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Analysis of Anthropometric Measurements Using Receiver Operating Characteristic Curve for Impaired Waist to Height Ratio Detection

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Advances in Emerging Trends and Technologies (ICAETT 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1302))

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Abstract

Metabolic dysfunctions such as obesity, insulin resistance, metabolic syndrome, and glucose tolerance are strongly related to each other. The presence of any of them in a person translates into a high risk of diseases such as diabetes, heart failure, and cardiovascular disease. Anthropometric measurements such as body circumferences, body folds, and anthropometric indices such as waist-height ratio (WHtR) and body mass index (BMI) have been widely used in the study of metabolic diseases. This study aims to look for relationships between WHtR and anthropometric measurements such as subcutaneous folds and body circumferences. For this purpose, a database of 1863 subjects was used, 16 anthropometric variables were measured for each participant in the database and the BMI was calculated. The receiver operating characteristic (ROC) curves were used to assess the ability of BMI and each anthropometric measurement was used to diagnose BMI impairment. The findings reported in this research strongly suggest that the diagnosis of WHtR deficiency can be made from circumferences, skinfolds, and BMI. In this study, the anthropometric measures that best detect subjects with WHtR deficiency were BMI, subscapular skinfold, supra iliac skinfold, and arm circumference with a high probability of detecting normal WHtR-deficient subjects. Abdominal circumference is one of the areas that have the most direct relationship with cardiac metabolic risk, however, the findings of this study open the possibility of studying accumulated fat tissue in the arms and back as areas that could also indicate a risk of metabolic dysfunction.

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References

  1. Ansaldo, A.M., Montecucco, F., Sahebkar, A., Dallegri, F., Carbone, F.: Epicardial adipose tissue and cardiovascular diseases. Int. J. Cardiol. 278, 254–260 (2019)

    Article  Google Scholar 

  2. Ashwell, M., Gunn, P., Gibson, S.: Waist-to-height ratio is a better screening tool than waist circumference and BMI for adult cardiometabolic risk factors: systematic review and meta-analysis. Obes. Rev. 13(3), 275–286 (2012)

    Article  Google Scholar 

  3. Barroso, T.A., Marins, L.B., Alves, R., Gonçalves, A.C.S., Barroso, S.G., Rocha, G.D.S.: Association of central obesity with the incidence of cardiovascular diseases and risk factors. Int. J. Cardiovasc. Sci. 30(5), 416–424 (2017)

    Google Scholar 

  4. Browning, L.M., Hsieh, S.D., Ashwell, M.: A systematic review of waist-to-height ratio as a screening tool for the prediction of cardiovascular disease and diabetes: 0.5 could be a suitable global boundary value. Nutr. Res. Rev. 23(2), 247–269 (2010)

    Article  Google Scholar 

  5. Cardinal, T.R., Vigo, A., Duncan, B.B., Matos, S.M.A., da Fonseca, M.D.J.M., Barreto, S.M., Schmidt, M.I.: Optimal cut-off points for waist circumference in the definition of metabolic syndrome in brazilian adults: baseline analyses of the longitudinal study of adult health (Elsa-Brasil). Diabetology Metab. Syndr. 10(1), 1–9 (2018)

    Google Scholar 

  6. Chen, X., Liu, Y., Sun, X., Yin, Z., Li, H., Deng, K., Cheng, C., Liu, L., Luo, X., Zhang, R., et al.: Comparison of body mass index, waist circumference, conicity index, and waist-to-height ratio for predicting incidence of hypertension: the rural Chinese cohort study. J. Hum. Hypertens. 32(3), 228–235 (2018)

    Article  Google Scholar 

  7. Chung, I.H., Park, S., Park, M.J., Yoo, E.G.: Waist-to-height ratio as an index for cardiometabolic risk in adolescents: results from the 1998–2008 knhanes. Yonsei Med. J. 57(3), 658–663 (2016)

    Article  Google Scholar 

  8. Correa, M.M., Thume, E., De Oliveira, E.R.A., Tomasi, E.: Performance of the waist-to-height ratio in identifying obesity and predicting non-communicable diseases in the elderly population: a systematic literature review. Arch. Gerontol. Geriatr. 65, 174–182 (2016)

    Article  Google Scholar 

  9. Dong, J., Wang, S.S., Chu, X., Zhao, J., Liang, Y.Z., Yang, Y.B., Yan, Y.X.: Optimal cut-off point of waist to height ratio in Beijing and its association with clusters of metabolic risk factors. Curr. Med. Sci. 39(2), 330–336 (2019)

