Cargando…

Impact of Mitochondrial DNA Mutations on Carotid Intima-Media Thickness in the Novosibirsk Region

The search for markers of predisposition to atherosclerosis development is very important for early identification of individuals with a high risk of cardiovascular disease. The aim of the present study was to investigate the association of mitochondrial DNA mutations with carotid intima-media thick...

Descripción completa

Detalles Bibliográficos
Autores principales: Kirichenko, Tatiana V., Ryzhkova, Anastasia I., Sinyov, Vasily V., Sazonova, Marina D., Orekhova, Varvara A., Karagodin, Vasily P., Gerasimova, Elena V., Voevoda, Mikhail I., Orekhov, Alexander N., Ragino, Yulia I., Sobenin, Igor A., Sazonova, Margarita A.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7554768/
https://www.ncbi.nlm.nih.gov/pubmed/32842589
http://dx.doi.org/10.3390/life10090160
Descripción
Sumario:The search for markers of predisposition to atherosclerosis development is very important for early identification of individuals with a high risk of cardiovascular disease. The aim of the present study was to investigate the association of mitochondrial DNA mutations with carotid intima-media thickness and to determine the impact of mitochondrial heteroplasmy measurements in the prognosis of atherosclerosis development. This cross-sectional, population-based study was conducted in 468 subjects from the Novosibirsk region. It was shown that the mean (carotid intima-media thickness) cIMT correlated with the following mtDNA mutations: m.15059G>A (r = 0.159, p = 0.001), m.12315G>A (r = 0.119; p = 0.011), m.5178C>A (r = 0.114, p = 0.014), and m.3256C>T (r = 0.130, p = 0.011); a negative correlation with mtDNA mutations m.14846G>A (r = −0.111, p = 0.042) and m.13513G>A (r = −0.133, p = 0.004) was observed. In the linear regression analysis, the addition of the set of mtDNA mutations to the conventional cardiovascular risk factors increased the ability to predict the cIMT variability from 17 to 27%. Multi-step linear regression analysis revealed the most important predictors of mean cIMT variability: age, systolic blood pressure, blood levels of total cholesterol, LDL and triglycerides, as well as the mtDNA mutations m.13513G>A, m.15059G>A, m.12315G>A, and m.3256C>T. Thus, a high predictive value of mtDNA mutations for cIMT variability was demonstrated. The association of mutation m.13513G>A and m.14846G>A with a low value of cIMT, demonstrated in several studies, represents a potential for the development of anti-atherosclerotic gene therapy.