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Patterns of beverages consumption and prevalence of non-communicable chronic diseases

Published online by Cambridge University Press:  11 October 2024

Layanne Cristina de Carvalho Lavôr Open the ORCID record for Layanne Cristina de Carvalho Lavôr [Opens in a new window] ,
Poliana Cristina de Almeida Fonseca Viola Open the ORCID record for Poliana Cristina de Almeida Fonseca Viola [Opens in a new window] ,
Paulo Víctor de Lima Sousa Open the ORCID record for Paulo Víctor de Lima Sousa [Opens in a new window] ,
Felipe da Costa Campos Open the ORCID record for Felipe da Costa Campos [Opens in a new window] ,
Jany de Moura Crisóstomo Open the ORCID record for Jany de Moura Crisóstomo [Opens in a new window] ,
Larisse Monteles Nascimento Open the ORCID record for Larisse Monteles Nascimento [Opens in a new window]  and
Karoline de Macêdo Gonçalves Frota Open the ORCID record for Karoline de Macêdo Gonçalves Frota [Opens in a new window]
Layanne Cristina de Carvalho Lavôr*
Affiliation:
Nutrition Department, Health Sciences Center, Federal University of Piauí, Ininga, Teresina 64049-550, PI, Brazil
Poliana Cristina de Almeida Fonseca Viola
Affiliation:
Nutrition Department, Health Sciences Center, Federal University of Piauí, Ininga, Teresina 64049-550, PI, Brazil
Paulo Víctor de Lima Sousa
Affiliation:
Nutrition Department, Health Sciences Center, Federal University of Piauí, Ininga, Teresina 64049-550, PI, Brazil
Felipe da Costa Campos
Affiliation:
Nutrition Department, Health Sciences Center, Federal University of Piauí, Ininga, Teresina 64049-550, PI, Brazil
Jany de Moura Crisóstomo
Affiliation:
Nutrition Department, Health Sciences Center, Federal University of Piauí, Ininga, Teresina 64049-550, PI, Brazil
Larisse Monteles Nascimento
Affiliation:
Nutrition Department, Health Sciences Center, Federal University of Piauí, Ininga, Teresina 64049-550, PI, Brazil
Karoline de Macêdo Gonçalves Frota
Affiliation:
Nutrition Department, Health Sciences Center, Federal University of Piauí, Ininga, Teresina 64049-550, PI, Brazil
*
*Corresponding author: Layanne Cristina de Carvalho Lavôr, email [email protected]
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Abstract

Beverages consumption influences diet quality in general and has been associated with the development of non-communicable chronic diseases (NCCD). We aimed to verify the association between beverage consumption patterns and the prevalence of NCCD. A cross-sectional household and population-based study was conducted with 489 individuals aged 20 years and older. The presence of NCCD (arterial hypertension, diabetes, cancer and hypercholesterolemia) was obtained by self-report, while obesity was diagnosed by measuring body weight, height and waist circumference. Beverage consumption patterns were obtained by principal component analysis. The association between beverages patterns and the prevalence of NCCD was verified using Poisson regression, expressed as prevalence ratio (PR) and adjusted for potential confounding factors. Three beverage patterns were identified: ‘ultra-processed beverages’, ‘alcoholic beverages’ and ‘healthy beverages’. Individuals with greater adherence to the Ultra-processed Beverages Pattern had a 2·77 times higher prevalence of cancer (PR: 3·77; 95 % CI 1·57, 9·07). Higher adherence to the Alcoholic Beverages Pattern was associated with a higher prevalence of obesity (PR: 1·97; 95 % CI 1·13, 3·44). In contrast, individuals in the second tertile of adherence to the Healthy Beverages Pattern had a 39 % lower prevalence of hypercholesterolemia (PR: 0·61; 95 % CI 0·40, 0·92), and individuals in the third tertile had a 10 % lower prevalence of abdominal obesity estimated by the waist-to-height ratio (PR: 0·90; 95 % CI 0·83, 0·97). Beverage consumption patterns may be associated with a higher prevalence of NCCD, regardless of other risk factors. It is therefore important to conduct more studies investigating the impact of beverages patterns on health.

