Risk Factors For Diabetic Retinopathy Research Articles

Lipidomic studies revealing serological markers associated with the occurrence of retinopathy in type 2 diabetes

PurposeThe duration of type 2 diabetes mellitus (T2DM) and blood glucose levels have a significant impact on the development of T2DM complications. However, currently known risk factors are not good predictors of the onset or progression of diabetic retinopathy (DR). Therefore, we aimed to investigate the differences in the serum lipid composition in patients with T2DM, without and with DR, and search for potential serological indicators associated with the development of DR.MethodsA total of 622 patients with T2DM hospitalized in the Department of Endocrinology of the First Affiliated Hospital of Xi’an JiaoTong University were selected as the discovery set. One-to-one case–control matching was performed according to the traditional risk factors for DR (i.e., age, duration of diabetes, HbA1c level, and hypertension). All cases with comorbid chronic kidney disease were excluded to eliminate confounding factors. A total of 42 pairs were successfully matched. T2DM patients with DR (DR group) were the case group, and T2DM patients without DR (NDR group) served as control subjects. Ultra-performance liquid chromatography–mass spectrometry (LC–MS/MS) was used for untargeted lipidomics analysis on serum, and a partial least squares discriminant analysis (PLS-DA) model was established to screen differential lipid molecules based on variable importance in the projection (VIP) > 1. An additional 531 T2DM patients were selected as the validation set. Next, 1:1 propensity score matching (PSM) was performed for the traditional risk factors for DR, and a combined 95 pairings in the NDR and DR groups were successfully matched. The screened differential lipid molecules were validated by multiple reaction monitoring (MRM) quantification based on mass spectrometry.ResultsThe discovery set showed no differences in traditional risk factors associated with the development of DR (i.e., age, disease duration, HbA1c, blood pressure, and glomerular filtration rate). In the DR group compared with the NDR group, the levels of three ceramides (Cer) and seven sphingomyelins (SM) were significantly lower, and one phosphatidylcholine (PC), two lysophosphatidylcholines (LPC), and two SMs were significantly higher. Furthermore, evaluation of these 15 differential lipid molecules in the validation sample set showed that three Cer and SM(d18:1/24:1) molecules were substantially lower in the DR group. After excluding other confounding factors (e.g., sex, BMI, lipid-lowering drug therapy, and lipid levels), multifactorial logistic regression analysis revealed that a lower abundance of two ceramides, i.e., Cer(d18:0/22:0) and Cer(d18:0/24:0), was an independent risk factor for the occurrence of DR in T2DM patients.ConclusionDisturbances in lipid metabolism are closely associated with the occurrence of DR in patients with T2DM, especially in ceramides. Our study revealed for the first time that Cer(d18:0/22:0) and Cer(d18:0/24:0) might be potential serological markers for the diagnosis of DR occurrence in T2DM patients, providing new ideas for the early diagnosis of DR.

The L-shape relationship between hemoglobin, albumin, lymphocyte, platelet score and the risk of diabetic retinopathy in the US population.

The primary aim of this study was to investigate the correlation between diabetic retinopathy (DR) and the HALP score (hemoglobin, albumin, lymphocyte, and platelet) in individuals with diabetes within the United States population. This cross-sectional investigation was based on the National Health and Nutrition Examination Survey (NHANES) database from 2003-2018. The following module calculated the HALP score: HALP score = [lymphocytes (/L) × hemoglobin (g/L) × albumin (g/L)]/platelets (/L). By performing the receiver operating characteristic (ROC) analysis, the optimal cutoff value of HALP was ascertained. Restricted cubic splines (RCS), multivariable logistic regression analysis, sensitivity analysis, and subgroup analysis were conducted to evaluate the effect of the HALP score on DR patients. Finally, the decision curve analysis (DCA) and clinical impact curve (CIC) were conducted to estimate the predictive power and clinical utility of the HALP score with clinical indicators. According to the cutoff value (42.9) determined by the ROC curve, the participants were stratified into a lower HALP group (HALPlow) and a higher HALP group (HALPhigh). An L-shaped relationship between HALP score and DR risk was presented in the RCS model (P for nonlinearity <0.001). The DR risk sharply decreased with the increase of HALP, and the decline reached a plateau when HALP was more than 42.9. After fully adjustment, the multivariate logistic regression analysis found that HALPlow was an independent risk factor for DR (OR = 1.363, 95% CI: 1.111-1.671, P < 0.001). Besides, sensitivity analysis showed consistent results. Furthermore, the combination of HALP score and clinical indicators demonstrated predictive power and clinical utility, as shown by the ROC curve, DCA, and CIC. The HALP score has an L-shaped correlation with the risk of DR, and thus, the HALP score may contribute to the timely intervention of diabetes patients.

