The global diet quality score as an indicator of adequate nutrient intake and dietary quality - a nation-wide representative study.
Brazil
Dietary diversity
Dietary quality metrics
Dietary risk
Double burden of malnutrition
Nutrient adequacy
Sustainable developmental goal
Ultra-processed food
Journal
Nutrition journal
ISSN: 1475-2891
Titre abrégé: Nutr J
Pays: England
ID NLM: 101152213
Informations de publication
Date de publication:
17 Apr 2024
17 Apr 2024
Historique:
received:
23
12
2023
accepted:
10
04
2024
medline:
17
4
2024
pubmed:
17
4
2024
entrez:
16
4
2024
Statut:
epublish
Résumé
The Global Diet Quality Score (GDQS) was developed to be a simple, timely and cost-effective tool to track, simultaneously, nutritional deficiency and non-communicable disease risks from diet in diverse settings. The objective was to investigate the performance of GDQS as an indicator of adequate nutrient intake and dietary quality in a national-representative sample of the Brazilian population. Nationally-representative data from 44,744 men and non-pregnant and non-lactating women aging ≥ 10 years, from the Brazilian National Dietary Survey were used. Dietary data were collected through two 24-h recalls (24HR). The GDQS was calculated and compared to a proxy indicator of nutrient adequate intake (the Minimum Dietary Diversity for Women-MDD-W) and to an indicator of high-risk diet for non-communicable diseases (caloric contribution from ultra-processed foods-UPF). To estimate the odds for overall nutrient inadequacy across MDD-W and GDQS quintiles, a multiple logistic regression was applied, and the two metrics' performances were compared using Wald's post-test. The mean GDQS for Brazilians was 14.5 (0-49 possible range), and only 1% of the population had a low-risk diet (GDQS ≥ 23). The GDQS mean was higher in women, elderly individuals and in higher-income households. An inverse correlation was found between the GDQS and UPF (rho (95% CI) = -0.20(-0.21;-0.19)). The odds for nutrient inadequacy were lower as quintiles of GDQS and MDD-W were higher (p-trend < 0.001), and MDD-W had a slightly better performance than GDQS (p-diff < 0.001). Having a low-risk GDQS (≥ 23) lowered the odds for nutrient inadequacy by 74% (95% CI:63%-81%). The GDQS is a good indicator of overall nutrient adequacy, and correlates well with UPF in a nationally representative sample of Brazil. Future studies must investigate the relationship between the GDQS and clinical endpoints, strengthening the recommendation to use this metric to surveillance dietary risks.
Sections du résumé
BACKGROUND
BACKGROUND
The Global Diet Quality Score (GDQS) was developed to be a simple, timely and cost-effective tool to track, simultaneously, nutritional deficiency and non-communicable disease risks from diet in diverse settings. The objective was to investigate the performance of GDQS as an indicator of adequate nutrient intake and dietary quality in a national-representative sample of the Brazilian population.
METHODS
METHODS
Nationally-representative data from 44,744 men and non-pregnant and non-lactating women aging ≥ 10 years, from the Brazilian National Dietary Survey were used. Dietary data were collected through two 24-h recalls (24HR). The GDQS was calculated and compared to a proxy indicator of nutrient adequate intake (the Minimum Dietary Diversity for Women-MDD-W) and to an indicator of high-risk diet for non-communicable diseases (caloric contribution from ultra-processed foods-UPF). To estimate the odds for overall nutrient inadequacy across MDD-W and GDQS quintiles, a multiple logistic regression was applied, and the two metrics' performances were compared using Wald's post-test.
RESULTS
RESULTS
The mean GDQS for Brazilians was 14.5 (0-49 possible range), and only 1% of the population had a low-risk diet (GDQS ≥ 23). The GDQS mean was higher in women, elderly individuals and in higher-income households. An inverse correlation was found between the GDQS and UPF (rho (95% CI) = -0.20(-0.21;-0.19)). The odds for nutrient inadequacy were lower as quintiles of GDQS and MDD-W were higher (p-trend < 0.001), and MDD-W had a slightly better performance than GDQS (p-diff < 0.001). Having a low-risk GDQS (≥ 23) lowered the odds for nutrient inadequacy by 74% (95% CI:63%-81%).
