Anthropometric indicators of obesity for the prediction of metabolic syndrome in the older adults
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-sect...
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Published in | ABCS health sciences Vol. 47; p. e022212 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Faculdade de Medicina do ABC
15.06.2022
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Subjects | |
Online Access | Get full text |
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Summary: | 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. |
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ISSN: | 2318-4965 2357-8114 |
DOI: | 10.7322/abcshs.2020087.1544 |