Retinal Vascular Signs as Screening and Prognostic Factors for Chronic Kidney Disease: A Systematic Review and Meta-Analysis of Current Evidence

Enhancing stroke risk and prognostic timeframe assessment with deep learning and a broad range of retinal biomarkers

Stroke stands as a major global health issue, causing high death and disability rates and significant social and economic burdens. The effectiveness of existing stroke risk assessment methods is questionable due to their use of inconsistent and varying biomarkers, which may lead to unpredictable risk evaluations. This study introduces an automatic deep learning-based system for predicting stroke risk (both ischemic and hemorrhagic) and estimating the time frame of its occurrence, utilizing a comprehensive set of known retinal biomarkers from fundus images. Our system, tested on the UK Biobank and DRSSW datasets, achieved AUROC scores of 0.83 (95% CI: 0.79-0.85) and 0.93 (95% CI: 0.9-0.95), respectively. These results not only highlight our system's advantage over established benchmarks but also underscore the predictive power of retinal biomarkers in assessing stroke risk and the unique effectiveness of each biomarker. Additionally, the correlation between retinal biomarkers and cardiovascular diseases broadens the potential application of our system, making it a versatile tool for predicting a wide range of cardiovascular conditions.

Performance of deep learning for detection of chronic kidney disease from retinal fundus photographs: A systematic review and meta-analysis

OBJECTIVE

Deep learning has been used to detect chronic kidney disease (CKD) from retinal fundus photographs. We aim to evaluate the performance of deep learning for CKD detection.

METHODS

The original studies in CKD patients detected by deep learning from retinal fundus photographs were eligible for inclusion. PubMed, Embase, the Cochrane Library, and Web of Science were searched up to October 31, 2022. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool was used to assess the risk of bias.

RESULTS

Four studies enrolled 114,860 subjects were included. The pooled sensitivity and specificity were 87.8% (95% confidence interval (CI): 61.6% to 98.3%), and 62.4% (95% CI: 44.9% to 78.7%). The area under the curve (AUC) was 0.864 (95%CI: 0.769, 0.986).

CONCLUSION

Deep learning based on retinal fundus photographs has the ability to detect CKD, but it currently has a lot of room for improvement. It is still a long way from clinical application.

Early choroidal and retinal changes detected by swept-source oct in type 2 diabetes and their association with diabetic kidney disease: a longitudinal prospective study

BACKGROUND

To evaluate structural changes in retina and choroid in patients with type 2 diabetes (T2D) and their association with diabetic kidney disease (DKD).

METHODS

T2D patients with mild or no diabetic retinopathy (DR) were followed for 3 years using structural SS-OCT and OCT angiography (OCT-A) taken every 6 months. Parameters were compared longitudinally and according to the DKD status on baseline.

RESULTS

One hundred and sixty eyes from 80 patients were followed for 3 years, 72 with no DKD (nDKD) at baseline and 88 with DKD. Trend analysis of T2D showed significant thinning in GCL + and circumpapillary retinal fiber neural layer (cRFNL), choroid, and decreased vascular density (VD) in superficial plexus and central choriocapillaris with foveal avascular zone (FAZ) enlargement. Patients with no DKD on baseline presented more significant declines in retinal center and choroidal thickness, increased FAZ and loss of nasal and temporal choriocapillaris volume. In addition, the nDKD group had worse glycemic control and renal parameters at the end of the study.

CONCLUSION

Our data suggests the potential existence of early and progressive neurovascular damage in the retina and choroid of patients with Type 2 Diabetes (T2D) who have either no or mild Diabetic Retinopathy (DR). The progression of neurovascular damage appears to be correlated with parameters related to glycemic control and renal damage.

