The influence of ultra-processed food consumption in anthropometric and atherogenic indices of adolescents
Keywords:
Adolescence, Cardiovascular risk, Food consumption, Processed foodsAbstract
Objective
To investigate the influence of ultra-processed food consumption on anthropometric and atherogenic indices.
Methods
A cross-sectional study was conducted with 327 adolescents aged 14 to 19 years. Sociodemographic, anthropometric, biochemical, and food consumption data were evaluated. The ratios of atherogenic indices were calculated using the Castelli I (Total Cholesterol/High Density Lipoprotein Cholesterol), Castelli II (Low Density Lipoprotein Cholesterol/High Density Lipoprotein Cholesterol), and estimated Low Density Lipoprotein Cholesterol particle size (Atherogenic Index of Plasma=Triglycerides/High-Density Lipoprotein Cholesterol) indices. Logistic regression was used for the unadjusted and adjusted analysis between ultra-processed foods consumption, anthropometric, and atherogenic indices. The level of significance was 5%.
Results
Most participants were female (59.3%). Girls had a higher consumption of ultra-processed foods (26.6% vs. 20.5%). Of the total number of adolescents, 16.5% were overweight and 65.7% were from public schools. Adolescents with altered values for the Castelli I and II Index, and for the Atherogenic Index of Plasma had significantly higher weights, Waist Circumference, Waist Circumference/ Height and Body Mass Index/ Age values. The adjusted analysis identified a significant association (Odds ratio=2.29; 95% Confidence interval: 1.23-4.28) between the high consumption of ultraprocessed foods and the Castelli II index.
Conclusion
The associations between atherogenic indices and anthropometric indices and the consumption of ultra-processed foods highlight the negative influence of these foods on adolescents’ cardiovascular health.
References
Ding W, Cheng H, Yan Y, Zhao X, Chen F, Huang G, et al. 10-Year trends in serum lipid levels and dyslipidemia among children and adolescents from several schools in Beijing, China. J Epidemiol. 2016;26:637-45. https://doi.og/10.2188/jea.JE20140252
Barbalho SM, Oshiiwa M, Fontana LCS, Finalli EFB, Paiva Filho ME, Spada APM. Metabolic syndrome and atherogenic indices in school children: a worrying panorama in Brazil. Diab Met Syndr. 2017;11:S397-S401. https://doi.org/10.1016/j.dsx.2017.03.024
Barbalho SM, Tofano RJ, Bechara MD, Quesada K, Coqueiro DP, Mendes CG. Castelli Index and estimative of LDL-c particle size may still help in the clinical practice? J Cardiovasc Disease Res. 2016;7:86-9. https://doi.org/10.5530/jcdr.2016.2.6
Barbalho SM, Tofano RJ, Oliveira MB, Quesada KR, Barion MR, Akuri MC, et al. HDL-C and non-HDL-C levels are associated with anthropometric and biochemical parameters. J Vasc Bras. 2019;18:e20180109. https://doi.org/10.1590/1677-5449.180109
Gadelha PCFP, Arruda IKG, Queiroz PMA, Maio R, Diniz AS. Consumption of ultraprocessed foods, nutritional status, and dyslipidemia in schoolchildren: a cohort study. Eur J Clin Nutr. 2019;73(8):1194-9. https://doi.org/10.1038/s41430-019-0404-2
Lima LR, Nascimento LM, Gomes KRO, Martins MCC, Rodrigues MTP, Frota KMG. Associação entre o consumo de alimentos ultraprocessados e parâmetros lipídicos em adolescentes. Ciênc Saúde Coletiva. 2020 [2020 Jan 15]. Available from: http://www.cienciaesaudecoletiva.com.br/artigos/associacao-entre-o-consumo-de-alimentos-ultraprocessados-e-parametros-lipidicos-em-adolescentes/17122?id=17122
Monteiro CA, Cannon G, Lawrence M, Costa Louzada ML, Pereira Machado P. Ultra-processed foods, diet quality, and health using the NOVA classification system. Rome: Food and Agriculture Organization; 2019.
