Genetic risk of dementia modifies obesity effects on white matter myelin in cognitively healthy adults

Mole, Jilu P.; Fasano, Fabrizio; Evans, John; Sims, Rebecca; Hamilton, Derek A.; Kidd, Emma Jane; Metzler‐Baddeley, Claudia

doi:10.1016/j.neurobiolaging.2020.06.014

Cited by 21 publications

(46 citation statements)

References 139 publications

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“…It is important to note that while we did not find any risk-related effects on brain morphology we did replicate the well-established pattern of widespread age-related thinning in frontal, temporal and parietal regions 93 as well as volume loss in subcortical structures including the hippocampi and thalami (Fig. 3).…”

Section: Discussionsupporting

confidence: 67%

APOE-ε4-related differences in left thalamic microstructure in cognitively healthy adults

Mole

Fasano

Evans

et al. 2020

Sci Rep

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APOE-ε4 is a main genetic risk factor for developing late onset Alzheimer’s disease (LOAD) and is thought to interact adversely with other risk factors on the brain. However, evidence regarding the impact of APOE-ε4 on grey matter structure in asymptomatic individuals remains mixed. Much attention has been devoted to characterising APOE-ε4-related changes in the hippocampus, but LOAD pathology is known to spread through the whole of the Papez circuit including the limbic thalamus. Here, we tested the impact of APOE-ε4 and two other risk factors, a family history of dementia and obesity, on grey matter macro- and microstructure across the whole brain in 165 asymptomatic individuals (38–71 years). Microstructural properties of apparent neurite density and dispersion, free water, myelin and cell metabolism were assessed with Neurite Orientation Density and Dispersion (NODDI) and quantitative magnetization transfer (qMT) imaging. APOE-ε4 carriers relative to non-carriers had a lower macromolecular proton fraction (MPF) in the left thalamus. No risk effects were present for cortical thickness, subcortical volume, or NODDI indices. Reduced thalamic MPF may reflect inflammation-related tissue swelling and/or myelin loss in APOE-ε4. Future prospective studies should investigate the sensitivity and specificity of qMT-based MPF as a non-invasive biomarker for LOAD risk.

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Section: Discussionsupporting

confidence: 67%

APOE-ε4-related differences in left thalamic microstructure in cognitively healthy adults

Mole

Fasano

Evans

et al. 2020

Sci Rep

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“…Details of the CARDS study have been previously published (Coad et al, 2020; Metzler-Baddeley, Mole, Leonaviciute, et al, 2019; Metzler-Baddeley, Mole, Sims, et al, 2019; Mole, Fasano, Evans, Sims, Hamilton, et al, 2020; Mole, Fasano, Evans, Sims, Kidd, et al, 2020) and will hence only be briefly described in the following. The CARDS study was approved by the School of Psychology Research Ethics Committee at Cardiff University (EC.14.09.09.3843R2) and all participants provided written informed consent.…”

Section: Methodsmentioning

confidence: 99%

“…Central obesity was defined as a WHR ≥ 0.9 for men and ≥ 0.85 for women (Table 1). Other metabolic risk factors were self-reported in a medical history questionnaire (see for details Mole et al, 2020 Neurobiology of Aging) but were not included in the present analysis.…”

Section: Methodsmentioning

confidence: 99%

“…MRI data were collected on a 3T MAGNETOM Prisma clinical scanner (Siemens Healthcare, Erlangen, Germany) (Coad et al, 2020; Metzler-Baddeley, Mole, Leonaviciute, et al, 2019; Metzler-Baddeley, Mole, Sims, et al, 2019; Mole, Fasano, Evans, Sims, Hamilton, et al, 2020) at the Cardiff University Brain Research Imaging Centre (CUBRIC). A 3D magnetization-prepared rapid gradient-echo (MP-RAGE) sequence was used to acquire T 1 -weighted anatomical images with the following parameters: 256×256 acquisition matrix, TR = 2300 ms, TE = 3.06 ms, TI = 850 ms, flip angle θ = 9°, 176 slices, 1 mm slice thickness, 1×1×1 mm isotropic resolution, FOV = 256 mm and acquisition time of ∼ 6 min.…”

