Ambulatory Blood Pressure Levels in the Prediction of Progression of Cerebral Small Vessel Disease

Impact of blood pressure variability and cerebral small vessel disease: A systematic review and meta-analysis

Importance

Abnormal blood pressure pattern is an independent risk factor for vascular events. Blood pressure variability can predict cardiovascular and cerebrovascular disease outcomes and is closely associated with the risk of cognitive impairment. However, the relationship between blood pressure variability and cerebral small vessel disease neuroimaging markers remains unclear. This study aimed to evaluate the relationship between blood pressure variability and cerebral small vessel disease neuroimaging markers.

Data sources

We searched multiple databases, including Embase, Web of Science, PubMed, Cochrane Library, UpToDate, and World of Science, from their inception until November 27, 2023.Main Outcomes and Measures: A meta-analysis of 19 observational studies involving 14519 participants was performed. Findings: ①Systolic blood pressure variability was correlated with the cerebral small vessel disease total burden, white matter hyperintensities and lacunar infarction; ② Diastolic blood pressure variability was correlated with the cerebral small vessel disease total burden, white matter hyperintensities and cerebral microbleeds; ③ Non-dipping patterns were correlated with white matter hyperintensities and lacunar infarction. ④ Reverse-dipping patterns were significantly correlated with white matter hyperintensities and cerebral microbleeds.

Conclusions

and Relevance: Blood pressure variability correlates with neuroimaging markers of cerebral small vessel disease and its burden. Hence, early monitoring and intervention of blood pressure variability may be essential for the early diagnosis, prevention and treatment of cerebral small vessel disease.

Development and validation of a nomogram predictive model for cognitive impairment in cerebral small vessel disease: a comprehensive retrospective analysis

Background

Cerebral small vessel disease (CSVD) is a common neurodegenerative condition in the elderly, closely associated with cognitive impairment. Early identification of individuals with CSVD who are at a higher risk of developing cognitive impairment is crucial for timely intervention and improving patient outcomes.

Objective

The aim of this study is to construct a predictive model utilizing LASSO regression and binary logistic regression, with the objective of precisely forecasting the risk of cognitive impairment in patients with CSVD.

Methods

The study utilized LASSO regression for feature selection and logistic regression for model construction in a cohort of CSVD patients. The model's validity was assessed through calibration curves and decision curve analysis (DCA).

Results

A nomogram was developed to predict cognitive impairment, incorporating hypertension, CSVD burden, apolipoprotein A1 (ApoA1) levels, and age. The model exhibited high accuracy with AUC values of 0.866 and 0.852 for the training and validation sets, respectively. Calibration curves confirmed the model's reliability, and DCA highlighted its clinical utility. The model's sensitivity and specificity were 75.3 and 79.7% for the training set, and 76.9 and 74.0% for the validation set.

Conclusion

This study successfully demonstrates the application of machine learning in developing a reliable predictive model for cognitive impairment in CSVD. The model's high accuracy and robust predictive capability provide a crucial tool for the early detection and intervention of cognitive impairment in patients with CSVD, potentially improving outcomes for this specific condition.

Chronic kidney disease and its association with cerebral small vessel disease in the general older hypertensive population

BACKGROUND

Cerebral small vessel disease can be identified using magnetic resonance imaging, and includes white matter hyperintensities, lacunar infarcts, cerebral microbleeds, and brain atrophy. Cerebral small vessel disease and chronic kidney disease share many risk factors, including hypertension. This study aims to explore an association between chronic kidney disease and cerebral small vessel disease, and also to explore the role of hypertension in this relationship.

METHODS

With a cross sectional study design, data from 390 older adults was retrieved from the general population study Good Aging in Skåne. Chronic kidney disease was defined as glomerular filtration rate < 60 ml/min/1,73m2. Associations between chronic kidney disease and magnetic resonance imaging markers of cerebral small vessel disease were explored using logistic regression models adjusted for age and sex. In a secondary analysis, the same calculations were performed with the study sample stratified based on hypertension status.

RESULTS

In the whole group, adjusted for age and sex, chronic kidney disease was not associated with any markers of cerebral small vessel disease. After stratification by hypertension status and adjusted for age and sex, we observed that chronic kidney disease was associated with cerebral microbleeds (OR 1.93, CI 1.04-3.59, p-value 0.037), as well as with cortical atrophy (OR 2.45, CI 1.34-4.48, p-value 0.004) only in the hypertensive group. In the non-hypertensive group, no associations were observed.

