Depressive symptoms and obesity in adolescents. An application from Data Mining

Main Article Content

Norma Cecilia Serrano Díaz
David Andrés Castro Ruiz
Paula Fernanda Pérez Rivero
Diana Paola Suárez Suárez
Doris Cristina Quintero Lesmes

Abstract

Objective: to describe who develops obesity and depressive symptoms (DS) in the adolescent population of the city of Bucaramanga by means of the data mining application. Methods: a descriptive and cross-sectional study nested in a population  cohort was applied. 432 adolescents were evaluated.  Information on sociodemographic variables, DS and anthropometric measures was collected.  Statistical analyzes were carried out for categorical and  continuous variables, as well as a data mining analysis. Results: 26.7% of the adolescents were overweight. It  was observed that more than half of the sample presented DS. The data mining analysis allowed the identification of six groups of participants according to their characteristics in relation to weight and DS. Conclusions: the relationship between DS and obesity was established in the group of adolescent women,  observing that those with a weight greater than 2 standard deviations presented all DS. 

Keywords:
Obesity Overweight Depression Adolescent Lifestyle Data mining

References

Ahamad, M.G., Ahmed, M.F., & Uddin, M.Y. (2016). Clustering as data mining technique in risk factors analysis of diabetes, hypertension, and obesity. EJERS, European Journal of Engineering Research and Science, 1(6), 88-93. Recuperado de https://www.ejers.org/index.php/ejers/article/view/202/110

Cabas, K., González, Y., & Mendoza, C. (2018). Funcionamiento ejecutivo y depresión en universitarios con normopeso, sobrepeso y obesidad Tipo I. Informes Psicológicos, 18(1), 133-144. http://dx.doi.org/10.18566/infpsic.v18n1a07

Curilem-Gatica, C., Rodríguez-Rodríguez, F., Almagià-Flores, A., Yuing-Farías, T., & Berral F. (2016). Ecuaciones para la evaluación de la composición corporal en niños y adolescentes. Cadernos Saúde Pública, 32(7), 1-6. http://dx.doi.org/10.1590/0102-311X00195314.

De Onis, M., Onyango, A.W., Borghi, E., Siyam, A., Nishida, C., & Siekmann, J. (2007). Development of a Who growth reference for school-aged children and adolescents. Bull World Health Organization, 85(9), 660-667.

Dipnall, J. F., Pasco, J. A., Berk, M., Williams, L. J., Dodd, S.…& Jacka, F. N. (2016). Fusing data mining, machine learning and traditional statistics to detect biomarkers associated with depression. PLoS One, 11(2), 1-23.

Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. American Association for Artificial Intelligence, 17(3), 37-54.

Hossain, R., Mahmud, H., Hossin, A., Noori, S., & Jahan, H. (2018) PRMT: Predicting Risk Factor of Obesity among Middle-Aged People Using Data Mining Techniques. Procedia Computer Science, 132, 1068-1076. https://doi.org/10.1016/j.procs.2018.05.022

Lasserre, A. M., Glaus, J., Vandeleur, C. L., Marques-Vidal, P., Vaucher, J., Bastardot, F., Waeber, G., Vollenweider, P., & Preisig, M. (2014). Depression with atypical features and increase in obesity, body mass index, waist circumference, and fat mass: a prospective, population-based study. JAMA Psychiatry, 71(8), 880-888. https://doi.org/10.1001/jamapsychiatry.2014.411

Lohman, T.G., Roche, A.F., & Martorell, R. (Eds.). (1988). Anthropometric standardization reference manual. Champaign. IL: Human Kinetics

Luppino, F.S., De Wit, L.M., Bouvy, P.F., Stijnen, T., Cuijpers, P., Penninx, B.W., & Zitman, F.G. (2010). Overweight, obesity, and depression: a systematic review and meta-analysis of longitudinal studies. Archives of General Psychiatry, 67(3), 220- 229.

Martinson, M. L., & Vasunilashorn, S. M. (2016). The long-arm of adolescent weight status on later life depressive symptoms. Age and Ageing, 45(3), 389-395. https://doi.org/10.1093/ageing/afw020

Milano, W., Ambrosio, P., Carizzone, F., De Biasio, V., Di Munzio, W., Foia, M. G., & Capasso, A. (2020). Depression and obesity: analysis of common biomarkers. Diseases (Basel, Switzerland), 8(2), 23-52. https://doi.org/10.3390/diseases8020023

Milovic, B. & Milovic, M. (2012). Prediction and decision making in health care using data mining. International Journal of Public Health Science, 1(2), 69-78.

