Dipeptidyl peptidase 4 inhibitors, sodium glucose cotransporter 2 inhibitors, and glucagon-like peptide 1 receptor agonists do not worsen diabetic macular edema.

AIMS: There are limited studies on dipeptidyl-peptidase 4 inhibitor (DPP-4i), sodium glucose cotransporter 2 inhibitor (SGLT2-i), and glucagon-like peptide 1 (GLP-1) receptor agonist use and occurrence of diabetic macular edema (DME). The objective of this study was to determine the association between DPP-4i, SGLT2-i, and GLP-1 receptor agonist use and occurrence of DME. METHODS: Proportional hazard models were used to evaluate the change in hazard of developing DME associated with DPP-4i, SGLT2-i, or GLP-1 receptor agonist use. Models accounted for age at DR diagnosis, DR severity (proliferative vs non-proliferative stage), time-weighted average of HbA1c level, sex, and self-reported race/ethnicity. A p-value ≤ 0.05 was considered statistically significant. RESULTS: The hazard ratio of developing DME after diagnosis of DR was 1.2 (CI = 0.75 to 1.99; p = 0.43) for DPP-4i use, 0.93 (CI = 0.54 to 1.61; p = 0.81) for GLP-1 receptor agonist use, 0.82 (CI = 0.20 to 3.34; p = 0.78) for SGLT2-i use, 1.1 (CI = 0.75 to 1.59; p = 0.66) for any one medication use, 1.1 (CI = 0.62 to 2.09; p = 0.68) and for any two or more medications use. CONCLUSIONS: We did not find an association between DPP-4i, SGLT2-i, or GLP-1 receptor agonist use and increased hazard of development of DME among patients with DR.

Autonomous artificial intelligence for diabetic eye disease increases access and health equity in underserved populations.

Diabetic eye disease (DED) is a leading cause of blindness in the world. Annual DED testing is recommended for adults with diabetes, but adherence to this guideline has historically been low. In 2020, Johns Hopkins Medicine (JHM) began deploying autonomous AI for DED testing. In this study, we aimed to determine whether autonomous AI implementation was associated with increased adherence to annual DED testing, and how this differed across patient populations. JHM primary care sites were categorized as "non-AI" (no autonomous AI deployment) or "AI-switched" (autonomous AI deployment by 2021). We conducted a propensity score weighting analysis to compare change in adherence rates from 2019 to 2021 between non-AI and AI-switched sites. Our study included all adult patients with diabetes (>17,000) managed within JHM and has three major findings. First, AI-switched sites experienced a 7.6 percentage point greater increase in DED testing than non-AI sites from 2019 to 2021 (p < 0.001). Second, the adherence rate for Black/African Americans increased by 12.2 percentage points within AI-switched sites but decreased by 0.6% points within non-AI sites (p < 0.001), suggesting that autonomous AI deployment improved access to retinal evaluation for historically disadvantaged populations. Third, autonomous AI is associated with improved health equity, e.g. the adherence rate gap between Asian Americans and Black/African Americans shrank from 15.6% in 2019 to 3.5% in 2021. In summary, our results from real-world deployment in a large integrated healthcare system suggest that autonomous AI is associated with improvement in overall DED testing adherence, patient access, and health equity.

Use of Diabetes Technologies and Retinopathy in Adults With Type 1 Diabetes.

