Age-Related Whole-Brain Structural Changes in Relation to Cardiovascular Risks Across the Adult Age Spectrum

Abstract

Background : The brain atrophy and lesion index (BALI) has been developed to assess whole-brain structural deficits that are commonly seen on magnetic resonance imaging (MRI) in aging. It is unclear whether such changes can be detected at younger ages and how they might relate to other exposures. Here, we investigate how BALI scores, and the subcategories that make the total score, compare across adulthood and whether they are related to the level of cardiovascular risks, in both young and old adulthood. Methods : Data were from 229 subjects (72% men; 24–80 years of age) whose annual health evaluation included a routine anatomical MRI examination. A BALI score was generated for each subject from T2-weighted MRI. Differences in the BALI total score and categorical subscores were examined by age and by the level of cardiovascular risk factors (CVRFs). Regression analysis was used to evaluate relationships between continuous variables. Relative risk ratios (RRRs) of CVRF on BALI were examined using a multinomial logistic regression. The area under the receiver operating characteristic (ROC) curve was used to estimate the classification accuracy. Results : Nearly 90% of the participants had at least one CVRF. Mean CVRF scores increased with age (slope = 0.03; r = 0.36, 95% confidence intervals: 0.23–0.48; p < 0.001). The BALI total score was closely related to age (slope = 0.18; r = 0.69, 95% confidence intervals: 0.59–0.78; p < 0.001), as so were the categorical subscores ( r ’s = 0.41–0.61, p < 0.001); each differed by the number of CVRF ( t -test: 4.16–14.83, χ 2 : 6.9–43.9, p ’s < 0.050). Multivariate analyses adjusted for age and sex suggested an independent impact of age and the CVRF on the BALI score (for each year of advanced age, RRR = 1.20, 95% CI = 1.11–1.29; for each additional CVRF, RRR = 3.63, 95% CI = 2.12–6.23). The CVRF and BALI association remained significant even in younger adults. Conclusion : The accumulation of MRI-detectable structural brain deficits can be evident from young adulthood. Age and the number of CVFR are independently associated with BALI score. Further research is needed to understand the extent to which other age-related health deficits can increase the risk of abnormalities in brain structure and function, and how these, with BALI scores, relate to cognition.

How can dementia and disability be prevented in older adults: where are we today and where are we going?

“…Vascular risk factors, such as midlife obesity, high total cholesterol level and high systolic blood pressure, are tightly associated with both dementia and disability risk later in life [80,81], and findings related to dementia suggest that clustering of vascular risk factors increases the risk additively [80]. Moreover, vascular risk factors have been shown to be associated with structural brain changes over the life course starting from young adulthood [82]. Treating vascular risk factors with antihypertensives and statins is associated with reduced dementia risk in observational studies [83,84], and there is some evidence from RCTs, suggesting beneficial effects of treating hypertension.…”

The effects of age on resting‐state BOLD signal variability is explained by cardiovascular and cerebrovascular factors

“…Some degree of association between age differences in RSFA and grey matter atrophy is expected given CVH has been linked to brain-wide atrophy (Gu et al, 2019;Srinivasa et al, 2016) and T1-weighted data are confounded by nonmorphological signals (Bhogal et al, 2017;Ge et al, 2017;Tardif et al, 2017). Therefore, to test whether the effects III); Cov, CBF, and CVH (Model IV); Cov and grey matter volume (GMV); and Cov and grey matter volume residuals (GMVr) after controlling for the effects of CVH (see text).…”

The effects of age on resting-state BOLD signal variability is explained by cardiovascular and cerebrovascular factors

“…To test whether the effects of brain atrophy on RSFA were independent of the effects of CVH on brain atrophy (Srinivasa et al, 2016;Gu et al, 2019), we controlled for the effects of CVH in GMV ICs. Then we used the GMV residuals after fitting CVH to GMV IC loadings (i.e.…”