Monteiro CA, Cannon G, Moubarac JC, Levy RB, Louzada MLC, Jaime PC. The UN decade of nutrition, the NOVA food classification and the trouble with ultra-processing. Public Health Nutr. 2018;21(1):5-17. https://doi.org/10.1017/S1368980017000234
Gomes KRO, Miranda CES, Frota KMG, Rodrigues MTP, Mascarenhas MDM, Araújo RSRM, et al. Análise da situação de saúde no ensino médio: metodologia. Rev Epidemiol Contr Infec. 2019;9(1). https://doi.org/10.17058/reci.v9i1.11873
Jelliffe DB, Jelliffe EFP. Anthropometry: major measurements. In: Jelliffe DB, Jelliffe EFP. Community nutritional assessment with special reference to less technically developed countries. 2nd. ed. London: Oxford University Press; 1989.
World Health Organization. WHO child growth standards: Length/height-forage, weight-for-age, weight-forlength, weight-for height and body mass index-for-age: methods and development. Geneva: Organization; 2007.
Callaway CW, Chumlea WC, Bouchard C, Himes JH, Lohman TG, Martin AD, et al. Circunferences. In: Lohman TG, Roche AF, Martorell R, editors. Anthropometric standardization reference manual. Champaign: Human Kinetics; 1988.
Fredriks AM, Van Buuren S, Verloove-Vanhorick MFSP, Wit JM. Are age references for waist circumference, hip circumference and waist-hip ratio in Dutch children useful in clinical practice? Eur J Pediatr. 2005;164:216-22. https://doi.org/10.1007/s00431-004-1586-7
Ashwell M, Hsieh SD. Six reasons why the waist-toheight 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. 2005;56:303-7. https://doi.org/10.1080/09637480500195066
Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem. 1972;18:499-502. https://doi.org/10.1093/clinchem/18.6.499
Maruyama C, Imamura K, Teramoto T. Assessment of LDL-c particle size by triglyceride/HDL-cholesterol ratio in non-diabetic, healthy subjects without prominent hyperlipidemia. J Atheroscler Thromb. 2003;10:186-91. https://doi.org/10.5551/jat.10.186
Faludi AA, Izar MCO, Saraiva JFK, Chacra APM, Bianco HT, Afiune Neto A, et al. Atualização da Diretriz Brasileira de Dislipidemias e Prevenção da Aterosclerose 2017. Arq Bras Cardiol. 2017;109:1-76.
Adamarczuk-Janczyszyn M, Zdrojowy-Wełna A, Rogala N, Zatonska K, Bednarek-Tupikowska G. Evaluation of selected atherosclerosis risk factors in women with subclinical hypothyroidism treated with L-thyroxine. Adv Clin Exp Med. 2016;25:457-63. https://doi.org/10.17219/acem/38555
Moshfegh AJ, Rhodes DG, Baer DJ, Murayi T, Clemens JC, Rumpler WV. 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. https://doi.org/10.1093/ajcn/88.2.324
Verly-Júnior E, Castro MA, Fisberg RM, Marchioni DML. Precision of usual food intake estimates according to the percentage of individuals with a second dietary measurement. J Acad Nutr Diet. 2012;122:1015-20. https://doi.org/10.1016/j.jand.2012.03.028
Monteiro CA, Cannon G, Levy RB, Moubarac JC, Jaime P, Martins AP, et al. NOVA: the star shines bright. World Nutr. 2016;7:28-38.
Universidade Estadual de Campinas. Tabela brasileira de composição de alimentos TACO. 4a. ed. Campinas: Unicamp; 2011 [cited 2020 Oct 9]. Available from: http://www.unicamp.br/nepa/taco/tabela.php?ativo= tabela
Instituto Brasileiro de Geografia e Estatística. Censo demográfico: resultados gerais da amostra. Rio de Janeiro: Instituto; 2011.
Raper N, Perloff B, Ingwersen L, Steinfeldt L, Anand J. An overview of USDA’s Dietary Intake Data System. J Food Compost Anal. 2004;17:545-55.
