Sex Differences in Cerebral Small Vessel Disease: A Systematic Review and Meta-Analysis

Background: Cerebral small vessel disease (SVD) is a common cause of stroke, mild cognitive impairment, dementia and physical impairments. Differences in SVD incidence or severity between males and females are unknown. We assessed sex differences in SVD by assessing the male-to-female ratio (M:F) of recruited participants and incidence of SVD, risk factor presence, distribution, and severity of SVD features. Methods: We assessed four recent systematic reviews on SVD and performed a supplementary search of MEDLINE to identify studies reporting M:F ratio in covert, stroke, or cognitive SVD presentations (registered protocol: CRD42020193995). We meta-analyzed differences in sex ratios across time, countries, SVD severity and presentations, age and risk factors for SVD. Results: Amongst 123 relevant studies (n = 36,910 participants) including 53 community-based, 67 hospital-based and three mixed studies published between 1989 and 2020, more males were recruited in hospital-based than in community-based studies [M:F = 1.16 (0.70) vs. M:F = 0.79 (0.35), respectively; p < 0.001]. More males had moderate to severe SVD [M:F = 1.08 (0.81) vs. M:F = 0.82 (0.47) in healthy to mild SVD; p < 0.001], and stroke presentations where M:F was 1.67 (0.53). M:F did not differ for recent (2015–2020) vs. pre-2015 publications, by geographical region, or age. There were insufficient sex-stratified data to explore M:F and risk factors for SVD. Conclusions: Our results highlight differences in male-to-female ratios in SVD severity and amongst those presenting with stroke that have important clinical and translational implications. Future SVD research should report participant demographics, risk factors and outcomes separately for males and females. Systematic Review Registration: [PROSPERO], identifier [CRD42020193995]

Topological relationships between perivascular spaces and progression of white matter hyperintensities:A pilot study in a sample of the Lothian Birth Cohort 1936

Enlarged perivascular spaces (PVS) and white matter hyperintensities (WMH) are features of cerebral small vessel disease which can be seen in brain magnetic resonance imaging (MRI). Given the associations and proposed mechanistic link between PVS and WMH, they are hypothesized to also have topological proximity. However, this and the influence of their spatial proximity on WMH progression are unknown. We analyzed longitudinal MRI data from 29 out of 32 participants (mean age at baseline = 71.9 years) in a longitudinal study of cognitive aging, from three waves of data collection at 3-year intervals, alongside semi-automatic segmentation masks for PVS and WMH, to assess relationships. The majority of deep WMH clusters were found adjacent to or enclosing PVS (waves−1: 77%; 2: 76%; 3: 69%), especially in frontal, parietal, and temporal regions. Of the WMH clusters in the deep white matter that increased between waves, most increased around PVS (waves−1–2: 73%; 2–3: 72%). Formal statistical comparisons of severity of each of these two SVD markers yielded no associations between deep WMH progression and PVS proximity. These findings may suggest some deep WMH clusters may form and grow around PVS, possibly reflecting the consequences of impaired interstitial fluid drainage via PVS. The utility of these relationships as predictors of WMH progression remains unclear

Magnetic Resonance Imaging Tissue Signatures Associated With White Matter Changes Due to Sporadic Cerebral Small Vessel Disease Indicate That White Matter Hyperintensities Can Regress

