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. 2023 Jul 18;12(14):e030676.
doi: 10.1161/JAHA.123.030676. Epub 2023 Jul 8.

Contribution of Conventional Cardiovascular Risk Factors to Brain White Matter Hyperintensities

Affiliations

Contribution of Conventional Cardiovascular Risk Factors to Brain White Matter Hyperintensities

Fatemeh Koohi et al. J Am Heart Assoc. .

Abstract

Background White matter hyperintensities (WMHs) are a major risk factor for stroke and dementia, but their pathogenesis is incompletely understood. It has been debated how much risk is accounted for by conventional cardiovascular risk factors (CVRFs), and this has major implications as to how effective a preventative strategy targeting these risk factors will be. Methods and Results We included 41 626 UK Biobank participants (47.2% men), with a mean age of 55 years (SD, 7.5 years), who underwent brain magnetic resonance imaging at the first imaging assessment beginning in 2014. The relationships among CVRFs, cardiovascular conditions, and WMH volume as a percentage of total brain volume were examined using correlations and structural equation models. Only 32% of the variance in WMH volume was explained by measures of CVRFs, sex, and age, of which age accounted for 16%. CVRFs combined accounted for ≈15% of the variance. However, a large portion of the variance (well over 60%) remains unexplained. Of the individual CVRFs, blood pressure parameters together accounted for ≈10.5% of the total variance (diagnosis of hypertension, 4.4%; systolic blood pressure, 4.4%; and diastolic blood pressure, 1.7%). The variance explained by most individual CVRFs declined with age. Conclusions Our findings suggest the presence of other vascular and nonvascular factors underlying the development of WMHs. Although they emphasize the importance of modification of conventional CVRFs, particularly hypertension, they highlight the need to better understand risk factors underlying the considerable unexplained variance in WMHs if we are to develop better preventative approaches.

Keywords: cardiovascular risk factors; magnetic resonance imaging; structural equation modeling; white matter hyperintensities.

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Figures

Figure 1
Figure 1. Path diagram of measurement models for the cardiovascular risk factors (CVRFs) and cardiovascular conditions (CVCs) latent constructs.
The rectangles denote measured variables, and the circles represent latent variables (CVRFs or CVCs) that are a weighted combination of the measured variables, and they were given labels based on the concept they represent. Single‐headed arrows are hypothesized causal pathways, and double‐headed arrows are correlations; numbers adjacent to the arrows are standardized loading factors and residual correlations, respectively. Model fit parameters are shown adjacent to each measurement model. The models and parameters are estimated by the confirmatory factor analysis. CFI indicates comparative fit index; HbA1c, glycated hemoglobin; RMSEA, root mean square error of approximation; and SRMR, standardized root mean square residual.
Figure 2
Figure 2. Path diagram of structural models for white matter hyperintensity (WMH) volume.
A, The total effect of cardiovascular risk factors (CVRFs) on WMH volume. B, The undirected association (correlation) between cardiovascular conditions (CVCs) and WMH volume. C, The effect of CVRFs on both CVCs and WMH volume. D, The direct, indirect, and total effect of CVRFs on WMH volume (the mediation model). A, C, and D were adjusted for sex and age at baseline. Standardized regression coefficients are shown adjacent to each path; significant coefficients are shown in bold. All the models and parameters are estimated by structural equation modeling. R 2 indicates the variance explained by the model; and S 2, the residual variance in WMH volume that is unexplained by the model.
Figure 3
Figure 3. Patterns of change in the shared variance between individual risk factors and white matter hyperintensity volume, calculated by multiplying the squared correlation coefficients by 100, according to age categories.
BMI indicates body mass index; DBP, diastolic blood pressure; HbA1c, glycated hemoglobin; and SBP, systolic blood pressure.

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