Cut-off Points of Anthropometric Markers Associated with Hypertension and Diabetes in Peru: Demographic and Health Survey 2018

Overview

Journal Public Health Nutr

Publisher Cambridge University Press

Date 2020 Oct 16

PMID 33059791

Citations 7

Authors

Akram Hernandez-Vasquez

Diego Azanedo

Rodrigo Vargas-Fernandez

Juan Pablo Aparco

Raul Martin Chaparro

Marilina Santero

Affiliations

Soon will be listed here.

Abstract

Objective: To determine the optimal anthropometric cut-off points for predicting the likelihood ratios of hypertension and diabetes in the Peruvian population.

Design: A cross-sectional study was performed to establish cut-off values for body mass index (BMI), waist circumference (WC), waist:height ratio (WHtR) and Conicity index (C-index) associated with increased risk of hypertension and diabetes. Youden's index (YIndex), area under the curve (AUC), sensitivity and specificity were calculated.

Setting: Peruvian households.

Participants: Peruvian population over the age of 18 years.

Results: A total of 31 553 subjects were included, 57 % being women. Among the women, 53·06 % belonged to the 25- to 44-year-old age group [mean age: 41·66 in men and 40·02 in women]. The mean BMI, WHtR and C-index values were higher in women 27·49, 0·61, 1·30, respectively, while the mean WC value was higher in men 92·12 cm (sd ± 11·28). The best predictors of hypertension in men were the WHtR (AUC = 0·64) and the C-index (AUC = 0·64) with an optimal cut-off point of 0·57 (YIndex = 0·284) and 1·301 (YIndex = 0·284), respectively. Women showed an AUC of 0·63 and 0·61 in the WHtR and C-index, respectively, with an optimal cut-off of 0·61 (YIndex = 0·236) and 1·323 (YIndex = 0·225). The best predictor for diabetes was the C-index: with an AUC = 0·67 and an optimal cut-off of 1·337 (YIndex = 0·346) for men and an AUC = 0·66 and optimal cut-off of 1·313 (YIndex = 0·319) for women.

Conclusions: Our findings show that in Peruvian adults, the WHtR and the C-index have the strongest association with hypertension in both sexes. Likewise, the C-index had the strongest association with diabetes.

Citing Articles

Predictive ability of anthropometric indices for risk of developing metabolic syndrome: a cross-sectional study.

Chaquila J, Ramirez-Jeri G, Miranda-Torvisco F, Baquerizo-Sedano L, Aparco J J Int Med Res. 2024; 52(11):3000605241300017.

PMID: 39578935 PMC: 11585023. DOI: 10.1177/03000605241300017.

Diagnostic Performance of the Measurement of Skinfold Thickness for Abdominal and Overall Obesity in the Peruvian Population: A 5-Year Cohort Analysis.

Rios-Escalante C, Alban-Fernandez S, Espinoza-Rojas R, Saavedra-Garcia L, Barengo N, Guerra Valencia J Int J Environ Res Public Health. 2023; 20(23).

PMID: 38063518 PMC: 10706145. DOI: 10.3390/ijerph20237089.

Correlation and agreement between the body mass index and abdominal perimeter with the waist-to-height ratio in peruvian adults aged 18 to 59 years.

Aparco J, Cardenas-Quintana H Rev Peru Med Exp Salud Publica. 2023; 39(4):392-399.

PMID: 36888800 PMC: 11397726. DOI: 10.17843/rpmesp.2022.394.11932.

[Cut-off points of anthropometric markers for hypertension and hyperglycemia in Argentine adults: a cross-sectional study from the 4th ENFR].

Farinola M, Sganga M Rev Fac Cien Med Univ Nac Cordoba. 2022; 79(3):260-266.

PMID: 36149065 PMC: 9590817. DOI: 10.31053/1853.0605.v79.n3.37313.

The Association between Altitude and Waist-Height Ratio in Peruvian Adults: A Cross-Sectional Data Analysis of a Population-Based Survey.

Hernandez-Vasquez A, Azanedo D Int J Environ Res Public Health. 2022; 19(18).

PMID: 36141764 PMC: 9517344. DOI: 10.3390/ijerph191811494.

References

Harris D, Song W, Shetty A, Levano K, Caceres O, Padilla C . Evolutionary genomic dynamics of Peruvians before, during, and after the Inca Empire. Proc Natl Acad Sci U S A. 2018; 115(28):E6526-E6535. PMC: 6048481. DOI: 10.1073/pnas.1720798115. View

Chun H, Kim I, Min K . Accuracy of Self-reported Hypertension, Diabetes, and Hypercholesterolemia: Analysis of a Representative Sample of Korean Older Adults. Osong Public Health Res Perspect. 2016; 7(2):108-15. PMC: 4850372. DOI: 10.1016/j.phrp.2015.12.002. View

