Retinal microvascular function predicts chronic kidney disease in patients with cardiovascular risk factors

Chronic kidney disease and dementia: an epidemiological perspective

Ageing populations worldwide face increasing challenges of multimorbidity (that is, the co-occurrence of two or more chronic conditions). The combination of chronic kidney disease (CKD) and dementia occurs more frequently than it would by simple coincidence, owing to several underlying biological and clinical mechanisms. Population-based cohort studies are an important epidemiological tool and have contributed to improved understanding of these mechanisms. These mechanisms include uniquely shared haemodynamic features of vasculature, overlapping risk factor profiles, and direct neurotoxic effects of accumulating waste products due to poor kidney function. The effect of these pathways is suggested to differ across gender, relevant demographic subgroups, and populations from low- to middle-income countries. Yet, given their study design, population-based cohort studies also inherently face several methodological challenges. These challenges pertain to the use of biomarkers that do not always fully capture the structure and function of the kidney or the brain; bidirectionality across the pathways under study; and practical issues of proper causal inference in light of incomplete distinction between confounders, mediators and effect modifiers. This Review describes our current understanding of the link between CKD and dementia, with a focus on knowledge synthesized from population-based cohort studies. Methodological challenges and possible solutions will be described and directions for future research areas will be outlined.

Early Renal Dysfunction and Reduced Retinal Vascular Density Assessed by Angio-OCT in Hypertensive Patients

Background: The eye and kidney share embryological, structural, and pathophysiological similarities, suggesting potential interconnections between retinal and renal microvascular changes. Hypertension, a major risk factor for renal impairment, also affects retinal microvasculature. This study investigates the relationship between retinal vascular density, assessed by Optical Coherence Tomography Angiography (OCT-A), and early renal dysfunction in hypertensive patients. Methods: A total of 142 hypertensive patients (mean age 47 ± 13 years; 74% male) were enrolled from the Nephrology and Hypertension Unit at the University of Palermo. Retinal vascular density was measured using OCT-A, and renal function was assessed using estimated glomerular filtration rate (eGFR). Clinical and hemodynamic parameters, including 24-h aortic blood pressure, were also analyzed. Results: Patients with eGFR < 60 mL/min/1.73 m2 exhibited significantly lower retinal vascular densities, particularly in the parafoveal region. Superficial parafoveal density was inversely associated with aortic pulse pressure (p = 0.012) and directly correlated with eGFR (p = 0.012). Deep parafoveal density was independently associated with eGFR (p = 0.001). Multiple linear regression confirmed that lower retinal vascular density was significantly linked to reduced renal function, independent of age and blood pressure. Conclusions: Retinal vascular density, particularly in the parafoveal region, is associated with renal function decline in hypertensive patients. These findings suggest that retinal microvascular changes could serve as a non-invasive biomarker for kidney dysfunction, with potential applications in early risk stratification and disease monitoring. Further research is needed to establish causality and clinical utility.

Screening chronic kidney disease through deep learning utilizing ultra-wide-field fundus images

To address challenges in screening for chronic kidney disease (CKD), we devised a deep learning-based CKD screening model named UWF-CKDS. It utilizes ultra-wide-field (UWF) fundus images to predict the presence of CKD. We validated the model with data from 23 tertiary hospitals across China. Retinal vessels and retinal microvascular parameters (RMPs) were extracted to enhance model interpretability, which revealed a significant correlation between renal function and RMPs. UWF-CKDS, utilizing UWF images, RMPs, and relevant medical history, can accurately determine CKD status. Importantly, UWF-CKDS exhibited superior performance compared to CTR-CKDS, a model developed using the central region (CTR) cropped from UWF images, underscoring the contribution of the peripheral retina in predicting renal function. The study presents UWF-CKDS as a highly implementable method for large-scale and accurate CKD screening at the population level.

