Importance of high triglycerides levels between novel coronary risk factors

Introduction: The analysis of new cardiovascular risk factors is under an extensive debate in the cardiology and metabolic research fields. Objective: To determine the main factors that contribute to the classification of individuals with higher coronary risk in the adult population from Maracaibo,...

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Autores:
Bermúdez, Valmore
Salazar, Juan
Calvo, María
Martínez, María
Añez, Roberto
Rivas-Ríos, José
Chacín, Maricarmen
Hernández, Juan
Graterol, Modesto
Rojas, Joselyn
Tipo de recurso:
Fecha de publicación:
2017
Institución:
Universidad Simón Bolívar
Repositorio:
Repositorio Digital USB
Idioma:
eng
OAI Identifier:
oai:bonga.unisimon.edu.co:20.500.12442/1861
Acceso en línea:
http://hdl.handle.net/20.500.12442/1861
Palabra clave:
Lipids
Insulin
Risk factors
Prevention
Lípidos
Insulina
Factores de riesgo
Prevención
Rights
License
Licencia de Creative Commons Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional
Description
Summary:Introduction: The analysis of new cardiovascular risk factors is under an extensive debate in the cardiology and metabolic research fields. Objective: To determine the main factors that contribute to the classification of individuals with higher coronary risk in the adult population from Maracaibo, Venezuela. Methods: A descriptive, cross-sectional study with multistage random sampling in 1379 individuals belonging to the Maracaibo City Metabolic Syndrome Prevalence Study (MMSPS) was performed. They were classified according to the coronary risk by Framingham-Wilson equation adapted to our population. The association between various risk factors was evaluated by ordinal logistic regression models. Results: 1,379 subjects (females 55.9%; n = 771) were evaluated, 66.2% (n = 913) were classified with low coronary risk. In univariate ( 2 = 112.35; p < 0.00001) and multivariate analysis [OR: 3.98 (2.39-6.63); p < 0.01], the main factors associated to be classified as the highest risk category were hypertriglyceridemia. Conclusion: There are several factors that should be included in predictive models use worldwide. The most important in our population were dyslipidemia such as hypertriglyceridemia, hyperlipoproteinemia (a) and insulin resistance.