CONCLUSION
CONCLUSIONS
The GDQS is a good indicator of overall nutrient adequacy, and correlates well with UPF in a nationally representative sample of Brazil. Future studies must investigate the relationship between the GDQS and clinical endpoints, strengthening the recommendation to use this metric to surveillance dietary risks.
Identifiants
pubmed: 38627669
doi: 10.1186/s12937-024-00949-x
pii: 10.1186/s12937-024-00949-x
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
42Subventions
Organisme : NIH HHS
ID : T32 DK 007703
Pays : United States
Informations de copyright
© 2024. The Author(s).
Références
Swinburn BA, Kraak VI, Allender S, Atkins VJ, Baker PI, Bogard JR, et al. The Global Syndemic of Obesity, Undernutrition, and Climate Change: The Lancet Commission report. Lancet. 2019;393:791–846 Lancet Publishing Group.
doi: 10.1016/S0140-6736(18)32822-8
pubmed: 30700377
Willett W, Rockström J, Loken B, Springmann M, Lang T, Vermeulen S, et al. Food in the Anthropocene: the EAT–Lancet Commission on healthy diets from sustainable food systems. Lancet. 2019;393:447–92 Lancet Publishing Group.
doi: 10.1016/S0140-6736(18)31788-4
pubmed: 30660336
Byaruhanga R, Isgren E. Rethinking the alternatives: food sovereignty as a prerequisite for sustainable food security. Food Ethics. 2023;8:16.
doi: 10.1007/s41055-023-00126-6
Institute for Health Metrics and Evaluation (IHME). GBD Compare Data Visualization. Available from http://vizhub.healthdata.org/gbd-compare . 2020.
World Health Organization (WHO), United Nations International Children’s Emergency Fund (UNICEF), Food and Agriculture Organization of the United Nations (FAO). Report of the technical consultation on measuring healthy diets: concepts, methods and metrics. 2021.
FAO - Food and Agriculture Organization of the United Nations. Sustainable food systems: Concept and framework. Rome: FAO; 2018.
Verger EO, Savy M, Martin-Prével Y, Coates J, Frongillo E, Neufeld L, et al. Healthy Diet Metrics: A suitability assessment of indicators for global and national monitoring purposes. 2023.
Bromage S, Batis C, Bhupathiraju SN, Fawzi WW, Fung TT, Li Y, et al. Development and Validation of a Novel Food-Based Global Diet Quality Score (GDQS). J Nutr. 2021;151:75S-92S.
doi: 10.1093/jn/nxab244
pubmed: 34689200
pmcid: 8542096
Moursi M, Bromage S, Fung TT, Isanaka S, Matsuzaki M, Batis C, et al. There’s an App for That: Development of an Application to Operationalize the Global Diet Quality Score. J Nutr. 2021;151:176S-184S.
doi: 10.1093/jn/nxab196
pubmed: 34689193
pmcid: 8542098
Castellanos-Gutiérrez A, Rodríguez-Ramírez S, Bromage S, Fung TT, Li Y, Bhupathiraju SN, et al. Performance of the Global Diet Quality Score with Nutrition and Health Outcomes in Mexico with 24-h Recall and FFQ Data. J Nutr. 2021;151:143S-151S.
doi: 10.1093/jn/nxab202
pubmed: 34689195
pmcid: 8542100
Angulo E, Stern D, Castellanos-Gutiérrez A, Monge A, Lajous M, Bromage S, et al. Changes in the Global Diet Quality Score, Weight, and Waist Circumference in Mexican Women. J Nutr. 2021;151:152S-161S.
doi: 10.1093/jn/nxab171
pubmed: 34689194
pmcid: 8542099
Birk N, Matsuzaki M, Fung TT, Li Y, Batis C, Stampfer MJ, et al. Exploration of Machine Learning and Statistical Techniques in Development of a Low-Cost Screening Method Featuring the Global Diet Quality Score for Detecting Prediabetes in Rural India. J Nutr. 2021;151:110S-118S.