    Article  Google Scholar 

  10. Ehrampoush, E., Arasteh, P., Homayounfar, R., Cheraghpour, M., Alipour, M., Naghizadeh, M.M., Davoodi, S.H., Askari, A., Razaz, J.M., et al.: New anthropometric indices or old ones: which is the better predictor of body fat? Diabetes Metab. Syndr.: Clinic. Res. Rev. 11(4), 257–263 (2017)

    Article  Google Scholar 

  11. Farina, P.V.R., Severeyn, E., Wong, S., Turiel, J.P.: Study of cardiac repolarization during oral glucose tolerance test in metabolic syndrome patients. In: 2012 Computing in Cardiology, pp. 429–432. IEEE (2012)

    Google Scholar 

  12. Gonzalez-Cantero, J., Martin-Rodriguez, J.L., Gonzalez-Cantero, A., Arrebola, J.P., Gonzalez-Calvin, J.L.: Insulin resistance in lean and overweight non-diabetic caucasian adults: study of its relationship with liver triglyceride content, waist circumference and BMI. PLoS One 13(2), e0192663 (2018)

    Article  Google Scholar 

  13. Greenfield, J.R., Campbell, L.V.: Relationship between inflammation, insulin resistance and type 2 diabetes:’cause or effect? Curr. Diab. Rev. 2(2), 195–211 (2006)

    Article  Google Scholar 

  14. Guan, X., Sun, G., Zheng, L., Hu, W., Li, W., Sun, Y.: Associations between metabolic risk factors and body mass index, waist circumference, waist-to-height ratio and waist-to-hip ratio in a chinese rural population. J. Diab. Investig. 7(4), 601–606 (2016)

    Article  Google Scholar 

  15. He, J., Ma, R., Liu, J., Zhang, M., Ding, Y., Guo, H., Mu, L., Zhang, J., Wei, B., Yan, Y., et al.: The optimal ethnic-specific waist-circumference cut-off points of metabolic syndrome among low-income rural Uyghur adults in far western China and implications in preventive public health. Int. J. Environ. Res. Public Health 14(2), 158 (2017)

    Article  Google Scholar 

  16. Herrera, H., Rebato, E., Arechabaleta, G., Lagrange, H., Salces, I., Susanne, C.: Body mass index and energy intake in Venezuelan university students. Nutr. Res. 23(3), 389–400 (2003)

    Article  Google Scholar 

  17. Hetherington-Rauth, M., Bea, J.W., Lee, V.R., Blew, R.M., Funk, J., Lohman, T.G., Going, S.B.: Comparison of direct measures of adiposity with indirect measures for assessing cardiometabolic risk factors in preadolescent girls. Nutr. J. 16(1), 15 (2017)

    Article  Google Scholar 

  18. Ho, S.Y., Lam, T.H., Janus, E.D.: Waist to stature ratio is more strongly associated with cardiovascular risk factors than other simple anthropometric indices. Ann. Epidemiol. 13(10), 683–691 (2003)

    Article  Google Scholar 

  19. Janssen, I., Ross, R.: Linking age-related changes in skeletal muscle mass and composition with metabolism and disease. J. Nutr. Health Aging 9(6), 408 (2005)

    Google Scholar 

  20. Ji, B., Qu, H., Wang, H., Wei, H., Deng, H.: Association between the visceral adiposity index and homeostatic model assessment of insulin resistance in participants with normal waist circumference. Angiol. 68(8), 716–721 (2017)

    Article  Google Scholar 

  21. Kaur, N., Barna, B., et al.: Inter-relationship of waist-to-hip ratio (WHR), body mass index (BMI) and subcutaneous fat with blood pressure among university-going Punjabi sikh and hindu females. Int. J. Med. Med. Sci. 2(1), 005–011 (2010)

    Google Scholar 

  22. Kopelman, P.G.: Obesity as a medical problem. Nature 404(6778), 635–643 (2000)

    Article  Google Scholar 

  23. Kumari, M., Heeren, J., Scheja, L.: Regulation of immunometabolism in adipose tissue. Semin Immunopathol 40(2), 189–202 (2018)

    Article  Google Scholar 

  24. Kurniawan, L.B., Bahrun, U., Hatta, M., Arif, M.: Body mass, total body fat percentage, and visceral fat level predict insulin resistance better than waist circumference and body mass index in healthy young male adults in Indonesia. J. Clin. Med. 7(5), 96 (2018)

    Article  Google Scholar 

  25. Lanas, F., Serón, P., Munoz, S., Margozzini, P., Puig, T.: Latin American clinical epidemiology network series-paper 7: central obesity measurements better identified risk factors for coronary heart disease risk in the chilean national health survey (2009–2010). J. Clin. Epidemiol. 86, 111–116 (2017)

    Article  Google Scholar 

  26. Lee, C.H., Lam, K.S.: Obesity-induced insulin resistance and macrophage infiltration of the adipose tissue: a vicious cycle. J. Diab. Investig. 10(1), 29 (2019)