Automated Retinal Vessel Analysis Based on Fundus Photographs as a Predictor for Non-Ophthalmic Diseases-Evolution and Perspectives

The study of retinal vessels in relation to cardiovascular risk has a long history. The advent of a dedicated tool based on digital imaging, i.e., the retinal vessel analyzer, and also other software such as Integrative Vessel Analysis (IVAN), Singapore I Vessel Assessment (SIVA), and Vascular Assessment and Measurement Platform for Images of the Retina (VAMPIRE), has led to the accumulation of a formidable body of evidence regarding the prognostic value of retinal vessel analysis (RVA) for cardiovascular and cerebrovascular disease (including arterial hypertension in children). There is also the potential to monitor the response of retinal vessels to therapies such as physical activity or bariatric surgery. The dynamic vessel analyzer (DVA) remains a unique way of studying neurovascular coupling, helping to understand the pathogenesis of cerebrovascular and neurodegenerative conditions and also being complementary to techniques that measure macrovascular dysfunction. Beyond cardiovascular disease, retinal vessel analysis has shown associations with and prognostic value for neurological conditions, inflammation, kidney function, and respiratory disease. Artificial intelligence (AI) (represented by algorithms such as QUantitative Analysis of Retinal vessel Topology and siZe (QUARTZ), SIVA-DLS (SIVA-deep learning system), and many others) seems efficient in extracting information from fundus photographs, providing prognoses of various general conditions with unprecedented predictive value. The future challenges will be integrating RVA and other qualitative and quantitative risk factors in a unique, comprehensive prediction tool, certainly powered by AI, while building the much-needed acceptance for such an approach inside the medical community and reducing the "black box" effect, possibly by means of saliency maps.

Deep learning-based fundus image analysis for cardiovascular disease: a review

It is well established that the retina provides insights beyond the eye. Through observation of retinal microvascular changes, studies have shown that the retina contains information related to cardiovascular disease. Despite the tremendous efforts toward reducing the effects of cardiovascular diseases, they remain a global challenge and a significant public health concern. Conventionally, predicting the risk of cardiovascular disease involves the assessment of preclinical features, risk factors, or biomarkers. However, they are associated with cost implications, and tests to acquire predictive parameters are invasive. Artificial intelligence systems, particularly deep learning (DL) methods applied to fundus images have been generating significant interest as an adjunct assessment tool with the potential of enhancing efforts to prevent cardiovascular disease mortality. Risk factors such as age, gender, smoking status, hypertension, and diabetes can be predicted from fundus images using DL applications with comparable performance to human beings. A clinical change to incorporate DL systems for the analysis of fundus images as an equally good test over more expensive and invasive procedures may require conducting prospective clinical trials to mitigate all the possible ethical challenges and medicolegal implications. This review presents current evidence regarding the use of DL applications on fundus images to predict cardiovascular disease.

Association between Kidney Function and the Burden of Cerebral Small Vessel Disease: An Updated Meta-Analysis and Systematic Review

INTRODUCTION

Due to anatomical and functional similarities in microvascular beds, the brain and kidney share distinctive susceptibilities to vascular injury and common risk factors of small vessel disease. The aim of this updated meta-analysis is to explore the association between kidney function and the burden of cerebral small vessel disease (CSVD).

METHODS

PubMed, EMBASE, and Cochrane Library were systematically searched for observational studies that explored the association between the indicators of kidney function and CSVD neuroimaging markers. The highest-adjusted risk estimates and their 95% confidence intervals (CIs) were aggregated using random-effect models.

RESULTS

Twelve longitudinal studies and 51 cross-sectional studies with 57,030 subjects met the inclusion criteria of systematic review, of which 52 were included in quantitative synthesis. According to the pooled results, we found that low estimated glomerular filtration rate (eGFR <60 mL/min/1.73 m2) was associated with cerebral microbleeds (odds ratio (OR) = 1.55, 95% CI = 1.26-1.90), white matter hyperintensities (OR = 1.40, 95% CI = 1.05-1.86), and lacunar infarctions (OR = 1.50, 95% CI = 1.18-1.92), but not with severe perivascular spaces (OR = 1.20, 95% CI = 0.77-1.88). Likewise, patients with proteinuria (OR = 1.75, 95% CI = 1.47-2.09) or elevated serum cystatin C (OR = 1.51, 95% CI = 1.25-1.83) also had an increased risk of CSVD.

CONCLUSION

The association between kidney function and CSVD has been comprehensively updated through this study, that kidney insufficiency manifested as low eGFR, proteinuria, and elevated serum cystatin C was independently associated with CSVD burden.