Role of cystatin C in the detection of sight-threatening diabetic retinopathy in Asian Indians with type 2 diabetes

AIM

To study the association between cystatin C and sight-threatening diabetic retinopathy (STDR) in Asian Indians with type 2 diabetes (T2DM).

METHODS

In a cross-sectional study carried out at two tertiary centres in India in 2022, individuals with T2DM underwent clinical and ophthalmic assessments and estimation of serum cystatin C. Grading of DR was done by retina specialists. STDR was defined by the presence of severe non-proliferative DR (NPDR), proliferative DR (PDR) and/or diabetic macular edema. Receiver operating characteristic (ROC) curves were used to identify cystatin C cut-off value for detecting STDR.

RESULTS

Among 420 individuals with T2DM (mean age 56 ± 9 years; mean duration of diabetes 14.5 ± 7.9 years), 121 (24.1 %) had No-DR, 119 (28.3 %) had No-STDR and 200 (49.6 %) had STDR. Mean cystatin C level was significantly higher in individuals with STDR compared to those with no-STDR and No-DR (1.34 vs 1.06 vs 0.93 mg/L, p < 0.001). Cystatin C cut-off value ≥1.11 mg/L had a C statistic of 0.944 (95 % CI: 0.909-0.968, p < 0.001), 96.8 % sensitivity and 78.2 % specificity for detection of STDR.

CONCLUSION

Elevated serum cystatin C was strongly associated with STDR and could possibly be used as a biomarker for screening for sight-threatening diabetic retinopathy.

Association Between Type 2 Diabetes Mellitus and Heart Failure: A Retrospective Study from a Tertiary Care Diabetes Centre in India

INTRODUCTION

The study aimed to explore the association between type 2 diabetes (T2D) and heart failure (HF) using echocardiography and NT-proBNP. The study also derived an NT-proBNP cut-off for diagnosing HF by echo in Asian Indians with T2D.

METHODS

A retrospective study was performed using data from individuals with T2D, aged ≥ 18 years, who visited diabetes clinics in India between March 2019 and December 2023. NT-proBNP levels were quantified by chemiluminescence, and left ventricular ejection fraction (LVEF) was assessed from echo using two-dimensional (2D) echocardiography. Heart failure was classified based on the European Society of Cardiology (ESC) guidelines. Receiver operating characteristic (ROC) curve was performed to determine the optimal NT-proBNP cut-off for diagnosing HF by echo.

RESULTS

Among the 1189 study individuals included in the study (714 men and 475 women), 5.9% were identified as having HF with reduced ejection fraction (HFrEF), 5.5% had mildly reduced ejection fraction (HFmrEF), and 14.1% had HF with preserved ejection fraction (HFpEF) while the rest (74.5%) had LVEF > 50%. Elevated NT-proBNP levels were observed in those with reduced ejection fraction. ROC analysis identified an optimal NT-proBNP threshold of 398 pg/mL for diagnosing HF, with 87% sensitivity and 78% specificity. HF prevalence increased with age, peaking at 30.6% in individuals aged 61-70 years. Women with HF had higher NT-proBNP levels than men.

CONCLUSIONS

In this diabetes clinic population, 11.5% of individuals with T2D had moderate to reduced LVEF. Early identification of HF using echocardiography and NT-proBNP in a diabetes clinic could help improve prognosis.

Wide-field imaging with smartphone based fundus camera: grading of severity of diabetic retinopathy and locating peripheral lesions in diabetic retinopathy

AIM

To assess the performance of smartphone based wide-field retinal imaging (WFI) versus ultra-wide-field imaging (UWFI) for assessment of sight-threatening diabetic retinopathy (STDR) as well as locating predominantly peripheral lesions (PPL) of DR.

METHODS

Individuals with type 2 diabetes with varying grades of DR underwent nonmydriatic UWFI with Daytona Plus camera followed by mydriatic WFI with smartphone-based Vistaro camera at a tertiary care diabetes centre in South India in 2021-22. Grading of DR as well as identification of PPL (DR lesions beyond the posterior pole) in the retinal images of both cameras was performed by senior retina specialists. STDR was defined by the presence of severe non-proliferative DR, proliferative DR or diabetic macular oedema (DME). The sensitivity and specificity of smartphone based WFI for detection of PPL and STDR was assessed. Agreement between the graders for both cameras was compared.

