Dataset Information

Low carotid wall shear stress independently accelerates the progression of cognitive impairment and white matter lesions in the elderly.

ABSTRACT: The association of hemodynamics with cognitive impairment and white matter lesions (WMLs) has come to the foreground in recent years. Six hundred eighty-nine elderly participants aged ?60 years were eligible enrolled. After an average of 5.4 years follow-up, there was a significant decline in Mini-Mental State Examination (MMSE) scores and increases in total white matter hyperintensities (WMH), periventricular (P)WMH, and deep (D)WMH (P < 0.001). The participants were grouped by the tertiles of carotid mean wall shear stress (WSS). The decline in MMSE scores and the increases in total WMH, PWMH, and DWMH decreased from the lowest group to the highest group. There were significant differences between each group comparison (all P <0.05). Mean WSS was an independent and significant factor for the changes in MMSE scores, total WMH, PWMH, and DWMH after adjustment for confounders (P <0.001). The risk of developing cognitive impairment was higher in the lowest (hazard ratio: 2.753; 95% CI: 1.945 to 3.895; P < 0.001) and intermediate (hazard ratio: 1.531; 95% CI: 1.084 to 2.162; P = 0.015) groups than in the highest group after adjustment for confounders. Similar associations were yielded between peak WSS and the changes in MMSE scores, total WMH, PWMH, and DWMH. Our results indicated that carotid WSS is an independent factor for the progression of cognitive impairment and WMLs in the elderly. Low WSS significantly deteriorates the progression of cognitive impairment and WMLs.

SUBMITTER: Zhang H

PROVIDER: S-EPMC5834267 | biostudies-other | 2018 Feb

REPOSITORIES: biostudies-other

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Low carotid wall shear stress independently accelerates the progression of cognitive impairment and white matter lesions in the elderly.

Zhang Hua H Liu Hongxia H Dong Yuanli Y Wang Juan J Zhao Yingxin Y Cui Yi Y Chai Qiang Q Liu Zhendong Z

Oncotarget 20171212 13

The association of hemodynamics with cognitive impairment and white matter lesions (WMLs) has come to the foreground in recent years. Six hundred eighty-nine elderly participants aged ≥60 years were eligible enrolled. After an average of 5.4 years follow-up, there was a significant decline in Mini-Mental State Examination (MMSE) scores and increases in total white matter hyperintensities (WMH), periventricular (P)WMH, and deep (D)WMH (<i>P</i> < 0.001). The participants were grouped by the terti ...[more]

PMID: 29541422

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Project description:Introduction: Carotid geometry and wall shear stress (WSS) have been proposed as independent risk factors for the progression of carotid atherosclerosis, but this has not yet been demonstrated in larger longitudinal studies. Therefore, we investigated the impact of these biomarkers on carotid wall thickness in patients with high cardiovascular risk. Methods: Ninety-seven consecutive patients with hypertension, at least one additional cardiovascular risk factor and internal carotid artery (ICA) plaques (wall thickness ≥ 1.5 mm and degree of stenosis ≤ 50%) were prospectively included. They underwent high-resolution 3D multi-contrast and 4D flow MRI at 3 Tesla both at baseline and follow-up. Geometry (ICA/common carotid artery (CCA)-diameter ratio, bifurcation angle, tortuosity and wall thickness) and hemodynamics [WSS, oscillatory shear index (OSI)] of both carotid bifurcations were measured at baseline. Their predictive value for changes of wall thickness 12 months later was calculated using linear regression analysis for the entire study cohort (group 1, 97 patients) and after excluding patients with ICA stenosis ≥10% to rule out relevant inward remodeling (group 2, 61 patients). Results: In group 1, only tortuosity at baseline was independently associated with carotid wall thickness at follow-up (regression coefficient = -0.52, p < 0.001). However, after excluding patients with ICA stenosis ≥10% in group 2, both ICA/CCA-ratio (0.49, p < 0.001), bifurcation angle (0.04, p = 0.001), tortuosity (-0.30, p = 0.040), and WSS (-0.03, p = 0.010) at baseline were independently associated with changes of carotid wall thickness at follow-up. Conclusions: A large ICA bulb and bifurcation angle and low WSS seem to be independent risk factors for the progression of carotid atherosclerosis in the absence of ICA stenosis. By contrast, a high carotid tortuosity seems to be protective both in patients without and with ICA stenosis. These biomarkers may be helpful for the identification of patients who are at particular risk of wall thickness progression and who may benefit from intensified monitoring and treatment.

Project description:BackgroundVascular risk factors like white matter lesions (WMLs) are increasingly recognized as risk factors for vascular dementia (VaD) and can predict Alzheimer's disease (AD) at least a decade before the clinical stage of the disease. This study aimed to predict cognitive decline and use machine learning techniques to classify older individuals (aged 50 years or older) with WMLs as having vascular mild cognitive impairment (VaMCI), VaD, or in good cognitive health (CH).MethodsA total of 79 individuals with WMLs were selected for this study and categorized into the following 3 groups: CH (n=25), VaMCI (n=33), and VaD (n=21). Data from the entire cohort was then divided into a training dataset (n=56) and testing dataset (n=23). The data were extracted from gray matter (GM) segmentations using voxel-based morphometry (VBM). A relevance vector regression (RVR) approach was used to test the relationship between the structural brain images and clinical scores. To predict the individual-level subtypes, we applied 2 different machine learning-based classifiers: support vector machine (SVM) and Gaussian process classification (GPC). All predictive models were trained on the training dataset and then validated on the testing dataset of age-matched participants.ResultsMulti-domain cognitive performance could be predicted based on the pattern of GM atrophy in older people with WMLs using a RVR approach. The classification of VaD versus CH (cross-validation accuracy =93.94%, test set accuracy =76.92%) and VaMCI versus CH (cross-validation accuracy =95.24%, test set accuracy =87.50%) could be successfully achieved using both SVM and GPC. However, SVM (cross-validation accuracy =67.57%, test set accuracy =70.59%) performed better than GPC in the classification of VaD versus VaMCI.ConclusionsBased on the patterns of gray matter and RVR-based model could achieve prediction of cognitive test scores, and SVM and GPC could classify the severity of cognitive impairment in older people with WMLs.

Dataset Information

Low carotid wall shear stress independently accelerates the progression of cognitive impairment and white matter lesions in the elderly.

Publications

Low carotid wall shear stress independently accelerates the progression of cognitive impairment and white matter lesions in the elderly.

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