Positive and negative likelihood ratios of two anthropometric indices in the diagnosis of nutritional situations overweight and obesity
Positive and negative likelihood ratios of two anthropometric indices in the diagnosis of nutritional situations overweight and obesity
DOI:
https://doi.org/10.54167/tch.v14i3.625Keywords:
likelihood ratios, waist/height index, abdominal circumference index, overweight, obesityAbstract
Objective. To determine whether two anthropometric indices have sufficient prognostic efficiency or moderate prognostic efficiency by combining sensitivity and specificity using positive and negative likelihood ratios in a single expression. Material and methods. Quantitative epistemological approach. Descriptive observational epidemiological study without directionality and with prospective temporality. Three hundred adult patients of both genders who attended the Hospital Integral "Jose Maria Morelos" were studied. As a reference test or Gold Standard was used the Equation of the Metropolitan Life Insurance Company. Results. The results for the positive likelihood ratios corresponded to 13.41 and 1.63 for the anthropometric indices, Waist / Height Index (WHI) and Abdominal Circumference (AC), respectively. The results for the negative likelihood ratios corresponded, respectively, to 0.07 and 0.38 for the WHI and AC anthropometric indices. Conclusions. It is concluded that the best anthropometric index for the diagnosis of pathological nutritional situations overweight and obesity corresponds to the WHI, since the results of the positive and negative likelihood ratios report sufficient prognostic efficiencies. On the other hand, the results of the positive and negative likelihood ratios report, respectively, negligible prognostic efficiency and poor prognostic efficiency for AC.
Downloads
References
Aznar–Orovala, E., Mancheño–Alvarob, A., García–Lozanoa, T., & Sánchez–Yepesa, M. (2013). Likelihood ratio and Fagan's nomogram: two basic tools for the rational use of clinical laboratory tests. Rev Calid Asist, 28(6): 390–393. https://doi.org/10.1016/j.cali.2013.04.002
Amirabdollahian, F., & Haghighatdoost, F. (2018). Anthropometric Indicators of Adiposity Related to Body Weight and Body Shape as Cardiometabolic Risk Predictors in British Young Adults: Superiority of Waist–to–Height Ratio. J Obes, 1: 8370304. https://doi.org/10.1155/2018/8370304
Centre for Evidence–Based Medicine (CEBM). (2009). Likelihood Ratios. 1–3. https://bit.ly/39BNYdI
Cochran, W.G. (1954). Some methods for strengthening the common x2 tests. Biometrics, 10(4): 417–451. https://doi.org/10.2307/3001616
Corrêa, M.M., Facchini, L.A., Thumé, E., Oliveira, E.R.A., Tomasi, E. (2019). The ability of waist–to–height ratio to identify health risk. Rev Saude Publica, 23(53): 66. https://doi.org/10.11606%2Fs1518-8787.2019053000895
Deeks, J., & Altman, D. (2004). Diagnostic tests 4: likelihood ratios. BMJ, 329: 168–169. https://doi.org/10.1136/bmj.329.7458.168
Donis, J.H. (2012). Evaluación de la validez y confiabilidad de una prueba diagnóstica. Avances en Biomedicina, 1(2): 73–81. https://www.redalyc.org/articulo.oa?id=331328015005
Fagan, T. (1975). Nomogram for Bayes's theorem. N Engl J Med, 293: 257. https://doi.org/10.1056/nejm197507312930513
Gordis, L. (2004). Epidemiology. Philadelphia: Elsevier Saunders. https://ak.sbmu.ac.ir/uploads/epidemiology_gordis_5_edi.pdf
Grimes, D.A., & Schulz, K.F. (2005). Refining clinical diagnosis with likelihood ratios. Lancet, 365(9469): 1500–1505. https://doi.org/10.1016/s0140-6736(05)66422-7
Grundy, S. (2008). Metabolic syndrome pandemic. Arter Trhromb Vasc, 28: 629–636. https://doi.org/10.1161/atvbaha.107.151092
Hernández–Ávila, M. (2007). Epidemiología. Diseño y Análisis de Estudios. México: Editorial Médica Panamericana.
