Association of Carotid Atherosclerosis With White Matter Hyperintensity in an Asymptomatic Japanese Population: A Cross-Sectional Study

Zhang, Li; Zhou, Quan; Shao, Li; Wen, Jun; Xia, Jun

doi:10.3389/fcvm.2021.665573

Cited by 4 publications

(1 citation statement)

References 31 publications

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“…The classification criteria (systolic blood pressure over 130 mm Hg or diastolic blood pressure over 85 mm Hg, no medical history) differed from the study mentioned above and the current study (systolic blood pressure over 140 mm Hg or diastolic blood pressure over 90 mm Hg, with medical history). Another Japanese cross-sectional study found that an association between hypertension and WMH volumes existed in participants with the mean age of 55, but not in the older subsample (mean age > 65) (47).…”

Section: Discussionmentioning

confidence: 99%

Risk factors and cognitive correlates of white matter hyperintensities in ethnically diverse populations without dementia: the COSMIC consortium

Lin,

Wen,

Lipnicki

et al. 2023

Preprint

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INTRODUCTION: White matter hyperintensities (WMH) are an important imaging marker for cerebral small vessel diseases, but their risk factors and cognitive associations have not been well-documented in populations of different ethnicities and/or from different geographical regions. METHOD: Magnetic resonance imaging data of five population-based cohorts of non-demented older individuals from Australia, Singapore, South Korea, and Sweden (N = 1,946) were examined for WMH and their associations with vascular risk factors and cognition. RESULT: Factors associated with larger whole brain WMH volumes included diabetes, hypertension, stroke, current smoking, body mass index, higher alcohol intake and insufficient physical activity. Participants with moderate or higher physical activity had less WMH than those who never exercised, but the former two groups did not differ. Hypertension and stroke had stronger associations with WMH volumes in the White, compared to Asian subsample. DISCUSSION: The current study highlighted the ethnic differences in the contributions of vascular risk factors to WMH.

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Section: Discussionmentioning

confidence: 99%

Risk factors and cognitive correlates of white matter hyperintensities in ethnically diverse populations without dementia: the COSMIC consortium

Lin,

Wen,

Lipnicki

et al. 2023

Preprint

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Development and Validation of a Fusion Model Based on Carotid Plaques and White Matter Lesion Burden Imaging Characteristics to Evaluate Ischemic Stroke Severity in Symptomatic Patients

Cui,

Xu,

Miu

et al. 2024

Magnetic Resonance Imaging

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BackgroundThe diagnostic value of carotid plaque characteristics based on higher–resolution vessel wall MRI (HRVW‐MRI) combined with white matter lesion (WML) burden for the risk of ischemic stroke is unclear.PurposeTo combine carotid plaque features and WML burden to construct a hybrid model for evaluating ischemic stroke severity and prognosis in patients with symptomatic carotid artery stenosis.Study TypeRetrospective.SubjectsOne hundred and ninty‐three patients with least one confirmed carotid atherosclerotic stenosis ≥30% and cerebrovascular symptoms within the last 2 weeks (136 in the training cohort and 57 in the test cohort).Field Strength/Sequence3.0T, T2‐weighted fluid attenuated inversion recovery (T2‐FLAIR) and diffusion‐weighted imaging (DWI); HRVW‐MRI: 3D T1‐weighted variable flip angle fast spin‐echo sequences (VISTA), T2‐weighted VISTA, simultaneous noncontrast angiography and intraplaque hemorrhage (SNAP), and contrast‐enhanced T1‐VISTA.AssessmentThe following features of the plaques or vessel wall were assessed by three MRI readers independently: calcification (CA), intraplaque hemorrhage (IPH), lipid‐rich necrotic core (LRNC), ulceration, plaque enhancement (PE), maximum vessel diameter (Max VD), maximum wall thickness (Max WT), total vessel area (TVA), lumen area (LA), plaque volume, and lumen stenosis. WMLs were graded visually and categorized as absent‐to‐mild WMLs (Fazekas score 0–2) or moderate–severe WMLs (Fazekas score 3–6). WML volumes were quantified using a semiautomated volumetric analysis program. Modified Rankin scores (mRS) were assessed at 90 days, following an outpatient interview, or by telephone.Statistical TestsLASSO‐logistic regression analysis was performed to construct a model. The performance of the model was evaluated using receiver operating characteristic (ROC) curve analyses, calibration curves, decision curve analyses, and clinical imaging curves. Conditional logistic regression analysis was used to explore the associations between the hybrid model‐derived score and the modified Rankin Scale (mRS) score at 90 days.ResultsThe model was constructed using five selected features, including IPH, plaque enhancement, ulceration, NWI, and total Fazekas score in deep WMLs (DWMLs). The hybrid model yielded an area under the curve of 0.92 (95% confidence interval [CI] 0.87–0.97) in the training cohort and 0.88 (0.80–0.96) in the test cohort. Furthermore, the hybrid model‐derived score (odds ratio = 1.28; 95% CI 1.06–1.53) was independently associated with the mRS score 90 days after stroke.Data ConclusionsThe hybrid model constructed using MRI plaque characteristics and WML burden has potential to be an effective noninvasive method of assessing ischemic stroke severity. The model‐derived score has promising utility in judging neurological function recovery.Level of Evidence4.Technical EfficacyStage 2.

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Development and validation of a predictive model for white matter lesions in young- and middle-aged people

Zhang,

Peng,

Cai

et al. 2023

Front. Neurol.

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BackgroundWhite matter lesion (WML) is an age-related disorder associated with stroke and cognitive impairment. This study aimed to investigate the risk factors and build a predictive model of WML in young- and middle-aged people.MethodsWe performed a second analysis of the data from the Dryad Digital Repository. We selected those people who are <60 years old and randomly divided them into the training group and the validation group. We investigated the risk factors of WML in the training group with logistic regression analysis and built a prediction nomogram based on multivariate logistic regression analysis; finally, the performance of the prediction nomogram was evaluated for discrimination, accuracy, and clinical utility.ResultsThere were 308 people in the training group and 723 people in the validation group. Multivariate regression analysis showed that the age (OR = 1.49, 95% CI: 1.31–1.70), diastolic blood pressure (OR = 1.02, 95% CI: 1.00–1.03), carotid plaque score (OR = 1.31, 95% CI: 1.14–1.50), female gender (OR = 2.27, 95% CI: 1.56–3.30), and metabolic syndrome (OR = 2.12, 95% CI: 1.22–3.70) were significantly associated with white matter lesions. The area under the curve value (AUC) of the receiver operating curve (ROC) was 0.734 for the training group and 0.642 for the validation group. The calibration curve and clinical impact curve showed that the prediction nomogram has good accuracy and clinical application value.ConclusionAge, diastolic blood pressure, carotid plaque score, female gender, and metabolic syndrome were risk factors in young- and middle-aged people <60 years old with WML, and the nomogram based on these risk factors showed good discrimination, accuracy, and clinical utility.

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Association of Carotid Atherosclerosis With White Matter Hyperintensity in an Asymptomatic Japanese Population: A Cross-Sectional Study

Cited by 4 publications

References 31 publications

Risk factors and cognitive correlates of white matter hyperintensities in ethnically diverse populations without dementia: the COSMIC consortium

Risk factors and cognitive correlates of white matter hyperintensities in ethnically diverse populations without dementia: the COSMIC consortium

Development and Validation of a Fusion Model Based on Carotid Plaques and White Matter Lesion Burden Imaging Characteristics to Evaluate Ischemic Stroke Severity in Symptomatic Patients

Development and validation of a predictive model for white matter lesions in young- and middle-aged people

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