CONCLUSIONS

In this exploratory cross-sectional study, we observed that chronic kidney disease was associated with markers of cerebral small vessel disease only in the hypertensive subgroup of a general population of older adults. This might indicate that hypertension is an important link between chronic kidney disease and cerebral small vessel disease. Further studies investigating the relationship between CKD, CSVD, and hypertension are warranted.

Blood Pressure Partially Mediated the Association of Insulin Resistance and Cerebral Small Vessel Disease: A Community-Based Study

BACKGROUND

Insulin resistance as a significant vascular risk factor has been studied in relation to cerebral small vessel disease (SVD). Evidence suggests that insulin resistance might trigger high blood pressure (BP). Therefore, we aimed to investigate whether insulin resistance impacts SVD with a mediating effect of BP in nondiabetic subjects.

METHODS AND RESULTS

PRECISE (Polyvascular Evaluation for Cognitive Impairment and Vascular Events) study participants underwent brain and vascular imaging techniques and metabolomic risk factors measurements. Insulin resistance was evaluated by the insulin sensitivity index and the Homeostatic Model Assessment for Insulin Resistance based on the standard oral glucose tolerance test. On average, 2752 nondiabetic subjects (47.1% men) aged 60.9 years were included. The multivariable logistic regression model and linear regression model tested the association of insulin resistance with BP components (including systolic BP [SBP], diastolic BP (DBP), and pulse pressure [PP]) and SVD, and of BP components with SVD. In the mediation analysis, SBP, DBP, and PP were found to partially mediate the detrimental effect of insulin resistance (assessed by the insulin sensitivity index) on lacunes (mediation percentage: SBP, 31.15%; DBP, 34.21%; PP, 10.43%), white matter hyperintensity (mediation percentage: SBP, 37.34%; DBP, 44.15%; PP, 9.80%), and SVD total burden (mediation percentage: SBP, 42.07%; DBP, 49.29%; PP, 11.71%) (all P<0.05). The mediation analysis results were not significant when using the Homeostatic Model Assessment for Insulin Resistance to assess insulin resistance.

CONCLUSIONS

Higher insulin resistance was associated with SVD in this community-dwelling population. The association of insulin resistance with lacunes, white matter hyperintensity, and SVD total burden was explained in part by BP.

REGISTRATION

URL: https://www.clinicaltrials.gov; Unique identifier: NCT03178448.

The etiology and evolution of magnetic resonance imaging-visible perivascular spaces: Systematic review and meta-analysis

Objectives

Perivascular spaces have been involved in neuroinflammatory and neurodegenerative diseases. Upon a certain size, these spaces can become visible on magnetic resonance imaging (MRI), referred to as enlarged perivascular spaces (EPVS) or MRI-visible perivascular spaces (MVPVS). However, the lack of systematic evidence on etiology and temporal dynamics of MVPVS hampers their diagnostic utility as MRI biomarker. Thus, the goal of this systematic review was to summarize potential etiologies and evolution of MVPVS.

Methods

In a comprehensive literature search, out of 1,488 unique publications, 140 records assessing etiopathogenesis and dynamics of MVPVS were eligible for a qualitative summary. 6 records were included in a meta-analysis to assess the association between MVPVS and brain atrophy.

Results

Four overarching and partly overlapping etiologies of MVPVS have been proposed: (1) Impairment of interstitial fluid circulation, (2) Spiral elongation of arteries, (3) Brain atrophy and/or perivascular myelin loss, and (4) Immune cell accumulation in the perivascular space. The meta-analysis in patients with neuroinflammatory diseases did not support an association between MVPVS and brain volume measures [R: −0.15 (95%-CI −0.40–0.11)]. Based on few and mostly small studies in tumefactive MVPVS and in vascular and neuroinflammatory diseases, temporal evolution of MVPVS is slow.

Conclusion

Collectively, this study provides high-grade evidence for MVPVS etiopathogenesis and temporal dynamics. Although several potential etiologies for MVPVS emergence have been proposed, they are only partially supported by data. Advanced MRI methods should be employed to further dissect etiopathogenesis and evolution of MVPVS. This can benefit their implementation as an imaging biomarker.

Systematic review registration

https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=346564, identifier CRD42022346564.