Importance: Diabetic retinopathy (DR) is a complication of diabetes that can lead to vision loss. Outcomes of continuous glucose monitoring (CGM) and insulin pump use in DR are not well understood. Objective: To assess the use of CGM, insulin pump, or both, and DR and proliferative diabetic retinopathy (PDR) in adults with type 1 diabetes (T1D). Design, Setting, and Participants: A retrospective cohort study of adults with T1D in a tertiary diabetes center and ophthalmology center was conducted from 2013 to 2021, with data analysis performed from June 2022 to April 2023. Exposure: Use of diabetes technologies, including insulin pump, CGM, and both CGM and insulin pump. Main Outcomes and Measures: The primary outcome was development of DR or PDR. A secondary outcome was the progression of DR for patients in the longitudinal cohort. Multivariable logistic regression models assessed for development of DR and PDR and association with CGM and insulin pump use. Results: A total of 550 adults with T1D were included (median age, 40 [IQR, 28-54] years; 54.4% female; 24.5% Black or African American; and 68.4% White), with a median duration of diabetes of 20 (IQR, 10-30) years, and median hemoglobin A1c (HbA1c) of 7.8% (IQR, 7.0%-8.9%). Overall, 62.7% patients used CGM, 58.2% used an insulin pump, and 47.5% used both; 44% (244 of 550) of the participants had DR at any point during the study. On univariate analysis, CGM use was associated with lower odds of DR and PDR, and CGM with pump was associated with lower odds of PDR (all P < .05), compared with no CGM use. Multivariable logistic regression adjusting for age, sex, race and ethnicity, diabetes duration, microvascular and macrovascular complications, insurance type, and mean HbA1c, showed that CGM was associated with lower odds of DR (odds ratio [OR], 0.52; 95% CI, 0.32-0.84; P = .008) and PDR (OR, 0.42; 95% CI, 0.23-0.75; P = .004), compared with no CGM use. In the longitudinal analysis of participants without baseline PDR, 79 of 363 patients (21.8%) had progression of DR during the study. Conclusions and Relevance: In this cohort study of adults with T1D, CGM use was associated with lower odds of developing DR and PDR, even after adjusting for HbA1c. These findings suggest that CGM may be useful for diabetes management to mitigate risk for DR and PDR.

Real-world use of glucagon-like peptide-1 receptor agonists in youth with type 2 diabetes is associated with short-term improvements in HbA1c.

AIM: To assess the short-term, real-world use and effectiveness of glucagon-like peptide-1 receptor agonist (GLP-1RA) medications in the management of type 2 diabetes (T2D) in a diverse cohort of youth. METHODS: This multicentre retrospective study analysed youth prescribed a GLP-1RA for the management of T2D at two academic paediatric diabetes centres prior to June 2022. Change in HbA1c and insulin use from baseline to first (median 91 days) and second (median 190 days) follow-up were evaluated for those taking a GLP-1RA. Multivariable linear mixed effects models adjusting for baseline sex, age, race/ethnicity, insurance, insulin regimen, metformin regimen, GLP-1RA dosing frequency and the body mass index Z-score (BMI-Z) examined the change in HbA1c for participants for up to 6 months after baseline. RESULTS: A total of 136 patients with T2D (median age 16.1 [interquartile range 13.9-18.0] years, 54% female, 56% non-Hispanic Black, 24% Hispanic, 77% with public insurance) were prescribed GLP-1RAs and taking them at first or second follow-up. Median HbA1c decreased from 7.9% to 7.6% (P < .001) at a median follow-up of 91 days (n = 109) and, among those with HbA1c available at baseline and second follow-up (n = 83), from 8.4% to 7.4%. The proportion of patients prescribed insulin decreased from baseline to the first follow-up visit (basal 69% to 60% [P = .008], prandial 46% to 38% [P = .03]). In multivariable analysis, there was a mean decrease in HbA1c by 0.09 percentage points per month (P = .005, 95% confidence interval -0.15, -0.03). CONCLUSIONS: Real-world use of GLP-1RAs in youth with T2D is associated with decreased HbA1c levels, despite challenges with access and adherence. GLP-1RA treatment may reduce insulin doses for youth with T2D.

Machine Learning Models for Prediction of Diabetic Microvascular Complications.

