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Comparison of the Ability of Anthropometric Indices to Predict the Risk of Diabetes Mellitus in South African Males: SANHANES-1

Author

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  • Machoene D. Sekgala

    (School of Public Health, University of the Western Cape, Bellville 7535, South Africa
    Human and Social Capabilities, Human Sciences Research Council, Cape Town 8000, South Africa)

  • Ronel Sewpaul

    (Human and Social Capabilities, Human Sciences Research Council, Cape Town 8000, South Africa)

  • Maretha Opperman

    (Department of Biotechnology and Consumer Science, Cape Peninsula University of Technology, Cape Town 7535, South Africa)

  • Zandile J. Mchiza

    (School of Public Health, University of the Western Cape, Bellville 7535, South Africa
    Non-Communicable Diseases Research Unit, South African Medical Research Council, Tygerberg, Cape Town 7505, South Africa)

Abstract

This study aimed to assess the sensitivity of body mass index (BMI) to predict the risk of diabetes mellitus (DM) and whether waist circumference (WC), waist-to-hip (WHR) and waist-to-height (WHtR) ratios are better predictors of the risk of DM than BMI in South African men aged 20 years and older. Data from the first South African National Health and Nutrition Examination Survey (SANHANES-1) were used. Overall, 1405 men who had valid HbA1c outcomes were included. The sensitivity, specificity, and optimal cut-off points for predicting DM were determined using the receiver operating characteristic (ROC) curve analysis. A total of 34.6% percent of the study participants were overweight/obese, while 10.5%, 10.4%, 36.6% and 61.0% had HbA1c, WC, WHR and WHtR above the normal reference ranges, respectively. Based on age-adjusted logistic regression analysis, the highest likelihood of DM was observed for those participants who had increased WC and WHtR (odds ratios [OR] were 6.285 (95% CI: 4.136–9.550; p < 0.001) and 8.108 (95% CI: 3.721–17.667; p < 0.001)). The ROC curve analyses for WC, WHR, and WHtR displayed excellent ability to predict the risk of DM, with their areas under the curve (AUC) being 80.4%, 80.2% and 80.8%, respectively. The overall cut-off points to predict the risk of DM for WC, WHR, and WHtR were ≥88.95 cm, ≥0.92, and >0.54, respectively. The ROC analysis for BMI, on the other hand, showed acceptable ability to predict the risk of DM (AUC = 75.6%), with its cut-off point being ≥24.64 kg/m 2 . Even after stratifying the data by two age groups, WHtR remained a superior index to predict DM, especially in the younger age group. To conclude, no significant differences were observed between the AUC for BMI the AUCs for other indices. However, the AUCs for these indices showed significant excellent ability as opposed to the significant acceptable ability of BMI to predict DM in adult South African men.

Suggested Citation

  • Machoene D. Sekgala & Ronel Sewpaul & Maretha Opperman & Zandile J. Mchiza, 2022. "Comparison of the Ability of Anthropometric Indices to Predict the Risk of Diabetes Mellitus in South African Males: SANHANES-1," IJERPH, MDPI, vol. 19(6), pages 1-16, March.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:6:p:3224-:d:767457
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    References listed on IDEAS

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    1. Zandile June-Rose Mchiza & Whadi-Ah Parker & Muhammad Zakir Hossin & Amy Heshmati & Demetre Labadarios & Daniel Falkstedt & Ilona Koupil, 2019. "Social and Psychological Predictors of Body Mass Index among South Africans 15 Years and Older: SANHANES-1," IJERPH, MDPI, vol. 16(20), pages 1-20, October.
    2. Qian Wang & Lingzhong Xu & Jiajia Li & Long Sun & Wenzhe Qin & Gan Ding & Jing Zhu & Jiao Zhang & Zihang Yu & Su Xie, 2018. "Association of Anthropometric Indices of Obesity with Hypertension in Chinese Elderly: An Analysis of Age and Gender Differences," IJERPH, MDPI, vol. 15(4), pages 1-14, April.
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    1. Cristian Rios-Escalante & Silvia Albán-Fernández & Rubén Espinoza-Rojas & Lorena Saavedra-Garcia & Noël C. Barengo & Jamee Guerra Valencia, 2023. "Diagnostic Performance of the Measurement of Skinfold Thickness for Abdominal and Overall Obesity in the Peruvian Population: A 5-Year Cohort Analysis," IJERPH, MDPI, vol. 20(23), pages 1-17, November.

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