Peripheral lipid oxidative stress markers are related to vascular risk factors and subcortical small vessel disease

Swardfager, Walter; Yu, Di; Scola, Gustavo; Cogo‐Moreira, Hugo; Zou, Yi; Herrmann, Nathan; Lanctôt, Krista L.; Ramirez, Joel; Gao, Fuqiang; Masellis, Mario; Swartz, Richard H.; Sahlas, Demetrios J.; Chan, Pak Cheung; Ojeda-López, Carmen; Milán-Tomás, Ángela; Pettersen, Jacqueline A.; Andreazza, Ana Cristina; Black, Sandra E.

doi:10.1016/j.neurobiolaging.2017.06.029

Cited by 28 publications

(14 citation statements)

References 30 publications

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“…This response is not seen in subcortical WMHs [23] . In contrast, subcortical (but not periventricular) WMH volume was associated with lipid peroxidation in blood, which mediated the effect of hypertension, adding biological validity to a vascular etiology for subcortical WMHs [21] . There may be further valid subdivisions within subcortical WMHs.…”

Section: Epidemiology Of Wmhsmentioning

confidence: 85%

“…The low molecular weight neurofilament marker (NF‐L), extracellular metalloproteinase matrix metalloproteinase‐9, tissue inhibitor of metalloproteinase‐1, the matrix metalloproteinase‐2 index, and the albumin brain‐plasma ratio are all increased in people with clinical diagnosis of SVD. Peripheral blood markers for WMHs, alongside fluid biomarkers related to AD pathology, will help to subtype patients according to their degree of AD pathology and brain vascular burden [13,20,21].…”

Section: Why Are Wmhs Important?mentioning

confidence: 99%

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White matter hyperintensities in vascular contributions to cognitive impairment and dementia (VCID): Knowledge gaps and opportunities

Alber

Alladi

Bae

et al. 2019

A&D Transl Res & Clin Interv

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303

293

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White matter hyperintensities (WMHs) are frequently seen on brain magnetic resonance imaging scans of older people. Usually interpreted clinically as a surrogate for cerebral small vessel disease, WMHs are associated with increased likelihood of cognitive impairment and dementia (including Alzheimer's disease [AD]). WMHs are also seen in cognitively healthy people. In this collaboration of academic, clinical, and pharmaceutical industry perspectives, we identify outstanding questions about WMHs and their relation to cognition, dementia, and AD. What molecular and cellular changes underlie WMHs? What are the neuropathological correlates of WMHs? To what extent are demyelination and inflammation present? Is it helpful to subdivide into periventricular and subcortical WMHs? What do WMHs signify in people diagnosed with AD? What are the risk factors for developing WMHs? What preventive and therapeutic strategies target WMHs? Answering these questions will improve prevention and treatment of WMHs and dementia.

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Section: Epidemiology Of Wmhsmentioning

confidence: 85%

Section: Why Are Wmhs Important?mentioning

confidence: 99%

White matter hyperintensities in vascular contributions to cognitive impairment and dementia (VCID): Knowledge gaps and opportunities

Alber

Alladi

Bae

et al. 2019

A&D Transl Res & Clin Interv

Self Cite

303

293

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“…To train the WMH segmentation model, a total of 432 subjects were recruited from four multicenter studies: 160 subjects with cerebrovascular disease +/- vascular cognitive impairment (CVD +/- VCI) or PD (55-86, 75% male) through the Ontario Neurodegenerative Disease Research Initiative (ONDRI) (Farhan et al 2017), 203 individuals with non-surgical carotid stenosis (47-92, 61% male) through the Canadian Atherosclerosis Imaging Network (CAIN) study (ClinicalTrials.gov: NCT01440296), 37 subjects with nonfluent progressive aphasia, semantic dementia (SD) and normal controls through the Language Impairment in Progressive Aphasia (LIPA) study (Marcotte et al 2017) (55-80), and 32 subjects with CVD, VCI or Alzheimer’s disease (AD) through the Vascular Brain Health (VBH) study (Swardfager et al 2017) (46-78, 50% male). Ground truth segmentations for WMH were generated using SABRE-Lesion Explorer semi-automated pipeline that generates intensity-based segmentations which are then manually edited by expert annotators trained by a neuroradiologist with an intraclass correlation of ≥ 0.9 (J.…”

Section: Methodsmentioning

confidence: 99%

Deep Bayesian networks for uncertainty estimation and adversarial resistance of white matter hyperintensity segmentation

