Anthropometric indicators of obesity for the prediction of metabolic syndrome in the older adults

Main Article Content

Mateus Carmo
Thainara Araújo Franklin
Lélia Lessa Teixeira Pinto
Claudio Bispo de Almeida
Adriana Alves Nery
Cezar Augusto Casotti

Abstract

Introduction: The anthropometric indicators of obesity may be important in predicting metabolic syndrome (MS). Objective: To evaluate the anthropometric indicators as predictors of MS and verify the association of these indicators with MS in older adult individuals of both sexes. Methods: Cross-sectional epidemiological study was carried out with 222 individuals aged 60 years or older residents in the urban area of Aiquara, Bahia state, Brazil. Older adults were measured for anthropometric indicators: body mass index (BMI), waist-to-height ratio (WHtR), waist circumference, conicity index, the sum of skinfolds; blood pressure; biochemical variables: fasting glucose, triglycerides, total cholesterol, and fractions. For the diagnosis of MS, the definition of the International Diabetes Federation was used. Descriptive and inferential data analysis was tested using correlation, the Poisson regression technique, and the Receiver Operating Characteristic (ROC) curve. Results: The prevalence of MS was 62.3%. There was a correlation of all anthropometric indicators with MS in both sexes. The indicators of visceral fat had a strong association in that these indicators had an area under the ROC curve higher than 0.76 (CI95% 0.66–0.85). Thus, most results showed a weak correlation. Conclusion: All anthropometric indicators can be used to predict MS in older adults for both sexes, however, BMI and WHtR showed the best predictions.

Downloads

Download data is not yet available.

Article Details

How to Cite
Carmo, M., Franklin, T. A., Pinto, L. L. T., Almeida, C. B. de, Nery, A. A., & Casotti, C. A. (2022). Anthropometric indicators of obesity for the prediction of metabolic syndrome in the older adults. ABCS Health Sciences, 47, e022212. https://doi.org/10.7322/abcshs.2020087.1544
Section
Original Articles
Author Biographies

Mateus Carmo, Universidade do Estado da Bahia (UNEB) - Guanambi (BA), Brazil

Profissional de Eduycação Física. Mestre. Doutorando pelo Programa Pós-graduação em Enfermagem e Saúde. Universidade Estadual do Sudoeste da Bahia. Professor da Universidade do Estado da Bahia.

Thainara Araújo Franklin, Universidade Estadual do Sudoeste da Bahia (UESB) - Jequié (BA), Brazil

Enfermeira. Mestre. Doutoranda pelo Programa Pós-graduação em Enfermagem e Saúde. Universidade Estadual do Sudoeste da Bahia.

Lélia Lessa Teixeira Pinto, Universidade Estadual do Sudoeste da Bahia (UESB) - Jequié (BA), Brazil

Profissional de Educação Física. Mestre. Doutora pelo Programa Pós-graduação em Enfermagem e Saúde. Universidade Estadual do Sudoeste da Bahia.

Adriana Alves Nery, Universidade Estadual do Sudoeste da Bahia (UESB) - Jequié (BA), Brazil

Enfermeira. Mestre. Doutora. Docente no Departamento de Saúde II e no Programa Pós-graduação em Enfermagem e Saúde da Universidade Estadual do Sudoeste da Bahia.

Cezar Augusto Casotti, Universidade Estadual do Sudoeste da Bahia (UESB) - Jequié (BA), Brazil

Cirurgião-dentista. Mestre. Doutor. Docente do Departamento de Saúde I e do Programa Pós-graduação em Enfermagem e Saúde na Universidade Estadual do Sudoeste da Bahia.

References

1. Grundy SM. Metabolic syndrome update. Trends Cardiovasc Med. 2016;26(4):364-73. https://doi.org/10.1016/j.tcm.2015.10.004

2. Rochlani Y, Pothineni NV, Kovelamudi S, Mehta JL. Metabolic syndrome: pathophysiology, management, and modulation by natural compounds. Ther Adv Cardiovasc Dis. 2017;11(8):215-25. https://doi.org10.1177/1753944717711379

3. Nóbrega OT, Faleiros VP, Telles JL. Gerontology in the developing Brazil: achievements and challenges in public policies. Geriatr Gerontol Int. 2009;9(2):135-9. https://doi.org/10.1111/j.1447-0594.2008.00499.x

4. McCracken E, Monaghan M, Sreenivasan S. Pathophysiology of the metabolic syndrome. Clin Dermatol. 2018;36(1):14-20. https://doi.org/10.1016/j.clindermatol.2017.09.004

5. Benedetti TRB, Meurer ST, Morini S. Índices antropométricos relacionados a doenças cardiovasculares e metabólicas em idosos. Rev Educ Fis UEM. 2012;23(1):123-30. https://doi.org/10.4025/reveducfis.v23i1.11393

6. Leal Neto JS, Coqueiro RS, Freitas RS, Fernandes MH, Oliveira DS, Barbosa AR. Anthropometric indicators of obesity as screening tools for high blood pressure in Older Adults. Int J Nurs Practice. 2013;19(4):360-7. https://doi.org/10.1111/ijn.12085

