Use of obesity markers for type 2 diabetes mellitus screening in Peru: a cross-sectional study in Peru
Obesity and diabetes screening
DOI:
https://doi.org/10.14306/renhyd.26.2.1513%20Keywords:
Obesity, Waist circumference, Body mass index, Diabetes mellitus, Type 2, Obesity, AbdominalAbstract
Aims: There are different methods to assess the fat body excess, but whether one of them is better to detect type 2 diabetes mellitus (T2DM) cases has not been fully explored in Peru. This study aimed to explore the diagnostic accuracy of some obesity anthropometric markers for newly-diagnosed T2DM at the population level and by sex.
Methodology: Secondary data analysis conducted using data from a population-based study carried out in Tumbes, Peru, with subjects aged between 30 and 69 years old. The outcome was newly diagnosed T2DM, defined using an oral glucose tolerance test. The index tests were obesity anthropometric markers: body mass index (BMI), waist circumference (WC), and waist-to-height ratio (WHR). Diagnostic accuracy of anthropometric markers was estimated using the area under the ROC curve (AUC); sensitivity and specificity were also estimated based on the Youden index.
Results: Data from 1500 participants were analyzed; and 4.7% were classified as having undiagnosed T2DM. Mean age was 47.6 (standard deviation: 10.6) and 50.1% were male. At the population level, diagnostic accuracy of anthropometric markers was: WHR (AUC: 0.67; 95% CI: 0.60–0.73), BMI (AUC: 0.65; 95% CI: 0.58–0.72), and WC (AUC: 0.65; 95% CI: 0.58–0.72). Stratified by sex, the results were similar except on the case of male WC, with an acceptable diagnostic accuracy (AUC: 0.70; 95% CI: 0.60-0.81).
Conclusion: The diagnostic accuracy of obesity anthropometric markers (BMI, WC and WHR) for T2DM screening was poor in the general population.
References
(1) Carrillo-Larco RM, Bernabé-Ortiz A. [Type 2 diabetes mellitus in peru: a systematic review of prevalence and incidence in the general population]. Rev Peru Med Exp Salud Publica. 2019;36(1):26-36, doi: 10.17843/rpmesp.2019.361.4027.
(2) NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in diabetes since 1980: a pooled analysis of 751 population-based studies with 4.4 million participants. Lancet. 2016;387(10027):1513-30, doi: 10.1016/S0140-6736(16)00618-8.
(3) International Diabetes Federation. IDF Diabetes Atlas 2021 10th edition. [accedido 6 febrero 2022]. Disponible en: https://diabetesatlas.org/atlas/tenth-edition/.
(4) World Health Organization. Obesity : preventing and managing the global epidemic : report of a WHO consultation. [accedido 6 febrero 2022]. Disponible en: https://apps.who.int/iris/handle/10665/42330.
(5) Barazzoni R, Gortan Cappellari G, Ragni M, Nisoli E. Insulin resistance in obesity: an overview of fundamental alterations. Eat Weight Disord. 2018;23(2):149-57, doi: 10.1007/s40519-018-0481-6.
(6) Chooi YC, Ding C, Magkos F. The epidemiology of obesity. Metabolism. 2019;92:6-10, doi: 10.1016/j.metabol.2018.09.005.
(7) Yamada G, Jones-Smith JC, Castillo-Salgado C, Moulton LH. Differences in magnitude and rates of change in BMI distributions by socioeconomic and geographic factors in Mexico, Colombia, and Peru, 2005-2010. Eur J Clin Nutr. 2020;74(3):472-80, doi: 10.1038/s41430-019-0479-9.
(8) Lebiedowska A, Hartman-Petrycka M, Błońska-Fajfrowska B. How reliable is BMI? Bioimpedance analysis of body composition in underweight, normal weight, overweight, and obese women. Ir J Med Sci. 2021;190(3):993-8, doi: 10.1007/s11845-020-02403-3.
(9) Nimptsch K, Konigorski S, Pischon T. Diagnosis of obesity and use of obesity biomarkers in science and clinical medicine. Metabolism. 2019;92:61-70, doi: 10.1016/j.metabol.2018.12.006.
(10) Naboush A, Hamdy O. Measuring visceral and hepatic fat in clinical practice and clinical research. Endocr Pract. 2013;19(4):587-9, doi: 10.4158/EP12331.OR.
(11) Bhowmik B, Munir SB, Ahmed KR, Siddiquee T, Diep LM, Wright E, et al. Anthropometric indices of obesity and type 2 diabetes in Bangladeshi population: Chandra Rural Diabetes Study (CRDS). Obes Res Clin Pract. 2014;8(3):e201-298, doi: 10.1016/j.orcp.2013.06.001.
(12) Huerta JM, Tormo M-J, Chirlaque M-D, Gavrila D, Amiano P, Arriola L, et al. Risk of type 2 diabetes according to traditional and emerging anthropometric indices in Spain, a Mediterranean country with high prevalence of obesity: results from a large-scale prospective cohort study. BMC Endocr Disord. 2013;13:7, doi: 10.1186/1472-6823-13-7.
(13) Ministerio de Salud. Guía de Práctica Clínica para el Diagnóstico, Tratamiento y Control de la Diabetes Mellitus Tipo 2 en el Primer Nivel de Atención. Lima, Perú: MINSA, 2016. Disponible en: http://bvs.minsa.gob.pe/local/MINSA/3466.pdf.