Epigenetic Clock Explains White Matter Hyperintensity Burden Irrespective of Chronological Age

In this manuscript we studied the relationship between WMH and biological age (B-age) in patients with acute stroke. We included in this study 247 patients with acute stroke recruited at Hospital del Mar having both epigenetic (DNA methylation) and magnetic resonance imaging data. WMH were measured using a semi-automated method. B-age was calculated using two widely used methods: the Hannum and Horvath formulas. We used multiple linear regression models to interrogate the role of B-age on WMH volume after adjusting for chronological age (C-age) and other covariables. Average C-age of the sample was 68.4 (±11.8) and we observed a relatively high median WMH volume (median = 8.8 cm³, Q1-Q3 = 4.05-18.8). After adjusting for potential confounders, we observed a significant effect of B-age_Hannum on WMH volume (β_Hannum = 0.023, p-value = 0.029) independently of C-age, which remained significant (β_C-age = 0.021, p-value = 0.036). Finally, we performed a mediation analysis, which allowed us to discover that 42.7% of the effect of C-age on WMH is mediated by B-age_Hannum. On the other hand, B-age_Hoarvath showed no significant associations with WMH after being adjusted for C-age. In conclusion, we show for the first time that biological age, measured through DNA methylation, contributes substantially to explain WMH volumetric burden irrespective of chronological age.

Silent brain infarcts, peripheral vascular disease and the risk of cardiovascular events in patients with hypertension

BACKGROUND AND AIMS

We aimed to study the relationship between cerebral small vessel disease (cSVD) lesions, as markers of subclinical target organ damage (TOD) in the brain, and incident cardiovascular events (CVE).

METHODS

Data from the ISSYS (Investigating Silent Strokes in hYpertensives Study), which is a longitudinal and observational study conducted in patients with hypertension aged 50-70 years, and stroke-free at the inclusion. At the baseline visit, participants underwent a clinical interview, a brain MRI, urine and blood sampling collection and vascular testing studies. Therefore, we obtained markers of TOD from the brain [white matter hyperintensities, silent brain infarcts (SBI), cerebral microbleeds and enlarged perivascular spaces (EPVS)], from kidney (microalbuminuria, glomerular filtration) and regarding large vessels [ankle-to-brachial index (ABI), carotid-femoral pulse wave velocity]. Survival analyses were used to assess the relationship between these predictors and the incidence of cardiovascular events (CVE).

RESULTS

We followed-up 964 individuals within a median time of 5 years (4.7-5), representing 4377.1 persons-year. We found 73 patients presenting incident CVE, which corresponds to a rate of 8.2%. We found ABI less than 0.9 [hazard ratio, 2.2; 95% confidence interval (CI) 1.17-4.13, P value = 0.014] and SBI (hazard ratio, 2.9; 95% CI 1.47-5.58, P value = 0.002) independently associated with higher risk of incident CVE. The inclusion of both variables in a clinical model resulted in an increased discrimination of individuals with new CVE of 4.72%, according to the integrated discrimination index.

CONCLUSION

Assessment of SBI and ABI less than 0.9 may refine the cardiovascular risk stratification in patients with hypertension.

Subclinical Magnetic Resonance Imaging Markers of Cerebral Small Vessel Disease in Relation to Office and Ambulatory Blood Pressure Measurements

Background

Twenty-four-hour and nighttime blood pressure (BP) levels are more strongly associated with cardiovascular risk than office or daytime BP measurements. However, it remains undocumented which of the office and ambulatory BP measurements have the strongest association and predictive information in relation to the presence of type I, or arteriolosclerosis type, cerebral small vessel diseases (CSVD).

Methods

A subset of 429 participants from the Maracaibo Aging Study [aged ≥40 years (women, 73.7%; mean age, 59.3 years)] underwent baseline brain magnetic resonance imaging (MRI) to visualize CSVD, which included log-transformed white matter hyperintensities (log-WMH) volume and the presence (yes/no) of lacunes, cerebral microbleeds (CMB), or enlarged perivascular spaces (EPVS). Linear and logistic regression models were applied to examine the association between CSVD and each +10-mmHg increment in the office and ambulatory systolic BP measurements. Improvement in the fit of nested logistic models was assessed by the log-likelihood ratio and the generalized R2 statistic.