Phenome- and genome-wide analyses of retinal optical coherence tomography images identify links between ocular and systemic health

The human retina is a multilayered tissue that offers a unique window into systemic health. Optical coherence tomography (OCT) is widely used in eye care and allows the noninvasive, rapid capture of retinal anatomy in exquisite detail. We conducted genotypic and phenotypic analyses of retinal layer thicknesses using macular OCT images from 44,823 UK Biobank participants. We performed OCT layer cross-phenotype association analyses (OCT-XWAS), associating retinal thicknesses with 1866 incident conditions (median 10-year follow-up) and 88 quantitative traits and blood biomarkers. We performed genome-wide association studies (GWASs), identifying inherited genetic markers that influence retinal layer thicknesses and replicated our associations among the LIFE-Adult Study (N = 6313). Last, we performed a comparative analysis of phenome- and genome-wide associations to identify putative causal links between retinal layer thicknesses and both ocular and systemic conditions. Independent associations with incident mortality were detected for thinner photoreceptor segments (PSs) and, separately, ganglion cell complex layers. Phenotypic associations were detected between thinner retinal layers and ocular, neuropsychiatric, cardiometabolic, and pulmonary conditions. A GWAS of retinal layer thicknesses yielded 259 unique loci. Consistency between epidemiologic and genetic associations suggested links between a thinner retinal nerve fiber layer with glaucoma, thinner PS with age-related macular degeneration, and poor cardiometabolic and pulmonary function with a thinner PS. In conclusion, we identified multiple inherited genetic loci and acquired systemic cardio-metabolic-pulmonary conditions associated with thinner retinal layers and identify retinal layers wherein thinning is predictive of future ocular and systemic conditions.

The automatic detection of diabetic kidney disease from retinal vascular parameters combined with clinical variables using artificial intelligence in type-2 diabetes patients

BACKGROUND

Diabetic kidney disease (DKD) has become the largest cause of end-stage kidney disease. Early and accurate detection of DKD is beneficial for patients. The present detection depends on the measurement of albuminuria or the estimated glomerular filtration rate, which is invasive and not optimal; therefore, new detection tools are urgently needed. Meanwhile, a close relationship between diabetic retinopathy and DKD has been reported; thus, we aimed to develop a novel detection algorithm for DKD using artificial intelligence technology based on retinal vascular parameters combined with several easily available clinical parameters in patients with type-2 diabetes.

METHODS

A total of 515 consecutive patients with type-2 diabetes mellitus from Xiangyang Central Hospital were included. Patients were stratified by DKD diagnosis and split randomly into either the training set (70%, N = 360) or the testing set (30%, N = 155) (random seed = 1). Data from the training set were used to develop the machine learning algorithm (MLA), while those from the testing set were used to validate the MLA. Model performances were evaluated.

RESULTS

The MLA using the random forest classifier presented optimal performance compared with other classifiers. When validated, the accuracy, sensitivity, specificity, F1 score, and AUC for the optimal model were 84.5%(95% CI 83.3-85.7), 84.5%(82.3-86.7), 84.5%(82.7-86.3), 0.845(0.831-0.859), and 0.914(0.903-0.925), respectively.

CONCLUSIONS

A new machine learning algorithm for DKD diagnosis based on fundus images and 8 easily available clinical parameters was developed, which indicated that retinal vascular changes can assist in DKD screening and detection.

Retinal Microvascular Diameters are Associated with Diabetic Kidney Disease in Patients with Type 2 Diabetes Mellitus

Objective

To investigate the association between retinal microvascular diameters and diabetic kidney disease (DKD) in patients with type 2 diabetes mellitus (T2DM).

Methods

A total of 690 patients with T2DM were included in this retrospective study. Patients were divided into DKD and non-DKD groups according to urine microalbumin/creatinine ratio and estimated glomerular filtration rate. Retinal microvascular diameters were measured by the automated retinal image analysis system. Multivariate logistic regression analysis and restricted cubic splines were used to assess the relationships between the retinal microvascular diameters and DKD in patients with T2DM.

Results

Multivariate logistic regression showed that widened diameters of retinal venules and narrowed diameters of retinal arterioles were associated with DKD after adjusting for potential confounding variables. There was a significant linear trend between the diameters of superior temporal retinal venula (P for trend < 0.001, P for non-linearity = 0.080), inferior temporal retinal venula (P for trend < 0.001, P for non-linearity = 0.111) and central retinal venular equivalent (CRVE) (P for trend < 0.001, P for non-linearity = 0.392) and risk of DKD in patients with T2DM. The restricted cubic splines showed that narrowed retinal arteriolar diameters, superior and inferior nasal retinal venulas were associated with the risk of DKD in a non-linear fashion (all P for non-linearity < 0.001).

