Skip to main content

Advertisement

Log in

Metabolic syndrome and its association with changes in modifiable risk factors: Epifloripa aging study

  • Research article
  • Published:
Journal of Diabetes & Metabolic Disorders Aims and scope Submit manuscript

Abstract

Purpose

To estimate the prevalence of Metabolic Syndrome (MetS) and its association with changes in modifiable risk factors in older adults from southern Brazil.

Methods

A longitudinal study was performed with data from EpiFloripa Aging study. We defined MetS by the existence of three or more of the following risk factors for cardiovascular disease (CVD): waist circumference (WC) (≥ 92 cm in men and ≥ 87 cm in women); fasting glucose (≥100 mg/dl); decreased HDL cholesterol (<40 mg/dl in men and <50 mg/dl in women); hypertriglyceridemia (≥150 mg/dl) and blood pressure (≥130/85 mmHg). We evaluated the changes in modifiable risk factors (smoking, alcohol consumption, fruit and vegetable consumption, physical activity, and body mass index) between the two moments of the study (2009/10 and 2013/14). Directed acyclic graph and logistic regression models were used.

Results

Among the 599 participants, the prevalence of MetS was 64.0% (95% CI, 58.7–68.9). In the adjusted analysis, those who remained or became persons who are overweight (OR = 4.59; 95% CI: 3.05–6.89) and those who remained or became insufficiently active (OR = 1.92; 95% CI: 1.23–2.98) were more likely to present MetS.

Conclusion

Our findings suggest that being or becoming overweight and being or becoming insufficiently active are modifiable factors associated with MetS. These results highlight the need for developing preventive strategies for the observed risk indicators to mitigate the prevalence of MetS in older adults.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Vanwormer JJ, Jackie LB, Abbey CS, Arthur S, Thomas K. Lifestyle changes and prevention of metabolic syndrome in the heart of New Ulm project. Prev Med Rep. 2017;6:242–5.

    Article  Google Scholar 

  2. Alberti KGMM, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the international diabetes federation task force on epidemiology and prevention; National Heart, Lung, and Blood Institute; American Heart Association; world heart federation; international atherosclerosis society; and international association for the study of obesity. Circulation. 2009;120(16):1640–5.

    Article  CAS  Google Scholar 

  3. Scuteri A, Stephane L, Francesco C, John C, Pedro GC, Leocadio RM, et al. Metabolic syndrome across Europe: different clusters of risk factors. Eur J Prev Cardiol. 2015;22(4):486–91.

    Article  Google Scholar 

  4. Ford ES, Li C, Zhao G. Prevalence and correlates of metabolic syndrome based on a harmonious definition among adults in the US. J Diabetes. 2010;2(3):180–93.

    Article  Google Scholar 

  5. Ramires EKNM, Menezes RCE, Longo-Silva G, Santos TG, Marinho PM, Silveira JAC. Prevalence and factors associated with metabolic syndrome among brazilian adult population: National Health Survey - 2013. Arq Bras Cardiol. 2018;110(5):455–66.

    PubMed  PubMed Central  Google Scholar 

  6. World Health Organization. Global Status Report on Noncommunicable Diseases 2018. [acessed on 2021 Apr 2] Available from: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases

  7. Pimenta AM, Gazzinelli A, Velásquez-Meléndez G. Prevalence of metabolic syndrome and its associated factors in a rural area of Minas Gerais state (MG, Brazil). Cienc Saude Colet. 2011;16(7):3297–306.

    Article  Google Scholar 

  8. Vieira EC, Peixoto MRG, Silveira EA. Prevalence and factors associated with metabolic syndrome in elderly users of the unified health system. Rev Bras Epidemiol. 2014;43:805–17.

    Article  Google Scholar 

  9. Silva PAB, Sacramento AJ, Carmo CID, Silva LB, Silqueira SMF, Soares SM. Factors associated with metabolic syndrome in older adults: a population-based study. Rev Bras Enferm. 2019;72:221–8.

    Article  Google Scholar 

  10. Confortin SC, Schneider IJC, Antes DL, Cembranel F, Ono LM, Marques LP, et al. Life and health conditions among elderly: results of the EpiFloripa Idoso cohort study. Epidemiol Serv Saude. 2017;26(2):305–17.

    Article  Google Scholar 

  11. Cardinal TR, Vigo A, Duncan BB, Matos SMA, da Fonseca MDJM, Barreto SM, et al. Optimal cut-off points for waist circumference in the definition of metabolic syndrome in Brazilian adults: baseline analyses of the longitudinal study of adult health (ELSA-Brasil). Diabetol Metab Syndr. 2018;10(1):1–9.