Risk factors for diabetic retinopathy, diabetic macular edema, and sight-threatening diabetic retinopathy

OBJECTIVETo identify the risk factors for the development of diabetic retinopathy (DR), diabetic macular edema (DME), and sight threatening DR (STDR) based on a city-wide diabetes screening program. RESEARCH DESIGN AND METHODSDiabetic patients were prospectively recruited between June 2016 and December 2022. All patients underwent dilated fundus photography centered on the disc and macula or macula spectral domain optical coherence tomography (SD-OCT) scan. Complete medical history was documented.Systematic examination, blood analysis, and urinalysis were performed. Multivariate logistic regression analysis adjusting for age and sex was conducted. RESULTSOut of 7,274 diabetic patients, 6,840 had gradable images, among which 3,054 (42.0%) were graded as DR, 1153 (15.9%) as DME, and 1,500 (20.6%) as STDR. The factors associated with DR, DME, and STDR included younger age (odds ratio [OR]: 0.96, 0.97, and 0.96 respectively), lower BMI (OR: 0.97, 0.95, and 0.95 respectively), longer duration of diabetes (OR: 1.07, 1.03, and 1.05 respectively) and positive of urinary albumin (OR: 2.22, 2.56, and 2.88 respectively). Other associated factors included elevated blood urea nitrogen (OR: 1.22, 1.28, and 1.27 respectively), higher LDL-cholesterol, lower blood haemoglobin (OR: 0.98, 0.98, and 0.98), insulin intake, presence of diabetic foot pathologies and diabetic peripheral neuropathy. We also identified novel risk factors, including high serum potassium (OR: 1.37, 1.46, and 1.55 respectively), high serum sodium (OR: 1.02, 1.02, and 1.04 respectively). Better family income was a protective factor for DR, DME, and STDR. Alcohol consumption once a week was also identified as a protective factor for DR. CONCLUSIONSThis study revealed high serum sodium, high serum potassium, low blood haemoglobin, and the level of family income as novel associated factors for DR, which can help with DR monitoring and management.

Prediction and analysis of risk factors for diabetic retinopathy based on machine learning and interpretable models

ObjectiveDiabetic retinopathy is one of the major complications of diabetes. In this study, a diabetic retinopathy risk prediction model integrating machine learning models and SHAP was established to increase the accuracy of risk prediction for diabetic retinopathy, explain the rationality of the findings from model prediction and improve the reliability of prediction results. MethodsData were preprocessed for missing values and outliers, features selected through information gain, a diabetic retinopathy risk prediction model established using the CatBoost and the outputs of the mode interpreted using the SHAP model. ResultsOne thousand early warning data of diabetes complications derived from diabetes complication early warning dataset from the National Clinical Medical Sciences Data Center were used in this study. The CatBoost-based model for diabetic retinopathy prediction performed the best in the comparative model test. ALB_CR, HbA1c, UPR_24, NEPHROPATHY and SCR were positively correlated with diabetic retinopathy, while CP, HB, ALB, DBILI and CRP were negatively correlated with diabetic retinopathy. The relationships between HEIGHT, WEIGHT and ESR characteristics and diabetic retinopathy were not significant. ConclusionThe risk factors for diabetic retinopathy include poor renal function, elevated blood glucose level, liver disease, hematonosis and dysarteriotony, among others. Diabetic retinopathy can be prevented by monitoring and effectively controlling relevant indices. In this study, the influence relationships between the features were also analyzed to further explore the potential factors of diabetic retinopathy, which can provide new methods and new ideas for the early prevention and clinical diagnosis of subsequent diabetic retinopathy.

