Challenging obesity and sex based differences in resting energy expenditure using allometric modeling, a sub-study of the DIETFITS clinical trial.


Journal

Clinical nutrition ESPEN
ISSN: 2405-4577
Titre abrégé: Clin Nutr ESPEN
Pays: England
ID NLM: 101654592

Informations de publication

Date de publication:
02 2023
Historique:
received: 15 11 2022
accepted: 21 11 2022
entrez: 19 1 2023
pubmed: 20 1 2023
medline: 24 1 2023
Statut: ppublish

Résumé

Resting energy expenditure (REE) is a major component of energy balance. While REE is usually indexed to total body weight (BW), this may introduce biases when assessing REE in obesity or during weight loss intervention. The main objective of the study was to quantify the bias introduced by ratiometric scaling of REE using BW both at baseline and following weight loss intervention. Participants in the DIETFITS Study (Diet Intervention Examining The Factors Interacting with Treatment Success) who completed indirect calorimetry and dual-energy X-ray absorptiometry (DXA) were included in the study. Data were available in 438 participants at baseline, 340 at 6 months and 323 at 12 months. We used multiplicative allometric modeling based on lean body mass (LBM) and fat mass (FM) to derive body size independent scaling of REE. Longitudinal changes in indexed REE were then assessed following weight loss intervention. A multiplicative model including LBM, FM, age, Black race and the double product (DP) of systolic blood pressure and heart rate explained 79% of variance in REE. REE indexed to [LBM Allometric scaling of REE based on LBM and FM removes body composition-associated biases and should be considered in obesity and weight-based intervention studies.

Sections du résumé

BACKGROUND & AIMS
Resting energy expenditure (REE) is a major component of energy balance. While REE is usually indexed to total body weight (BW), this may introduce biases when assessing REE in obesity or during weight loss intervention. The main objective of the study was to quantify the bias introduced by ratiometric scaling of REE using BW both at baseline and following weight loss intervention.
DESIGN
Participants in the DIETFITS Study (Diet Intervention Examining The Factors Interacting with Treatment Success) who completed indirect calorimetry and dual-energy X-ray absorptiometry (DXA) were included in the study. Data were available in 438 participants at baseline, 340 at 6 months and 323 at 12 months. We used multiplicative allometric modeling based on lean body mass (LBM) and fat mass (FM) to derive body size independent scaling of REE. Longitudinal changes in indexed REE were then assessed following weight loss intervention.
RESULTS
A multiplicative model including LBM, FM, age, Black race and the double product (DP) of systolic blood pressure and heart rate explained 79% of variance in REE. REE indexed to [LBM
CONCLUSION
Allometric scaling of REE based on LBM and FM removes body composition-associated biases and should be considered in obesity and weight-based intervention studies.

Identifiants

pubmed: 36657929
pii: S2405-4577(22)01402-4
doi: 10.1016/j.clnesp.2022.11.015
pii:
doi:

Types de publication

Clinical Trial Journal Article Research Support, Non-U.S. Gov't Research Support, N.I.H., Extramural

Langues

eng

Sous-ensembles de citation

IM

Pagination

43-52

Subventions

Organisme : NIDDK NIH HHS
ID : R01 DK091831
Pays : United States

Informations de copyright

Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of Competing Interest No author has any conflict of interest to disclose for this work.

Auteurs

Francois Haddad (F)

Department of Medicine, Division of Cardiovascular Medicine, Stanford Cardiovascular Institute, Stanford University, CA, USA; Stanford Cardiovascular Institute, CA, USA; Stanford Diabetes Research Center, Stanford, CA, 94305, USA. Electronic address: fhaddad@stanford.edu.

Xiao Li (X)

Department of Genetics, Stanford University, CA, USA. Electronic address: xxl1015@case.edu.

Dalia Perelman (D)

Department of Genetics, Stanford University, CA, USA. Electronic address: xxl1015@case.edu.

Everton Jose Santana (EJ)

Department of Medicine, Division of Cardiovascular Medicine, Stanford Cardiovascular Institute, Stanford University, CA, USA; Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, Belgium. Electronic address: evertonsantana@stanford.edu.

Tatiana Kuznetsova (T)

Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, Belgium. Electronic address: tatiana.kouznetsova@kuleuven.be.

Nicholas Cauwenberghs (N)

Research Unit Hypertension and Cardiovascular Epidemiology, KU Leuven Department of Cardiovascular Sciences, Belgium. Electronic address: nicholas.cauwenberghs@kuleuven.be.

Vincent Busque (V)

Department of Medicine, Division of Cardiovascular Medicine, Stanford Cardiovascular Institute, Stanford University, CA, USA; Department of Medicine, Stanford Prevention Research Center, CA, USA; Stanford Diabetes Research Center, Stanford, CA, 94305, USA. Electronic address: vbusque@stanford.edu.

Kevin Contrepois (K)

Department of Genetics, Stanford University, CA, USA; Department of Medicine, Stanford Prevention Research Center, CA, USA. Electronic address: kcontrep@stanford.edu.

Michael P Snyder (MP)

Stanford Cardiovascular Institute, CA, USA; Department of Genetics, Stanford University, CA, USA; Stanford Diabetes Research Center, Stanford, CA, 94305, USA. Electronic address: mpsnyder@stanford.edu.

Mary B Leonard (MB)

Department of Pediatrics, Stanford University, CA, USA; Department of Medicine, Stanford Prevention Research Center, CA, USA. Electronic address: leonard5@stanford.edu.

Christopher Gardner (C)

Department of Medicine, Stanford Prevention Research Center, CA, USA; Stanford Diabetes Research Center, Stanford, CA, 94305, USA. Electronic address: cgardner@stanford.edu.

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