    Article  Google Scholar 

  27. Luna-Luna, M., Medina-Urrutia, A., Vargas-Alarcón, G., Coss-Rovirosa, F., Vargas-Barrón, J., Perez-Mendez, O.: Adipose tissue in metabolic syndrome: onset and progression of atherosclerosis. Arch. Med. Res. 46(5), 392–407 (2015)

    Article  Google Scholar 

  28. Marusteri, M., Bacarea, V.: Comparing groups for statistical differences: how to choose the right statistical test? Biochemia Medica: Biochemia Medica 20(1), 15–32 (2010)

    Article  Google Scholar 

  29. Miyazaki, Y., Mahankali, A., Matsuda, M., Mahankali, S., Hardies, J., Cusi, K., Mandarino, L.J., DeFronzo, R.A.: Effect of pioglitazone on abdominal fat distribution and insulin sensitivity in type 2 diabetic patients. J. Clin. Endocrinol. Metab. 87(6), 2784–2791 (2002)

    Article  Google Scholar 

  30. Mohamed-Ali, V., Pinkney, J., Coppack, S.: Adipose tissue as an endocrine and paracrine organ. Int. J. Obes. 22(12), 1145–1158 (1998)

    Article  Google Scholar 

  31. Moretto, M.C., Fontaine, A.M., Garcia, C.D.A.M.S., Neri, A.L., Guariento, M.E.: Association between race, obesity and diabetes in elderly community dwellers: data from the fibra study. Cadernos de saude publica, 32, e00081315 (2016)

    Google Scholar 

  32. Mortensen, M.B., Fuster, V., Muntendam, P., Mehran, R., Baber, U., Sartori, S., Falk, E.: Negative risk markers for cardiovascular events in the elderly. J. Am. Coll. Cardiol. 74(1), 1–11 (2019)

    Article  Google Scholar 

  33. World Health Organization., et al.: Global status report on non communicable diseases 2014. World Health Organization, vol. 2014 (2014)

    Google Scholar 

  34. Özer, S., Kazanc, N.Ö., Sönmezgöz, E., Karaaslan, E., Altunta, B., Kuyucu, Y.E., et al.: Higher HDL levels are a preventive factor for metabolic syndrome in obese Turkish children. Nutricion hospitalaria 31(1), 307–312 (2015)

    Google Scholar 

  35. Parikh, R., Mathai, A., Parikh, S., Sekhar, G.C., Thomas, R.: Understanding and using sensitivity, specificity and predictive values. Ind. J. Ophthalmol. 56(1), 45 (2008)

    Article  Google Scholar 

  36. Park, S.H., Choi, S.J., Lee, K.S., Park, H.Y.: Waist circumference and waist-to-height ratio as predictors of cardiovascular disease risk in Korean adults. Circ. J. 73(9), 1643–1650 (2009)

    Article  Google Scholar 

  37. Perpiñan, G., Severeyn, E., Altuve, M., Wong, S.: Classification of metabolic syndrome subjects and marathon runners with the k-means algorithm using heart rate variability features. In: 2016 XXI Symposium on Signal Processing, Images and Artificial Vision (STSIVA), pp. 1–6. IEEE (2016)

    Google Scholar 

  38. Perrone-Filardi, P., Paolillo, S., Costanzo, P., Savarese, G., Trimarco, B., Bonow, R.O.: The role of metabolic syndrome in heart failure. Eur. Heart J. 36(39), 2630–2634 (2015)

    Article  Google Scholar 

  39. Polsky, S., Ellis, S.L.: Obesity, insulin resistance, and type 1 diabetes mellitus. Curr. Opinion Endocrinol. Diab. Obes. 22(4), 277–282 (2015)

    Article  Google Scholar 

  40. Raz, I., Eldor, R., Cernea, S., Shafrir, E.: Diabetes: insulin resistance and derangements in lipid metabolism. Cure through intervention in fat transport and storage. Diab. Metab. Res. Rev. 21(1), 3–14 (2005)

    Article  Google Scholar 

  41. Reitsma, J.B., Glas, A.S., Rutjes, A.W., Scholten, R.J., Bossuyt, P.M., Zwinderman, A.H.: Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews. J. Clin. Epidemiol. 58(10), 982–990 (2005)

    Article  Google Scholar 

  42. Rönnecke, E., Vogel, M., Bussler, S., Grafe, N., Jurkutat, A., Schlingmann, M., Koerner, A., Kiess, W.: Age-and sex-related percentiles of skinfold thickness, waist and hip circumference, waist-to-hip ratio and waist-to-height ratio: results from a population-based pediatric cohort in Germany (life child). Obes. Facts 12(1), 25–39 (2019)

