Anthropometric indicators of obesity for the prediction of metabolic syndrome in the older adults
Main Article Content
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.
Authors who publish with this journal agree to the following terms:
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (CC BY) that allows others to share and adapt the work with an acknowledgement of the work's authorship and initial publication in this journal.Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
Grundy SM. Metabolic syndrome update. Trends Cardiovasc Med. 2016;26(4):364-73. https://doi.org/10.1016/j.tcm.2015.10.004
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
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
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
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
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
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
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
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
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
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
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
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.
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.
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
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
Mukaka MM. Statistic corner: a guide to the appropriate use of correlation coefficient in medical research. Malawi M J. 2012;24(3):69-71.
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
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
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
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
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
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
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
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
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