Section: Methodsmentioning

confidence: 99%

“…In the present study, we used graph theory to characterize the mesoscale of structural brain networks for the whole-brain connectome and for systems that are affected in AD, namely the default mode network (DMN), as well as the visual network as a control (Badhwar et al, 2017), in 165 cognitively healthy individuals from the Cardiff Ageing and Risk of Dementia Study (CARDS) (38-71 years) (Coad et al, 2020; Metzler-Baddeley, Mole, Leonaviciute, et al, 2019; Metzler-Baddeley, Mole, Sims, et al, 2019; Mole, Fasano, Evans, Sims, Hamilton, et al, 2020; Mole, Fasano, Evans, Sims, Kidd, et al, 2020) with different risk factors for AD. The risk factors investigated were Apolipoprotein-E ε4 (APOE4), family history of dementia (FH) and central obesity.…”

Section: Introductionmentioning

confidence: 99%

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Dementia risk factors modify hubs but leave other connectivity measures unchanged in asymptomatic individuals: a graph theoretical analysis

Clarke

Messaritaki

Dimitriadis

et al. 2020

Preprint

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BackgroundAlzheimer’s Disease (AD) is the most common form of dementia with genetic and environmental risk contributing to its development. Graph theoretical analyses of brain networks constructed from structural and functional MRI measurements have identified connectivity changes in AD and individuals with mild cognitive impairment (MCI). However, brain connectivity in asymptomatic individuals at risk of AD remains poorly understood.MethodsWe acquired diffusion-weighted magnetic resonance imaging (dMRI) data from 165 asymptomatic individuals (38-71 years) from the Cardiff Ageing and Risk of Dementia Study (CARDS). We calculated white matter tracts and constructed whole-brain, default-mode-network and visual structural brain networks that incorporate multiple structural metrics as edge weights. We then calculated the relationship of three AD risk factors, namely Apolipoprotein-E ɛ4 genotype (APOE4), family history (FH) of dementia, and central obesity, on graph theoretical measures and hubs.ResultsWe observed no risk-related differences in clustering coefficients, characteristic path lengths, eccentricity, diameter and radius across the whole-brain, default-mode-network or visual system. However, a hub in the right paracentral lobule was present in all high-risk groups (FH, APOE4, obese) but absent in low-risk groups (no FH, APOE4-ve, healthy weight).DiscussionWe identified no risk-related effects on graph theoretical metrics in the structural brain networks of cognitively healthy individuals. However, high-risk was associated with a hub in the right paracentral lobule, an area with motor and sensory functions related to the lower limb. If this phenotype is shown to predict symptom development in longitudinal studies, it could be used as an early biomarker of AD.Impact StatementAlzheimer’s Disease is a common form of dementia which to date has no cure. Identifying early biomarkers will aid the discovery and development of treatments that may slow AD progression in the future. In this paper we report that asymptomatic individuals at heightened risk of dementia due to their family history, Apolipoprotein-E ɛ4 genotype and body adiposity have a hub in the right paracentral lobule which is absent in low-risk groups. If this phenotype were to predict the development of symptoms in a longitudinal study of the same cohort, it could provide an early biomarker of disease progression.

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Socioeconomic Deprivation, Genetic Risk, and Incident Dementia

Klee¹,

Leist²,

Veldsman³

et al. 2023

American Journal of Preventive Medicine

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Genetic risk of dementia modifies obesity effects on white matter myelin in cognitively healthy adults

Abstract: Title: Genetic risk of dementia modifies obesity effects on white matter myelin in cognitively healthy adults.

Cited by 21 publications

References 139 publications

APOE-ε4-related differences in left thalamic microstructure in cognitively healthy adults

APOE-ε4-related differences in left thalamic microstructure in cognitively healthy adults

Dementia risk factors modify hubs but leave other connectivity measures unchanged in asymptomatic individuals: a graph theoretical analysis

Socioeconomic Deprivation, Genetic Risk, and Incident Dementia

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