Anthropometric, metabolic and hemodynamic indicators as predictors of metabolic syndrome in adolescents

Authors

DOI:

https://doi.org/10.5020/18061230.2018.7967

Keywords:

Metabolic Syndrome, Anthropometry, Adolescent, Dyslipidemia, Blood Pressure, Abdominal Obesity.

Abstract

Objective: To predict metabolic syndrome in adolescents using anthropometric, metabolic and hemodynamic indicators based on analysis of sensitivity and specificity. Methods: Cross-sectional study carried out from July 2015 to March 2016 with 186 adolescents from eight private schools in the municipality of Picos, Piauí, Brazil. The data were individually collected using a form adapted with information on anthropometric, metabolic and blood pressure measures in a room reserved for such within the premises of the schools. After a 12-hour overnight fast, venous blood was collected for further biochemical analysis. The T-test was used for comparison of means in independent samples with significance level of p < 0.05. Cut-off scores were selected based on the receiver operating characteristic (ROC) curves using the values with sensitivity and specificity closest to each other and not below 60%. Results: There was a predominance of women (61.8%; n= 114), age between 15 and 19 years (57.5%; n=106) and syndrome in 2.7% (n=5) of the sample. When the area under the curve (AUC) was analyzed, conicity index (AUC=0.83), high-density lipoprotein (AUC= 0.88), systolic blood pressure (AUC=0.86) and mean arterial pressure (AUC=0.84) were found to be significant predictors of the syndrome in the total sample. Conclusion: The indicators analyzed proved to be predictors of metabolic syndrome, particularly conicity index, high-density lipoprotein, systolic blood pressure and mean arterial pressure.

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References

Ribeiro AC, Padoin SMM, Paula CC, Terra MG. The daily living of adolescentes with HIV/Aids: impersonality and tendency to fear. Text Context Nursing [Internet]. 2013 [acesso 2018 Set 15];22(3):680-6. Disponível em: http://www.scielo.br/pdf/tce/v22n3/en_v22n3a14.pdf

Pereira AMVB, Gomes I, Schwanke CHA. Metabolic syndrome in elderly assisted in tertiary health care in Curitiba, Paraná, Brazil: prevalence and association with health, functional capacity, life style, and sociodemographic factors. Sci Med [Internet]. 2016 [acesso em 2018 Set 08];26(3):1-9. Disponível em: http://revistaseletronicas.pucrs.br/ojs/index.php/scientiamedica/article/view/23444/14874

Leitão MP, Martins IS. Prevalence and factors associated with metabolic syndrome in users of primary healthcare units in São Paulo - SP. Rev Assoc Med Bras [Internet]. 2012 [acesso em 2018 Ago 15];58(1):60-9. Disponível em: http://www.scielo.br/pdf/ramb/v58n1/v58n1a16.pdf.

Araújo MFM, Freitas RWJF, Lima ACS, Pereira DCR, Zanetti ML, Damasceno MMC.

Relation between sleep quality and metabolic syndrome among university students. Texto & Contexto Enferm [Internet]. 2015 [acesso em 2018 Set 08];24(2):505-12. Disponível em:

www.scielo.br/pdf/tce/v24n2/pt_0104-0707-tce-24-02-00505.pdf

Silva ARV. Risk factors for metabolic syndrome in adolescents [editorial]. Rev Enferm UFPI [Internet]. 2014 [acesso em 2018 Ago 15];3(2):1-3. Disponível em: http://www.ojs.ufpi.br/index.php/reufpi

Barreto AC Neto, Andrade MIS, Lima VLM, Diniz AS. Peso corporal e escores de consumo alimentar em adolescentes no nordeste brasileiro. Rev Paul Pediatr [Internet]. 2015 [acesso em 2018 Set 08];33(3):318-25. Disponível em: http://www.scielo.br/pdf/rpp/v33n3/0103-0582-rpp-33-03-0318.pdf

Faria FR, Faria ER, Faria FR, Paula HAA, Franceschini SCC, Priore SE. Association between metabolic syndrome and anthropometric and body composition indicators in adolescents. Rev Assoc Bras Nutrição [Internet]. 2014 [acesso em 2018 Set 08];6(1):13-20. Disponível em: https://www.rasbran.com.br/rasbran/article/view/163/123

Faial LCM, Silva RMCRA, Pereira ER, Refrande SM, Souza LMC, Faial CSG. The school as an environment for health promotion during adolescence: literature review. Rev Pró-UniverSUS [Internet]. 2016 [acesso em 2018 Set 15];7(2):22-9. Disponível em: http://editora.universidadedevassouras.edu.br/index.php/RPU/article/view/344/525

