrition, Groupement Hospitalier Sud, Hospices Civils de Lyon, Lyon, France; Centre de Recherche en Nutrition Humaine Rhône-Alpes (CRNH-RA), Centre Européen Nutrition et Santé (CENS), Lyon, France; Laboratoire CarMeN, Unité INSERM U1060 - INRA 1235 - INSA-Lyon, Université Claude Bernard Lyon 1, Lyon, France. Electronic address: emmanuel.disse@chu-lyon.fr. 2. Centre Intégré Nord Francilien de l'Obésité (CINFO), Service des Explorations Fonctionnelles, Centre de référence de prise en charge de l'obésité, Hôpital Louis Mourier (AP-HP), Université Paris Diderot, Sorbonne Paris Cité, France. 3. Centre Intégré de l'Obésité Rhône-Alpes, Fédération Hospitalo-Universitaire DO-iT, Department of Endocrinology and Nutrition, Groupement Hospitalier Sud, Hospices Civils de Lyon, Lyon, France. 4. Centre Intégré de l'Obésité Rhône-Alpes, Fédération Hospitalo-Universitaire DO-iT, Department of Endocrinology and Nutrition, Groupement Hospitalier Sud, Hospices Civils de Lyon, Lyon, France; Centre de Recherche en Nutrition Humaine Rhône-Alpes (CRNH-RA), Centre Européen Nutrition et Santé (CENS), Lyon, France; Laboratoire CarMeN, Unité INSERM U1060 - INRA 1235 - INSA-Lyon, Université Claude Bernard Lyon 1, Lyon, France. 5. Centre de Recherche en Nutrition Humaine Rhône-Alpes (CRNH-RA), Centre Européen Nutrition et Santé (CENS), Lyon, France.
Abstract
BACKGROUND & AIMS: The resting energy expenditure (REE) determination is important in nutrition for adequate dietary prescription. The gold standard i.e. indirect calorimetry is not available in clinical settings. Thus, several predictive equations have been developed, but they lack of accuracy in subjects with extreme weight including obese populations. Artificial neural networks (ANN) are useful predictive tools in the area of artificial intelligence, used in numerous clinical fields. The aim of this study was to determine the relevance of ANN in predicting REE in obesity. METHODS: A Multi-Layer Perceptron (MLP) feed-forward neural network with a back propagation algorithm was created and cross-validated in a cohort of 565 obese subjects (BMI within 30-50 kg m-2) with weight, height, sex and age as clinical inputs and REE measured by indirect calorimetry as output. The predictive performances of ANN were compared to those of 23 predictive REE equations in the training set and in two independent sets of 100 and 237 obese subjects for external validation. RESULTS: Among the 23 established prediction equations for REE evaluated, the Harris & Benedict equations recalculated by Roza were the most accurate for the obese population, followed by the USA DRI, Müller and the original Harris & Benedict equations. The final 5-fold cross-validated three-layer 4-3-1 feed-forward back propagation ANN model developed in that study improved precision and accuracy of REE prediction over linear equations (precision = 68.1%, MAPE = 8.6% and RMSPE = 210 kcal/d), independently from BMI subgroups within 30-50 kg m-2. External validation confirmed the better predictive performances of ANN model (precision = 73% and 65%, MAPE = 7.7% and 8.6%, RMSPE = 187 kcal/d and 200 kcal/d in the 2 independent datasets) for the prediction of REE in obese subjects. CONCLUSIONS: We developed and validated an ANN model for the prediction of REE in obese subjects that is more precise and accurate than established REE predictive equations independent from BMI subgroups. For convenient use in clinical settings, we provide a simple ANN-REE calculator available at: https://www.crnh-rhone-alpes.fr/fr/ANN-REE-Calculator.
BACKGROUND & AIMS: The resting energy expenditure (REE) determination is important in nutrition for adequate dietary prescription. The gold standard i.e. indirect calorimetry is not available in clinical settings. Thus, several predictive equations have been developed, but they lack of accuracy in subjects with extreme weight including obese populations. Artificial neural networks (ANN) are useful predictive tools in the area of artificial intelligence, used in numerous clinical fields. The aim of this study was to determine the relevance of ANN in predicting REE in obesity. METHODS: A Multi-Layer Perceptron (MLP) feed-forward neural network with a back propagation algorithm was created and cross-validated in a cohort of 565 obese subjects (BMI within 30-50 kg m-2) with weight, height, sex and age as clinical inputs and REE measured by indirect calorimetry as output. The predictive performances of ANN were compared to those of 23 predictive REE equations in the training set and in two independent sets of 100 and 237 obese subjects for external validation. RESULTS: Among the 23 established prediction equations for REE evaluated, the Harris & Benedict equations recalculated by Roza were the most accurate for the obese population, followed by the USA DRI, Müller and the original Harris & Benedict equations. The final 5-fold cross-validated three-layer 4-3-1 feed-forward back propagation ANN model developed in that study improved precision and accuracy of REE prediction over linear equations (precision = 68.1%, MAPE = 8.6% and RMSPE = 210 kcal/d), independently from BMI subgroups within 30-50 kg m-2. External validation confirmed the better predictive performances of ANN model (precision = 73% and 65%, MAPE = 7.7% and 8.6%, RMSPE = 187 kcal/d and 200 kcal/d in the 2 independent datasets) for the prediction of REE in obese subjects. CONCLUSIONS: We developed and validated an ANN model for the prediction of REE in obese subjects that is more precise and accurate than established REE predictive equations independent from BMI subgroups. For convenient use in clinical settings, we provide a simple ANN-REE calculator available at: https://www.crnh-rhone-alpes.fr/fr/ANN-REE-Calculator.
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Authors: Valentina De Cosmi; Alessandra Mazzocchi; Gregorio Paolo Milani; Edoardo Calderini; Silvia Scaglioni; Silvia Bettocchi; Veronica D'Oria; Thomas Langer; Giulia C I Spolidoro; Ludovica Leone; Alberto Battezzati; Simona Bertoli; Alessandro Leone; Ramona Silvana De Amicis; Andrea Foppiani; Carlo Agostoni; Enzo Grossi Journal: J Clin Med Date: 2020-04-05 Impact factor: 4.241