Hernández–Sampieri, R., Fernández–Collado, C., & Baptista–Lucio, M.P. (2006). Metodología de la Investigación. México: McGrawHill/Interamericana Editores, S.A. de C.V. https://bit.ly/3sMNO9U
Huamán, J., Alvarez, M., Gamboa, L., & Marino, F. (2017). Índice cintura–estatura como prueba diagnóstica del síndrome metabólico en adultos de Trujillo. Rev Med Hered, 28(1): 13–20. https://doi.org/10.20453/rmh.v28i1.3068
INEGI. INSP. (2018). Encuesta Nacional de Salud y Nutrición 2018. https://bit.ly/3afbRYX
Isomaa, B., Almgren, P., Tuomi, T., et al. (2001). Cardiovascular Morbidity and Mortality. Diabetes Care, 224(4): 683–688. https://doi.org/10.2337/diacare.24.4.683
Jaeschke, R., Guyatt, G., & Lijmer, J. (2002). Diagnostic Tests. En: Guyatt G, Drummond R, ed. Users' guides to the medical literature. Essentials of evidence–based clinical practice. Chicago: Editorial: JAMA Press, 187–217. https://www.ebcp.com.br/simple/upfiles/livros/005EEBM.pdf
Koch, E., Romero, T., Manríquez, L., Taylor, A., Román, C., Paredes, M., Díaz, C., & Kirschbaum, A. (2008). Razón cintura–estatura: un mejor predictor antropométrico de riesgo cardiovascular y mortalidad en adultos chilenos. Nomograma diagnóstico utilizado en el Proyecto San Francisco. Revista Chilena de Cardiología, 27(1): 23–35. https://repositorio.uchile.cl/handle/2250/128451
Lee, C.M., Huxley, R.R., Wildman R.P., & Woodward, M. (2008). Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI: a meta–analysis. J Clin Epidemiol, 61(7): 646–653. https://doi.org/10.1016/j.jclinepi.2007.08.012
Loong, T.W. (2003). Understanding sensitivity and specificity with the right side of the brain. BMJ, 327(7417): 716–719. https://doi.org/10.1136%2Fbmj.327.7417.716
Manterola, C. (2009). Cómo interpretar un artículo sobre pruebas diagnósticas. Rev Med Clin Condes, 20(5): 708–717. http://dx.doi.org/10.4067/S0718-40262010000300018
Mataix–Verdú, J. (2009). Nutrición y Alimentación Humana. II. Situaciones Fisiológicas y Patológicas. España: Ergon.
Metropolitan Life Insurance Company. (1983). Metropolitan height and weight tables. New York. Stat Bull Metropolitan Life Insurance Company, 64: 19.
Molinero, L.M. (2002). Valoración de pruebas diagnósticas. Asociación de la Sociedad Española de Hipertensión, 6–7. https://www.alceingenieria.net/bioestadistica/pdiagnos.pdf
Nevill, A.M., Stewart, A.D., Olds, T., & Duncan, M.J. (2020). A new waist–to–height ratio predicts abdominal adiposity in adults. Res Sports Med, 28(1): 15–26. https://doi.org/10.1080/15438627.2018.1502183
OMS. (2016). Obesidad y sobrepeso. Nota descriptiva N°. 311. http://www.who.int/mediacentre/factsheets/fs311/es/.
OMS. (2016). Temas de salud: obesidad. https://www.who.int/es/health-topics/obesity#tab=tab_1
Ruiz–Morales, A., & Morrillo–Zarate, L. (2004). Epidemiología Clínica Investigación Aplicada. Bogotá DC Colombia: Editorial Médica Panamericana.
Saderi, N., Escobar, C., & Salgado–Delgado, R. (2013). La alteración de los ritmos biológicos causa enfermedades metabólicas y obesidad. Rev Neurol, 57: 71–78. https://doi.org/10.33588/rn.5702.2013007
Tapia–Conyer, R., & Kuri–Morales, P. (1999). Epidemiología de la obesidad en México. Gac Med Mex, 135(5): 477–479. https://www.imbiomed.com.mx/articulo.php?id=27631
WHO MONICA. (1989). Project: risk factors. Int J Epidemiol, 18(Suppl 1): S46–S55. https://pubmed.ncbi.nlm.nih.gov/2807707/
WHO. (1990). World Health Organization Study Group. Diet, nutrition, and the prevention of chronic diseases. Ginebra: WHO (Technical Report Series 797), 203. https://apps.who.int/iris/handle/10665/39426
WHO. (2000). Obesity: preventing and managing the global epidemic. Report of a WHO consultation. Ginebra: WHO. (Technical Report Series 894), 203. https://apps.who.int/iris/handle/10665/42330