Results

Office and ambulatory systolic BP measurements were related to log-WMH (β-correlation coefficients ≥0.08; P &lt; 0.001). Lacunes and CMB were only associated with ambulatory systolic BP measurements (odds ratios [OR] ranged from 1.31 [95% confidence interval, 1.10-1.55] to 1.46 [1.17–1.84], P ≤ 0.003). Accounted for daytime systolic BP, both the 24-h (β-correlation, 0.170) and nighttime (β-correlation, 0.038) systolic BP measurements remained related to log-WMH. When accounted for 24-h or daytime systolic BP levels, the nighttime systolic BP retained the significant association with lacunes (ORs, 1.05–1.06; 95% CIs, ≥1.01 to ≤ 1.13), whereas the 24-h and daytime systolic BP levels were not associated with lacunes after adjustments for nighttime systolic BP (ORs, ≤ 0.88; 95% CI, ≥0.77 to ≤ 1.14). On top of covariables and office systolic BP, ambulatory systolic BP measurements significantly improved model performance (1.05% ≥ R2 ≤ 3.82%). Compared to 24-h and daytime systolic BP, nighttime systolic BP had the strongest improvement in the model performance; for WMH (1.46 vs. 1.05%) and lacunes (3.06 vs. ≤ 2.05%).

Conclusions

Twenty-four-hour and nighttime systolic BP were the more robust BP measurements associated with CSVD, but the nighttime systolic BP level had the strongest association. Controlling ambulatory BP levels might provide additional improvement in the prevention of CSVD.

Effects of white matter hyperintensities distribution and clustering on late-life cognitive impairment

White matter hyperintensities (WMH) are a key hallmark of subclinical cerebrovascular disease and are known to impair cognition. Here, we parcellated WMH using a novel system that segments WMH based on both lobar regions and distance from the ventricles, dividing the brain into a coordinate system composed of 36 distinct parcels ('bullseye' parcellation), and then investigated the effect of distribution on cognition using two different analytic approaches. Data from a well characterized sample of healthy older adults (58 to 84 years) who were free of dementia were included. Cognition was evaluated using 12 computerized tasks, factored onto 4 indices representing episodic memory, speed of processing, fluid reasoning and vocabulary. We first assessed the distribution of WMH according to the bullseye parcellation and tested the relationship between WMH parcellations and performance across the four cognitive domains. Then, we used a data-driven approach to derive latent variables within the WMH distribution, and tested the relation between these latent components and cognitive function. We observed that different, well-defined cognitive constructs mapped to specific WMH distributions. Speed of processing was correlated with WMH in the frontal lobe, while in the case of episodic memory, the relationship was more ubiquitous, involving most of the parcellations. A principal components analysis revealed that the 36 bullseye regions factored onto 3 latent components representing the natural aggrupation of WMH: fronto-parietal periventricular (WMH principally in the frontal and parietal lobes and basal ganglia, especially in the periventricular region); occipital; and temporal and juxtacortical WMH (involving WMH in the temporal lobe, and at the juxtacortical region from frontal and parietal lobes). We found that fronto-parietal periventricular and temporal & juxtacortical WMH were independently associated with speed of processing and episodic memory, respectively. These results indicate that different cognitive impairment phenotypes might present with specific WMH distributions. Additionally, our study encourages future research to consider WMH classifications using parcellations systems other than periventricular and deep localizations.

New candidate blood biomarkers potentially associated with white matter hyperintensities progression

We aimed to discover blood biomarkers associated with longitudinal changes in white matter hyperintensities (WMH). This study was divided into a discovery phase and a replication phase. Subjects in both studies were patients with hypertension, aged 50-70, who underwent two magnetic resonance imaging (MRI) sessions and blood extractions over a 4-year follow-up period. In the discovery phase, we screened 1305 proteins in 12 subjects with WMH progression and in 12 matched control subjects. We found that 41 proteins were differentially expressed: 13 were upregulated and 28 were downregulated. We subsequently selected three biomarkers for replication in baseline and follow-up samples in 80 subjects with WMH progression and in 80 control subjects. The selected protein candidates for the replication were MMP9 (matrix metalloproteinase-9), which was higher in cases, MET (hepatocyte growth factor receptor) and ASAH2 (neutral ceramidase), which were both lower in cases of WMH progression. Baseline biomarker concentrations did not predict WMH progression. In contrast, patients with WMH progression presented a steeper decline in MET over time. Furthermore, cases showed higher MMP9 and lower ASAH2 levels than controls at the follow-up. These results indicate that MMP9, MET, and ASAH2 are potentially associated with the progression of WMH, and could therefore be interesting candidates to validate in future studies.