    Article  Google Scholar 

  12. Confortin SC, Ono LM, Barbosa AR, d’Orsi E. Sarcopenia and its association with changes in socioeconomic, behavioral, and health factors: the EpiFloripa Elderly Study. Cad Saude Publica. 2018; 34(12): e00164917.

  13. Babor TF, Higgins-Biddle JC, Saunders JB, Monteiro MG. The alcohol use disorders identification test: guidelines for use in primary care. 2ª ed. Geneva: World Health Organization; 2001.

    Google Scholar 

  14. Brasil. Ministério da Saúde. Secretaria de Atenção à Saúde. Departamento de Atenção Básica. Orientações para a coleta e análise de dados antropométricos em serviços de saúde: Norma Técnica do Sistema de Vigilância Alimentar e Nutricional - SISVAN. 2011. [acessed 2021 Apr 2] Available from http://189.28.128.100/dab/docs/portaldab/publicacoes/orientacoes_coleta_analise_dados_antropometricos.pdf. Portuguese.

  15. Craig CL, Marshall AL, Sjostrom M, Bauman AE, Booth ML, Ainsworth BE, et al. International physical activity questionnaire: 12-country reliability and validity. Med Sci Sports Exerc. 2003;35(8):1381–95.

    Article  Google Scholar 

  16. Bertolucci PHF, Brucki SMD, Campacci SR, Juliano Y. O Mini-Exame do estado mental em uma população geral: impacto da escolaridade. Arq Neuro-Psiquiatr.1994; 52 (1):1–7.

  17. Textor J, Hardt J, Knüppel S. DAGitty: a graphical tool for analyzing causal diagrams. Epidemiol. 2011;22(5):745.

    Article  Google Scholar 

  18. Hernán MA, Cole SR. Invited commentary: causal diagrams and measurement bias. Am J Epidemiol. 2009;170(8):959–62.

    Article  Google Scholar 

  19. Kim S, So WY. Prevalence and correlates of metabolic syndrome and its components in elderly Korean adults. Exp Gerontol. 2016;100(84):107–12.

    Article  Google Scholar 

  20. Li W, Song F, Wang X, Wang L, Wang D, Yin X, et al. Prevalence of metabolic syndrome among middle-aged and elderly adults in China: current status and temporal trends. Ann Med. 2018;50(4):345–53.

    Article  Google Scholar 

  21. Barranco-Ruiz Y, Villa-González E, Venegas-Sanabria LC, Chavarro-Carvajal DA, Cano-Gutiérrez CA, Izquierdo M, et al. Metabolic syndrome and its associated factors in older adults: a secondary analysis of SABE Colombia in 2015. Metab Syndr Relat Disord. 2020;18(8):389–98.

    Article  Google Scholar 

  22. Orces CH, Gavilanez EL. The prevalence of metabolic syndrome among older adults in Ecuador: results of the SABE survey. Diabetes Metab Syndr. 2017;11:S555–60.

    Article  Google Scholar 

  23. Ortiz-Rodríguez MA, Yáñez-Velasco L, Carnevale A, Romero-Hidalgo S, Bernal D, Aguilar-Salinas C, et al. Prevalence of metabolic syndrome among elderly Mexicans. Arch Gerontol Geriatr. 2017;73:288–93.

    Article  Google Scholar 

  24. Costa ACDO, Duarte YADO, Andrade FBD. Metabolic syndrome: physical inactivity and socioeconomic inequalities among non-institutionalized Brazilian elderly. Rev Bras Epidemiol. 2020;23:e200046.

    Article  Google Scholar 

  25. Van Ancum JM, Jonkman NH, Schoor NM, Tressel E, Meskers CGM, Pijnappels M, et al. Predictors of metabolic syndrome in community-dwelling older adults. PLoS One. 2018;13(10):e0206424.

    Article  Google Scholar 

  26. Gray N, Picone G, Sloan F, Yanskin A. The relationship between BMI and onset of diabetes mellitus and its complications. South Med J. 2015;108(1):29.

    Article  Google Scholar 

  27. Luz RH, Barbosa AR, d'ORSI E. Waist circumference, body mass index and waist-height ratio: are two indices better than one for identifying hypertension risk in older adults? Prev Med. 2016;93:76–81.

    Article  Google Scholar 

  28. Coqueiro RS, Fares D, Barbosa AR, Passos TDRO, Reis-Júnior WM, Fernandes MH. Anthropometric indicators as predictors of serum triglycerides and hypertriglyceridemia in older adults. Med Express. 2014;1(4):202–5.