Conclusion

Wider retinal venular diameters and narrower retinal arteriolar diameters were associated with an increased risk of DKD in patients with T2DM. Widened retinal venular diameters, especially CRVE, superior and inferior temporal retinal venula, were positively associated with an increased risk of DKD in a linear fashion. In contrast, narrowed retinal arteriolar diameters were associated with the risk of DKD in a non-linear fashion.

Insights into human health from phenome- and genome-wide analyses of UK Biobank retinal optical coherence tomography phenotypes

The human retina is a complex multi-layered tissue which offers a unique window into systemic health and disease. Optical coherence tomography (OCT) is widely used in eye care and allows the non-invasive, rapid capture of retinal measurements in exquisite detail. We conducted genome- and phenome-wide analyses of retinal layer thicknesses using macular OCT images from 44,823 UK Biobank participants. We performed phenome-wide association analyses, associating retinal thicknesses with 1,866 incident ICD-based conditions (median 10-year follow-up) and 88 quantitative traits and blood biomarkers. We performed genome-wide association analyses, identifying inherited genetic markers which influence the retina, and replicated our associations among 6,313 individuals from the LIFE-Adult Study. And lastly, we performed comparative association of phenome- and genome- wide associations to identify putative causal links between systemic conditions, retinal layer thicknesses, and ocular disease. Independent associations with incident mortality were detected for photoreceptor thinning and ganglion cell complex thinning. Significant phenotypic associations were detected between retinal layer thinning and ocular, neuropsychiatric, cardiometabolic and pulmonary conditions. Genome-wide association of retinal layer thicknesses yielded 259 loci. Consistency between epidemiologic and genetic associations suggested putative causal links between thinning of the retinal nerve fiber layer with glaucoma, photoreceptor segment with AMD, as well as poor cardiometabolic and pulmonary function with PS thinning, among other findings. In conclusion, retinal layer thinning predicts risk of future ocular and systemic disease. Furthermore, systemic cardio-metabolic-pulmonary conditions promote retinal thinning. Retinal imaging biomarkers, integrated into electronic health records, may inform risk prediction and potential therapeutic strategies.

A Simple Review of Small Vessel Disease Manifestation in the Brain, Retina, and Kidneys

Small blood vessels express specific phenotypical and functional characteristics throughout the body. Alterations in the microcirculation contribute to many correlated physiological and pathological events in related organs. Factors such as comorbidities and genetics contribute to the complexity of this topic. Small vessel disease primarily affects end organs that receive significant cardiac output, such as the brain, kidney, and retina. Despite the differences in location, concurrent changes are seen in the micro-vasculature of the brain, retina, and kidneys under pathological conditions due to their common histological, functional, and embryological characteristics. While the cardiovascular basis of pathology in association with the brain, retina, or kidneys has been well documented, this is a simple review that uniquely considers the relationship between all three organs and highlights the prevalence of coexisting end organ injuries in an attempt to elucidate connections between the brain, retina, and kidneys, which has the potential to transform diagnostic and therapeutic approaches.

Schizophrenia in Translation: Why the Eye?

Schizophrenia is increasingly recognized as a systemic disease, characterized by dysregulation in multiple physiological systems (eg, neural, cardiovascular, endocrine). Many of these changes are observed as early as the first psychotic episode, and in people at high risk for the disorder. Expanding the search for biomarkers of schizophrenia beyond genes, blood, and brain may allow for inexpensive, noninvasive, and objective markers of diagnosis, phenotype, treatment response, and prognosis. Several anatomic and physiologic aspects of the eye have shown promise as biomarkers of brain health in a range of neurological disorders, and of heart, kidney, endocrine, and other impairments in other medical conditions. In schizophrenia, thinning and volume loss in retinal neural layers have been observed, and are associated with illness progression, brain volume loss, and cognitive impairment. Retinal microvascular changes have also been observed. Abnormal pupil responses and corneal nerve disintegration are related to aspects of brain function and structure in schizophrenia. In addition, studying the eye can inform about emerging cardiovascular, neuroinflammatory, and metabolic diseases in people with early psychosis, and about the causes of several of the visual changes observed in the disorder. Application of the methods of oculomics, or eye-based biomarkers of non-ophthalmological pathology, to the treatment and study of schizophrenia has the potential to provide tools for patient monitoring and data-driven prediction, as well as for clarifying pathophysiology and course of illness. Given their demonstrated utility in neuropsychiatry, we recommend greater adoption of these tools for schizophrenia research and patient care.

