Retinal Vascular Pathology in a Rat Model of Cerebral Small Vessel Disease

Retinal microvascular phenotypes can track small vessel disease burden and CPAP treatment effectiveness in obstructive sleep apnoea

Optical coherence tomography angiography (OCT-A) retinal imaging enables in vivo visualization of the retinal microvasculature that is developmentally related to the brain and can offer insight on cerebrovascular health. We investigated retinal phenotypes and neuroimaging markers of small vessel disease (SVD) in individuals with obstructive sleep apnoea (OSA). We enrolled 44 participants (mean age 50.1 ± SD 9.1 years) and performed OCT-A imaging before and after continuous positive airway pressure (CPAP) therapy. Pre-treatment analyses using a generalized estimating equations model adjusted for relevant covariates, revealed perivascular spaces (PVS) volume in basal ganglia associated with greater foveal vessel density (fVD) (p-value < 0.001), and smaller foveal avascular zone area (p-value = 0.01), whereas PVS count in centrum semiovale associated with lower retinal vessel radius (p-value = 0.02) and higher vessel tortuosity (p-value = 0.01). A reduction in retinal vessel radius was also observed with increased OSA severity (p-value = 0.05). Post-treatment analyses showed greater CPAP usage was associated with a decrease in fVD (p-value = 0.02), and increased retinal vessel radius (p-value = 0.01). The findings demonstrate for the first time the potential use of OCT-A to monitor CPAP treatment and its possible impact on both retinal and brain vascular health.

Spatio-temporal dynamics of microglia phenotype in human and murine cSVD: impact of acute and chronic hypertensive states

Vascular risk factors such as chronic hypertension are well-established major modifiable factors for the development of cerebral small vessel disease (cSVD). In the present study, our focus was the investigation of cSVD-related phenotypic changes in microglia in human disease and in the spontaneously hypertensive stroke-prone rat (SHRSP) model of cSVD. Our examination of cortical microglia in human post-mortem cSVD cortical tissue revealed distinct morphological microglial features specific to cSVD. We identified enlarged somata, an increase in the territory occupied by thickened microglial processes, and an expansion in the number of vascular-associated microglia. In parallel, we characterized microglia in a rodent model of hypertensive cSVD along different durations of arterial hypertension, i.e., early chronic and late chronic hypertension. Microglial somata were already enlarged in early hypertension. In contrast, at late-stage chronic hypertension, they further exhibited elongated branches, thickened processes, and a reduced ramification index, mirroring the findings in human cSVD. An unbiased multidimensional flow cytometric analysis revealed phenotypic heterogeneity among microglia cells within the hippocampus and cortex. At early-stage hypertension, hippocampal microglia exhibited upregulated CD11b/c, P2Y12R, CD200R, and CD86 surface expression. Detailed analysis of cell subpopulations revealed a unique microglial subset expressing CD11b/c, CD163, and CD86 exclusively in early hypertension. Notably, even at early-stage hypertension, microglia displayed a higher association with cerebral blood vessels. We identified several profound clusters of microglia expressing distinct marker profiles at late chronic hypertensive states. In summary, our findings demonstrate a higher vulnerability of the hippocampus, stage-specific microglial signatures based on morphological features, and cell surface protein expression in response to chronic arterial hypertension. These results indicate the diversity within microglia sub-populations and implicate the subtle involvement of microglia in cSVD pathogenesis.

Predicting the severity of white matter lesions among patients with cerebrovascular risk factors based on retinal images and clinical laboratory data: a deep learning study

Background and purpose

As one common feature of cerebral small vascular disease (cSVD), white matter lesions (WMLs) could lead to reduction in brain function. Using a convenient, cheap, and non-intrusive method to detect WMLs could substantially benefit to patient management in the community screening, especially in the settings of availability or contraindication of magnetic resonance imaging (MRI). Therefore, this study aimed to develop a useful model to incorporate clinical laboratory data and retinal images using deep learning models to predict the severity of WMLs.

Methods

Two hundred fifty-nine patients with any kind of neurological diseases were enrolled in our study. Demographic data, retinal images, MRI, and laboratory data were collected for the patients. The patients were assigned to the absent/mild and moderate-severe WMLs groups according to Fazekas scoring system. Retinal images were acquired by fundus photography. A ResNet deep learning framework was used to analyze the retinal images. A clinical-laboratory signature was generated from laboratory data. Two prediction models, a combined model including demographic data, the clinical-laboratory signature, and the retinal images and a clinical model including only demographic data and the clinical-laboratory signature, were developed to predict the severity of WMLs.

Results

Approximately one-quarter of the patients (25.6%) had moderate-severe WMLs. The left and right retinal images predicted moderate-severe WMLs with area under the curves (AUCs) of 0.73 and 0.94. The clinical-laboratory signature predicted moderate-severe WMLs with an AUC of 0.73. The combined model showed good performance in predicting moderate-severe WMLs with an AUC of 0.95, while the clinical model predicted moderate-severe WMLs with an AUC of 0.78.

Conclusion

Combined with retinal images from conventional fundus photography and clinical laboratory data are reliable and convenient approach to predict the severity of WMLs and are helpful for the management and follow-up of WMLs patients.

Study on the Interaction between the Characteristics of Retinal Microangiopathy and Risk Factors for Cerebral Small Vessel Disease

Objective

This study was designed to explore the characteristics of retinal microangiopathy in patients with cerebral small vessel disease (CSVD) and clarify its interaction with the risk factors for CSVD.

Methods

Sixty patients with CSVD and 15 healthy individuals were enrolled. Demographic data, risk factors, and medical history were recorded, and magnetic resonance imaging was performed to detect and analyze the characteristics of retinal microangiopathy in the two groups. The interaction among retinal microangiopathy, vascular risk factors, and total imaging load of CSVD was compared.

Results

(1) Hypertension, standard deviation of systolic blood pressure (SBPSD), standard deviation of blood glucose (SDBG), and atherogenic index of plasma (AIP) were independent vascular risk factors for CSVD. (2) Statistically significant differences in hypertension, SBPSD, SDBG, and AIP were observed between the two groups in terms of the total imaging burden of CSVD (p < 0.05). (3) Multivariate logistic linear regression showed that CSVD was associated with a wider central retinal vein equivalent (CRVE) (p = 0.015), a smaller arteriole-to-venule ratio (AVR) (p = 0.001), and a higher incidence of vessel tortuosity (p = 0.027). (4) When the total imaging burden of CSVD ranges from 0 to 4 points, the CRVE is larger, the AVR is smaller, and the incidence of vascular tortuosity is higher, with a statistically significant difference (p < 0.05). (5) The characteristics of retinal microangiopathy were correlated with hypertension, SBPSD, SDBG, and AIP (p < 0.05). (6) An association was observed between the characteristics of retinal microangiopathy and vascular risk factors and the total imaging burden of CSVD (p < 0.05).

Conclusions

(1) Hypertension, SBP variability, BG fluctuation, and AIP are independent vascular risk factors for CSVD. (2) Retinal microvessels are changed in patients with CSVD, and venous dilatation, decreased arteriovenous ratio, and vascular tortuosity are the main characteristics of the disease. (3) The characteristics of retinal microangiopathy are interactively correlated with the total imaging load and risk factors for CSVD and can be used as indicators of the severity of CSVD.