Epidemiology and risk factors of diabetic retinopathy in upper Egypt: a 3-year prospective study

Objective To determine the prevalence and risk factors of diabetic retinopathy (DR) in adult diabetic patients during 3 years. Patients and methods This is a cross-sectional study that was carried out on the adult diabetic population who attended a large private referral ophthalmology center in Assiut City, Upper Egypt for 3 years. All cases were subjected to full history taking, ophthalmological examination, and dilated fundus examination for evidence of DR, and besides, the associated systemic complications were assessed. Results The study included a total of 2178 cases and the prevalence of DR was 18.2%. DR was more prevalent in females than males (67.9% vs. 32.1%). Also, DR was significantly associated with insulin use, moderate and poor control of diabetes mellitus, uncontrolled hypertension (HTN), dyslipidemia, and nephropathy (P&lt;0.01 for all). The risk factors for DR were; older age (adjusted odds ratio (AOR) 1.69; 95% CI =0.91–1.97; P≤0.01), female sex (AOR 1.65; 95% CI =1.21–1.86; P =0.02), smoking (AOR 1.73; 95% CI =1.41–2.11; P =0.02), longer duration of DM (&gt; 15 years) (AOR 2.87; 95% CI =2.48–3.29; P ≤0.01), insulin use (AOR 1.42; 95% CI =0.94–1.65; P=0.03), poor control of DM (AOR 3.98; 95% CI =3.69–5.41; P≤0.01), presence of HTN (AOR 2.62; 95% CI =2.37–2.89; P≤0.01) and dyslipidemia (AOR 1.65; 95% CI =1.42–2.02; P=0.03). Conclusion The prevalence of DR in adult diabetic patients was 18.2% and the significant risk factors for DR (arranged descendingly in power) were; poor control of DM, longer duration of DM (&gt; 15 years), presence of HTN, smoking, high age, dyslipidemia, female sex, and insulin use.

Risk factors for diabetic retinopathy: a case-control study

Risk factors for diabetic retinopathy: a case-control study

Prevalence of Diabetic Retinopathy and Associated Risk Factors in Diabetic Type II Patients in Ajdabiya, Libya

Diabetic retinopathy (DR) is One of the most well-known microvascular complications of diabetes mellitus (DM) and is a frequent side effect of untreated diabetes that can lead to blindness and visual impairment. This study was conducted to assess the prevalence of diabetic retinopathy and associated factors among type 2 diabetic patients. A cross sectional study was done at Ajdabiya diabetic center from November 2023 to January 2024. Subjects and Methods: 52 patients (104 eyes) with T2DM were included in this study, their fasting blood sugar and glycosylated hemoglobin level, lipid profile measured.in addition to fundus examination (done by noncontact +90-diopter lens), fundus photographs taken by Non-Mydriatic Fundus Camera TFC-1000. Around 61 eyes (58.7%) had diabetic retinopathy, 27 eyes (26%) had back ground DR, 6 eyes (5.8 %) had pre-proliferative DR, 4 eyes (3.8%) had proliferative DR, 2 eyes (1.9%) had advanced diabetic eye disease, 22 eyes (21.2%) had CSME. Their ages ranges between 36–74 years with mean (58.90) years, (26.9%) were males, (73.1%) were females. There was a significant association between the development of DR and duration of DM (p=0.003), The FBS levels were also determined to be a significant risk factor for DR (p = 0.031), also LDL with (p=0.039). No other factors were found to have a significant association with DR. In this study more than half of diabetic patients had diabetic retinopathy. There was a significant association between the development of DR and duration of DM, FBS, LDL levels, there was no significant relation between diabetic retinopathy and hypertension, HgA1c Serum cholesterol or triglyceride levels or previous cataract surgery.

Associations between lipid abnormalities and diabetic retinopathy across a large United States national database.