Keywords

Non-communicable DiseasesCancerObesityBeveragesDietary Patterns
Type
Research Article
Information
British Journal of Nutrition , Volume 132 , Issue 6 , 28 September 2024 , pp. 794 - 804
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Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of The Nutrition Society

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References

Scander, H, Monteagudo, C, Nilsen, B, et al. (2018) Beverage consumption patterns and energy contribution from beverages per meal type: results from a national dietary survey in Sweden. Public Health Nutr 21, 3318–3327.CrossRefGoogle ScholarPubMed
Hernández-López, R, Canto-Osorio, F, Vidaña-Pérez, D, et al. (2022) Soft drink and non-caloric soft drink intake and their association with blood pressure: the Health Workers Cohort Study. Nutr J [Internet] 21, 1–10.CrossRefGoogle ScholarPubMed
Malik, VS & Hu, FB (2022) The role of sugar-sweetened beverages in the global epidemics of obesity and chronic diseases. Nat Rev Endocrinol 18, 205–218.CrossRefGoogle ScholarPubMed
Krittanawong, C, Isath, A, Rosenson, RS, et al. (2022) Alcohol consumption and cardiovascular health. Am J Med [Internet] 135, 1213–1230.e3.CrossRefGoogle ScholarPubMed
Rebholz, CM, Young, BA, Katz, R, et al. (2019) Patterns of beverages consumed and risk of incident kidney disease. Clin J Am Soc Nephrol 14, 49–56.CrossRefGoogle Scholar
Salinas-Mandujano, RG, Laiseca-Jácome, E, Ramos-Gómez, M, et al. (2023) Beverage consumption patterns and nutrient intake are associated with cardiovascular risk factors among urban mexican young adults. Nutrients 15, 1817.CrossRefGoogle ScholarPubMed
Lee, KW & Shin, D (2018) A healthy beverage consumption pattern is inversely associated with the risk of obesity and metabolic abnormalities in Korean adults. J Med Food 21, 935–945.CrossRefGoogle ScholarPubMed
Leal, JS, Vegi, AS, Meireles, AL, et al. (2022) Burden of non-communicable chronic diseases attributable to the consumption of sugar-sweetened beverage, 1990–2019. Clin Nutr ESPEN 51, 253–261.CrossRefGoogle Scholar
Instituto Brasileiro de Geografia e Estatística (2011) Instituto Brasileiro de Geografia e Estatística. Censo Demográfico 2010: Características Gerais Da População e Dos Domicílios, Resultados Do Universo (Brazilian Institute of Geography and Statistics. 2010 Demographic Census: General Characteristics of the Population and Households, Results of the Universe). Rio de Janeiro: IBGE.Google Scholar
Rodrigues, LARL, Costa e Silva, DM, Oliveira, EAR, et al. (2021) Plano de amostragem e aspectos metodológicos: inquérito de saúde domiciliar no Piauí (Sampling plan and methodological aspects: a household healthcare survey in Piauí). Rev Saude Publica 55, 118.CrossRefGoogle Scholar
Szwarcwald, CL, Malta, DC, Pereira, CA, et al. (2014) Pesquisa nacional de saúde no Brasil: concepção e metodologia de aplicação (National Health Survey in Brazil: design and methodology of application). Cienc e Saude Coletiva 19, 333–342.CrossRefGoogle Scholar
BRASIL (2019) Vigitel Brasil 2018: Vigilância de fatores de risco e proteção para doenças crônicas por inquerito telefônico: estimativas sobre frequência e distribuição sociodemográfica de fatores de risco e proteção para doenças crônicas nas capitais dos 26 estados br [Internet] (Surveillance of risk and protective factors for chronic diseases through telephone surveys: estimates of the frequency and sociodemographic distribution of risk and protective factors for chronic diseases in the capitals of the 26 states). Ministério da Saúde. 2019. 131 p. http://bvsms.saude.gov.br/bvs/publicacoes/vigitel_brasil_2011_fatores_risco_doencas_cronicas.pdf (accessed October 2023).Google Scholar
Jelliffe, D & Jelliffe, PE (1989) Anthropometry: major measurements. In Community Nutritional Assessment, pp. 68–105 [Jelliffe, D and Jelliffe, EFP, editors]. Oxford: Oxford University Press.Google Scholar
Cameron, N (1984) Anthropometric measurements. In The Measurement of Human Growth, pp. 56–99 [Tanner, JM, editor]. London: Croom Helm.Google Scholar
World Health Organization (2000) Obesity: Preventing and Managing the Global Epidemic. Geneva: World Health Organization.Google Scholar