Instituto Brasileiro de Geografia e Estatística, Diretoria de Pesquisas, Coordenação de População e Indicadores Sociais. Estimativas da população residente com data de referência 2017. [Internet]. 2017 [acesso em 2018 Set 08]. Disponível em: https://www.ibge.gov.br/estatisticas-novoportal/por-cidade-estado-estatisticas.html?t=destaques&c=2208007

Miot HA. Sample size in clinical and experimental trials. J Vasc Bras [Internet]. 2011 [acesso em 2018 Ago 15];10(4):275-8. Disponível em: http://www.scielo.br/pdf/jvb/v10n4/en_v10n4a01

Marcarini M, Mendes GK. Metabolic syndrome and its relationship with the nutritional status inadolescents – variability of diagnostic criteria. Scientia Medica [Internet]. 2013 [acesso em 2018 Ago 16];23(2):108-18. Disponível em: http://revistaseletronicas.pucrs.br/ojs/index.php/scientiamedica/article/view/12819/9661

Ferreira RW, Rombaldi AJ, Ricardo LIC, Hallal PC, Azevedo MR. Prevalence of sedentary behavior and its correlates among primary and secondary school students. Rev Paul

Pediatr [Internet]. 2016 [acesso em 2018 Ago 16];34(1):56-63. Disponível em: http://www.scielo.br/pdf/rpp/v34n1/0103-0582-rpp-34-01-0056.pdf

Ferreira MG, Valente JG, Gonçalves-Silva RMV, Sichieri R. Accuracy of waist circumference and waist-to-hip ratio as predictors of dyslipidemia in a cross-sectional study among blood donors in Cuiabá, Mato Grosso State, Brazil. Cad Saúde Pública [Internet]. 2006 [acesso em 2018 Ago 16];22(2):307-14. Disponível em: http://www.scielo.br/pdf/csp/v22n2/08.pdf

Pereira PF, Serrano HMS, Carvalho GQ, Lamounier JA, Peluzio MCC, Franceschini SCC, et al. Waist and waist-to-height ratio: useful to identify the metabolic risk of female adolescents? Rev Paul Pediatr [Internet]. 2011 [acesso em 2018 Ago 16];29(3):372-7. Disponível em: http://www.scielo.br/pdf/rpp/v29n3/en_a11v29n3.pdf

Pitanga FJG, Lessa I. Sensitivity and specificity of the conicity index as a coronary risk predictor among adults in Salvador, Brazil. Rev Bras Epidemiol [Internet]. 2004 [acesso em 2018 Ago 16];7(3):259-69. Disponível em: http://www.scielo.br/pdf/rbepid/v7n3/04.pdf

Malachias MVB, Souza WKSB, Plavnik FL, Rodrigues CIS, Brandão AA, Neves MFT, et al. 7th Brazilian Guideline of Arterial Hypertension. Arq Bras Cardiol [Internet]. 2016 [acesso em 2018 Ago 16];107(3 Supl 3):1-83. Disponível em: http://www.scielo.br/pdf/abc/v107n3s3/0066-782X-abc-107-03-s3-0000.pdf

Molina MCB, Faria CP, Montero MP, Cade NV, Mill JG. Cardiovascular risk factors in 7-to-10-year-old children in Vitória, Espírito Santo State, Brazil. Cad Saúde Pública [Internet]. 2010 [acesso em 2018 Ago 16];26(5):909-17. Disponível em: http://www.scielo.br/pdf/csp/v26n5/13.pdf

Silva JLN, Silva FL Júnior, Ferreira AP, Simões HG. Characterization and influence of indicators of central obesity, fitness cardiorespiratory and level of physical activity on blood pressure of school. Rev Andal Med Deporte [Internet]. 2017 [acesso em 2018 Ago 16];10(1):25–30. Disponível em: www.elsevier.es/pt-revista-revista-andaluza-medicina-del-deporte-284-pdf-S1888754616300089-S300

Brito BB, Leal JDV, Formiga LMF, Frota KMG, Silva ARV, Lima LHO. Cardiovascular diseases: risk factors in adolescents. Cogitare Enferm [Internet]. 2016 [acesso em 2018 Ago 16];21(2):1-8. Disponível em: http://www.saude.ufpr.br/portal/revistacogitare/wp-content/uploads/sites/28/2016/10/41848-177492-1-PB.pdf

Gauche R, Gadelha AB, Paiva FML, Oliveira PFA, Lima RM. Strength, muscle quality and markers of cardiometabolic risk in older women. Rev Bras Cineantropom Desempenho Hum [Internet]. 2015 [acesso em 2018 Ago 16];17(2):186-94. Disponível em: https://periodicos.ufsc.br/index.php/rbcdh/article/download/1980-0037.2015v17n2p186/28728

Cook S, Weitzman M, Auinger P, Nguyen M, Dietz WH. Prevalence of a metabolic syndrome phenotype in adolescents: findings from the third National Health and Nutrition Examination Survey, 1988-1994. Arch Pediatr Adolesc Med [Internet]. 2003 [acesso em 2018 Ago 16];157(8):821-7. Disponível em: https://jamanetwork.com/journals/jamapediatrics/fullarticle/481403