While dyslipidaemia has been suggested as a potential risk factor for diabetic retinopathy (DR), previous studies have reported conflicting findings. This study aimed to better characterize the relationship between abnormal serum levels of various lipid markers and the risk of the development and progression of DR. This retrospective cohort study utilized a United States national database of electronic medical records. Adults with a history of type 2 diabetes mellitus without type 1 diabetes mellitus were divided into cohorts based on the presence of abnormal serum levels of various lipid markers. Propensity score matching was performed to match cohorts with abnormal lipid levels to those with normal lipid levels on covariates. The cohorts were then compared to evaluate the hazard ratios (HR) of receiving a new DR diagnosis, pars plana vitrectomy, panretinal photocoagulation, vitreous haemorrhage, proliferative diabetic retinopathy, diabetic macular oedema (DMO), and traction retinal detachment. The database contained 1,126,231 eligible patients (mean age: 60.8 [14.2] years; 46.0% female). Among patients without prior DR, low HDL (HR = 0.94, CI = 0.90-0.98), total cholesterol (HR = 0.88, CI = 0.85-0.91), and high triglyceride (HR = 0.91, CI = 0.86-0.97) levels were associated with a decreased risk of receiving a DR diagnosis. Among patients with preexisting DR, high LDL levels was associated with an increased risk of DMO (HR = 1.42, CI = 1.15-1.75), whereas low HDL levels was associated with a marginally decreased risk (HR = 0.92, CI = 0.85-0.99). Elevated levels of markers of dyslipidaemia are inversely associated with the risk of receiving a DR diagnosis, but this relationship is blunted after the onset of DR.

Statins as a risk factor for diabetic retinopathy: a Mendelian randomization and cross-sectional observational study

BackgroundDiabetic retinopathy (DR) is the foremost cause of vision loss among the global working-age population, and statins are among the most frequently prescribed drugs for lipid management in patients with DR. The exact relationship between statins and DR has not been determined. This study sought to validate the causal association between statins usage and diabetic retinopathy.MethodsThe summary-data-based Mendelian randomization (SMR) method and inverse-variance-weighted Mendelian randomization (IVW-MR) were used to identify the causal relationship between statins and DR via the use of expression quantitative trait loci (eQTL) data for 3-hydroxy-3-methylglutaryl-coenzyme A reductase (HMGCR) (31,684 blood samples), low density lipoprotein cholesterol-related GWAS data (sample size: 440,546), and DR-related GWAS data (14,584 cases and 176,010 controls). Additionally, a cross-sectional observational study based on the data from the National Health and Nutrition Examination Survey (NHANES) was conducted to supplement the association between DR and statins (sample size: 106,911). The odds ratios (ORs) with corresponding 95% confidence intervals (CIs) was employed to evaluate the results.ResultsBased on the results of the MR analysis, HMGCR inhibitors were causally connected with a noticeably greater incidence of DR (IVW: OR = 0.54, 95% CI [0.42, 0.69], p = 0.000002; SMR: OR = 0.66, 95% CI [0.52, 0.84], p = 0.00073). Subgroup analysis revealed that the results were not affected by the severity of DR. The sensitivity analysis revealed the stability and reliability of the MR analysis results. The results from the cross-sectional study based on NHANES also support the association between not taking statins and a decreased risk of DR (OR = 0.54, 95% CI [0.37, 0.79], p = 0.001).ConclusionsThis study revealed that a significant increase in DR risk was causally related to statins use, providing novel insights into the role of statins in DR. However, further investigations are needed to verify these findings.

Mendelian randomization analysis reveals a causal effect of Streptococcus salivarius on diabetic retinopathy through regulating host fasting glucose.

Diabetic retinopathy (DR) is one of leading causes of vision loss in adults with increasing prevalence worldwide. Increasing evidence has emphasized the importance of gut microbiome in the aetiology and development of DR. However, the causal relationship between gut microbes and DR remains largely unknown. To investigate the causal associations of DR with gut microbes and DR risk factors, we employed two-sample Mendelian Randomization (MR) analyses to estimate the causal effects of 207 gut microbes on DR outcomes. Inputs for MR included Genome-wide Association Study (GWAS) summary statistics of 207 taxa of gut microbes (the Dutch Microbiome Project) and 21 risk factors for DR. The GWAS summary statistics data of DR was from the FinnGen Research Project. Data analysis was performed in May 2023. We identified eight bacterial taxa that exhibited significant causal associations with DR (FDR < 0.05). Among them, genus Collinsella and species Collinsella aerofaciens were associated with increased risk of DR, while the species Bacteroides faecis, Burkholderiales bacterium_1_1_47, Ruminococcus torques, Streptococcus salivarius, genus Burkholderiales_noname and family Burkholderiales_noname showed protective effects against DR. Notably, we found that the causal effect of species Streptococcus salivarius on DR was mediated through the level of host fasting glucose, a well-established risk factor for DR. Our results reveal that specific gut microbes may be causally linked to DR via mediating host metabolic risk factors, highlighting potential novel therapeutic or preventive targets for DR.

