Andrea Oviedo Pérez¹, Graciela Vimos Ibáñez² and Melba Vertel Morinson³*

¹Research Group Statistics and Mathematical Modeling Applied to Educational Quality, Biology Program, University of Sucre, Colombia
²Doctor, Master’s in Occupational Health, Colombia
³Teacher-Researcher, Master in Statistical Sciences, University of Sucre, Sincelejo, Sucre, Colombia

*Corresponding Author: Melba Vertel Morinson, Teacher-Researcher, Master in Statistical Sciences, University of Sucre, Sincelejo, Sucre, Colombia.

Received: August 02, 2022; Published: September 21, 2022

Abstract

Metabolic syndrome (MS) is an alteration of metabolic origin, product of the simultaneous manifestation of cardiovascular diseases (CVD) and abdominal obesity, characterized by increasing cardiovascular risk and diabetes. Therefore, we sought to determine the incidence of MS in a sample of adults over 60-63 years of age in the department of Sucre. Initially, bivariate analysis was performed, using chi-square test (Independence Test) for each of the factors (socio-demographic, physiological and lifestyle). Data Visualization comprised a mapping with principal component analysis (PCA) weighted - Multiple Correspondence Analysis (MCA) for qualitative variables and K-media Clustering to identify groups expressing common relationships. As well as Logistic Regression to determine the presence of metabolic syndrome with respect to the study variables. Physiological variables had greater significance in the presence of MS; logistic regression indicated that the variables were not significant for developing MS. The PCA showed that a disordered lifestyle alters the metabolism, and therefore develops CVD and Chronic non-communicable disease (CNCD) that lead to the appearance of MS, likewise a low academic training predisposes to have an unhealthy diet, rich in flours and fats that lead to the increase in Body Mass Index (BMI).

Keywords: Metabolic Syndrome; Cardiovascular Diseases; Obesity; Descriptive Statistics

References

1. Álvarez Álvarez AM., et al. “Role of testosterone and cortisol in metabolic syndrome and type 2 diabetes mellitus”. Revista Cubana de Endocrinología1 (2010): 80-90.
2. Barrera MDP., et al. “Metabolic syndrome: an interdisciplinary look”. Colombian Journal of Cardiology (2008).
3. Bernal García JJ., et al. “Modelling of the most important factors that characterize a website”. Spain: ASEPUMA (2004).
4. Chessel D and Dufour AB. “The ade4-package: implementing the duality diagram for ecologists”. Journal of Statistical Software. Paris4 (2007): 1-20.
5. De Salazar DIM and Munoz-Gómez D. “Metabolic syndrome in women”. Revista Colombiana de Cardiología 25 (2018): 21-29.
6. Díaz granados IL., et al. “Metabolic syndrome in workers of the Universidad Libre Seccional Barranquilla, 2009”. Uninorte Health1 (2010): 41-53.
7. Díaz-Realpe JE., et al. “Risk factors for cardiovascular disease in workers of a health service provider institution, Colombia”. Journal of Public Health 9 (2007): 64-75.
8. Escobedo J., et al. “Prevalence of the metabolic syndrome in Latin America and its association with sub-clinical carotid atherosclerosis: the CARMELA cross sectional study”. Cardiovascular Diabetology1 (2009): 52.
9. Galán Morillo M., et al. “Effects of smoking on blood pressure of 24 h-evaluation by ambulatory blood pressure monitoring (MAP)”. Revista Cubana de Medicina5-6 (2004): 0-0.
10. Giraldo-Giraldo NA., et al. “Metabolic syndrome in a group of older adults not institutionalized according to criteria of international organizations”. Perspectives on Human Nutrition1 (2016): 25-35.
11. Gomez LA. “Cardiovascular diseases: a public health problem and a global challenge”. Biomedical4 (2011).
12. López-Jaramillo P., et al. “Latin American consensus of hypertension in patients with type 2 diabetes and metabolic syndrome”. Arteriosclerosis Clinical and Research2 (2014): 85-103.
13. Manzur F., et al. “Metabolic syndrome prevalence in the town of Arjona, Colombia”. Revista Colombiana de Cardiología5 (2008): 215-222.
14. Pardo C-E and Del Campo PC. “Combination -of factorial methods and cluster analysis in R: the FactoClass package”. Revista Colombiana de Estadística 2 (2007): 231-245.
15. Pedrozo W., et al. “Metabolic syndrome and risk factors associated with the lifestyle of adolescents in a city in Argentina, 2005”. Pan American Journal of Public Health 24 (2008): 149-160.
16. Pinzón A., et al. “Metabolic syndrome in workers of a level III care hospital SIMETRA study”. Acta Medica Colombiana 39.4 (2014): 327-335.
17. R Development Core Team. “R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria (2012).
18. Song S and Song Y. “Dietary Fiber and Its Source Are Associated with Cardiovascular Risk Factors in Korean Adults”. Nutrients1 (2021): 160.
19. Uriel E and Manzano JA. “Applied multivariate analysis”. Auditorium 76 (2002): 270-271.
20. Worachartcheewan A., et al. “Data mining for the identification of metabolic syndrome status”. EXCLI Journal17 (2018): 72.

Citation

Citation: Melba Vertel Morinson., et al. “Incidence of Metabolic Syndrome in Older Adults in Sucre (Colombia): Socio-Demographic, Physiological Variables and Lifestyles". Acta Scientific Nutritional Health 6.10 (2022): 32-42.

Copyright

Copyright: © 2022 Melba Vertel Morinson., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.