Forooshani

Biparva

Ntiri

et al. 2021

Preprint

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White matter hyperintensities (WMH) are frequently observed on structural neuroimaging of elderly populations and are associated with cognitive decline and increased risk of dementia. Many existing WMH segmentation algorithms produce suboptimal results in populations with vascular lesions or brain atrophy, or require parameter tuning and are computationally expensive. Additionally, most algorithms do not generate a confidence estimate of segmentation quality, limiting their interpretation. MRI-based segmentation methods are often sensitive to acquisition protocols, scanners, noise-level, and image contrast, failing to generalize to other populations and out-of-distribution datasets. Given these concerns, we propose a novel Bayesian 3D Convolutional Neural Network (CNN) with a U-Net architecture that automatically segments WMH, provides uncertainty estimates of the segmentation output for quality control and is robust to changes in acquisition protocols. We also provide a second model to differentiate deep and periventricular WMH. 432 subjects were recruited to train the CNNs from four multi-site imaging studies. A separate test set of 158 subjects was used for evaluation, including an unseen multi-site study. We compared our model to two established state-of-the-art techniques (BIANCA and DeepMedic), highlighting its accuracy and efficiency. Our Bayesian 3D U-Net achieved the highest Dice similarity coefficient of 0.89 ± 0.08 and the lowest modified Hausdorff distance of 2.98 ± 4.40 mm. We further validated our models highlighting their robustness on ′clinical adversarial cases′ simulating data with low signal-to-noise ratio, low resolution, and different contrast (stemming from MRI sequences with different parameters). Our pipeline and models are available at: https://hypermapp3r.readthedocs.io

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“…However, studies in AD and non-AD patient groups have reported collinearity between blood and CSF measurements of OS markers 85,86 and inflammatory cytokines. 87,88 Both peripheral OS and inflammatory markers have also been associated with poor health outcomes 89 and brain-related changes associated with cognitive impairment, such as increased white matter hyperintensities, 90 suggesting that they may be appropriate markers of central processes. Additionally, collecting biomarkers from the periphery is less invasive and more appropriate for a frail and agitated study population, compared to a lumbar puncture which would be required for CSF collection.…”

Section: Limitationsmentioning

confidence: 99%

Agitation, Oxidative Stress, and Cytokines in Alzheimer Disease: Biomarker Analyses From a Clinical Trial With Nabilone for Agitation

Ruthirakuhan

Herrmann

Andreazza

et al. 2019

J Geriatr Psychiatry Neurol

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The endocannabinoid system has been a target of interest for agitation in Alzheimer disease (AD) because of potential behavioral effects and its potential impact on mechanisms implicated in AD such as oxidative stress (OS) and neuroinflammation. We explored whether serum markers of OS and neuroinflammation were associated with response to the cannabinoid nabilone in agitated patients with AD (N = 38). All participants were enrolled in a 14-week, double-blind, cross-over trial comparing nabilone to placebo (6 weeks each) with a 1-week washout between phases. Samples were collected at the start and end of each phase. The cross-sectional relationship agitation (Cohen Mansfield Agitation Inventory) and OS and inflammatory markers were investigated to select markers of interest. Significant markers were then explored for their relationship with response. The OS marker, 4-hydroxynonenal (4-HNE; F1, 35 = 6.41, P = .016), and the proinflammatory cytokine, tumor necrosis factor-α (TNF-α; F1, 29 = 3.97, P = .06), were associated with agitation severity, and TNF-α remained significantly associated ( F2, 25 = 3.69, P = .04) after adjustment for cognition. In the placebo phase, lower baseline 4-HNE was associated with decreases in agitation severity only (b = 0.01, P = .01), while lower baseline TNF-α was associated with decreases in agitation severity in the nabilone phase only (b = 1.14, P = .045). Changes in 4-HNE were not associated with changes in agitation severity in either phase. In the nabilone phase, lower baseline TNF-α was associated with decreases in agitation severity (b = 1.14, P = .045), and decreases in TNF-α were associated with decreases in agitation severity (b = 1.12, P = .006). These findings suggest that OS and neuroinflammation may be associated with agitation severity, while nabilone may have anti-inflammatory effects.

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Peripheral lipid oxidative stress markers are related to vascular risk factors and subcortical small vessel disease

Cited by 28 publications

References 30 publications

White matter hyperintensities in vascular contributions to cognitive impairment and dementia (VCID): Knowledge gaps and opportunities

White matter hyperintensities in vascular contributions to cognitive impairment and dementia (VCID): Knowledge gaps and opportunities

Deep Bayesian networks for uncertainty estimation and adversarial resistance of white matter hyperintensity segmentation

Agitation, Oxidative Stress, and Cytokines in Alzheimer Disease: Biomarker Analyses From a Clinical Trial With Nabilone for Agitation

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