Serum vitamin D is substantially reduced and predicts flares in diabetic retinopathy patients.

Research has suggested that vitamin D deficiency is associated with diabetic retinopathy (DR). Our study aimed to determine whether vitamin D deficiency is the cause of diabetic retinopathy or if diabetic retinopathy reduces vitamin D levels. Participants with type-2 diabetes were recruited for this prospective observational clinical study and were divided into a diabetic group without retinopathy and a diabetic group with retinopathy, with additional healthy volunteers serving as a control group. The differences in clinical characteristics among the three groups were also compared. Patients without retinopathy were then followed for 1 year to monitor the incidence of diabetic retinopathy. After follow-up, participants were divided into subgroups based on whether diabetic retinopathy occurred. The baseline data of the subgroups were compared, and the independent risk factors were analyzed. Vitamin D levels were generally low. Participants with diabetic retinopathy had significantly lower vitamin D levels than did those without retinopathy (P < 0.01). A comparison of the two subgroups revealed lower baseline vitamin D concentrations in the new-DR subgroup than in the non-DR subgroup (P < 0.01). Vitamin D deficiency and elevated HbA1c levels were found to be independent risk factors for diabetic retinopathy (OR = 0.935, 95% CI: 0.867-0.981, P = 0.006; OR = 2.208, 95% CI: 1.764-2.764, P < 0.01). The limit of vitamin D intake according to the receiver-operating characteristic (ROC) curve was 26.01 ng/mL, and the area under the ROC curve was 0.603 (95% CI: 0.559-0.706, P = 0.002). Vitamin D levels were significantly lower in patients diagnosed with diabetic retinopathy. More importantly, vitamin D deficiency may accelerate the onset of diabetic retinopathy.

Remnant cholesterol is correlated with retinal vascular morphology and diabetic retinopathy in type 2 diabetes mellitus: a cross-sectional study

BackgroundThe association between remnant cholesterol (RC) and diabetic retinopathy (DR) in type 2 diabetes mellitus (T2DM) remains unclear. Morphological changes in retinal vessels have been reported to predict vascular complications of diabetes, including DR.MethodsThis cross-sectional study included 6535 individuals with T2DM. The RC value was calculated using the recognized formula. The retinal vascular parameters were measured using fundus photography. The independent relationship between RC and DR was analyzed using binary logistic regression models. Multiple linear regression and subgroup analyses were employed to investigate the link between RC and vascular parameters, including the retinal arteriolar diameter (CRAE), venular diameter (CRVE), and fractal dimension (Df). Mediation analysis was performed to assess whether the vascular morphology could explain the association between RC and DR.ResultsRC was independently associated with DR in patients with a longer duration of T2DM (> 7 years). Patients with the highest quartile RC levels had larger CRAE (5.559 [4.093, 7.025] μm), CRVE (7.620 [5.298, 9.941] μm) and Df (0.013 [0.009, 0.017]) compared with patients with the lowest quartile RC levels. Results were robust across different subgroups. The association between RC and DR was mediated by CRVE (0.020 ± 0.005; 95% confidence interval: 0.012–0.032).ConclusionsRC may be a risk factor for DR among those who have had T2DM for a longer period of time. Higher RC levels were correlated with wider retinal arterioles and venules as well as higher Df, and it may contribute to DR through the dilation of retinal venules.

IDF23-0018 Elevated Non-HDL Cholesterol Levels as A Risk Factor for Diabetic Retinopathy Among Type 2 Diabetes Mellitus Patients

IDF23-0018 Elevated Non-HDL Cholesterol Levels as A Risk Factor for Diabetic Retinopathy Among Type 2 Diabetes Mellitus Patients

Correlation Between Thyroid-Related Hormones and Diabetic Retinopathy in Type 2 Diabetes Mellitus Patients with Normal Thyroid Function: A Retrospective Study.

