Contribution of Conventional Cardiovascular Risk Factors to Brain White Matter Hyperintensities
Open accessAbstract
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.
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Only 15% of the variance in WMH volume is explained by measures of vascular risks, and another 16% by age.
“A recent large sample study using UK Biobank datasets also reported that only 15% of the variance in WMH volume was explained by measures of vascular risks, and another 16% by age. 49”
From White matter hyperintensity modulates the amyloid‐tau‐cognition association and anti‐amyloid treatment efficacy in asymptomatic older adults (2025) · cites this paper as [49]
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- Authors
- Koohi, F., Harshfield, E. L., & Markus, H. S.
- Affiliations
- University of Cambridge (GB)
- Corresponding author
- Koohi, Fatemeh
- Venue
- Journal of the American Heart Association
- Published
- 2023-07-18
- Volume
- 12
- Issue
- 14
- Pages
- e030676-e030676
- ISSN
- 2047-9980 (ISSN-L)
- PMC
- PMC10382123
- OpenAlex
- W4383621477
- Cited by
- 38
- References
- 36
- Citations by year
- 2026: 11 · 2025: 15 · 2024: 9 · 2023: 3
- FWCI
- 6.46
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Journal of the American Heart Association (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institute for Health and Care Research ((NIHR203312)
- British Heart Foundation (RE/18/1/34212, RG/F/22/110052)
- University of Cambridge
- Cambridge University Hospitals
- Topics
- Health Sciences › Medicine › Neurology › Cerebrovascular and genetic disorders
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Neurology › Intracerebral and Subarachnoid Hemorrhage Research
- Keywords
- white matter hyperintensities, cardiovascular risk factors, hypertension, blood pressure, stroke risk, dementia risk, vascular risk factors, unexplained variance, brain MRI, UK Biobank
- MeSH terms
- Heart Disease Risk Factors, Brain, Cardiovascular Diseases, Female, Humans, Hypertension, Magnetic Resonance Imaging, Male, Middle Aged, Risk Factors, White Matter
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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