Heuristic Search of Cut-Off Points for Clinical Parameters: Defining the Limits of Obesity

  • Miguel Murguía-Romero
  • Rafael Villalobos-Molina
  • René Méndez-Cruz
  • Rafael Jiménez-Flores
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7094)


We studied the variability of obesity in a sample of 4,164 young Mexicans (17-24 years old) measured through the waist circumference. According to the American Heart Association, obesity is one of the five clinical alterations to define the Metabolic Syndrome (MS); the other four are low levels of HDL cholesterol, and high values of triglycerides, glucose, and blood pressure. It has been proposed a cut-off point of 80 cm for women and 90 cm for men to define a normal or altered value of waist circumference for Mexicans. We assume that the waist circumference in healthy population has a normal distribution, so a monolithic cut-off point is only an upper limit for normal values. The objective of this work is to estimate the subjacent normal distribution of the waist circumference of healthy people, involving in this analysis the other four components of the MS, and approaching the problem as a combinatory one. We defined a combination of cut-off points for the other four components of the MS; if considering a set of 50 cut-off points candidates for each of the five parameters, then results in a searching space of 505 (more than 300 millions of combinations). Each particular combination of cut-off points (excluding waist circumference) sets a subpopulation in which parameter values fall into normal ranges so defined; then for each subpopulation we calculated the histogram of the waist circumference values. Using a heuristic function involving the symmetry value of the histogram (skewness), we applied a ‘best first search’ on the combination of cut-off points. We found a combination of cut-off point values that generates the more symmetrical histogram, so we propose it as a useful criterion to set cut-off points of MS parameters for Mexicans. Finally, the obtained histogram is proposed as the normal distribution of healthy population, and represents the variability of the waist circumference of non-obese young Mexicans.


Heuristic search best first search obesity metabolic syndrome normal distribution 


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  1. 1.
    World-Health-Organization: Obesity and overweight, Fact Sheet No. 311. WHO (2011),
  2. 2.
    World-Health-Organization: Obesity: preventing and managing the global epidemic, Report of a WHO Consultation. World Health Organ. Tech. Rep. Ser. 894 (2000)Google Scholar
  3. 3.
    Popkin, B., Gordon-Larsen, P.: The nutrition transition: worldwide obesity dynamics and their determinants. Int. J. Obes. 28, S2–S9(2004)CrossRefGoogle Scholar
  4. 4.
    Gautier, A., Roussel, R., Ducluzeau, P., Lange, C., Vol, S., Balkau, B., Bonnet, F., Group, D.S.: Increases in waist circumference and weight as predictors of type 2 diabetes in individuals with impaired fasting glucose: influence of baseline BMI: data from the DESIR study. Diabetes Care 33, 1850–1852 (2010)CrossRefGoogle Scholar
  5. 5.
    Barquera, S., Campos-Nonato, I., Hernández-Barrera, L., Flores, M., Durazo-Arvizu, R., Kanter, R., Rivera, J.: Obesity and central adiposity in mexican adults: results from the Mexican National Health and Nutrition Survey 2006. Salud Pública Méx. 51(suppl. 4), S595–S603 (2009)Google Scholar
  6. 6.
    Secretaría-De-Salud: Manual de procedimientos. Toma de medidas clínicas y antropométricas en el adulto mayor, Secretaría de Salud. Mexico Federal Goverment (2002),
  7. 7.
    Shamah-Levy, T., Cuevas-Nasu, L., Mundo-Rosas, V., Morales-Ruán, C., Cervantes-Turrubiates, L., Villalpando-Hernández, S.: Estado de salud y nutrición de los adultos mayores en México: resultados de una encuesta probabilística nacional. Salud Pública Méx. 50, 383–389 (2008)CrossRefGoogle Scholar
  8. 8.
    Grundy, S., Cleeman, J., Daniels, S., Donato, K., Eckel, R., Franklin, B., Gordon, D., Krauss, R., Savage, P., Smith, S.J., Spertus, J., Costa, F.: Diagnosis and management of the metabolic syndrome. an American Heart Association/National Heart, Lung, and Blood Institute Scientific Statement. Curculation 112, 2735–2752 (2005)CrossRefGoogle Scholar
  9. 9.
    Alberti, K., Eckel, R., Grundy, S., Zimmet, P., Cleeman, J., Donato, K., Fruchart, J.C., James, W., Loria, C., Smith, S.J.: International-Diabetes-Federation-Task-Force-On-Epidemiology-And-Prevention, National-Heart-Lung-And-Blood-Institute, American-Heart-Association, World-Heart-Federation, International-Atherosclerosis-Society, International-Association-For-The-Study-Of-Obesity: Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 120, 1640–1645 (2009)CrossRefGoogle Scholar
  10. 10.
    World-Health-Organization: Use and interpretation of anthropometric indicators of nutritional status. Bull. World Health Organ. 64, 929–941 (1986)Google Scholar
  11. 11.
    de Onis, M., Habicht, J.: Anthropometric reference data for international use: recommendations from a World Health Organization expert committee. Am. J. Clin. Nutr. 64, 650–658 (1996)Google Scholar
  12. 12.
    Pearl, J.: Heuristics: Intelligent Search Strategies for Computer Problem Solving. Addison-Wesley (1984)Google Scholar
  13. 13.
    Hart, P., Nilsson, N., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. 4, 100–107 (1968)CrossRefGoogle Scholar
  14. 14.
    Bratko, I.: Prolog Programming for Artificial Intelligence, 3rd edn. Addison Wesley (2000)Google Scholar
  15. 15.
    Groeneveld, R., Meeden, G.: Measuring skewness and kurtosis. The Statistician 33, 391–399 (1984)CrossRefGoogle Scholar
  16. 16.
    D’Agostino, R., Belanger, A., D’Agostino, J.R.: A suggestion for using powerful and informative tests of normality. The American Statistician 44, 316–321 (1990)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Miguel Murguía-Romero
    • 1
  • Rafael Villalobos-Molina
    • 1
  • René Méndez-Cruz
    • 2
  • Rafael Jiménez-Flores
    • 2
  1. 1.Unidad de BiomedicinaTlalnepantlaMéxico
  2. 2.Carrera de Médico Cirujano, Facultad de Estudios Superiores IztacalaUniversidad Nacional Autónoma de MéxicoTlalnepantlaMéxico

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