IMPORTANCE AND AIMS: Diabetic microvascular complications significantly impact morbidity and mortality. This review focuses on machine learning/artificial intelligence (ML/AI) in predicting diabetic retinopathy (DR), diabetic kidney disease (DKD), and diabetic neuropathy (DN). METHODS: A comprehensive PubMed search from 1990 to 2023 identified studies on ML/AI models for diabetic microvascular complications. The review analyzed study design, cohorts, predictors, ML techniques, prediction horizon, and performance metrics. RESULTS: Among the 74 identified studies, 256 featured internally validated ML models and 124 had externally validated models, with about half being retrospective. Since 2010, there has been a rise in the use of ML for predicting microvascular complications, mainly driven by DKD research across 27 countries. A more modest increase in ML research on DR and DN was observed, with publications from fewer countries. For all microvascular complications, predictive models achieved a mean (standard deviation) c-statistic of 0.79 (0.09) on internal validation and 0.72 (0.12) on external validation. Diabetic kidney disease models had the highest discrimination, with c-statistics of 0.81 (0.09) on internal validation and 0.74 (0.13) on external validation, respectively. Few studies externally validated prediction of DN. The prediction horizon, outcome definitions, number and type of predictors, and ML technique significantly influenced model performance. CONCLUSIONS AND RELEVANCE: There is growing global interest in using ML for predicting diabetic microvascular complications. Research on DKD is the most advanced in terms of publication volume and overall prediction performance. Both DR and DN require more research. External validation and adherence to recommended guidelines are crucial.

Autonomous artificial intelligence increases screening and follow-up for diabetic retinopathy in youth: the ACCESS randomized control trial.

Diabetic retinopathy can be prevented with screening and early detection. We hypothesized that autonomous artificial intelligence (AI) diabetic eye exams at the point-of-care would increase diabetic eye exam completion rates in a racially and ethnically diverse youth population. AI for Children's diabetiC Eye ExamS (NCT05131451) is a parallel randomized controlled trial that randomized youth (ages 8-21 years) with type 1 and type 2 diabetes to intervention (autonomous artificial intelligence diabetic eye exam at the point of care), or control (scripted eye care provider referral and education) in an academic pediatric diabetes center. The primary outcome was diabetic eye exam completion rate within 6 months. The secondary outcome was the proportion of participants who completed follow-through with an eye care provider if deemed appropriate. Diabetic eye exam completion rate was significantly higher (100%, 95%CI: 95.5%, 100%) in the intervention group (n = 81) than the control group (n = 83) (22%, 95%CI: 14.2%, 32.4%)(p < 0.001). In the intervention arm, 25/81 participants had an abnormal result, of whom 64% (16/25) completed follow-through with an eye care provider, compared to 22% in the control arm (p < 0.001). Autonomous AI increases diabetic eye exam completion rates in youth with diabetes.

Stakeholder Engagement in Type 1 Diabetes Research, Quality Improvement, and Clinical Care.

The integration of stakeholder engagement (SE) in research, quality improvement (QI), and clinical care has gained significant traction. Type 1 diabetes is a chronic disease that requires complex daily management and care from a multidisciplinary team across the lifespan. Inclusion of key stakeholder voices, including patients, caregivers, health care providers and community advocates, in the research process and implementation of clinical care is critical to ensure representation of perspectives that match the values and goals of the patient population. This review describes the current framework for SE and its application to research, QI, and clinical care across the lifespan.

A New Approach to Staging Diabetic Eye Disease: Staging of Diabetic Retinal Neurodegeneration and Diabetic Macular Edema.

Topic: The goal of this review was to summarize the current level of evidence on biomarkers to quantify diabetic retinal neurodegeneration (DRN) and diabetic macular edema (DME). Clinical relevance: With advances in retinal diagnostics, we have more data on patients with diabetes than ever before. However, the staging system for diabetic retinal disease is still based only on color fundus photographs and we do not have clear guidelines on how to incorporate data from the relatively newer modalities into clinical practice. Methods: In this review, we use a Delphi process with experts to identify the most promising modalities to identify DRN and DME. These included microperimetry, full-field flash electroretinogram, spectral-domain OCT, adaptive optics, and OCT angiography. We then used a previously published method of determining the evidence level to complete detailed evidence grids for each modality. Results: Our results showed that among the modalities evaluated, the level of evidence to quantify DRN and DME was highest for OCT (level 1) and lowest for adaptive optics (level 4). Conclusion: For most of the modalities evaluated, prospective studies are needed to elucidate their role in the management and outcomes of diabetic retinal diseases. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.