Ministerio de Salud (2010). Presentación de lanzamiento de Encuesta Nacional de la Situación Nutricional ENSIN 2010. Recuperado de https://www.icbf.gov.co/sites/default/files/resumenfi.pdf

Ministerio de Salud (2015). Presentación de lanzamiento de Encuesta Nacional de la Situación Nutricional ENSIN 2015. Recuperado de https://www.minsalud.gov.co/Paginas/Gobierno-presenta-Encuesta-Nacional-de-Situaci%C3%B3n-Nutricional-de-Colombia-ENSIN-2015.aspx

Ministerio de Salud. (2017). Boletín de salud mental: Depresión. Subdirección de Enfermedades No Transmisibles. Recuperado de https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/VS/PP/ENT/boletin-depresion-marzo-2017.pdf

Mühlig, Y., Antel, J., Föcker, M., & Hebebrand, J. (2015). Are bidirectional associations of obesity and depression already apparent in childhood and adolescence as based on high‐quality studies? A systematic review. Obesity Reviews, 17(3), 235-249. https://doi.org/10.1111/obr.12357

Ni, H., Yang, X., Fang, C., Guo, Y., Xu, M. & He, Y. (2014). Data mining-based study on sub-mentally healthy state among residents in eight provinces and cities in China. Journal of Traditional Chinese Medicine, 34(4), 511-517.

Organización Mundial de la Salud OMS. (1994). Guía de bolsillo de la clasificación de la CIE-10. Madrid: Panamericana.

Organización Mundial de la Salud OMS. (2017). Depresión. Recuperado de http://www.who.int/topics/depression/es/

Organización Mundial de la Salud OMS. (2015). Enfermedades Cardiovasculares. Recuperado de http://www.who.int/mediacentre/factsheets/fs317/es/

Organización Mundial de la Salud OMS. (2002). Temas de salud: Definición y evaluación de riesgos en salud. Recuperado de https://www.who.int ›whr

Pereira-Miranda, E., Costa, P., Queiroz, V., Pereira-Santos, M., & Santana, M. (2017). Overweight and obesity associated with higher depression prevalence in adults: a systematic review and meta-analysis. Journal of the American College of Nutrition, 36(3), 223-233. doi: 10.1080/07315724.2016.1261053.

Pirooznia, M., Seifuddin, F., Judy, J., Mahon, P., Potash, J., & Zandi, P. (2012). Data mining approaches for genome-wide asscociation of mood disorders. Psychiatric Genetics, 22(2), 55-61.

Quek, Y., Tam, W., Zhang, M. & Ho, R. (2017). Exploring the association between childhood and adolescent obesity and depression: a meta‐analysis. Obesity Reviews, 18(7), 742-754. https://doi.org/10.1111/obr.12535

Ranta, K., Väänänen, J., Fröjd, S., Isomaa, R., Kaltiala-Heino, R., & Marttunen, M. (2017). Social phobia, depression and eating disorders during middle adolescence: longitudinal associations and treatment seeking. Nordic Journal of Psychiatry, 71(8), 605-613. doi: 10.1080/08039488.2017.1366548

Stice, E., & Desjardins, C. (2018). Interactions between risk factors in the prediction of onset of eating disorders: Exploratory hypothesis generating analyses. Behaviour Research and Therapy, 105, 52-62. https://doi.org/10.1016/j.brat.2018.03.005

Two Crows Corporation. (2005). Introduction to Data Mining and Knowledge Discovery. Recuperado de http://www.stat.ucla.edu/~hqxu/stat19/intro-dm.pdf

Villa-Roel, C., Buitrago, A., Rodríguez, D., Cano, D., Martínez, M.P., Camacho, P.A, Ruiz, A., & Durán, A. (2009). Prevalence of metabolic syndrome in scholars from Bucaramanga, Colombia: a population-based study. Study protocol. BMC Pediatrics, 9(28), 1-6. Recuperado de https://bmcpediatr.biomedcentral.com/articles/10.1186/1471-2431-9-28

Weka Company (s.f.). Software Weka.Waikato Environment for Knowledge Analysis. Versión 3.8. Recuperado de https://www.cs.waikato.ac.nz/ml/weka/

Yoon, S., Taha, B., & Bakken S. (2014). Using a data mining approach to discover behavior correlates of chronic disease: a case study of depression. Studies in Health Technology and Informatics, 201, 71-78. Recuperado de https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4580372

Article Details

Author Biographies

Norma Cecilia Serrano Díaz, Fundación Cardiovascular de Colombia

Médica y genetista. Magister en bioética. Fundación Cardiovascular de Colombia

David Andrés Castro Ruiz, Fundación Cardiovascular de Colombia

Magister en Gestión, Aplicación y Desarrollo de Software. Fundación Cardiovascular de Colombia

Paula Fernanda Pérez Rivero, Universidad Pontificia Bolivariana, Seccional Bucaramanga

Magíster en Ciencias Básicas Biomédicas. Especialista en Psicología Clínica. Universidad Pontificia Bolivariana, Seccional Bucaramanga

Diana Paola Suárez Suárez, Fundación Cardiovascular de Colombia

Enfermera. Fundación para la Excelencia de la Medicina Clínica en Colombia. Fundación Cardiovascular de Colombia

Doris Cristina Quintero Lesmes, Fundación Cardiovascular de Colombia

PhD. demografía. Fundación Cardiovascular de Colombia

Most read articles by the same author(s)