doi: 10.1093/jn/nxab281
pubmed: 34689190
pmcid: 8542097
Matsuzaki M, Birk N, Bromage S, Bowen L, Batis C, Fung TT, et al. Validation of Global Diet Quality Score among Nonpregnant Women of Reproductive Age in India: Findings from the Andhra Pradesh Children and Parents Study (APCAPS) and the Indian Migration Study (IMS). J Nutr. 2021;151:101S-109S.
doi: 10.1093/jn/nxab217
pubmed: 34689191
pmcid: 8564710
Bromage S, Pongcharoen T, Prachansuwan A, Sukboon P, Srichan W, Purttiponthanee S, et al. Performance of the Global Diet Quality Score (GDQS) App in predicting nutrient adequacy and metabolic risk factors among Thai adults. J Nutr. 2023;153(12):3576–94. https://doi.org/10.1016/j.tjnut.2023.10.007 .
He Y, Fang Y, Bromage S, Fung TT, Bhupathiraju SN, Batis C, et al. Application of the Global Diet Quality Score in Chinese Adults to Evaluate the Double Burden of Nutrient Inadequacy and Metabolic Syndrome. J Nutr. 2021;151:93S-100S.
doi: 10.1093/jn/nxab162
pubmed: 34689199
pmcid: 8542094
Bromage S, Andersen CT, Tadesse AW, Passarelli S, Hemler EC, Fekadu H, et al. The Global Diet Quality Score is Associated with Higher Nutrient Adequacy, Midupper Arm Circumference, Venous Hemoglobin, and Serum Folate among Urban and Rural Ethiopian Adults. J Nutr. 2021;151:130S-142S.
doi: 10.1093/jn/nxab264
pubmed: 34689198
pmcid: 8564694
Yaregal Z, Baye K. The Global Diet Quality Score predicts diet quality of women of reproductive age in Addis Ababa, Ethiopia. Br J Nutr. 2023;130(9):1573–9.
Bromage S, Zhang Y, Holmes MD, Sachs SE, Fanzo J, Remans R, et al. The Global Diet Quality Score Is Inversely Associated with Nutrient Inadequacy, Low Midupper Arm Circumference, and Anemia in Rural Adults in Ten Sub-Saharan African Countries. J Nutr. 2021;151:119S-129S.
doi: 10.1093/jn/nxab161
pubmed: 34689197
pmcid: 8542095
Fung TT, Li Y, Bhupathiraju SN, Bromage S, Batis C, Holmes MD, et al. Higher Global Diet Quality Score Is Inversely Associated with Risk of Type 2 Diabetes in US Women. J Nutr. 2021;151:168S-175S.
doi: 10.1093/jn/nxab195
pubmed: 34689196
pmcid: 8542093
Fung TT, Li Y, Bromage S, Bhupathiraju SN, Batis C, Fawzi W, et al. Higher Global Diet Quality Score Is Associated with Less 4-Year Weight Gain in US Women. J Nutr. 2021;151:162S-167S.
doi: 10.1093/jn/nxab170
pubmed: 34689192
pmcid: 8542092
Food and Agriculture Organization of the United Nations (FAO). Minimum dietary diversity for women. Rome: FAO; 2021.
Chiuve SE, Fung TT, Rimm EB, Hu FB, McCullough ML, Wang M, et al. Alternative Dietary Indices Both Strongly Predict Risk of Chronic Disease. J Nutr. 2012;142:1009–18.
doi: 10.3945/jn.111.157222
pubmed: 22513989
pmcid: 3738221
Norde MM, Porciuncula L, Garrido G, Nunes-Galbes NM, Sarti FM, Marchioni DML, et al. Measuring food systems sustainability in heterogenous countries: The Brazilian multidimensional index updated version applicability. Sustain Dev. 2023;31(1):91–107.