To investigate the correlation between thyroid-related hormones and diabetic retinopathy (DR) in euthyroid patients with type 2 diabetes mellitus (T2DM). Patients with T2DM admitted to our hospital between January 2023 and June 2023 were retrospectively analyzed. The patients were divided into DR and non-diabetic retinopathy (NDR) groups according to whether DR occurred. Thyroid function-related hormones (TSH, FT3, and FT4), blood glucose indices (FBG and HbA1c), and blood lipid indices (HDL-C, LDL-C, TC, and TG) of the two groups were analyzed by univariate and multivariate logistic regression to explore the risk factors for DR. Pearson correlation analysis and multiple stepwise regression analysis were used to investigate the correlation of TSH or FT3 with FBG, HbA1c, and TG in DR patients. Of the 286 patients with T2DM included in this study, 101 (35.31%) developed DR and 185 (64.69%) did not. High TG, FBG, HbA1c, and TSH and low FT3 levels were independent risk factors for DR in T2DM patients. TSH positively correlated with TG, whereas FT3 negatively correlated with TG and HbA1c in T2DM patients with DR. Higher TSH and lower FT3 in T2DM patients with normal thyroid function may affect glucose and lipid metabolism, thereby increasing the risk of DR.

Trends of Diabetic Retinopathy Research from 2018-2022: A bibliometric analysis

Introduction &amp; Objectives : Diabetic Retinopathy (DR) is one of the leading cause of visual impairment and blindness. Abundant studies have been done around the topic. This study aims to identify research hotspot of DR during the last five years.&#x0D; Methods : Document search in the Scopus database was done using the term “diabetic retinopathy” in Title, Abstracts, and Author Keywords. We included only articles written in English published between 2018 and 2022. The dataset were analyzed using VOSviewer to conduct bibliometric analysis, such as citation analysis of sources and institutions, co-authorship analysis of countries, and co- occurrence analysis of author keywords.&#x0D; Results : A total of 8.377 articles on DR were retrieved. Number of articles in DR had been increasing steadily overtime. China had the most publications with over 2.000 articles, while United States had the most international collaboration. The most active institution was Guangdong Provincial Clinical Research Center for Ocular Diseases. Scientific Reports was the most prolific journal in DR research. Co- occurrence analysis of author keywords formed 5 clusters; DR management, risk factors for DR, examination for DR, pathogenesis of DR, and diagnostic method for DR.&#x0D; Conclusion : This study revealed research trends on diabetic retinopathy research for the last five years. It may provide information for researchers to identify potential collaborator and give better insight on DR research topics

The causal effect of hypertension, intraocular pressure, and diabetic retinopathy: a Mendelian randomization study.

Previous research has indicated a vital association between hypertension, intraocular pressure (IOP), and diabetic retinopathy (DR); however, the relationship has not been elucidated. In this study, we aim to investigate the causal association of hypertension, IOP, and DR. The genome-wide association study (GWAS) IDs for DR, hypertension, and IOP were identified from the Integrative Epidemiology Unit (IEU) Open GWAS database. There were 33,519,037 single-nucleotide polymorphisms (SNPs) and a sample size of 1,030,836 for DR. There were 16,380,466 SNPs and 218,754 participants in the hypertension experiment. There were 9,851,867 SNPs and a sample size of 97,465 for IOP. Univariable, multivariable, and bidirectional Mendelian randomization (MR) studies were conducted to estimate the risk of hypertension and IOP in DR. Moreover, causality was examined using the inverse variance weighted method, and MR results were verified by numerous sensitivity analyses. A total of 62 SNPs at the genome-wide significance level were selected as instrumental variables (IVs) for hypertension-DR. The results of univariable MR analysis suggested a causal relationship between hypertension and DR and regarded hypertension as a risk factor for DR [p = 0.006, odds ratio (OR) = 1.080]. A total of 95 SNPs at the genome-wide significance level were selected as IVs for IOP-DR. Similarly, IOP was causally associated with DR and was a risk factor for DR (p = 0.029, OR = 1.090). The results of reverse MR analysis showed that DR was a risk factor for hypertension (p = 1.27×10-10, OR = 1.119), but there was no causal relationship between DR and IOP (p > 0.05). The results of multivariate MR analysis revealed that hypertension and IOP were risk factors for DR, which exhibited higher risk scores (p = 0.001, OR = 1.121 and p = 0.030, OR = 1.124, respectively) than those in univariable MR analysis. Therefore, hypertension remained a risk factor for DR after excluding the interference of IOP, and IOP was still a risk factor for DR after excluding the interference of hypertension. This study validated the potential causal relationship between hypertension, IOP, and DR using MR analysis, providing a reference for the targeted prevention of DR.

