Severity of diabetic retinopathy and its relationship with age at onset of diabetes mellitus in India: A multicentric study

Relationship Between Disease and Treatment Factors in Diabetes With Vision Difficulty: A Cross-Sectional, Population-Based Analysis

BACKGROUND AND OBJECTIVE

Relationships between disease and treatment factors of diabetes and vision difficulty remain largely unknown. This study investigated the relationship between disease and treatment factors in diabetes and participants' self-reported vision difficulty.

PATIENTS AND METHODS

This was a cross-sectional, population-based analysis. Data from the National Health Interview Survey was used and analyzed through univariable and multivariable logistic regression.

RESULTS

From the 29,464 included participants, logistic regression showed increased odds of self-reported vision difficulty among participants with diabetes (odds ratio [OR] = 2.14, P < 0.001), prediabetes (OR = 1.95, P < 0.001), or gestational diabetes (OR = 1.54, P < 0.001) compared to participants without diabetes. Those who reported having diabetes for more than a year had higher odds of vision difficulty (OR = 1.97, P = 0.02), as did those who were taking insulin (OR = 1.62, P < 0.001), those who had taken less insulin to save money within the past year (OR = 1.87, P = 0.01), and those who reported experiencing diabetes-related stress (OR = 2.14, P < 0.001).

CONCLUSIONS

Duration of diabetes, diabetes-related stress, and taking less insulin than recommended to save money were associated with vision difficulty. [Ophthalmic Surg Lasers Imaging Retina 2024;55:142-153.].

Triplet-branch network with contrastive prior-knowledge embedding for disease grading

Since different disease grades require different treatments from physicians, i.e., the low-grade patients may recover with follow-up observations whereas the high-grade may need immediate surgery, the accuracy of disease grading is pivotal in clinical practice. In this paper, we propose a Triplet-Branch Network with ContRastive priOr-knoWledge embeddiNg (TBN-CROWN) for the accurate disease grading, which enables physicians to accordingly take appropriate treatments. Specifically, our TBN-CROWN has three branches, which are implemented for representation learning, classifier learning and grade-related prior-knowledge learning, respectively. The former two branches deal with the issue of class-imbalanced training samples, while the latter one embeds the grade-related prior-knowledge via a novel auxiliary module, termed contrastive embedding module. The proposed auxiliary module takes the features embedded by different branches as input, and accordingly constructs positive and negative embeddings for the model to deploy grade-related prior-knowledge via contrastive learning. Extensive experiments on our private and two publicly available disease grading datasets show that our TBN-CROWN can effectively tackle the class-imbalance problem and yield a satisfactory grading accuracy for various diseases, such as fatigue fracture, ulcerative colitis, and diabetic retinopathy.

Diabetic Retinopathy: Clinical Features, Risk Factors, and Treatment Options

Diabetic retinopathy is a common complication of diabetes that affects the eyes and can lead to severe vision loss or blindness if left untreated. Chronic hyperglycemia destroys the blood vessels in the retina, resulting in diabetic retinopathy. The damage can lead to leakage of fluid and blood into the retina, causing edema, hemorrhages, and ischemia. A thorough evaluation by an ophthalmologist is necessary to determine the most appropriate course of treatment for each patient with diabetic retinopathy. The article discusses various surgical treatment options for diabetic retinopathy, including vitrectomy, scleral buckling, epiretinal membrane peeling, retinal detachment repair, and the risk factors of diabetic retinopathy. These surgical techniques can help to address the underlying causes of vision loss and prevent further complications from developing or worsening. To avoid complications and maintain vision, this review emphasizes the significance of early detection and treatment of diabetic retinopathy. Patients with diabetic retinopathy can improve their eyesight and quality of life with the help of some surgical treatments. The article also highlights some case studies in the field of diabetic retinopathy.

Precision subclassification of type 2 diabetes: a systematic review

BACKGROUND

Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients.

METHODS

We searched PubMed and Embase for publications that used 'simple subclassification' approaches using simple categorisation of clinical characteristics, or 'complex subclassification' approaches which used machine learning or 'omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches.

RESULTS

Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes.

CONCLUSION

Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.

Systematic review of precision subclassification of type 2 diabetes

Heterogeneity in type 2 diabetes presentation, progression and treatment has the potential for precision medicine interventions that can enhance care and outcomes for affected individuals. We undertook a systematic review to ascertain whether strategies to subclassify type 2 diabetes are associated with improved clinical outcomes, show reproducibility and have high quality evidence. We reviewed publications that deployed 'simple subclassification' using clinical features, biomarkers, imaging or other routinely available parameters or 'complex subclassification' approaches that used machine learning and/or genomic data. We found that simple stratification approaches, for example, stratification based on age, body mass index or lipid profiles, had been widely used, but no strategy had been replicated and many lacked association with meaningful outcomes. Complex stratification using clustering of simple clinical data with and without genetic data did show reproducible subtypes of diabetes that had been associated with outcomes such as cardiovascular disease and/or mortality. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into meaningful groups. More studies are needed to test these subclassifications in more diverse ancestries and prove that they are amenable to interventions.

Association between Age at Type 2 Diabetes Onset and Diabetic Retinopathy: A Double-Center Retrospective Study

BACKGROUND

With the decreasing age of type 2 diabetes mellitus (T2DM) onset, the incidence of diabetic complications is gradually increasing. We evaluated the independent effect of age at diabetes onset on diabetic retinopathy (DR) development.

METHODS

A total of 7472 patients with T2DM were enrolled in the National Metabolic Management Center from September 2017 to May 2022. Anthropometry data, laboratory reports, and medical history were collected. The independent association of DR with age at diabetes onset was analyzed using multivariable logistic regression models. In addition, a stratified analysis was performed to determine the effect of confounding variables.

RESULTS

Of the 7472 patients recruited, 1642 (21.98%) had DR. Patients with DR had considerably younger ages of diabetes onset than those without DR (45 (38-53) years vs. 50 (43-57) years, P < 0.001). The proportion of patients with T2DM onset at a younger age was higher in the DR group than that in the non-DR group. Participants were divided into four groups according to their age at diabetes onset, namely, ≥60, <40, 40-49, and 50-59 years. Compared with patients with diabetes onset at age ≥ 60 years, those with diabetes onset at <40 years (odds ratio (OR): 5.56, 95% confidence interval (CI): 3.731-8.285, P < 0.001), 40-49 years (OR: 2.751, 95% CI: 2.047-3.695, P < 0.001), and 50-59 years (OR: 1.606, 95% CI: 1.263-2.042, P < 0.001) were at an increased risk of DR after adjusting for potential confounding factors. Furthermore, stratification analyses demonstrated that young age at diabetes onset is an independent risk factor for DR.

CONCLUSIONS

Compared with diabetes onset at an older age, diabetes onset at a younger age is associated with a significantly increased DR risk.