RESULTS

Retinal imaging was carried out in 318 eyes of 160 individuals (mean age 54.7 ± 9 years; mean duration of diabetes 16.6 ± 7.9 years). The sensitivity and specificity for detection of STDR by Vistaro camera was 92.7% (95% CI 80.1-98.5) and 96.6% (95% CI 91.5-99.1) respectively and 95.1% (95% CI 83.5-99.4) and 95.7% (95% CI 90.3-98.6) by Daytona Plus respectively. PPL were detected in 89 (27.9%) eyes by WFI by Vistaro camera and in 160 (50.3%) eyes by UWFI. However, this did not translate to any significant difference in the grading of STDR between the two imaging systems. In both devices, PPL were most common in supero-temporal quadrant (34%). The prevalence of PPL increased with increasing severity of DR with both cameras (p < 0.001). The kappa comparison between the 2 graders for varying grades of severity of DR was 0.802 (p < 0.001) for Vistaro and 0.753 (p < 0.001) for Daytona Plus camera.

CONCLUSION

Mydriatic smartphone-based widefield imaging has high sensitivity and specificity for detecting STDR and can be used to screen for peripheral retinal lesions beyond the posterior pole in individuals with diabetes.

Leveraging Artificial Intelligence for Diabetic Retinopathy Screening and Management: History and Current Advances

AIM

Regular screening of large number of people with diabetes for diabetic retinopathy (DR) with the support of available human resources alone is a global challenge. Digital health innovation is a boon in screening for DR. Multiple artificial intelligence (AI)-based deep learning (DL) algorithms have shown promise for accurate diagnosis of referable DR (RDR). The aim of this review is to evaluate the use of AI for DR screening and the various currently available automated DR detection algorithms.

METHODS

We reviewed articles published up to May 15th 2024, on the use of AI for DR by searching PubMed, Medline, Embase, Scopus, and Google Scholar using keywords like diabetic retinopathy, retinal imaging, teleophthalmology, automated detection, artificial intelligence, deep learning and fundus photography.

RESULTS

This narrative review, traces the advent of AI and its use in digital health, the key concepts in AI and DL algorithm development for diagnosis of DR, some crucial AI algorithms that have been validated for detection of DR and the benefits and challenges of use of AI in detection and management of DR. While there are many approved AI algorithms that are in use globally for DR detection, IDx-DR, EyeArt, and AEYE Diagnostic Screening (AEYE-DS) are the algorithms that have been approved so far by USFDA for automated DR screening.

CONCLUSION

AI has revolutionized screening of DR by enabling early automated detection. Continuous advances in AI technology, combined with high-quality retinal imaging, can lead to early diagnosis of sight-threatening DR, appropriate referrals, and better outcomes.

Creating a retinal image database to develop an automated screening tool for diabetic retinopathy in India

Diabetic retinopathy (DR), a prevalent microvascular complication of diabetes, is the fifth leading cause of blindness worldwide. Given the critical nature of the disease, it is paramount that individuals with diabetes undergo annual screening for early and timely detection of DR, facilitating prompt ophthalmic assessment and intervention. However, screening for DR, which involves assessing visual acuity and retinal examination through ophthalmoscopy or retinal photography, presents a significant global challenge due to the massive volume of individuals requiring annual reviews. To counter this challenge, there has been an increasing interest in the potential of artificial intelligence (AI) tools for automated diagnosis of DR. The AI tools primarily utilize deep learning (DL) techniques and are tailored to analyse extensive medical image data and provide diagnostic outputs, essentially streamline the DR screening process. However, the development of such AI tools requires access to a comprehensive retinal image database with a plethora of high-resolution fundus images from various cameras, covering all DR lesions. Additionally, the accurate training of these AI algorithms necessitates skilled professionals, such as optometrists or ophthalmologists, to provide reliable ground truths that ensure the precision of the diagnostic outputs. To address these prerequisites, we have initiated a study involving multiple institutions to establish a large-scale online 'Retinal Image Database' in India, aiming to contribute significantly to DR research. This paper delineates the methodology employed for this significant undertaking, detailing the steps taken to create the large retinal image database, as well as the framework for developing a cost-effective, robust AI-based DR diagnostic tool. Our work is expected to mark a significant stride in DR detection and management, promising a more efficient and scalable solution for tackling this global health challenge.