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Campos TADM, Mariz VG, Mulder AP, Curioni CC, Bezerra FF. Adequacy of basal metabolic rate prediction equations in individuals with severe obesity: A systematic review and meta-analysis. Obes Rev 2024; 25:e13739. [PMID: 38548479 DOI: 10.1111/obr.13739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2023] [Revised: 02/22/2024] [Accepted: 02/26/2024] [Indexed: 05/14/2024]
Abstract
The determination of energy requirements in clinical practice is based on basal metabolic rate (BMR), frequently predicted by equations that may not be suitable for individuals with severe obesity. This systematic review and meta-analysis examined the accuracy and precision of BMR prediction equations in adults with severe obesity. Four databases were searched in March 2021 and updated in May 2023. Eligible studies compared BMR prediction equations with BMR measured by indirect calorimetry. Forty studies (age: 28-55 years, BMI: 40.0-62.4 kg/m2) were included, most of them with a high risk of bias. Studies reporting bias (difference between estimated and measured BMR) were included in the meta-analysis (n = 20). Six equations were meta-analyzed: Harris & Benedict (1919); WHO (weight) (1985); Owen (1986); Mifflin (1990); Bernstein (1983); and Cunningham (1980). The most accurate and precise equations in the overall analysis were WHO (-12.44 kcal/d; 95%CI: -81.4; 56.5 kcal/d) and Harris & Benedict (-18.9 kcal/d; 95%CI -73.2; 35.2 kcal/d). All the other equations tended to underestimate BMR. Harris & Benedict and WHO were the equations with higher accuracy and precision in predicting BMR in individuals with severe obesity. Additional analyses suggested that equations may perform differently according to obesity BMI ranges, which warrants further investigation.
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Milani GP, Silano M, Mazzocchi A, Bettocchi S, De Cosmi V, Agostoni C. Personalized nutrition approach in pediatrics: a narrative review. Pediatr Res 2021; 89:384-388. [PMID: 33230198 DOI: 10.1038/s41390-020-01291-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Revised: 11/07/2020] [Accepted: 11/10/2020] [Indexed: 01/30/2023]
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.
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Affiliation(s)
- Gregorio P Milani
- Department of Clinical Sciences and Community Health, University of Milan, 20122, Milan, Italy.,Pediatric Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, 20122, Milan, Italy
| | - Marco Silano
- Unit of Human Nutrition and Health, Department of Food Safety Nutrition and Veterinary Public Health, Istituto Superiore di Sanità, 00161, Rome, Italy
| | - Alessandra Mazzocchi
- Department of Clinical Sciences and Community Health, University of Milan, 20122, Milan, Italy
| | - Silvia Bettocchi
- Pediatric Intermediate Care Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, 20122, Milan, Italy
| | - Valentina De Cosmi
- Department of Clinical Sciences and Community Health, University of Milan, 20122, Milan, Italy. .,Pediatric Intermediate Care Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, 20122, Milan, Italy.
| | - Carlo Agostoni
- Department of Clinical Sciences and Community Health, University of Milan, 20122, Milan, Italy.,Pediatric Intermediate Care Unit, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, 20122, Milan, Italy
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