Meller FO, Schäfer AA, Santos LP, Quadra MR, Miranda VIA. Double Burden of Malnutrition and Inequalities in the Nutritional Status of Adults: A Population-Based Study in Brazil, 2019. Int J Public Health. 2021;66:609179.
doi: 10.3389/ijph.2021.609179
pubmed: 34744575
pmcid: 8565287
Silva NDJ, Ribeiro-Silva RDC, Rasella D, Alves FJO, Campello T, Fiaccone RL, et al. Shifts towards overweight and double burden of malnutrition among socio-economically vulnerable children: A longitudinal ecological analysis of Brazilian municipalities. Public Health Nutr. 2021;24:4908–17.
doi: 10.1017/S1368980020004735
pubmed: 33222714
Dulgheroff PT, Da Silva LS, Rinaldi AEM, Rezende LFM, Marques ES, Azeredo CMH. Educational disparities in hypertension, diabetes, obesity and smoking in Brazil: A trend analysis of 578 977 adults from a national survey, 2007-2018. BMJ Open. BMJ Publishing Group; 2021;11:e046154.
doi: 10.1136/bmjopen-2020-046154
pubmed: 34281920
pmcid: 8291309
Brasil. Vigitel Brasil 2021 : vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico : estimativas sobre frequência e distribuição sociodemográfica de fatores de risco e proteção para doenças crônicas nas capitais dos 26 estados brasileiros e no Distrito Federal em 2021. Brasília: Ministério da Saúde; 2021. Available from: www.saude.gov.br/svs .
Fernanda do Nascimento Jacinto de Souza B, Serenini Bernardes M, Cristina Ribeiro Vieira V, Maria Stolses Bergamo Francisco P, Marín-León L, Flaviane Mendes Camargo D, et al. (In)segurança alimentar no Brasil no pré e pós pandemia da COVID-19: reflexões e perspectivas. InterAmerican J Med Health. 2021;4:1–10. Article ID: e202101001.
Rede Brasileira de Pesquisa em Soberania e Segurança Alimentar. II Inquérito Nacional sobre Insegurança Alimentar no Contexto da Pandemia da COVID-19 no Brasil: II VIGISAN. Fundação Friedrich Ebert. 2022;1–112. Available from: https://olheparaafome.com.br/wp-content/uploads/2022/06/Relatorio-II-VIGISAN-2022.pdf . Cited 2022 Oct 24.
Brazilian Institute of Geography and Statistics (IBGE). Pesquisa de orçamentos familiares : 2017–2018 : análise do consumo alimentar pessoal no Brasil / IBGE, Coordenação de Trabalho e Rendimento. Rio de Janeiro: IBGE; 2020.
Moshfegh AJ, Rhodes DG, Baer DJ, Murayi T, Clemens JC, Rumpler WV, et al. The US Department of Agriculture Automated Multiple-Pass Method reduces bias in the collection of energy intakes. Am J Clin Nutr. 2008;88:324–32.
doi: 10.1093/ajcn/88.2.324
pubmed: 18689367
Rodrigues RM, De Carli E, Araújo MC, Junior EV, Marchioni DML, Bezerra IN, et al. Limitações na comparação dos Inquéritos Nacionais de Alimentação de 2008–2009 e 2017–2018. Rev Saude Publica. 2021;55:1–10.
Food Research Center (FoRC). Tabela Brasileira de Composição de Alimentos (TBCA). Universidade de São Paulo (USP). Version 7.1. http://www.fcf.usp.br/tbca . 2022.
U.S. Department of Agriculture. USDA Table of Nutrient Retention Factors - Release 6. Maryland: USDA; 2007.
Charrondiere R, Haytowitz D, Stadlmayr B. FAO/INFOODS Density Databases - Density Database Version 2.0. Rome: FAO; 2012.
Matthews RH, Garrison YJ. USDA Handbook No. 102: Food Yields Summarized by Different Stages of Preparation. Washington DC: USDA; 1975.
Vásquez-Caicedo AL, Bell S, Hartmann B. Report on Collection of Rules on Use of Recipe Calculation Procedures Including the Use of Yield and Retention Factors for Imputing Nutrient Values for Composite Foods. Brussels: EuroFIR; 2008.
Bognar A. Tables on Weight Yield of Food and Retention Factors of Food Constituents for the Calculation of Nutrition Composition of Cooked Foods (Dishes). Karlsruhe: Bundesforschungsanstalt für Ernährung; 2022.