Arterial Hypertension and the Hidden Disease of the Eye: Diagnostic Tools and Therapeutic Strategies

Hypertension is a major cardiovascular risk factor that is responsible for a heavy burden of morbidity and mortality worldwide. A critical aspect of cardiovascular risk estimation in hypertensive patients depends on the assessment of hypertension-mediated organ damage (HMOD), namely the generalized structural and functional changes in major organs induced by persistently elevated blood pressure values. The vasculature of the eye shares several common structural, functional, and embryological features with that of the heart, brain, and kidney. Since retinal microcirculation offers the unique advantage of being directly accessible to non-invasive and relatively simple investigation tools, there has been considerable interest in the development and modernization of techniques that allow the assessment of the retinal vessels’ structural and functional features in health and disease. With the advent of artificial intelligence and the application of sophisticated physics technologies to human sciences, consistent steps forward have been made in the study of the ocular fundus as a privileged site for diagnostic and prognostic assessment of diverse disease conditions. In this narrative review, we will recapitulate the main ocular imaging techniques that are currently relevant from a clinical and/or research standpoint, with reference to their pathophysiological basis and their possible diagnostic and prognostic relevance. A possible non pharmacological approach to prevent the onset and progression of retinopathy in the presence of hypertension and related cardiovascular risk factors and diseases will also be discussed.

Long-term prediction models for vision-threatening diabetic retinopathy using medical features from data warehouse

We sought to evaluate the performance of machine learning prediction models for identifying vision-threatening diabetic retinopathy (VTDR) in patients with type 2 diabetes mellitus using only medical data from data warehouse. This is a multicenter electronic medical records review study. Patients with type 2 diabetes screened for diabetic retinopathy and followed-up for 10 years were included from six referral hospitals sharing same electronic medical record system (n = 9,102). Patient demographics, laboratory results, visual acuities (VAs), and occurrence of VTDR were collected. Prediction models for VTDR were developed using machine learning models. F1 score, accuracy, specificity, and area under the receiver operating characteristic curve (AUC) were analyzed. Machine learning models revealed F1 score, accuracy, specificity, and AUC values of up 0.89, 0.89.0.95, and 0.96 during training. The trained models predicted the occurrence of VTDR at 10-year with F1 score, accuracy, and specificity up to 0.81, 0.70, and 0.66, respectively, on test set. Important predictors included baseline VA, duration of diabetes treatment, serum level of glycated hemoglobin and creatinine, estimated glomerular filtration rate and blood pressure. The models could predict the long-term occurrence of VTDR with fair performance. Although there might be limitation due to lack of funduscopic findings, prediction models trained using medical data can facilitate proper referral of subjects at high risk for VTDR to an ophthalmologist from primary care.

Early neurovascular retinal changes detected by swept-source OCT in type 2 diabetes and association with diabetic kidney disease

Purpose

To evaluate retinal thickness and capillary density in patients with type 2 diabetes (T2D) and their association with diabetic kidney disease (DKD) using swept-source optical coherence tomography (SS-OCT).

Methods

A cross-sectional study was conducted with T2D patients with mild or no diabetic retinopathy (DR) and nondiabetic controls. Inner retinal layer thickness was measured with SS-OCT. Retinal capillary density and the foveal avascular zone (FAZ) were measured with SS-OCT angiography (OCTA). SS-OCT parameters were compared in patients with and without diabetic kidney disease (DKD) and nondiabetic controls.

Results

131 DKD eyes showed decreased ganglion cell layer plus (GCL+) (p = 0.005 TI; p = 0.022 I), retinal nerve fiber layer (RNFL) (p = 0.003), and central retinal thickness (CRT) (p = 0.032), as well as foveal avascular zone (FAZ) enlargement (p = 0.003) and lower capillary density in the superficial vascular plexus (p = 0.016, central quadrant), compared to controls. No statistically significant changes were found between diabetic patients without significant DKD and controls.

Conclusion

Our findings suggest early neurovascular damage in patients with T2D; these changes were more significant in patients with DKD. Larger longitudinal studies are warranted to determine the role of early neurovascular damage in the pathophysiology of severe DR.