Contribution of Conventional Cardiovascular Risk Factors to Brain White Matter Hyperintensities

My doctor told me I had a bunch of white matter hyperintensities but never showed me them on any scan, so I don't know the size, location or any intervention needed, because my doctor knew nothing and did nothing.

This told me nothing useful. Like how to reverse white matter hyperintensities.

 

Contribution of Conventional Cardiovascular Risk Factors to Brain White Matter Hyperintensities

Fatemeh Koohi,

Eric L. Harshfield and

Hugh S. Markus

Originally published8 Jul 2023https://doi.org/10.1161/JAHA.123.030676Journal of the American Heart Association. 2023;12:e030676

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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.

Clinical Perspective

What Is New?

• This is the largest study to date assessing the proportion of white matter hyperintensity (WMH) risk accounted for by conventional cardiovascular risk factors.

• We found that all common conventional cardiovascular risk factors combined explained only 15% of the variance in WMHs, highlighting the limited explanatory power of cardiovascular risk factors alone in understanding the development of WMHs.

What Are the Clinical Implications?

• Despite the importance of conventional cardiovascular risk factors, other vascular and nonvascular factors are involved in the development of WMHs.

• Further research is needed to identify and understand additional factors underlying the considerable unexplained variance in WMHs, which may provide insights into novel targets for intervention and prevention strategies for WMHs.

Cerebral small‐vessel disease (SVD) is a major global cause of stroke and dementia.¹ White matter hyperintensities (WMHs) are a key magnetic resonance imaging (MRI) marker of SVD, and they have been shown to predict both stroke and dementia.² Increasingly, WMHs are being used to monitor SVD progression and as a surrogate disease marker for clinical trials in SVD.^{3, 4}

Currently, there are few proven treatments for SVD, and better understanding of the underlying disease mechanisms has been highlighted as important in developing better treatment approaches.⁵ One approach has been to investigate and target risk factors for WMHs. Epidemiologic studies suggest a familial component,⁶ and recent genome‐wide association studies have identified multiple genetic loci associated with WMH risk.^{7, 8} Cardiovascular risk factors, particularly hypertension, have also been implicated as risk factors for WMHs.^{9, 10, 11}

However, there has been uncertainty about the proportion of WMH risk that is accounted for by conventional cardiovascular risk factors (CVRFs). A recent study suggested that only 2% of the total risk could be accounted for by all common vascular risk factors.¹² This finding was perhaps unexpected in view of the many previously reported associations between conventional CVRFs and WMHs but has major implications for the proportion of WMH risk that could be targeted by risk factor control. In addition, it is unclear whether vascular risk factors are independently associated with increased WMHs, or whether there are common underlying factors that influence both vascular risk factors and the presence of WMHs.¹³

Therefore, using the large and well‐characterized population of the UK Biobank (UKB), we sought to determine the proportion of variance in WMHs accounted for by conventional CVRFs.

Methods

The UKB data that support the findings of this study are publicly available to bona fide researchers on application at http://www.ukbiobank.ac.uk/using‐the‐resource/.

Study Population

The UKB is a large, prospective, population‐based cohort study that recruited >500 000 community‐dwelling participants, aged 40 to 69 years, across Great Britain between 2006 and 2010. The UKB study design and population have been described in more detail elsewhere.¹⁴ Following the initial assessment, starting in 2014, a subset of 100 000 participants began undergoing brain MRI.¹⁵ In this study, we included all 42 940 UKB participants who had undergone brain MRI at the first imaging assessment.

Standard Protocol Approvals, Registration, and Patient Consents

UKB received ethical approval from the National Information Governance Board for Health and Social Care and the National Health Service Northwest Multicenter research ethics committee. All participants provided informed consent through electronic signature. The present analyses were conducted under UKB application number 36509.

Measures of CVRFs and Conditions

CVRFs were assessed at baseline recruitment for each participant at a UKB assessment center via a touchscreen questionnaire and physical measurements.

Weight was measured using the Tanita BC‐418MA body composition analyzer (Tanita Corp, Tokyo, Japan). Height was measured using the Saca 202 device in a barefoot standing position. Body mass index was derived as weight in kilograms divided by height in meters squared. Waist circumference was measured with a Wessex nonstretchable sprung tape measure (Andover, UK).

Systolic blood pressure and diastolic blood pressure were taken as the average of 2 measurements in the sitting position after a 5‐minute rest using an Omron 705IT digital monitor. Hypertension was defined as systolic blood pressure ≥140 mm Hg, diastolic blood pressure ≥90 mm Hg, or taking blood pressure medications.

Glycated hemoglobin was measured by high‐performance liquid chromatography analysis on a Bio‐Rad VARIANT II Turbo. Diabetes was defined on the basis of elevated levels of glycated hemoglobin, taking high blood glucose medications, self‐reported data, interviews, or hospital inpatient records. Cholesterol, high‐density lipoprotein cholesterol, low‐density lipoprotein cholesterol, and triglycerides were measured by direct enzymatic methods. Hyperlipidemia was defined as elevated levels of total cholesterol (≥240 mL/dL), low‐density lipoprotein cholesterol (≥160 mg/dL), or triglycerides (≥200 mg/dL), or low levels of high‐density lipoprotein cholesterol (<40 mg/dL).

Cardiovascular conditions (CVCs) were defined for a history of clinical outcomes (namely, stroke, coronary artery disease, and myocardial infarction) and clinical risk factors (ie, atrial fibrillation), using algorithmically defined outcomes, from hospital admission records, self‐report at nurse interview, or death certificate records, which were recorded before the date of imaging assessment.

Measures of WMHs

We used image‐derived variables provided by the UKB team for total WMH volume (using T1‐ and T2‐weighted fluid‐attenuated inversion recovery images) and total brain volume (derived as the sum of white matter volume and gray matter volume from T1 images, normalized for head size, and measured in cubic millimeters).¹⁵

The details of the MRI acquisition protocol and pipeline for the production of imaging‐derived phenotypes have been described elsewhere.¹⁶ Briefly, all brain MRI data were acquired on a single standard Siemens Skyra 3T scanner with 32‐channel head coils. To transform the original T1‐ and T2‐weighted fluid‐attenuated inversion recovery images into MNI152 space, spatial normalization procedures were performed on these images. After gradient distortion correction and reduction of the field of view to remove nonbrain tissue, a nonlinear registration to 1‐mm resolution MNI152 space was done using the functional magnetic resonance imaging of the brain (FNIRT) nonlinear image registration tool. All of the above transformations estimated are then combined into 1 single nonlinear and reversible transformation.¹⁶

WMHs were automatically segmented using the Brain Intensity Abnormality Classification Algorithm tool¹⁷ and the combined T1‐ and T2‐weighted fluid‐attenuated inversion recovery data as input. Brain Intensity Abnormality Classification Algorithm is an automated supervised method for WMH segmentation based on the k‐nearest neighbor algorithm and voxel intensity. The total WMH volume was calculated from the voxels inside a white matter mask that had a probability of being WMH >0.9.¹⁷

We calculated WMH percentage volume by dividing total WMH volume by total brain volume and applied a log transformation to approximate a normal distribution.

Statistical Analysis

Descriptive statistics were presented as means (SDs) for continuous data and frequencies (percentages) for categorical data. Continuous variables with highly skewed distributions, such as triglycerides, were log transformed.

We imputed missing data for CVRFs and CVCs based on chained equation methods (10 imputations) using the mice (3.15.0) package.¹⁸ Among the included participants with complete data on WMH volume, the proportion of originally missing values that were substituted with values obtained by multiple imputation ranged from 0.0% to 8.8% per variable (Table 1).

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