Retinal microvascular signs and risk of diabetic kidney disease in asian and white populations

Evaluating the potential of retinal photography in chronic kidney disease detection: a review

Background

Chronic kidney disease (CKD) is a significant global health concern, emphasizing the necessity of early detection to facilitate prompt clinical intervention. Leveraging the unique ability of the retina to offer insights into systemic vascular health, it emerges as an interesting, non-invasive option for early CKD detection. Integrating this approach with existing invasive methods could provide a comprehensive understanding of patient health, enhancing diagnostic accuracy and treatment effectiveness.

Objectives

The purpose of this review is to critically assess the potential of retinal imaging to serve as a diagnostic tool for CKD detection based on retinal vascular changes. The review tracks the evolution from conventional manual evaluations to the latest state-of-the-art in deep learning.

Survey Methodology

A comprehensive examination of the literature was carried out, using targeted database searches and a three-step methodology for article evaluation: identification, screening, and inclusion based on Prisma guidelines. Priority was given to unique and new research concerning the detection of CKD with retinal imaging. A total of 70 publications from 457 that were initially discovered satisfied our inclusion criteria and were thus subjected to analysis. Out of the 70 studies included, 35 investigated the correlation between diabetic retinopathy and CKD, 23 centered on the detection of CKD via retinal imaging, and four attempted to automate the detection through the combination of artificial intelligence and retinal imaging.

Results

Significant retinal features such as arteriolar narrowing, venular widening, specific retinopathy markers (like microaneurysms, hemorrhages, and exudates), and changes in arteriovenous ratio (AVR) have shown strong correlations with CKD progression. We also found that the combination of deep learning with retinal imaging for CKD detection could provide a very promising pathway. Accordingly, leveraging retinal imaging through this technique is expected to enhance the precision and prognostic capacity of the CKD detection system, offering a non-invasive diagnostic alternative that could transform patient care practices.

Conclusion

In summary, retinal imaging holds high potential as a diagnostic tool for CKD because it is non-invasive, facilitates early detection through observable microvascular changes, offers predictive insights into renal health, and, when paired with deep learning algorithms, enhances the accuracy and effectiveness of CKD screening.

Continuous glucose monitoring metrics in pregnancy with type 1 diabetes mellitus

Managing diabetes during pregnancy is challenging, given the significant risk it poses for both maternal and foetal health outcomes. While traditional methods involve capillary self-monitoring of blood glucose level monitoring and periodic HbA1c tests, the advent of continuous glucose monitoring (CGM) systems has revolutionized the approach. These devices offer a safe and reliable means of tracking glucose levels in real-time, benefiting both women with diabetes during pregnancy and the healthcare providers. Moreover, CGM systems have shown a low rate of side effects and high feasibility when used in pregnancies complicated by diabetes, especially when paired with continuous subcutaneous insulin infusion pump as hybrid closed loop device. Such a combined approach has been demonstrated to improve overall blood sugar control, lessen the occurrence of preeclampsia and neonatal hypoglycaemia, and minimize the duration of neonatal intensive care unit stays. This paper aims to offer a comprehensive evaluation of CGM metrics specifically tailored for pregnancies impacted by type 1 diabetes mellitus.

Deep learning algorithms to detect diabetic kidney disease from retinal photographs in multiethnic populations with diabetes

OBJECTIVE

To develop a deep learning algorithm (DLA) to detect diabetic kideny disease (DKD) from retinal photographs of patients with diabetes, and evaluate performance in multiethnic populations.

MATERIALS AND METHODS

We trained 3 models: (1) image-only; (2) risk factor (RF)-only multivariable logistic regression (LR) model adjusted for age, sex, ethnicity, diabetes duration, HbA1c, systolic blood pressure; (3) hybrid multivariable LR model combining RF data and standardized z-scores from image-only model. Data from Singapore Integrated Diabetic Retinopathy Program (SiDRP) were used to develop (6066 participants with diabetes, primary-care-based) and internally validate (5-fold cross-validation) the models. External testing on 2 independent datasets: (1) Singapore Epidemiology of Eye Diseases (SEED) study (1885 participants with diabetes, population-based); (2) Singapore Macroangiopathy and Microvascular Reactivity in Type 2 Diabetes (SMART2D) (439 participants with diabetes, cross-sectional) in Singapore. Supplementary external testing on 2 Caucasian cohorts: (3) Australian Eye and Heart Study (AHES) (460 participants with diabetes, cross-sectional) and (4) Northern Ireland Cohort for the Longitudinal Study of Ageing (NICOLA) (265 participants with diabetes, cross-sectional).

