Dietary habits represent the main determinant of health. Although extensive research has been conducted to modify unhealthy dietary behaviors across the lifespan, obesity and obesity-associated comorbidities are increasingly observed worldwide. Individually tailored interventions are nowadays considered a promising frontier for nutritional research. In this narrative review, the technologies of importance in a pediatric clinical setting are discussed. The first determinant of the dietary balance is represented by energy intakes matching individual needs. Most emerging studies highlight the opportunity to reconsider the widely used prediction equations of resting energy expenditure. Artificial Neural Network approaches may help to disentangle the role of single contributors to energy expenditure. Artificial intelligence is also useful in the prediction of the glycemic response, based on the individual microbiome. Other factors further concurring to define individually tailored nutritional needs are metabolomics and nutrigenomic. Since most available data come from studies in adult groups, new efforts should now be addressed to integrate all these aspects to develop comprehensive and—above all—effective interventions for children. Impact: Personalized dietary advice, specific to individuals, should be more effective in the prevention of chronic diseases than general recommendations about diet.Artificial Neural Networks algorithms are technologies of importance in a pediatric setting that may help practitioners to provide personalized nutrition.Other approaches to personalized nutrition, while promising in adults and for basic research, are still far from practical application in pediatrics.

Personalized nutrition approach in pediatrics: a narrative review / G.P. Milani, M. Silano, A. Mazzocchi, S. Bettocchi, V. De Cosmi, C. Agostoni. - In: PEDIATRIC RESEARCH. - ISSN 0031-3998. - (2020). [Epub ahead of print] [10.1038/s41390-020-01291-8]

Personalized nutrition approach in pediatrics: a narrative review

G.P. Milani;A. Mazzocchi;V. De Cosmi;C. Agostoni
2020

Abstract

Dietary habits represent the main determinant of health. Although extensive research has been conducted to modify unhealthy dietary behaviors across the lifespan, obesity and obesity-associated comorbidities are increasingly observed worldwide. Individually tailored interventions are nowadays considered a promising frontier for nutritional research. In this narrative review, the technologies of importance in a pediatric clinical setting are discussed. The first determinant of the dietary balance is represented by energy intakes matching individual needs. Most emerging studies highlight the opportunity to reconsider the widely used prediction equations of resting energy expenditure. Artificial Neural Network approaches may help to disentangle the role of single contributors to energy expenditure. Artificial intelligence is also useful in the prediction of the glycemic response, based on the individual microbiome. Other factors further concurring to define individually tailored nutritional needs are metabolomics and nutrigenomic. Since most available data come from studies in adult groups, new efforts should now be addressed to integrate all these aspects to develop comprehensive and—above all—effective interventions for children. Impact: Personalized dietary advice, specific to individuals, should be more effective in the prevention of chronic diseases than general recommendations about diet.Artificial Neural Networks algorithms are technologies of importance in a pediatric setting that may help practitioners to provide personalized nutrition.Other approaches to personalized nutrition, while promising in adults and for basic research, are still far from practical application in pediatrics.
Resting energy-expenditure; predictive equations; glucose-tolerance; birth-weight; obesity; nutrigenomics; microbiome; risk; overweight; accuracy
Settore MED/38 - Pediatria Generale e Specialistica
23-nov-2020
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/809714
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