Recommendations for initial diabetic retinopathy screening of diabetic patients using large language model-based artificial intelligence in real-life case scenarios

PurposeTo study the role of artificial intelligence (AI) to identify key risk factors for diabetic retinopathy (DR) screening and develop recommendations based on clinician and large language model (LLM) based AI platform opinions for newly detected diabetes mellitus (DM) cases.MethodsFive clinicians and three AI applications were given 20 AI-generated hypothetical case scenarios to assess DR screening timing. We calculated inter-rater agreements between clinicians, AI-platforms, and the “majority clinician response” (defined as the maximum number of identical responses provided by the clinicians) and “majority AI-platform” (defined as the maximum number of identical responses among the 3 distinct AI). Scoring was used to identify risk factors of different severity. Three, two, and one points were given to risk factors requiring screening immediately, within a year, and within five years, respectively. After calculating a cumulative screening score, categories were assigned.ResultsClinicians, AI platforms, and the “majority clinician response” and “majority AI response” had fair inter-rater reliability (k value: 0.21–0.40). Uncontrolled DM and systemic co-morbidities required immediate screening, while family history of DM and a co-existing pregnancy required screening within a year. The absence of these risk factors required screening within 5 years of DM diagnosis. Screening scores in this study were between 0 and 10. Cases with screening scores of 0–2 needed screening within 5 years, 3–5 within 1 year, and 6–12 immediately.ConclusionBased on the findings of this study, AI could play a critical role in DR screening of newly diagnosed DM patients by developing a novel DR screening score. Future studies would be required to validate the DR screening score before it could be used as a reference in real-life clinical situations.Clinical trial registrationNot applicable.

Evaluation and Optimization of Diabetic Retinopathy Screenings for Uninsured Latinx Patients in a Resource-Limited Student-Run Free Clinic.

Diabetic retinopathy (DR) is a sight-threatening condition that causes progressive retina damage. Student-run free clinics represent a valuable opportunity to provide DR screenings to high-risk populations. We characterized the patient population, evaluated the performance, and conducted a needs assessment of DR screenings at the University of California, San Diego Student-Run Ophthalmology Free Clinic, which provides care to predominantly uninsured, Latino patients. Retrospective chart review was conducted of all patients seen at the free clinic since 2019 with a diagnosis of type II diabetes. Date and outcome of all DR-related screenings or visits from 2015 onward, demographics information, and DR risk factors such as A1c and insulin dependence were recorded. Predictors of diabetic retinopathy and frequency of DR screenings for each patient were analyzed using multiple logistic regression, t-test for equality of means, and Pearson's correlation. Of 179 uninsured diabetic patients receiving care at the free clinic, 71% were female and average age was 59. 83% had hypertension, 93% had hyperlipidemia, and 79% had metabolic syndrome. Prevalence of non-proliferative DR was 34% and that of proliferative DR was 15% in diabetic patients. The free clinic capacity in recent years plateaued at just under 50% of patients seen for DR screening or visit per year, though average wait time was over 2 years between visits. Patients with higher no-show rates had less frequent DR screenings. Chronic kidney disease and poor glycemic control were the strongest predictors of DR. The student-run free ophthalmology clinic has been effective in providing screening and follow-up care for DR patients. Creation of a protocol to identify which patients are at highest risk of DR and should be seen more urgently, addressing no-shows, and implementation of a tele-retina program are potential avenues for improving clinic efficiency in a resource-limited setting for vulnerable populations.