RESULTS

In SiDRP validation, area under the curve (AUC) was 0.826(95% CI 0.818-0.833) for image-only, 0.847(0.840-0.854) for RF-only, and 0.866(0.859-0.872) for hybrid. Estimates with SEED were 0.764(0.743-0.785) for image-only, 0.802(0.783-0.822) for RF-only, and 0.828(0.810-0.846) for hybrid. In SMART2D, AUC was 0.726(0.686-0.765) for image-only, 0.701(0.660-0.741) in RF-only, 0.761(0.724-0.797) for hybrid.

DISCUSSION AND CONCLUSION

There is potential for DLA using retinal images as a screening adjunct for DKD among individuals with diabetes. This can value-add to existing DLA systems which diagnose diabetic retinopathy from retinal images, facilitating primary screening for DKD.

The LongitudinAl Nationwide stuDy on Management And Real-world outComes of diabetes in India over 3 years (LANDMARC trial)

INTRODUCTION

LANDMARC (CTRI/2017/05/008452), a prospective, observational real-world study, evaluated the occurrence of diabetes complications, glycemic control and treatment patterns in people with type 2 diabetes mellitus (T2DM) from pan-India regions over a period of 3 years.

METHODS

Participants with T2DM (≥25 to ≤60 years old at diagnosis, diabetes duration ≥2 years at the time of enrollment, with/without glycemic control and on ≥2 antidiabetic therapies) were included. The proportion of participants with macrovascular and microvascular complications, glycemic control and time to treatment adaptation over 36 months were assessed.

RESULTS

Of the 6234 participants enrolled, 5273 completed 3 years follow-up. At the end of 3-years, 205 (3.3%) and 1121 (18.0%) participants reported macrovascular and microvascular complications, respectively. Nonfatal myocardial infarction (40.0%) and neuropathy (82.0%) were the most common complications. At baseline and 3-years, 25.1% (1119/4466) and 36.6% (1356/3700) of participants had HbA1c <7%, respectively. At 3-years, population with macrovascular and microvascular complications had higher proportion of participants with uncontrolled glycemia (78.2% [79/101] and 70.3% [463/659], respectively) than those without complications (61.6% [1839/2985]). Over 3-years, majority (67.7%-73.9%) of the participants were taking only OADs (biguanides [92.2%], sulfonylureas [77.2%] and DPP-IV inhibitors [62.4%]). Addition of insulin was preferred in participants who were only on OADs at baseline, and insulin use gradually increased from 25.5% to 36.7% at the end of 3 years.

CONCLUSION

These 3-year trends highlight the burden of uncontrolled glycemia and cumulative diabetes-related complications, emphasizing the importance of optimizing diabetes management in India.

Diagnostic and prognostic biomarkers for tubulointerstitial fibrosis

Renal fibrosis is the final common pathophysiological pathway in chronic kidney disease (CKD) regardless of the underlying cause of kidney injury. Tubulointerstitial fibrosis (TIF) is considered to be the key pathological predictor of CKD progression. Currently, the gold-standard tool to identify TIF is kidney biopsy, an invasive method that carries risks. Non-invasive diagnostics rely on an estimation of glomerular filtration rate and albuminuria to assess kidney function, but these fail to diagnose early CKD accurately or to predict progressive decline in kidney function. In this review, we summarize the current and emerging molecular biomarkers that have been studied in various clinical settings and in animal models of kidney disease and that are correlated with the degree of TIF. We examine the potential of these biomarkers to diagnose TIF non-invasively and to predict disease progression. We also examine the potential of new technologies and non-invasive diagnostic approaches in assessing TIF. Limitations of current and potential biomarkers are discussed and knowledge gaps identified.