Multiple Source Method. Multiple Source Method (MSM) for estimating usual dietary intake from short-term measurement data: user guide. Potsdam: Efcoval; 2011 [cited 2017 Sept 20]. Available from: https://msm.dife.de
Instituto Brasileiro de Geografia e Estatística. Pesquisa de orçamentos familiares 2017-2018 primeiros resultados/IBGE, Coordenação de Trabalho e Rendimento. Rio de Janeiro: Instituto; 2019.
Carneiro CS, Peixoto MRG, Mendonça KL. Excesso de peso e fatores associados em adolescentes de uma capital brasileira. Rev Bras Epidemiol. 2017;20:260-73. https://doi.org/10.1590/1980-5497201700020007
Bloch KV, Klein CH, Szklo M, Kuschnir MCC, Abreu GA, Barufaldi LA, et al. Prevalências de hipertensão arterial e obesidade em adolescentes brasileiros. Rev Saúde Pública. 2016;50:9s. https://doi.org/10.1590/S01518-8787.2016050006685
Enes CC, Camargo CM, Justino MIC. Ultra-processed food consumption and obesity in adolescents. Rev Nutr. 2019;32:e180170. https://doi.org/10.1590/1678-9865201932e180170
Juul F, Martinez-Steele E, Parekh N, Monteiro CA, Chang VW. Ultra-processed food consumption and excesso weight among US adults. Br J Nutr. 2018;120(1):90-100.
Barroso TA, Marins LB, Alves R, Gonçalves ACS, Barroso SG, Rocha GS. Associação entre a obesidade central e a incidência de doenças e fatores de risco cardiovascular. Int J Cardiovasc Sci. 2017;30:416-24. https://doi.org/10.5935/2359-4802.20170073
Corrêa MM, Tomasi E, Thumé E, Oliveira ERA, Facchini LA. Razão cintura-estatura como marcador antropométrico de excesso de peso em idosos brasileiros. Cad Saúde Pública. 2017;33:e00195315. https://doi.org/10.1590/0102-311x00195315
Sapunar J, Aguilar-Farías N, Navarro J, Araneda G, Chandía-Poblete D, Manríquez V, et al. Alta prevalencia de dislipidemias y riesgo aterogénico en una población infanto-juvenil. Rev Med Chile. 2018;146:1112-22. https://doi.org/10.4067/S0034-98872018001001112
Feoli AMP, Ribeiro ECT, Piovesan CH, Macagnan FE, Oliveira M, Gustavo AS. Melhora do estilo de vida reduz o Índice de Castelli 1 em indivíduos com Síndrome Metabólica. Rev Saúde Pesquisa. 2018;11:467-74. https://doi.org/10.17765/2176-9206.2018v11n3p467-474
Rauber F, Campagnolo P, Hoffman DJ, Vitolo MR. Consumption of ultra-processed food products and its effects on children’s lipid profiles: a longitudinal study. Nutr Metab Cardiovasc. 2015;25:116-22. https://doi.org/10.1016/j.numecd.2014.08.001
Rocha NP, Milagresa LC, Longo GZ, Ribeiro AQ, Novaes JF. Association between dietary pattern and cardiometabolic risk in children and adolescents: a systematic review. J Pediatr. 2017;93:214-22. https://doi.org/10.1016/j.jped.2017.01.002
Scaglioni S, De Cosmi V, Ciappolino V, Parazzini F, Brambilla P, Agostoni C. Factors influencing children’s eating behaviours. Nutrients. 2018;10:706. https://doi.org/10.3390/nu10060706
Beserra JB, Soares NIS, Marreiros CS, Carvalho CMRG, Martins MCC, Freitas BJSA, et al. Crianças e adolescentes que consomem alimentos ultraprocessados possuem pior perfil lipídico? Uma revisão sistemática. Ciênc Saúde Coletiva. 2020;25:4979-89. https://doi.org/10.1590/1413-812320202512.29542018
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Copyright (c) 2022 Larisse Monteles NASCIMENTO, Nayara Vieira do Nascimento MONTEIRO, Thiana Magalhães VILAR, Cyntia Regina Lúcio de Sousa IBIAPINA, Karoline de Macedo Gonçalves FROTA
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