    Article  Google Scholar 

  43. Rose, D., Komninou, D., Stephenson, G.: Obesity, adipocytokines, and insulin resistance in breast cancer. Obes. Rev. 5(3), 153–165 (2004)

    Article  Google Scholar 

  44. Schneider, H.J., Friedrich, N., Klotsche, J., Pieper, L., Nauck, M., John, U., Dorr, M., Felix, S., Lehnert, H., Pittrow, D., et al.: The predictive value of different measures of obesity for incident cardiovascular events and mortality. J. Clin. Endocrinol. Metab. 95(4), 1777–1785 (2010)

    Article  Google Scholar 

  45. Sjöström, C.D., Håkangård, A.C., Lissner, L., Sjöström, L.: Body compartment and subcutaneous adipose tissue distribution-risk factor patterns in obese subjects. Obes. Res. 3(1), 9–22 (1995)

    Article  Google Scholar 

  46. Velásquez, J., Severeyn, E., Herrera, H., Encalada, L., Wong, S.: Anthropometric index for insulin sensitivity assessment in older adults from Ecuadorian highlands. In: 12th International Symposium on Medical Information Processing and Analysis, vol. 10160, p. 101600S. International Society for Optics and Photonics (2017)

    Google Scholar 

  47. Velásquez, J., Herrera, H., Encalada, L., Wong, S., Severeyn, E.: Análisis dimensional de variables antropométricas y bioquímicas para diagnosticar el síndrome metabólico. Maskana 8, 57–67 (2017)

    Google Scholar 

  48. Vintimilla, C., Wong, S., Astudillo-Salinas, F., Encalada, L., Severeyn, E.: An AIDE diagnosis system based on k-means for insulin resistance assessment in eldery people from the Ecuadorian highlands. In: 2017 IEEE Second Ecuador Technical Chapters Meeting (ETCM), pp. 1–6. IEEE (2017)

    Google Scholar 

  49. Wei, Y., Wang, J., Han, X., Yu, C., Wang, F., Yuan, J., Miao, X., Yao, P., Wei, S., Wang, Y., et al.: Metabolically healthy obesity increased diabetes incidence in a middle-aged and elderly Chinese population. Diab./Metab. Res. Rev. 36(1), e3202 (2020)

    Google Scholar 

  50. Zhou, C., Zhan, L., Yuan, J., Tong, X., Peng, Y., Zha, Y.: Comparison of visceral, general and central obesity indices in the prediction of metabolic syndrome in maintenance hemodialysis patients, pp. 1–8. Eating and Weight Disorders-Studies on Anorexia, Bulimia and Obesity (2019)

    Google Scholar 

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Acknowledgment

This work was funded by the Research and Development Deanery of the Simón Bolívar University (DID) and the Research Direction of the Ibagué University. Full acknowledgment is given to Rajul Parikh, author of “Understanding and using sensitivity, specificity, and predictive values” (BioInfo Publications™).

Author information

Authors and Affiliations

  1. Department of Thermodynamics and Transfer Phenomena, Universidad Simón Bolívar, Caracas, Venezuela

    Erika Severeyn

  2. Faculty of Engineering, Universidad de Ibagué, Ibagué, Tolima, Colombia

    Alexandra La Cruz

  3. Department of Electronics and Circuits, Universidad Simón Bolívar, Caracas, Venezuela

    Sara Wong

  4. Faculty of Electronic and Biomedical Engineering, Universidad Atonio Nariño, Cartagena, Colombia

    Gilberto Perpiñan

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  1. Erika Severeyn
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  2. Alexandra La Cruz
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  3. Sara Wong
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  4. Gilberto Perpiñan
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Correspondence to Erika Severeyn .

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Editors and Affiliations

  1. Eindhoven University of Technology, Eindhoven, Noord-Brabant, The Netherlands

    Miguel Botto-Tobar

  2. Escuela Superior Politécnica de Chimbor, Riobamba, Ecuador

    Omar S. Gómez

  3. Escuela Superior Politécnica del Chimbo, Riobamba, Ecuador

    Raúl Rosero Miranda

  4. Universitat de Valencia, Valencia, Valencia, Spain

    Angela Díaz Cadena

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Severeyn, E., La Cruz, A., Wong, S., Perpiñan, G. (2021). Analysis of Anthropometric Measurements Using Receiver Operating Characteristic Curve for Impaired Waist to Height Ratio Detection. In: Botto-Tobar, M., S. Gómez, O., Rosero Miranda, R., Díaz Cadena, A. (eds) Advances in Emerging Trends and Technologies. ICAETT 2020. Advances in Intelligent Systems and Computing, vol 1302. Springer, Cham. https://doi.org/10.1007/978-3-030-63665-4_13

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