Background White matter hyperintensities (WMHs) might regress and progress contemporaneously, but we know little about underlying mechanisms. We examined WMH change and underlying quantitative magnetic resonance imaging tissue measures over 1 year in patients with minor ischemic stroke with sporadic cerebral small vessel disease. Methods and Results We defined areas of stable normal‐appearing white matter, stable WMHs, progressing and regressing WMHs based on baseline and 1‐year brain magnetic resonance imaging. In these areas we assessed tissue characteristics with quantitative T1, fractional anisotropy (FA), mean diffusivity (MD), and neurite orientation dispersion and density imaging (baseline only). We compared tissue signatures cross‐sectionally between areas, and longitudinally within each area. WMH change masks were available for N=197. Participants' mean age was 65.61 years (SD, 11.10), 59% had a lacunar infarct, and 68% were men. FA and MD were available for N=195, quantitative T1 for N=182, and neurite orientation dispersion and density imaging for N=174. Cross‐sectionally, all 4 tissue classes differed for FA, MD, T1, and Neurite Density Index. Longitudinally, in regressing WMHs, FA increased with little change in MD and T1 (difference estimate, 0.011 [95% CI, 0.006–0.017]; −0.002 [95% CI, −0.008 to 0.003] and −0.003 [95% CI, −0.009 to 0.004]); in progressing and stable WMHs, FA decreased (−0.022 [95% CI, −0.027 to −0.017] and −0.009 [95% CI, −0.011 to −0.006]), whereas MD and T1 increased (progressing WMHs, 0.057 [95% CI, 0.050–0.063], 0.058 [95% CI, 0.050 –0.066]; stable WMHs, 0.054 [95% CI, 0.045–0.063], 0.049 [95% CI, 0.039–0.058]); and in stable normal‐appearing white matter, MD increased (0.004 [95% CI, 0.003–0.005]), whereas FA and T1 slightly decreased and increased (−0.002 [95% CI, −0.004 to −0.000] and 0.005 [95% CI, 0.001–0.009]). Conclusions Quantitative magnetic resonance imaging shows that WMHs that regress have less abnormal microstructure at baseline than stable WMHs and follow trajectories indicating tissue improvement compared with stable and progressing WMHs

Integrated methylome and phenome study of the circulating proteome reveals markers pertinent to brain health

Characterising associations between the methylome, proteome and phenome may provide insight into biological pathways governing brain health. Here, we report an integrated DNA methylation and phenotypic study of the circulating proteome in relation to brain health. Methylome-wide association studies of 4058 plasma proteins are performed (N = 774), identifying 2928 CpG-protein associations after adjustment for multiple testing. These are independent of known genetic protein quantitative trait loci (pQTLs) and common lifestyle effects. Phenome-wide association studies of each protein are then performed in relation to 15 neurological traits (N = 1,065), identifying 405 associations between the levels of 191 proteins and cognitive scores, brain imaging measures or APOE e4 status. We uncover 35 previously unreported DNA methylation signatures for 17 protein markers of brain health. The epigenetic and proteomic markers we identify are pertinent to understanding and stratifying brain health

Lifetime influences on imaging markers of adverse brain health and vascular disease

Cerebral small vessel disease (cSVD) is highly prevalent in the general population, increases with age and vascular risk factor exposure, and is a common cause of stroke and dementia. There is great variation in cSVD burden experienced in older age, and maintaining brain health across the life course requires looking beyond an individual's current clinical status and traditional vascular risk factors. Of particular importance are social determinants of health which can be more important than healthcare or lifestyle choices in influencing later life health outcomes, including brain health. In this paper we discuss the social determinants of cerebrovascular disease, focusing on the impact of socioeconomic status on markers of cSVD. We outline the potential mechanisms behind these associations, including early life exposures, health behaviours and brain reserve and maintenance, and we highlight the importance of public health interventions to address the key determinants and risk factors for cSVD from early life stages

PRISMA flow chart of search process.

<p>PRISMA flow chart of search process.</p

Forest plot showing correlation between education and depressive symptoms in stroke patients.

<p>Negative correlation = low education increases depressive symptoms; Positive correlation = low education decreases depressive symptoms. DS = depressive symptoms. * adjusted for sex, income, smoking, age, cognitive dysfunction and activities of daily living. DS = depressive symptoms.</p

Sensitivity analysis comparing studies with depression defined as mild symptoms and above vs clinical depression or severe depressive symptoms only.

<p>Sensitivity analysis comparing studies with depression defined as mild symptoms and above vs clinical depression or severe depressive symptoms only.</p

Mean years of education for those with and without post-stroke depression.

<p>Random effects model for the mean difference. Negative mean difference = lower education decreases risk of post-stroke depression and positive mean difference = higher education decreases risk of post-stroke depression.</p