Alzheimer's disease as a causal risk factor for diabetic retinopathy: a Mendelian randomization study.

This study aims to investigate the causal relationship between Alzheimer's Disease (AD) and Diabetic Retinopathy (DR). Employing Mendelian Randomization (MR), Generalized Summary-data-based Mendelian Randomization (GSMR), and the MR-Steiger test, this study scrutinizes the genetic underpinnings of the hypothesized causal association between AD and DR, as well as its Proliferative DR (PDR) and Non-Proliferative DR (NPDR) subtypes. Comprehensive data from Genome-Wide Association Studies (GWAS) were analyzed, specifically AD data from the Psychiatric Genomics Consortium (71,880 cases/383,378 controls), and DR, PDR, and NPDR data from both the FinnGen consortium (FinnGen release R8, DR: 5,988 cases/314,042 controls; PDR: 8,383 cases/329,756 controls; NPDR: 3,446 cases/314,042 controls) and the IEU OpenGWAS (DR: 14,584 cases/176,010 controls; PDR: 8,681 cases/204,208 controls; NPDR: 2,026 cases/204,208 controls). The study also incorporated Functional Mapping and Annotation (FUMA) for an in-depth analysis of the GWAS results. The MR analyses revealed that genetic susceptibility to AD significantly increases the risk of DR, as evidenced by GWAS data from the FinnGen consortium (OR: 2.5090; 95% confidence interval (CI):1.2102-5.2018, false discovery rate P-value (PFDR)=0.0201; GSMR: bxy=0.8936, bxy_se=0.3759, P=0.0174), NPDR (OR: 2.7455; 95% CI: 1.3178-5.7197, PFDR=0.0166; GSMR: bxy=0.9682, bxy_se=0.3802, P=0.0126), and PDR (OR: 2.3098; 95% CI: 1.2411-4.2986, PFDR=0.0164; GSMR: bxy=0.7962, bxy_se=0.3205, P=0.0129) using DR GWAS from FinnGen consortium. These results were corroborated by DR GWAS datasets from IEU OpenGWAS. The MR-Steiger test confirmed a significant association of all identified instrumental variables (IVs) with AD. While a potential causal effect of DR and its subtypes on AD was identified, the robustness of these results was constrained by a low power value. FUMA analysis identified OARD1, NFYA, TREM1 as shared risk genes between DR and AD, suggesting a potential genetic overlap between these complex diseases. This study underscores the contribution of AD to an increased risk of DR, as well as NPDR and PDR subtypes, underscoring the necessity of a holistic approach in the management of patients affected by these conditions.

TCERG1L hypermethylation is a risk factor of diabetic retinopathy in Chinese children with type 1 diabetes.

To identify the differential methylation sites (DMS) and their according genes associated with diabetic retinopathy (DR) development in type 1 diabetes (T1DM) children. This study consists of two surveys. A total of 40 T1DM children was included in the first survey. Because no participant has DR, retina thinning was used as a surrogate indicator for DR. The lowest 25% participants with the thinnest macular retinal thickness were included into the case group, and the others were controls. The DNA methylation status was assessed by the Illumina methylation 850K array BeadChip assay, and compared between the case and control groups. Four DMS with a potential role in diabetes were identified. The second survey included 27 T1DM children, among which four had DR. The methylation patterns of the four DMS identified by 850K were compared between participants with and without DR by pyrosequencing. In the first survey, the 850K array revealed 751 sites significantly and differentially methylated in the case group comparing with the controls (|Δβ|>0.1 and Adj.P<0.05), and 328 of these were identified with a significance of Adj.P<0.01. Among these, 319 CpG sites were hypermethylated and 432 were hypomethylated in the case group relative to the controls. Pyrosequencing revealed that the transcription elongation regulator 1 like (TCERG1L, cg07684215) gene was hypermethylated in the four T1DM children with DR (P=0.018), which was consistent with the result from the first survey. The methylation status of the other three DMS (cg26389052, cg25192647, and cg05413694) showed no difference (all P>0.05) between participants with and without DR. The hypermethylation of the TCERG1L gene is a risk factor for DR development in Chinese children with T1DM.