Cargando…

Association Between Behavioral, Biological, and Genetic Markers of Cardiovascular Health and MRI Markers of Brain Aging: A Cohort Study

BACKGROUND AND OBJECTIVE: The life’s simple 7 approach was proposed to define cardiovascular health (CVH) metrics. We sought to investigate the associations between behavioral, biological, and genetic markers for CVH and vascular brain aging in older adults. METHODS: This population-based cohort stu...

Descripción completa

Detalles Bibliográficos
Autores principales: Li, Yuanjing, Laukka, Erika J., Dekhtyar, Serhiy, Papenberg, Goran, Speh, Andreja, Fratiglioni, Laura, Kalpouzos, Grégoria, Qiu, Chengxuan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9827130/
https://www.ncbi.nlm.nih.gov/pubmed/36319110
http://dx.doi.org/10.1212/WNL.0000000000201346
Descripción
Sumario:BACKGROUND AND OBJECTIVE: The life’s simple 7 approach was proposed to define cardiovascular health (CVH) metrics. We sought to investigate the associations between behavioral, biological, and genetic markers for CVH and vascular brain aging in older adults. METHODS: This population-based cohort study included participants who had repeated brain MRI measures from 2001 to 2003 to 2007–2010 (i.e., count of perivascular spaces, volumes of white matter hyperintensity [WMH] and gray matter, and lacunes). At baseline, global, behavioral, and biological CVH metrics were defined and scored following the life’s simple 7 approach and categorized into unfavorable, intermediate, and favorable profiles according to tertiles. The metabolic genetic risk score was calculated by counting 15 risk alleles associated with hypertension, diabetes, or dyslipidemia. Data were analyzed using linear mixed-effects and Cox proportional hazards models, adjusting for age, sex, and education. RESULTS: The study sample consisted of 317 participants (age 60 years or older; 61.8% women). Favorable and intermediate (vs unfavorable) global CVH profiles were related to slower WMH progression, with β-coefficients (95% CI) being −0.019(-0.035–0.002) and −0.018(-0.034–0.001), respectively. Favorable and intermediate (vs unfavorable) biological CVH profiles were significantly related to slower WMH increase only in people aged 60–72 years. CVH profiles were not related to progression of other brain measures. Furthermore, a higher metabolic genetic risk score (range: 6–21) was associated with faster WMH increase (β-coefficient = 0.005; 95% CI: 0.003–0.008). There were statistical interactions of metabolic genetic risk score with global and behavioral CVH profiles on WMH accumulation. A higher metabolic genetic risk score was related to faster WMH accumulation, with β-coefficients being 0.015(0.007–0.023), 0.005(0.001–0.009), and 0.003(-0.001 to 0.006) among people with unfavorable, intermediate, and favorable global CVH profiles, respectively; the corresponding β-coefficients were 0.013(0.006–0.020), 0.006(0.003–0.009), and 0.002(-0.002 to 0.006) among people with unfavorable, intermediate, and favorable behavioral CVH profiles. DISCUSSION: Intermediate to favorable global CVH profiles in older adults are associated with slower vascular brain aging. The association of metabolic genetic risk load with accelerated vascular brain aging was evident among people with unfavorable to intermediate, but not favorable, CVH profiles. These findings highlight the importance of adhering to favorable CVH profiles, especially healthy behaviors, in vascular brain health.