Cargando…

A Comparison of the Australian Dietary Guidelines to the NOVA Classification System in Classifying Foods to Predict Energy Intakes and Body Mass Index

NOVA classification distinguishes foods by level of processing, with evidence suggesting that a high intake of ultra-processed foods (UPFs, NOVA category 4) leads to obesity. The Australian Dietary Guidelines, in contrast, discourage excess consumption of “discretionary foods” (DFs), defined accordi...

Descripción completa

Detalles Bibliográficos
Autores principales: Grech, Amanda, Rangan, Anna, Allman-Farinelli, Margaret, Simpson, Stephen J., Gill, Tim, Raubenheimer, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571644/
https://www.ncbi.nlm.nih.gov/pubmed/36235595
http://dx.doi.org/10.3390/nu14193942
_version_ 1784810413783777280
author Grech, Amanda
Rangan, Anna
Allman-Farinelli, Margaret
Simpson, Stephen J.
Gill, Tim
Raubenheimer, David
author_facet Grech, Amanda
Rangan, Anna
Allman-Farinelli, Margaret
Simpson, Stephen J.
Gill, Tim
Raubenheimer, David
author_sort Grech, Amanda
collection PubMed
description NOVA classification distinguishes foods by level of processing, with evidence suggesting that a high intake of ultra-processed foods (UPFs, NOVA category 4) leads to obesity. The Australian Dietary Guidelines, in contrast, discourage excess consumption of “discretionary foods” (DFs), defined according to their composition. Here, we (i) compare the classification of Australian foods under the two systems, (ii) evaluate their performance in predicting energy intakes and body mass index (BMI) in free-living Australians, and (iii) relate these outcomes to the protein leverage hypothesis of obesity. Secondary analysis of the Australian National Nutrition and Physical Activity Survey was conducted. Non-protein energy intake increased by 2.1 MJ (p < 0.001) between lowest and highest tertiles of DF intake, which was significantly higher than UPF (0.6 MJ, p < 0.001). This demonstrates that, for Australia, the DF classification better distinguishes foods associated with high energy intakes than does the NOVA system. BMI was positively associated with both DFs (−1. 0, p = 0.0001) and UPFs (−1.1, p = 0.0001) consumption, with no difference in strength of association. For both classifications, macronutrient and energy intakes conformed closely to the predictions of protein leverage. We account for the similarities and differences in performance of the two systems in an analysis of Australian foods.
format Online
Article
Text
id pubmed-9571644
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-95716442022-10-17 A Comparison of the Australian Dietary Guidelines to the NOVA Classification System in Classifying Foods to Predict Energy Intakes and Body Mass Index Grech, Amanda Rangan, Anna Allman-Farinelli, Margaret Simpson, Stephen J. Gill, Tim Raubenheimer, David Nutrients Article NOVA classification distinguishes foods by level of processing, with evidence suggesting that a high intake of ultra-processed foods (UPFs, NOVA category 4) leads to obesity. The Australian Dietary Guidelines, in contrast, discourage excess consumption of “discretionary foods” (DFs), defined according to their composition. Here, we (i) compare the classification of Australian foods under the two systems, (ii) evaluate their performance in predicting energy intakes and body mass index (BMI) in free-living Australians, and (iii) relate these outcomes to the protein leverage hypothesis of obesity. Secondary analysis of the Australian National Nutrition and Physical Activity Survey was conducted. Non-protein energy intake increased by 2.1 MJ (p < 0.001) between lowest and highest tertiles of DF intake, which was significantly higher than UPF (0.6 MJ, p < 0.001). This demonstrates that, for Australia, the DF classification better distinguishes foods associated with high energy intakes than does the NOVA system. BMI was positively associated with both DFs (−1. 0, p = 0.0001) and UPFs (−1.1, p = 0.0001) consumption, with no difference in strength of association. For both classifications, macronutrient and energy intakes conformed closely to the predictions of protein leverage. We account for the similarities and differences in performance of the two systems in an analysis of Australian foods. MDPI 2022-09-23 /pmc/articles/PMC9571644/ /pubmed/36235595 http://dx.doi.org/10.3390/nu14193942 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Grech, Amanda
Rangan, Anna
Allman-Farinelli, Margaret
Simpson, Stephen J.
Gill, Tim
Raubenheimer, David
A Comparison of the Australian Dietary Guidelines to the NOVA Classification System in Classifying Foods to Predict Energy Intakes and Body Mass Index
title A Comparison of the Australian Dietary Guidelines to the NOVA Classification System in Classifying Foods to Predict Energy Intakes and Body Mass Index
title_full A Comparison of the Australian Dietary Guidelines to the NOVA Classification System in Classifying Foods to Predict Energy Intakes and Body Mass Index
title_fullStr A Comparison of the Australian Dietary Guidelines to the NOVA Classification System in Classifying Foods to Predict Energy Intakes and Body Mass Index
title_full_unstemmed A Comparison of the Australian Dietary Guidelines to the NOVA Classification System in Classifying Foods to Predict Energy Intakes and Body Mass Index
title_short A Comparison of the Australian Dietary Guidelines to the NOVA Classification System in Classifying Foods to Predict Energy Intakes and Body Mass Index
title_sort comparison of the australian dietary guidelines to the nova classification system in classifying foods to predict energy intakes and body mass index
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9571644/
https://www.ncbi.nlm.nih.gov/pubmed/36235595
http://dx.doi.org/10.3390/nu14193942
work_keys_str_mv AT grechamanda acomparisonoftheaustraliandietaryguidelinestothenovaclassificationsysteminclassifyingfoodstopredictenergyintakesandbodymassindex
AT rangananna acomparisonoftheaustraliandietaryguidelinestothenovaclassificationsysteminclassifyingfoodstopredictenergyintakesandbodymassindex
AT allmanfarinellimargaret acomparisonoftheaustraliandietaryguidelinestothenovaclassificationsysteminclassifyingfoodstopredictenergyintakesandbodymassindex
AT simpsonstephenj acomparisonoftheaustraliandietaryguidelinestothenovaclassificationsysteminclassifyingfoodstopredictenergyintakesandbodymassindex
AT gilltim acomparisonoftheaustraliandietaryguidelinestothenovaclassificationsysteminclassifyingfoodstopredictenergyintakesandbodymassindex
AT raubenheimerdavid acomparisonoftheaustraliandietaryguidelinestothenovaclassificationsysteminclassifyingfoodstopredictenergyintakesandbodymassindex
AT grechamanda comparisonoftheaustraliandietaryguidelinestothenovaclassificationsysteminclassifyingfoodstopredictenergyintakesandbodymassindex
AT rangananna comparisonoftheaustraliandietaryguidelinestothenovaclassificationsysteminclassifyingfoodstopredictenergyintakesandbodymassindex
AT allmanfarinellimargaret comparisonoftheaustraliandietaryguidelinestothenovaclassificationsysteminclassifyingfoodstopredictenergyintakesandbodymassindex
AT simpsonstephenj comparisonoftheaustraliandietaryguidelinestothenovaclassificationsysteminclassifyingfoodstopredictenergyintakesandbodymassindex
AT gilltim comparisonoftheaustraliandietaryguidelinestothenovaclassificationsysteminclassifyingfoodstopredictenergyintakesandbodymassindex
AT raubenheimerdavid comparisonoftheaustraliandietaryguidelinestothenovaclassificationsysteminclassifyingfoodstopredictenergyintakesandbodymassindex