Visceral Adiposity Predicts Subclinical White Matter Hyperintensities in Middle-Aged Adults

Pasha, Evan; Birdsill, Alex C.; Parker, Paige; Elmenshaway, Ahmed; Tanaka, Hirofumi; Haley, Andreana P.

doi:10.1249/01.mss.0000486282.41474.0a

Cited by 6 publications

(8 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous work examining deep versus periventricular WMHs associated with visceral obesity also found increased probability of WMHs in deep white matter associated with raised levels of proinflammatory cytokines (Lampe et al, 2019b). Proinflammatory cytokines are elevated in obesity and have been associated with cognitive decline and neurodegenerative processes associated with dementia (Pasha et al, 2017). This may help to explain the observed relationship to speed of processing we found with mediation analysis.…”

Section: The Spatial Distribution Of Individual Cerebrovascular Risk supporting

confidence: 57%

Spatial distribution and cognitive impact of cerebrovascular risk-related white matter hyperintensities

Veldsman

Kindalova

Kosmidis

et al. 2020

Preprint

View full text Add to dashboard Cite

12Objectives White matter hyperintensities (WMHs) are considered macroscale markers of cerebrovascular 13 burden and are associated with increased risk of vascular cognitive impairment and dementia. However, 14 the spatial location of WMHs has typically been considered in broad categories of periventricular versus 15 deep white matter. The spatial distribution of WHMs associated with individual cerebrovascular risk 16 factors (CVR), controlling for frequently comorbid risk factors, has not been systematically investigated 17 at the population level in a healthy ageing cohort. Furthermore, there is an inconsistent relationship 18 between total white matter hyperintensity load and cognition, which may be due to the confounding of 19 several simultaneous risk factors in models based on smaller cohorts. 20 Methods We examined trends in individual CVR factors on total WMH burden in 13,680 individuals 21 (aged 45-80) using data from the UK Biobank. We estimated the spatial distribution of white matter 22 hyperintensities associated with each risk factor and their contribution to explaining total WMH load 23 using voxel-wise probit regression and univariate linear regression. Finally, we explored the impact of 24 CVR-related WMHs on speed of processing using regression and mediation analysis. 25 Results Contrary to the assumed dominance of hypertension as the biggest predictor of WMH burden, 26 we show associations with a number of risk factors including diabetes, heavy smoking, APOE ε4/ε4 status 27 and high waist-to-hip ratio of similar, or greater magnitude to hypertension. The spatial distribution of 28 WMHs varied considerably with individual cerebrovascular risk factors. There were independent effects 29 of visceral adiposity, as measured by waist-to-hip ratio, and carriage of the APOE ε4 allele in terms of the 30 unique spatial distribution of CVR-related WMHs. Importantly, the relationship between total WMH 31 load and speed of processing was mediated by waist-to-hip ratio suggesting cognitive consequences to 32 WMHs associated with excessive visceral fat deposition. 33Conclusion Waist-to-hip ratio, diabetes, heavy smoking, hypercholesterolemia and homozygous APOE 34 ε4 status are important risk factors, beyond hypertension, associated with WMH total burden and warrant 35 careful control across ageing. The spatial distribution associated with different risk factors may provide 36 important clues as to the pathogenesis and cognitive consequences of WMHs. High waist-to-hip ratio is 37 a key risk factor associated with slowing in speed of processing. With global obesity levels rising, focused 38 management of visceral adiposity may present a useful strategy for the mitigation of cognitive decline in 39 ageing. 40 1 42White matter hyperintensities (WMHs) of presumed vascular origin (Wardlaw et al., 2013) are widely 43 recognised as an indicator of poor brain health (Wardlaw et al., 2015). Age remains the strongest pre-44 dictor for the presence of WMHs. However, the total burden of WMHs...

show abstract

Section: The Spatial Distribution Of Individual Cerebrovascular Risk supporting

confidence: 57%

Spatial distribution and cognitive impact of cerebrovascular risk-related white matter hyperintensities

Veldsman

Kindalova

Kosmidis

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…26 Distally, obesity increases the risk of small vessel disease (marked by WMH) which may manifest beyond a CVD event and may lead to neurodegeneration, vascular cognitive impairment, and dementia. 27 Obesity also has an insidious effect, whereby obesity over the life-course may lead to an accumulation of age-related WMH, leukoaraiosis, and therefore may lead to elevated risk of mild cognitive impairment, dementia, depression, falls, gait disturbance, and stroke later in life. 28 Our findings suggest that obese African Americans are more susceptible to WMH, independent of the usual risk factors of WMH.…”

Section: Discussionmentioning

confidence: 99%

Obesity and Race May Explain Differential Burden of White Matter Hyperintensity Load

