Retinal image measurements and their association with chronic kidney disease in Chinese patients with type 2 diabetes: the NCD study

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.

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 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.

Comprehensive retinal vascular measurements: time in range is associated with peripheral retinal venular calibers in type 2 diabetes in China

AIM

Retinal vascular parameters are biomarkers of diabetic microangiopathy. We aimed to investigate the relationship between time in range (TIR) assessed by continuous glucose monitoring (CGM) and retinal vascular parameters in patients with type 2 diabetes in China.

METHODS

The TIR assessed by CGM and retinal photographs were obtained at the same time from adults with type 2 diabetes who were recruited. Retinal vascular parameters were extracted from retinal photographs by a validated fully automated computer program, and TIR was defined as between 3.9-7.8 mmol/L over a 24-h period. The association between TIR and caliber of retinal vessels distributed in different zones were analyzed using multivariable linear regression analyses.

RESULTS

For retinal vascular parameters measurements, the peripheral arteriovenous and middle venular calibers widen with decreasing TIR quartiles (P < 0.05). Lower TIR was associated with wider peripheral venule after adjusting for potential confounders. Even after further correction for GV, there was still a significant correlation between TIR and peripheral vascular calibers (CV: β = - 0.015 [- 0.027, - 0.003], P = 0.013; MAGE: β = - 0.013 [- 0.025, - 0.001], P = 0.038) and SD: β = - 0.013 [- 0.026, - 0.001], P = 0.004). Similar findings were not found for the middle and central venular calibers or arterial calibers located in different zones.

CONCLUSIONS

The TIR was associated with adverse changes to peripheral retinal venules but not central and middle vessels in patients with type 2 diabetes, suggesting that peripheral retinal vascular calibers may be affected by glycemic fluctuations earlier.

Current progress in artificial intelligence-assisted medical image analysis for chronic kidney disease: A literature review

Chronic kidney disease (CKD) causes irreversible damage to kidney structure and function. Arising from various etiologies, risk factors for CKD include hypertension and diabetes. With a progressively increasing global prevalence, CKD is an important public health problem worldwide. Medical imaging has become an important diagnostic tool for CKD through the non-invasive identification of macroscopic renal structural abnormalities. Artificial intelligence (AI)-assisted medical imaging techniques aid clinicians in the analysis of characteristics that cannot be easily discriminated by the naked eye, providing valuable information for the identification and management of CKD. Recent studies have demonstrated the effectiveness of AI-assisted medical image analysis as a clinical support tool using radiomics- and deep learning-based AI algorithms for improving the early detection, pathological assessment, and prognostic evaluation of various forms of CKD, including autosomal dominant polycystic kidney disease. Herein, we provide an overview of the potential roles of AI-assisted medical image analysis for the diagnosis and management of CKD.

Association of Obesity Indices with Diabetic Kidney Disease and Diabetic Retinopathy in Type 2 Diabetes: A Real-World Study

Background

Diabetic microvascular complications mainly include diabetic kidney disease (DKD) and diabetic retinopathy (DR). Obesity was recognized as a risk factor for DKD, while the reported relationship between obesity and DR was inconsistent. Moreover, whether the associations can be attributed to C-peptide levels is unknown.

Methods

Data from 1142 sequential inpatients with T2DM at Xiangyang Central Hospital between June 2019 and March 2022 were extracted retrospectively from the electronic medical record system. The associations between four obesity indices (body mass index (BMI), waist-hip circumference ratio (WHR), visceral fat tissue area (VFA), and subcutaneous fat tissue area (SFA)) and DKD and DR were evaluated. Whether the associations can be attributed to C-peptide levels was also explored.

Results

Obesity was a risk factor for DKD after adjusting for sex, HbA1c, TG, TC, HDL, LDL, smoking history, education, duration of diabetes, and insulin use (obesity indices: BMI (OR 1.050: 95% CI: 1.008-1.094; P = 0.020); WHR (OR 10.97; 95% CI: 1.250-92.267; P = 0.031); VFA (OR 1.005; 95% CI: 1.001-1.008; P = 0.008)), but it became insignificant after further adjusting for fasting C-peptide. The associations between BMI, WHR, VFA, and DKD might be U-shaped. Obesity and FCP tended to protect against DR; however, they became insignificant after adjusting for multiple potential confounders. C2/C0 (the ratio of the postprandial serum C-peptide to fasting C-peptide) was a protective factor for both DKD (OR 0.894, 95% CI: 0.833-0.959, P < 0.05) and DR (OR 0.851, 95% CI: 0.787-0.919; P < 0.05).

Conclusions

Obesity was a risk factor for DKD, and the effect may be attributable to C-peptide, which represents insulin resistance. The protective effect of obesity or C-peptide on DR was not independent and could be confounded by multiple factors. Higher C2/C0 was associated with both decreased DKD and DR.

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.

Correlation between retinal vascular parameters and cystatin C in patients with type 2 diabetes

AIMS

To investigate the relationship between retinal vascular parameters and cystatin C in patients with type 2 diabetes in northwestern China.

METHODS

This was a cross-sectional study of 1689 patients with type 2 diabetes. A validated fully automated computer program was used to extract retinal vascular parameters from the entire vascular tree. Multiple logistic regression analyses were conducted to investigate the relationship between these vascular measurements and cystatin C.

RESULTS

For retinal vascular geometrical measurements, smaller arteriolar fractal dimension was related to high cystatin C after adjusting for multiple variables (odds ratio [OR] 0.149, 95% CI 0.042-0.532). For retinal vascular caliber measurements, narrower central and middle arteriolar calibers were related to high cystatin C after adjusting for multiple variables (central: OR 0.922, 95% CI 0.886-0.960; middle: OR 0.940, 95% CI 0.901-0.981). Wider central, middle and peripheral venular calibers were associated with high cystatin C after adjusting for multiple variables (central: OR 1.058, 95% CI 1.003-1.117; middle: OR 1.094, 95% CI 1.040-1.150; peripheral: OR 1.075, 95% CI 1.023-1.130).

CONCLUSIONS

Multiple retinal vascular geometrical and caliber measurements are associated with cystatin C in type 2 diabetic patients. Further studies are needed to explore whether these retinal vascular changes can predict the incidence and progress of diabetic nephropathy.

Systemic retinal biomarkers

PURPOSE OF REVIEW

Systemic retinal biomarkers are biomarkers identified in the retina and related to evaluation and management of systemic disease. This review summarizes the background, categories and key findings from this body of research as well as potential applications to clinical care.

RECENT FINDINGS

Potential systemic retinal biomarkers for cardiovascular disease, kidney disease and neurodegenerative disease were identified using regression analysis as well as more sophisticated image processing techniques. Deep learning techniques were used in a number of studies predicting diseases including anaemia and chronic kidney disease. A virtual coronary artery calcium score performed well against other competing traditional models of event prediction.

SUMMARY

Systemic retinal biomarker research has progressed rapidly using regression studies with clearly identified biomarkers such as retinal microvascular patterns, as well as using deep learning models. Future systemic retinal biomarker research may be able to boost performance using larger data sets, the addition of meta-data and higher resolution image inputs.