Diabetic vascular diseases: molecular mechanisms and therapeutic strategies

Vascular complications of diabetes pose a severe threat to human health. Prevention and treatment protocols based on a single vascular complication are no longer suitable for the long-term management of patients with diabetes. Diabetic panvascular disease (DPD) is a clinical syndrome in which vessels of various sizes, including macrovessels and microvessels in the cardiac, cerebral, renal, ophthalmic, and peripheral systems of patients with diabetes, develop atherosclerosis as a common pathology. Pathological manifestations of DPDs usually manifest macrovascular atherosclerosis, as well as microvascular endothelial function impairment, basement membrane thickening, and microthrombosis. Cardiac, cerebral, and peripheral microangiopathy coexist with microangiopathy, while renal and retinal are predominantly microangiopathic. The following associations exist between DPDs: numerous similar molecular mechanisms, and risk-predictive relationships between diseases. Aggressive glycemic control combined with early comprehensive vascular intervention is the key to prevention and treatment. In addition to the widely recommended metformin, glucagon-like peptide-1 agonist, and sodium-glucose cotransporter-2 inhibitors, for the latest molecular mechanisms, aldose reductase inhibitors, peroxisome proliferator-activated receptor-γ agonizts, glucokinases agonizts, mitochondrial energy modulators, etc. are under active development. DPDs are proposed for patients to obtain more systematic clinical care requires a comprehensive diabetes care center focusing on panvascular diseases. This would leverage the advantages of a cross-disciplinary approach to achieve better integration of the pathogenesis and therapeutic evidence. Such a strategy would confer more clinical benefits to patients and promote the comprehensive development of DPD as a discipline.

Broadening horizons in mechanisms, management, and treatment of diabetic kidney disease

Diabetic kidney disease (DKD) is the first cause of end-stage kidney disease in patients with diabetes and its prevalence is increasing worldwide. It encompasses histological alterations that mainly affect the glomerular filtration unit, which include thickening of the basement membrane, mesangial cell proliferation, endothelial alteration, and podocyte injury. These morphological abnormalities further result in a persistent increase of urinary albumin-to-creatinine ratio and in a reduction of the estimated glomerular filtration rate. Several molecular and cellular mechanisms have been recognized, up to date, as major players in mediating such clinical and histological features and many more are being under investigation. This review summarizes the most recent advances in understanding cell death mechanisms, intracellular signaling pathways and molecular effectors that play a role in the onset and progression of diabetic kidney damage. Some of those molecular and cellular mechanisms have been already successfully targeted in preclinical models of DKD and, in some cases, strategies have been tested in clinical trials. Finally, this report sheds light on the relevance of novel pathways that may become therapeutic targets for future applications in DKD.

Identifying myoglobin as a mediator of diabetic kidney disease: a machine learning-based cross-sectional study

In view of the alarming increase in the burden of diabetes mellitus (DM) today, a rising number of patients with diabetic kidney disease (DKD) is forecasted. Current DKD predictive models often lack reliable biomarkers and perform poorly. In this regard, serum myoglobin (Mb) identified by machine learning (ML) may become a potential DKD indicator. We aimed to elucidate the significance of serum Mb in the pathogenesis of DKD. Electronic health record data from a total of 728 hospitalized patients with DM (286 DKD vs. 442 non-DKD) were used. We developed DKD ML models incorporating serum Mb and metabolic syndrome (MetS) components (insulin resistance and β-cell function, glucose, lipid) while using SHapley Additive exPlanation (SHAP) to interpret features. Restricted cubic spline (RCS) models were applied to evaluate the relationship between serum Mb and DKD. Serum Mb-mediated renal function impairment induced by MetS components was verified by causal mediation effect analysis. The area under the receiver operating characteristic curve of the DKD machine learning models incorporating serum Mb and MetS components reached 0.85. Feature importance analysis and SHAP showed that serum Mb and MetS components were important features. Further RCS models of DKD showed that the odds ratio was greater than 1 when serum Mb was > 80. Serum Mb showed a significant indirect effect in renal function impairment when using MetS components such as HOMA-IR, HGI and HDL-C/TC as a reason. Moderately elevated serum Mb is associated with the risk of DKD. Serum Mb may mediate MetS component-caused renal function impairment.

Status and Trends of the Association Between Diabetic Nephropathy and Diabetic Retinopathy From 2000 to 2021: Bibliometric and Visual Analysis

Background: Diabetic nephropathy (DN) and diabetic retinopathy (DR) are microvascular complications of diabetes that share a similar pathogenesis and clinical relevance. The study aimed to visually analyze the research status and development trend of the relationship between DN and DR by means of bibliometrics and knowledge mapping.

Methods: Publications were collected from the Science Citation Index-Expanded of the Web of Science Core Collection between 2000 and 2021. CiteSpace, Alluvial Generator, and Microsoft Excel were used to analyze and present the data.