Seixas¹,

Turner²,

Bubu³

et al. 2021

CIA

View full text Add to dashboard Cite

Compared to European Americans, research indicates that African Americans have higher white matter hyperintensity (WMH) load; however, the clinical and biological bases underlying this higher burden are poorly understood. We hypothesize that obesity may explain differences in WMH between African and European Americans. Methods: Participants enrolled in longitudinal brain aging studies (n=292; 61% Female; 92% European American; mean age=69.6±7.7) completed evaluations including medical exams, neuroimaging, and sociodemographic surveys. Overweight/obese status defined as body mass index ≥30 kg/m 2 , and WMH load, captured by FLAIR images, as sum of deep and periventricular volumes, scored using the Fazekas scale (0-6), WMH≥4 considered high. Results: Logistic regression analyses, adjusted for age, sex, hypertension, and smoking history, indicated that age and interaction between race and obesity were significant predictors of WMH, demonstrating that obesity significantly moderated the relationship between race and WMH. Age independently increased the odds of high WMH by 16% (OR=1.16, 95% CI=1.09-1.23, p<0.001). Stratified analysis indicates that older European Americans had increased WMH (OR=1.17, 95% CI=1.09-1.23, p<0.001), while obese African Americans had increased WMH (OR=27.65, 95% CI=1.47-519.13, p<0.05). In a case controlled subgroup matched by age, sex, and education (n=48), African Americans had significantly higher WMH load (27% vs 4%, Χ 2 =5.3, p=0.02). Conclusion:Results denote that age predicted WMH among European Americans, while obesity predicted WMH among African Americans. Matched sample analyses indicate that obesity increases the odds of WMH, though more pronounced in African Americans. These findings suggest that obesity may explain the differential burden of white matter hyperintensity load, signifying public health and clinical importance.

show abstract

“…In order to avoid confounding interpretations due to actually detecting demyelination effects caused by other pathologies, subjects with abnormal WMH were excluded. This was because WMH is known to have myelin loss as an underlying pathology and the occurrence of this is higher in obese individuals …”

Section: Discussionmentioning

confidence: 99%

Increasing body mass index in an elderly cohort: Effects on the quantitative MR parameters of the brain

Schall¹,

Iordanishvili²,

Mauler³

et al. 2019

Magnetic Resonance Imaging

View full text Add to dashboard Cite

Background Body mass index (BMI) is increasing in a large number of elderly persons. This increase in BMI is known to put one at risk for many "diseases of aging," although less is known about how a change in BMI may affect the brains of the elderly. Purpose To investigate the relationship between BMI and quantitative water content, T1, T2*, and the semi‐quantitative magnetization transfer ratio (MTR) of various structures in elderly brains. Study Type Cross‐sectional. Subjects Forty‐two adults (BMI range: 19.1–33.5 kg/m2, age range: 58–80 years). Field Strength 3T MRI (two multi‐echo gradient echoes, actual flip angle imaging, magnetization prepared rapid gradient echo, fluid attenuated inversion recovery). Assessment The 3D two‐point method was used to derive (semi‐)quantitative parameters in global white (WM) and gray matter (GM) and their regions as defined by the Johns Hopkins University and the Montreal Neurological Institute atlases. Statistical Tests Multivariate linear regression with BMI as principal regressor, corrected for the additional regressors age, gender, and glycated hemoglobin. Spearman correlation between quantitative parameters of the regions showing significant changes and the lipid spectra / C‐reactive protein (CRP). Voxel‐based morphometry and analysis of covariance (ANCOVA) to explore changes in the GM volume. Results T1 increased significantly (P < 0.05) in the frontal, temporal, and parietal cortices, while the bilateral corona radiata, right superior longitudinal fasciculus, as well as the corpus callosum showed significant changes in the WM regions. T2* increased significantly in the global WM and left corona radiata. Changes in MTR and the free water content did not reach significance. No significant correlation between any quantitative parameter and the lipid spectra or CRP could be identified. Data Conclusion These results suggest that an elevated BMI predominantly affects T1 in WM as well as GM structures in the elderly human brain. Level of Evidence: 3 Technical Efficacy: Stage 3 J. Magn. Reson. Imaging 2020;51:514–523.

show abstract

Visceral Adiposity Predicts Subclinical White Matter Hyperintensities in Middle-Aged Adults

Cited by 6 publications

References 0 publications

Spatial distribution and cognitive impact of cerebrovascular risk-related white matter hyperintensities

Spatial distribution and cognitive impact of cerebrovascular risk-related white matter hyperintensities

Obesity and Race May Explain Differential Burden of White Matter Hyperintensity Load

Increasing body mass index in an elderly cohort: Effects on the quantitative MR parameters of the brain

Contact Info

Product

Resources

About