Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 2 de 2
Filter
Add filters








Year range
1.
Arch. endocrinol. metab. (Online) ; 62(1): 79-86, Jan.-Feb. 2018. tab
Article in English | LILACS | ID: biblio-887629

ABSTRACT

ABSTRACT Objective Monocyte chemoattractant protein 1 (MCP-1) has been suggested to be involved in the pathophysiology of insulin resistance (IR); therefore, variants in the MCP-1 gene may contribute to the development of this disease. The aim of this study was to analyze the relationship of the -2518 A>G MCP-1 (rs1024611) gene polymorphism with insulin resistance in Mexican children. Subjects and methods A cross-sectional study was performed in 174 children, including 117 children without insulin resistance and 57 children with IR, with an age range of 6-11 years. Levels for serum insulin and high-sensitivity C-reactive protein were determined. The -2518 A>G MCP-1 polymorphism was identified by the polymerase chain reaction-restriction fragment length polymorphism method. Insulin resistance was defined as a HOMA-IR in the upper 75th percentile, which was ≥ 2.4 for all children. Results Genotype frequencies of the rs1024611 polymorphism for the insulin-sensitive group were 17% AA, 48% AG and 35% GG, and the frequency of G allele was 59%, whereas frequencies for the insulin-resistant group were 12% AA, 37% AG and 51% GG, and the frequency of G allele was 69%. The genotype and allele frequencies between groups did not show significant differences. However, the GG genotype was the most frequent in children with IR. The GG genotype was associated with insulin resistance (OR = 2.2, P = 0.03) in a genetic model. Conclusion The -2518 A>G MCP-1 gene polymorphism may be related to the development of insulin resistance in Mexican children.


Subject(s)
Humans , Male , Female , Child , Insulin Resistance/genetics , Chemokine CCL2/genetics , Polymorphism, Single Nucleotide/genetics , Genetic Markers/genetics , Case-Control Studies , Cross-Sectional Studies , Genetic Predisposition to Disease , Gene Frequency , Genotype
2.
Invest. clín ; 58(3): 284-308, sep. 2017. ilus, tab
Article in Spanish | LILACS | ID: biblio-893542

ABSTRACT

La obesidad es una enfermedad compleja y multifactorial, caracterizada por un aumento de grasa corporal que puede ser ocasionado por un desequilibrio entre la ingesta de alimentos y el gasto energético. En el proceso de aumento de peso intervienen factores como la susceptibilidad genética, factores ambientales y el estilo de vida. Está bien documentado que la obesidad aumenta el riesgo de padecer numerosas enfermedades y trastornos metabólicos como la resistencia a la insulina, diabetes tipo 2, hipercolesterolemia, enfermedades cardiovasculares, hígado graso, inflamación de bajo grado, algunos tipos de cáncer y trastornos sicológicos. Debido al incremento de la obesidad y sus comorbilidades en los últimos años en la población mundial, los gastos médicos erogados para su tratamiento representan un problema grave para los sistemas de salud pública. El análisis proteómico a gran escala, es una herramienta potente y prometedora para el descubrimiento de biomarcadores tempranos y para la comprensión de los mecanismos moleculares que subyacen a las alteraciones metabólicas asociadas con la obesidad. No obstante, es imprescindible considerar las limitaciones técnicas y el análisis e interpretación de los hallazgos proteómicos, para avanzar en la comprensión funcional integral de la dinámica del proteoma ligado a la obesidad. Adicionalmente, los abordajes con un enfoque proteómico, permitirán el desarrollo de nuevas terapias preventivas, así como el descubrimiento de agentes terapéuticos potenciales en el tratamiento de disfunciones metabólicas. El objetivo de esta revisión es analizar las contribuciones más recientes de la proteómica en la búsqueda de biomarcadores relacionados con la obesidad.


Obesity is a complex and multifactorial disease characterized by an increase in body fat that can be caused by an imbalance between food intake and energy expenditure. In the process of weight gain, factors such as genetic susceptibility, environmental factors and lifestyle are involved. It is well documented that obesity increases the risk of numerous diseases and metabolic disorders such as insulin resistance, type 2 diabetes, hypercholesterolemia, cardiovascular disease, fatty liver, low grade inflammation, some types of cancer and psychological disorders. Due to the increase in obesity and its comorbidities in recent years at the global level, medical expenses incurred for its treatment represent a serious problem for public health systems. Large-scale proteomic analysis is a powerful and promising tool for the discovery of early biomarkers and for the understanding of the molecular mechanisms underlying the metabolic alterations associated with obesity. Nevertheless, it is essential to consider the technical limitations and the analysis and interpretation of the proteomic findings, to advance in the integral functional understanding of the dynamics of the proteome linked to obesity. In addition, approaches with a proteomic viewpoint will allow the development of new preventive therapies, as well as the discovery of potential therapeutic agents in the treatment of metabolic dysfunctions. The aim of this review is to analyze the most recent contributions of proteomics in the search for biomarkers related to obesity.

SELECTION OF CITATIONS
SEARCH DETAIL