Unicef. Fase inicial e fase final da adolescência [Internet]. 2011 [acesso em 2018 Set 15]. Disponível em: http://www.unicef.org/brazil/sowc2011/foco1.html

Erdreich LS, Lee ET. Use of relative operating characteristic analysis in epidemiology: method for dealing with subjective judgement. Am J Epidemiol [Internet]. 1981 [acesso em 2018 Ago 16];114(5):649-62. Disponível em: https://doi.org/10.1093/oxfordjournals.aje.a113236

Kuschnir MCC, Bloch KV, Szklo M, Klein CH, Barufaldi LA, Abreu GA, et al. ERICA: prevalence of metabolic syndrome in Brazilian adolescents. Rev Saúde Pública [Internet]. 2016 [acesso em 2018 Ago 16];50(Supl 1):11s. Disponível em: http://www.scielo.br/pdf/rsp/v50s1/0034-8910-rsp-S01518-87872016050006701.pdf

Pontes LM, Amorim RJM, Lira PIC. Components of metabolic syndrome and associated factors in adolescents: a case-control study. Rev AMRIGS [Internet]. 2016 [acesso em 2018 Ago 16];60(2):121-8. Disponível em: http://www.amrigs.org.br/revista/60-02/10_1598_Revista%20AMRIGS.PDF

Mbowe O, Diaz A, Wallace J, Mazariegos M, Jolly P. Prevalence of metabolic syndrome and associated cardiovascular risk factors in Guatemalan school children. Matern Child Health J [Internet]. 2014 [acesso em 2018 Ago 16];18(7):1619-27. Disponível em: http://europepmc.org/articles/PMC4055521

Park SH, Park JH, Kang JW, Park HY, Park J, Shin KJ. Prevalence of the metabolic syndrome and abnormal lipid levels among Korean adolescents. J Paediatr Child Health [Internet]. 2013 [acesso em 2018 Ago 16];49(7):582-7. Disponível em: https://doi.org/10.1111/jpc.12284

Ribeiro-Silva RC, Florence TCM, Conceição-Machado MEP, Fernandes GB, Couto RD. Anthropometric indicators for prediction of metabolic syndrome in children and adolescents: a population-based study. Rev Bras Saude Mater Infant [Internet]. 2014 [acesso em 2018 Ago 16];14(2):173-81. Disponível em: http://www.scielo.br/pdf/rbsmi/v14n2/1519-3829-rbsmi-14-02-0173.pdf

Özer S, Yılmaz R, Özlem KN, Sönmezgöz E, Karaaslan E, Altuntaş B, et al. Higher hdl levels are a preventive factor for metabolic syndrome in obese Turkish children. Nutr Hosp [Internet]. 2014 [acesso em 2018 Ago 16];31(1):307-12. Disponível em: https://www.researchgate.net/publication/270655893/download

Pereira PF, Faria FR, Faria ER, Hermsdorff HHM, Peluzio MCG, Franceschini SCC, et al. Anthropometric indices to identify metabolic syndrome and hypertriglyceridemic waist

phenotype: a comparison between the three stages of adolescence. Rev Paul Pediatr [Internet]. 2015 [acesso em 2018 Ago 16];33(2):194-203. Disponível em: http://www.scielo.br/pdf/rpp/v33n2/0103-0582-rpp-33-02-00194.pdf

Rosini N, Moura SAZO, Rosini RD, Machado MJ, Silva EL. Metabolic syndrome and importance of associated variables in children and adolescents in Guabiruba - SC, Brazil. Arq Bras Cardiol [Internet]. 2015 [acesso em 2018 Ago 16];105(1):37-44. Disponível em: http://www.scielo.br/pdf/abc/v105n1/0066-782X-abc-20150040.pdf

Lima LHO. Promoção da saúde na infância e adolescência: perspectivas e desafios. In: Silva ARV, Oliveira AKS, Lima LHO, Machado ALG. Interlocuções entre vivências coletivas na promoção da saúde [Internet]. Teresina: EDUFPI; 2018 [acesso em 2018 Set 08]. p. 27-30. Disponível em: http://saudecoletivapicos.com.br/media/livros/Interloc.pdf

Published

2018-10-31

How to Cite

Moura, T. N. B. de, Leal, J. D. V., Sousa, G. S., Sousa, R. K. C. de, Oliveira, E. A. R., & Lima, L. H. de O. (2018). Anthropometric, metabolic and hemodynamic indicators as predictors of metabolic syndrome in adolescents. Brazilian Journal in Health Promotion, 31(3). https://doi.org/10.5020/18061230.2018.7967

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Section

Original Articles