(14) Bernabe-Ortiz A, Perel P, Miranda JJ, Smeeth L. Diagnostic accuracy of the Finnish Diabetes Risk Score (FINDRISC) for undiagnosed T2DM in Peruvian population. Prim Care Diabetes. 2018;12(6):517-25, doi: 10.1016/j.pcd.2018.07.015.
(15) Instituto Nacional de Estadística e Informática. Perú: Resultados definitivos de los Censos Nacionales 2017. Lima, Perú.; 2017.
(16) Bernabé-Ortiz A, Carrillo-Larco RM, Gilman RH, Checkley W, Smeeth L, Miranda JJ, et al. Contribution of modifiable risk factors for hypertension and type-2 diabetes in Peruvian resource-limited settings. J Epidemiol Community Health. 2016;70(1):49-55, doi: 10.1136/jech-2015-205988.
(17) American Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes—2021. Diabetes Care. 2020;44(Supplement_1):S15-33, doi: 10.2337/dc21-S002.
(18) Aráuz-Hernández AG, Guzmán-Padilla S, Roselló-Araya M. La circunferencia abdominal como indicador de riesgo de enfermedad cardiovascular. Acta Médica Costarricense. 2013;55(3):122-7.
(19) Ashwell M, Gibson S. Waist-to-height ratio as an indicator of «early health risk»: simpler and more predictive than using a «matrix» based on BMI and waist circumference. BMJ Open. 2016;6(3):e010159, doi: 10.1136/bmjopen-2015-010159.
(20) Youden WJ. Index for rating diagnostic tests. Cancer. 1950;3(1):32-5, doi: 10.1002/1097-0142(1950)3:1<32::aid-cncr2820030106>3.0.co;2-3.
(21) Fischer JE, Bachmann LM, Jaeschke R. A readers’ guide to the interpretation of diagnostic test properties: clinical example of sepsis. Intensive Care Med. 2003;29(7):1043-51, doi: 10.1007/s00134-003-1761-8.
(22) Ye M, Robson PJ, Eurich DT, Vena JE, Xu J-Y, Johnson JA. Anthropometric changes and risk of diabetes: are there sex differences? A longitudinal study of Alberta’s Tomorrow Project. BMJ Open. 2019;9(7):e023829, doi: 10.1136/bmjopen-2018-023829.
(23) Brown N, Critchley J, Bogowicz P, Mayige M, Unwin N. Risk scores based on self-reported or available clinical data to detect undiagnosed type 2 diabetes: a systematic review. Diabetes Res Clin Pract. 2012;98(3):369-85, doi: 10.1016/j.diabres.2012.09.005.
(24) Mbanya V, Hussain A, Kengne AP. Application and applicability of non-invasive risk models for predicting undiagnosed prevalent diabetes in Africa: A systematic literature search. Prim Care Diabetes. 2015;9(5):317-29, doi: 10.1016/j.pcd.2015.04.004.
(25) Kodama S, Horikawa C, Fujihara K, Heianza Y, Hirasawa R, Yachi Y, et al. Comparisons of the strength of associations with future type 2 diabetes risk among anthropometric obesity indicators, including waist-to-height ratio: a meta-analysis. Am J Epidemiol. 2012;176(11):959-69, doi: 10.1093/aje/kws172.
(26) Zafra-Tanaka JH, Miranda JJ, Gilman RH, Checkley W, Smeeth L, Bernabe-Ortiz A. Obesity markers for the prediction of incident type 2 diabetes mellitus in resource-poor settings: The CRONICAS Cohort Study. Diabetes Res Clin Pract. 2020;170:108494, doi: 10.1016/j.diabres.2020.108494.
(27) Berber A, Gómez-Santos R, Fanghänel G, Sánchez-Reyes L. Anthropometric indexes in the prediction of type 2 diabetes mellitus, hypertension and dyslipidaemia in a Mexican population. Int J Obes Relat Metab Disord. 2001;25(12):1794-9, doi: 10.1038/sj.ijo.0801827.
(28) Petermann-Rocha F, Ulloa N, Martínez-Sanguinetti MA, Leiva AM, Martorell M, Villagrán M, et al. Is waist-to-height ratio a better predictor of hypertension and type 2 diabetes than body mass index and waist circumference in the Chilean population? Nutrition. 2020;79-80:110932, doi: 10.1016/j.nut.2020.110932.
(29) Lee BJ, Ku B, Nam J, Pham DD, Kim JY. Prediction of fasting plasma glucose status using anthropometric measures for diagnosing type 2 diabetes. IEEE J Biomed Health Inform. 2014;18(2):555-61, doi: 10.1109/JBHI.2013.2264509.
(30) Mirzaei M, Khajeh M. Comparison of anthropometric indices (body mass index, waist circumference, waist to hip ratio and waist to height ratio) in predicting risk of type II diabetes in the population of Yazd, Iran. Diabetes Metab Syndr. 2018;12(5):677-82, doi: 10.1016/j.dsx.2018.04.026.
(31) Seclen SN, Rosas ME, Arias AJ, Huayta E, Medina CA. Prevalence of diabetes and impaired fasting glucose in Peru: report from PERUDIAB, a national urban population-based longitudinal study. BMJ Open Diabetes Res Care. 2015;3(1):e000110, doi: 10.1136/bmjdrc-2015-000110.
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Jocelyn Chac-Camasca, Engell Flores-Vargas , Antonio Bernabé-Ortiz
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
This journal 