Despres J, Moorjani S, Lupien P, Tremblay A, Nadeau A, Bouchard C . Regional distribution of body fat, plasma lipoproteins, and cardiovascular disease. Arteriosclerosis. 1990; 10(4):497-511. DOI: 10.1161/01.atv.10.4.497. View

Aschner P, Ruiz A, Balkau B, Massien C, Haffner S . Association of abdominal adiposity with diabetes and cardiovascular disease in Latin America. J Clin Hypertens (Greenwich). 2009; 11(12):769-74. PMC: 8673028. DOI: 10.1111/j.1559-4572.2008.00051.x. View

Chobanian A, Bakris G, Black H, Cushman W, Green L, Izzo Jr J . The Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure: the JNC 7 report. JAMA. 2003; 289(19):2560-72. DOI: 10.1001/jama.289.19.2560. View

Wang H, Liu A, Zhao T, Gong X, Pang T, Zhou Y . Comparison of anthropometric indices for predicting the risk of metabolic syndrome and its components in Chinese adults: a prospective, longitudinal study. BMJ Open. 2017; 7(9):e016062. PMC: 5623484. DOI: 10.1136/bmjopen-2017-016062. View

Dutra M, Reis D, Martins K, Gadelha A . Comparative Evaluation of Adiposity Indices as Predictors of Hypertension among Brazilian Adults. Int J Hypertens. 2018; 2018:8396570. PMC: 6009023. DOI: 10.1155/2018/8396570. View

Naing N . Easy way to learn standardization : direct and indirect methods. Malays J Med Sci. 2012; 7(1):10-5. PMC: 3406211. View

Aschner P, Buendia R, Brajkovich I, Gonzalez A, Figueredo R, Juarez X . Determination of the cutoff point for waist circumference that establishes the presence of abdominal obesity in Latin American men and women. Diabetes Res Clin Pract. 2011; 93(2):243-247. DOI: 10.1016/j.diabres.2011.05.002. View

10.

Wang Z, He S, Chen X . Capacity of different anthropometric measures to predict diabetes in a Chinese population in southwest China: a 15-year prospective study. Diabet Med. 2019; 36(10):1261-1267. DOI: 10.1111/dme.14055. View

11.

Almeida R, Almeida M, Araujo T . Abdominal obesity and cardiovascular risk: performance of anthropometric indexes in women. Arq Bras Cardiol. 2009; 92(5):345-50, 362-7, 375-80. DOI: 10.1590/s0066-782x2009000500007. View

12.

Savva S, Lamnisos D, Kafatos A . Predicting cardiometabolic risk: waist-to-height ratio or BMI. A meta-analysis. Diabetes Metab Syndr Obes. 2013; 6:403-19. PMC: 3810792. DOI: 10.2147/DMSO.S34220. View

13.

Asgari S, Luo Y, Akbari A, Belbin G, Li X, Harris D . A positively selected FBN1 missense variant reduces height in Peruvian individuals. Nature. 2020; 582(7811):234-239. PMC: 7410362. DOI: 10.1038/s41586-020-2302-0. View

14.

Ninatanta-Ortiz J, Romani F . [Metabolic syndrome and abdominal obesity criteria for andean populations - reply]. Rev Peru Med Exp Salud Publica. 2017; 34(1):148-149. DOI: 10.17843/rpmesp.2017.341.2720. View

15.

Barbosa P, Lessa I, Almeida Filho N, Magalhaes L, Araujo J . Criteria for central obesity in a Brazilian population: impact on metabolic syndrome. Arq Bras Cardiol. 2006; 87(4):407-14. DOI: 10.1590/s0066-782x2006001700003. View

16.

Carrillo-Larco R, Bernabe-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. PMC: 7613195. DOI: 10.17843/rpmesp.2019.361.4027. View

17.

Pitanga F, Lessa I . [Anthropometric indexes of obesity as an instrument of screening for high coronary risk in adults in the city of Salvador--Bahia]. Arq Bras Cardiol. 2005; 85(1):26-31. DOI: 10.1590/s0066-782x2005001400006. View

18.

Nishida C, Ko G, Kumanyika S . Body fat distribution and noncommunicable diseases in populations: overview of the 2008 WHO Expert Consultation on Waist Circumference and Waist-Hip Ratio. Eur J Clin Nutr. 2009; 64(1):2-5. DOI: 10.1038/ejcn.2009.139. View

19.

Zazai R, Wilms B, Ernst B, Thurnheer M, Schultes B . Waist circumference and related anthropometric indices are associated with metabolic traits in severely obese subjects. Obes Surg. 2013; 24(5):777-82. DOI: 10.1007/s11695-013-1141-6. View

20.

Caminha T, Ferreira H, Costa N, Nakano R, Carvalho R, Xavier Jr A . Waist-to-height ratio is the best anthropometric predictor of hypertension: A population-based study with women from a state of northeast of Brazil. Medicine (Baltimore). 2017; 96(2):e5874. PMC: 5266188. DOI: 10.1097/MD.0000000000005874. View