OPAS (2002) Reunión Del Comitê Asesor de Ivestigaciones En Salud – Encuestra Multicêntrica – Salud Beinestar y Envejecimeiento (SABE) En América Latina e El Caribe. https://www.google.com.br/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwigxvvL-eiIAxWgu5UCHWIEIGkQFnoECB8QAQ&url=http%3A%2F%2Fenvejecimiento.csic.es%2Fdocumentos%2Fdocumentos%2Fpaho-salud-01.pdf&usg=AOvVaw1ke5u1-Bo6zh0jF2oQrBq4&opi=89978449 (accessed October 2023).Google Scholar
World Health Organization (2008) Waist Circumference and Waist–Hip Ratio: Report of a WHO Expert Consultation Health. Geneva: World Health Organization.Google Scholar
Ashwell, M & Hsieh, SD (2005) Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. Int J Food Sci Nutr 56, 303–307.CrossRefGoogle ScholarPubMed
Moshfegh, AJ, Rhodes, DG, Baer, DJ, et al. (2008) The US Department of Agriculture Automated Multiple-Pass Method reduces bias in the collection of energy intakes. Am J Clin Nutr 88, 324–332.CrossRefGoogle ScholarPubMed
Verly-Jr, E, Castro, MA, Fisberg, RM, et al. (2012) Precision of usual food intake estimates according to the percentage of individuals with a second dietary measurement. J Acad Nutr Diet [Internet] 112, 1015–1020.CrossRefGoogle Scholar
Pinheiro, AB, Lacerda, EM, Benzecry, EH, et al. (2008) Tabela Para Avaliação de Consumo Alimentar Em Medidas Caseiras (Table for assessing food consumption in household measurements), 5th ed. São Paulo: Atheneu.Google Scholar
Harttig, U, Haubrock, J, Knüppel, S, et al. (2011) The MSM program: web-based statistics package for estimating usual dietary intake using the multiple source method. Eur J Clin Nutr 65, S87–S91.CrossRefGoogle ScholarPubMed
Mumme, KD, Conlon, C, Von Hurst, PR, et al. (2022) Associations between dietary patterns and the metabolic syndrome in older adults in New Zealand: the REACH study. Br J Nutr 128, 1806–1816.CrossRefGoogle ScholarPubMed
Matsudo, S, Araújo, T, Matsudo, V, et al. (2012) Questionário Internacional de Atividade Física (IPAQ): estudo de validade e reprodutibilidade no Brasil (International Physical Activity Questionnaire (lPAQ): study of validity and reliability in Brazil). Rev Bras Atividade Física Saúde [Internet] 6, 5–18. https://periodicos.ufpel.edu.br/ojs2/index.php/RBAFS/article/view/931 Google Scholar
World Health Organization (2020) Guidelines on Physical Activity and Sedentary Behaviour. Geneva: WHO.Google Scholar
Rivera-Paredez, B, Muñoz-Aguirre, P, Torres-Ibarra, L, et al. (2018) Patterns of beverage consumption and risk of CHD among Mexican adults. Br J Nutr 120, 210–219.CrossRefGoogle ScholarPubMed
Garduño-Alanís, A, Malyutina, S, Pajak, A, et al. (2020) Association between soft drink, fruit juice consumption and obesity in Eastern Europe: cross-sectional and longitudinal analysis of the HAPIEE study. J Hum Nutr Diet 33, 66–77.CrossRefGoogle ScholarPubMed
Ruxton, CHS & Myers, M (2021) Fruit juices: are they helpful or harmful? An evidence review. Nutrients 13, 1–14.CrossRefGoogle ScholarPubMed
D’Elia, L, Dinu, M, Sofi, F, et al. (2021) 100 % Fruit juice intake and cardiovascular risk: a systematic review and meta-analysis of prospective and randomised controlled studies. Eur J Nutr 60, 2449–2467.CrossRefGoogle ScholarPubMed
Schwingshackl, L, Hoffmann, G, Schwedhelm, C, et al. (2016) Consumption of dairy products in relation to changes in anthropometric variables in adult populations: a systematic review and meta-analysis of cohort studies. PLoS One 11, 1–15.CrossRefGoogle ScholarPubMed
Stobiecka, M, Król, J & Brodziak, A (2022) Antioxidant activity of milk and dairy products. Animals 12, 245.CrossRefGoogle ScholarPubMed
Inan-Eroglu, E, Powell, L, Hamer, M, et al. (2020) Is there a link between different types of alcoholic drinks and obesity? An Analysis of 280,183 UK Biobank Participants. Int J Environ Res Public Health. 17, 1–18.Google Scholar
Butler, L, Popkin, BM & Poti, JM (2018) Associations of alcoholic beverage consumption with dietary intake, waist circumference, and Body Mass Index in US Adults: National Health and Nutrition Examination Survey 2003–2012. J Acad Nutr Diet 118, 409–420.e3.CrossRefGoogle Scholar
Debras, C, Chazelas, E, Srour, B, et al. (2020) Total and added sugar intakes, sugar types, and cancer risk: results from the prospective NutriNet-Santé cohort. Am J Clin Nutr [Internet] 112, 1267–1279.CrossRefGoogle ScholarPubMed
De Lorgeril, M, Salen, P & Rabaeus, M (2020) Sugary drinks and cancer risk. Transl Cancer Res 9, 3172–3176.CrossRefGoogle ScholarPubMed
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