Giuntini EB, Coelho KS, Grande F, Marchioni DML, De Carli E, Sichieri R, et al. 12th IFDC 2017 Special issue – Brazilian Nutrient Intake Evaluation Database: An essential tool for estimating nutrient intake data. J Food Compos Anal. 2019;83:103286.
doi: 10.1016/j.jfca.2019.103286
Monteiro CA, Cannon G, Levy RB, Moubarac J-C, Louzada ML, Rauber F, et al. Ultra-processed foods: what they are and how to identify them. Public Health Nutr. 2019;22:936–41.
doi: 10.1017/S1368980018003762
pubmed: 30744710
pmcid: 10260459
Pagliai G, Dinu M, Madarena MP, Bonaccio M, Iacoviello L, Sofi F. Consumption of ultra-processed foods and health status: A systematic review and meta-Analysis. Br J Nutr. 2021;125:308–18 Cambridge University Press.
doi: 10.1017/S0007114520002688
pubmed: 32792031
Brasil. Dietary Guidelines for the Brazilian Population. Brasília: Ministry of Health; 2014.
Tooze JA, Kipnis V, Buckman DW, Carroll RJ, Freedman LS, Guenther PM, et al. A mixed-effects model approach for estimating the distribution of usual intake of nutrients: The NCI method. Stat Med. 2010;29:2857–68.
doi: 10.1002/sim.4063
pubmed: 20862656
Willett WC, Howe GR, Kushi LH. Adjustment for total energy intake in epidemiologic studies. Am J Clin Nutr. 1997;65:1220S-1228S.
doi: 10.1093/ajcn/65.4.1220S
pubmed: 9094926
Institute of Medicine (US) Subcommittee on Interpretation and Uses of Dietary Reference Intakes, Institute of Medicine (US) Standing Committee on the Scientific Evaluation of Dietary Reference Intakes. DRI Dietary Reference Intakes: Applications in Dietary Assessment. Washington (DC): National Academies Press (US); 2000.
Rodrigues RM, Souza ADM, Bezerra IN, Pereira RA, Yokoo EM, Sichieri R. Most consumed foods in Brazil: evolution between 2008–2009 and 2017–2018. Rev Saude Publica. 2021;55:4s.
doi: 10.11606/s1518-8787.2021055003406
pubmed: 34852162
pmcid: 8659613
Junior EV, de Oliveira DCRS, Sichieri R. Cost of healthy and culturally acceptable diets in Brazil in 2009 and 2018. Rev Saude Publica. 2021;55:7s.
De Mello AV, Sarti FM, Pereira JL, Goldbaum M, Cesar CLG, Alves MCGP, et al. Determinants of inequalities in the quality of Brazilian diet: Trends in 12-year population-based study (2003–2015). Int J Equity Health. 2018;17:72.
doi: 10.1186/s12939-018-0784-2
Marchioni DM, Cacau LT, De Carli E, de Carvalho AM, Rulli MC. Low Adherence to the EAT-Lancet Sustainable Reference Diet in the Brazilian Population: Findings from the National Dietary Survey 2017–2018. Nutrients. 2022;14:1187.
doi: 10.3390/nu14061187
pubmed: 35334839
pmcid: 8956109
Delpino FM, Figueiredo LM, Bielemann RM, Da Silva BGC, Dos Santos FS, Mintem GC, et al. Ultra-processed food and risk of type 2 diabetes: A systematic review and meta-analysis of longitudinal studies. Int J Epidemiol. 2022;51:1120–41 Oxford University Press.
doi: 10.1093/ije/dyab247
pubmed: 34904160
Intake Center for Dietary Assessment. The Global Diet Quality Score: Data collection options and tabulation guidelines. https://www.intake.org/resource/global-diet-quality-score-data-collection-options-and-tabulation-guidelines .
Miller V, Webb P, Micha R, Mozaffarian D. Defining diet quality: a synthesis of dietary quality metrics and their validity for the double burden of malnutrition. Lancet Planet Health. 2020;4:e352–70.
doi: 10.1016/S2542-5196(20)30162-5
pubmed: 32800153
pmcid: 7435701