Results: A total of 3,348 publications were retrieved and 3,285 were included in the analysis after deduplication. The publications demonstrated an annually increasing trend. The results of the collaborative network analysis showed that the United States, Steno Diabetes Center, and Tien Y. Wong were the most influential country, institution and author, in this field of research, respectively. The analysis of references and keywords showed that the pathogenesis of DN and DR and their relationship with cardiovascular disease are research hotspots. The clinical relevance and drug therapy for DN and DR will become frontiers of future research in this field.

Conclusion: This study is the first to visualize the correlation between DN and DR using a bibliometric approach. This study provides a reference of research trends for scholars.

Retinal Vascular Profile in Predicting Incident Cardiometabolic Diseases among Individuals with Diabetes

OBJECTIVE

To determine the longitudinal associations between retinal vascular profile (RVP) and four major cardiometabolic diseases; and to quantify the predictive improvements when adding RVP beyond traditional risk factors in individuals with diabetes.

METHODS

Subjects were enrolled from the Singapore Epidemiology of Eye Disease (SEED) study, a multi-ethnic population-based cohort. Four incident cardiometabolic diseases, calculated over a ~6-year period, were considered: cardiovascular disease (CVD), hypertension (HTN), diabetic kidney disease (DKD) and hyperlipidaemia (HLD). The RVP - vessel tortuosity, branching angle, branching coefficient, fractal dimension, vessel calibre, and DR status - was characterized at baseline using a computer-assisted program. Traditional risk factors at baseline included age, gender, ethnicity, smoking, blood pressure (BP), HbA1c, estimated glomerular filtration rate (eGFR) or cholesterol. The improvements in predictive performance when adding RVP (compared to only traditional risk factors) was calculated using several metrics including area under the receiver operating characteristics curve (AUC) and Net Reclassification Improvement (NRI).

RESULTS

Among 1,770 individuals with diabetes, incidences were 6.3% (n=79/1259) for CVD, 48.7% (n=166/341) for HTN, 14.6% (n=175/1199) for DKD, and 59.4% (n=336/566) for HLD. DR preceded the onset of CVD (RR 1.85[1.14;3.00]) and DKD (1.44 [1.06;1.96]). Narrower arteriolar calibre preceding the onset of HTN (0.84 [0.72;0.99]), and changes in arteriolar branching angle preceded the onset of CVD (0.78 [0.62;0.98]) and HTN (1.15 [1.03;1.29]). The largest predictive improvement was found for HTN with AUC increment of 3.4% (p=0.027) and better reclassification of 11.4% of the cases and 4.6% of the controls (p=0.008).

CONCLUSION

We found that RVPs improved the prediction of HTN in individuals with diabetes, but add limited information for CVD, DKD and HLD predictions.

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

BACKGROUND AND AIMS

Endothelial dysfunction is a precursor to atherosclerosis and is implicated in the coexistence between cardiovascular disease (CVD) and chronic kidney disease (CKD). We examined whether retinal microvascular dysfunction is present in subjects with renal impairment and predictive of long-term CKD progression in patients with CVD.

METHODS

In a single centre prospective observational study, 253 subjects with coronary artery disease and CVD risk factors underwent dynamic retinal vessel analysis. Retinal microvascular dysfunction was quantified by measuring retinal arteriolar and venular dilatation in response to flicker light stimulation. Serial renal function assessment was performed over a median period of 9.3 years using estimated GFR (eGFR).

RESULTS

Flicker light-induced retinal arteriolar dilatation (FI-RAD) was attenuated in patients with baseline eGFR <90 mL/min/1.73 m², compared to those with normal renal function (eGFR ≥90 mL/min/1.73 m²) (1.0 [0.4-2.1]% vs. 2.0 [0.8-3.6]%; p < 0.01). In patients with normal renal function, subjects with the lowest FI-RAD responses exhibited the greatest annual decline in eGFR. In uni- and multivariable analysis, among subjects with normal renal function, a 1% decrease in FI-RAD was associated with an accelerated decline in eGFR of 0.10 (0.01, 0.15; p = 0.03) and 0.07 mL/min/1.73 m² per year (0.00, 0.14; p = 0.06), respectively. FI-RAD was not predictive of CKD progression in subjects with baseline eGFR <90 mL/min/1.73 m².

CONCLUSIONS

Retinal arteriolar endothelial dysfunction is present in patients with CVD who have early-stage CKD, and serves as an indicator of long-term CKD progression in those with normal renal function.