7. Zhang Z, He L, Xie X, Ling W, Deng J, Su Y, et al. Association of simple anthropometric indices and body fat with early atherosclerosis and lipid profiles in Chinese adults. PloS One. 2014;9(8):e104361. https://doi.org/10.1371/journal.pone.0104361

8. Oliveira CC, Roriz AK, Ramos LB, Gomes Neto M. Indicators of Adiposity Predictors of Metabolic Syndrome in the Older Adults. Ann Nutr Metab. 2017;70(1):9-15. https://doi.org/10.1159/000455333

9. Brasil. DATASUS. Departamento de Informática do SUS. População Residente. Estimativas para o TCU – Bahia. População estimada segundo ano. Aiquara. Available from: http://tabnet.datasus.gov.br/cgi/tabcgi.exe?ibge/cnv/poptba.def

10. Brasil. Ministério da Saúde. Sistema de Informação e Atenção Básica (SIAB). Cadastramento familiar - Bahia. Available from: http://tabnet.datasus.gov.br/cgi/deftohtm.exe?siab/cnv/SIABFBA.def

11. 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. https://doi.org/10.1161/CIRCULATIONAHA.109.192644

12. Frisancho AR. New standards of weight and body composition by frame size and height for assessment of the nutritional status of adults and Older Adults. Am J Clin Nutr. 1984;40(4):808-19. https://doi.org/10.1093/ajcn/40.4.808

13. Callaway WC, Chumlea WC, Bouchard C, Himes JH, Lohman TG, Martin AD, et al. Circumferences. In: Lohman TG, Roche AF, Martorell R. Anthropometric standardization reference manual. Champaign: Human Kinetics, 1988; p.39-54.

14. Harrison, GC, Buskirk ER, Carter JEL, Johnston FE, Lohman TG, Pollock ML, et al. Skinfold thickness and measurement technique. In: Lohman TG, Roche AF, Martorell R. Anthropometric standardization reference manual. Champaing: Human Kinetics, 1988; p.55-80.

15. Woolcott OO, Bergman RN. Relative fat mass (RFM) as a new estimator of whole-body fat percentage - A cross-sectional study in American adult individuals. Sci Rep. 2018;8:10980. https://doi.org/10.1038/s41598-018-29362-1

16. Valdez, R. A simple model-based index of abdominal adiposity. J Clin Epidemiol. 1991;44(9):955-6. https://doi.org/10.1016/0895-4356(91)90059-i

17. Mukaka MM. Statistic corner: a guide to the appropriate use of correlation coefficient in medical research. Malawi M J. 2012;24(3):69-71.

18. Gadelha AB, Myers J, Moreira S, Dutra MT, Safons MP, Lima RM. Comparison of adiposity indices and cut-off values in the prediction of metabolic syndrome in postmenopausal women. Diabetes Metab Syndr. 2016;10(3):143-8. https://dx.doi.org/10.1016/j.dsx.2016.01.005

19. Gharipour M, Sadeghi M, Dianatkhah M, Bidmeshgi S, Ahmadi A, Tahri M, et al. The cut-off values of anthropometric indices for identifying subjects at risk for Metabolic Syndrome in Iranian Older Adults. J Obes. 2014;2014:907149. https://doi.org/10.1155/2014/907149

20. Wians FH. Clinical laboratory tests: which, why, and what do the results mean? Lab Med. 2009;40(2):105-13. https://doi.org/10.1309/LM4O4L0HHUTWWUDD

21. Chu FL, Hsu CH, Jeng C. Low predictability of anthropometric indicators of obesity in metabolic syndrome (MS) risks among Older Adults women. Arch Gerontol Geriatr. 2012;55(3):718-23. https://doi.org/10.1016/j.archger.2012.02.005

22. Zeng Q, He Y, Dong S, Zhao X, Chen Z, Song Z, et al. Optimal cut-off values of BMI, waist circumference and waist: height ratio for defining obesity in Chinese adults. Br J Nutr. 2014;112(10):1735-44. https://doi.org/10.1017/S0007114514002657

23. Abulmeaty MMA, Almajwal AM, Almadani NK, Aldosari MS, Alnajim AA, Ali SB, et al. Anthropometric and central obesity indices as predictors of long-term cardiometabolic risk among Saudi young and middle-aged men and women. Saudi Med J. 2017;38(4):372-80. https://doi.org/10.15537/smj.2017.4.18758

24. Krause MP, Hallage T, Gama MPR, Sasaki JE, Miculis CP, Buzzachera CF, et al. Associação entre Perfil Lipídico e Adiposidade Corporal em Mulheres com Mais de 60 Anos de Idade. Arq Bras Cardiol. 2007;89(3):163-9. https://doi.org/10.1590/S0066-782X2007001500004

25. Oliveira MAM, Fagundes RLM, Moreira EAM, Trindade EBSM, Carvalho T. Relação de Indicadores Antropométricos com Fatores de Risco para Doença Cardiovascular. Arq Bras Cardiol. 2010;94(4):478-85. https://doi.org/10.1590/S0066-782X2010005000012

26. Hsu CH, Lin J, Hsieh C, Lau SC, Chiang W, Chen Y, et al. Adiposity measurements in association with metabolic syndrome in older men have different clinical implications. Nutr Res. 2014;34(3):219-25. https://doi.org/10.1016/j.nutres.2014.01.004