    Article  Google Scholar 

  29. St-Onge MP, Gallagher D. Body composition changes with aging: the cause or the result of alterations in metabolic rate and macronutrient oxidation? Nutrition. 2010;26(2):152–5.

    Article  CAS  Google Scholar 

  30. Chait A, den Hartigh LJ. Adipose tissue distribution, inflammation and its metabolic consequences, including diabetes and cardiovascular disease. Front Card Med. 2020;7:22.

    Article  CAS  Google Scholar 

  31. Brandão I, Martins MJ, Monteiro R. Metabolically healthy obesity - heterogeneity in definitions and unconventional factors. Metabolites. 2020;10(2):48.

    Article  Google Scholar 

  32. Engin A. The definition and prevalence of obesity and metabolic syndrome. Adv Exp Med Biol. 2017;960:1–17.

    Article  CAS  Google Scholar 

  33. Serrano-Sánchez J., Fernández-Rodríguez MJ, Sanchis-Moysi J, Rodríguez-Pérez MD., Marcelino-Rodríguez I, Cabrera de León A. Domain and intensity of physical activity are associated with metabolic syndrome: A population-based study. PLoS One. 2019; 14(7):e0219798.

  34. He D. Xi. B., Xue, J., Huai, P., Zhang, M., and Li, J. association between leisure time physical activity and metabolic syndrome: a meta-analysis of prospective cohort studies. Endocrine. 2014;46:231–40.

    Article  CAS  Google Scholar 

  35. Galmes-Panades AM, Konieczna J, Varela-Mato V, Abete I, Babio N, Fiol M, et al. Targeting body composition in an older population: do changes in movement behaviours matter? Longitudinal analyses in the PREDIMED-plus trial. BMC Med. 2021;19(1):3.

    Article  Google Scholar 

  36. Stetic L, Belcic I, Sporis G, Stetic L, Starcevic N. Influence of physical activity on the regulation of disease of elderly persons with metabolic syndrome. Int J Environ Res Public Health. 2021;18(1):275.

    Article  CAS  Google Scholar 

  37. Slagter NS, van Vliet-Ostaptchouk JV, Vonk JM, Boezen M, Dullaart RPF, Kobold ACM, et al. Associations between smoking, components of metabolic syndrome and lipoprotein particle size. BMC Med. 2013;11:195.

    Article  Google Scholar 

  38. Raposo L, Severo M, Barros H, Santos AC. The prevalence of metabolic syndrome in Portugal: the PORMETS study. BMC Public Health. 2017;17:555.

    Article  Google Scholar 

  39. Buja A, Scafato E, Sergi G, Maggi S, Suhad MA, Rausa G, Coin A, Baldi I, Manzato E, Galluzzo L, Enzi G. Perissinotto E; ILSA working group. Alcohol consumption and metabolic syndrome in the elderly: results from the Italian longitudinal study on aging. Eur J Clin Nutr. 2010;64(3):297–307.

    Article  CAS  Google Scholar 

  40. Kim SK, Hong S-H, Chung J-H, Cho KB. Association between alcohol consumption and metabolic syndrome in a community-based cohort of Korean adults. Med Sci Monit. 2017;23:2104–10.

    Article  CAS  Google Scholar 

  41. Stoutenberg M, Lee D, Sui X, Hooker S, Horigian V, Perrino T, Blair S. Prospective study of alcohol consumption and the incidence of the metabolic syndrome in US men. Br J Nutr. 2013;110(5):901–10.

    Article  CAS  Google Scholar 

  42. Chang SH, Chang YY, Wu LY. Gender differences in lifestyle and risk factors of metabolic syndrome: do women have better health habits than men? J Clin Nurs. 2019;28(11–12):2225–34.

    Article  Google Scholar 

  43. Santos IKS, Conde WL. Trend in dietary patterns among adults from Brazilian state capitals. Rev Bras Epidemiol. 2020;23:e200035.

    Article  Google Scholar 

Download references

Acknowledgments

We would like to thank the Florianopolis Health Authority staff for their useful help with the practical aspects of the study.

Funding

This work was supported by the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), process number 475.904/2013–3. Garcia K.C. received a master’s scholarship grant from the Coordenação de Aperfeiçoamento de Pessoal de Ensino Superior (Capes) - Finance Code 001.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Aline Rodrigues Barbosa.

Ethics declarations

The authors have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Garcia, K.C., Confortin, S.C., Meneghini, V. et al. Metabolic syndrome and its association with changes in modifiable risk factors: Epifloripa aging study. J Diabetes Metab Disord 21, 77–84 (2022). https://doi.org/10.1007/s40200-021-00937-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40200-021-00937-6

Keywords

Navigation