Nomogram for prediction of non-proliferative diabetic retinopathy in juvenile-onset type 1 diabetes: a cohort study in an Asian population

Role of Systemic Factors in Improving the Prognosis of Diabetic Retinal Disease and Predicting Response to Diabetic Retinopathy Treatment

Topic

To review clinical evidence on systemic factors that might be relevant to update diabetic retinal disease (DRD) staging systems, including prediction of DRD onset, progression, and response to treatment.

Clinical relevance

Systemic factors may improve new staging systems for DRD to better assess risk of disease worsening and predict response to therapy.

Methods

The Systemic Health Working Group of the Mary Tyler Moore Vision Initiative reviewed systemic factors individually and in multivariate models for prediction of DRD onset or progression (i.e., prognosis) or response to treatments (prediction).

Results

There was consistent evidence for associations of longer diabetes duration, higher glycosylated hemoglobin (HbA1c), and male sex with DRD onset and progression. There is strong trial evidence for the effect of reducing HbA1c and reducing DRD progression. There is strong evidence that higher blood pressure (BP) is a risk factor for DRD incidence and for progression. Pregnancy has been consistently reported to be associated with worsening of DRD but recent studies reflecting modern care standards are lacking. In studies examining multivariate prognostic models of DRD onset, HbA1c and diabetes duration were consistently retained as significant predictors of DRD onset. There was evidence of associations of BP and sex with DRD onset. In multivariate prognostic models examining DRD progression, retinal measures were consistently found to be a significant predictor of DRD with little evidence of any useful marginal increment in prognostic information with the inclusion of systemic risk factor data apart from retinal image data in multivariate models. For predicting the impact of treatment, although there are small studies that quantify prognostic information based on imaging data alone or systemic factors alone, there are currently no large studies that quantify marginal prognostic information within a multivariate model, including both imaging and systemic factors.

Conclusion

With standard imaging techniques and ways of processing images rapidly evolving, an international network of centers is needed to routinely capture systemic health factors simultaneously to retinal images so that gains in prediction increment may be precisely quantified to determine the usefulness of various health factors in the prognosis of DRD and prediction of response to treatment.

Financial Disclosures

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Predictive model for diabetic retinopathy under limited medical resources: A multicenter diagnostic study

Background

Comprehensive eye examinations for diabetic retinopathy is poorly implemented in medically underserved areas. There is a critical need for a widely available and economical tool to aid patient selection for priority retinal screening. We investigated the possibility of a predictive model for retinopathy identification using simple parameters.

Methods

Clinical data were retrospectively collected from 4, 159 patients with diabetes admitted to five tertiary hospitals. Independent predictors were identified by univariate analysis and least absolute shrinkage and selection operator (LASSO) regression, and a nomogram was developed based on a multivariate logistic regression model. The validity and clinical practicality of this nomogram were assessed using concordance index (C-index), area under the receiver operating characteristic curve (AUROC), calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC).

Results

The predictive factors in the multivariate model included the duration of diabetes, history of hypertension, and cardiovascular disease. The three-variable model displayed medium prediction ability with an AUROC of 0.722 (95%CI 0.696-0.748) in the training set, 0.715 (95%CI 0.670-0.754) in the internal set, and 0.703 (95%CI 0.552-0.853) in the external dataset. DCA showed that the threshold probability of DR in diabetic patients was 17-55% according to the nomogram, and CIC also showed that the nomogram could be applied clinically if the risk threshold exceeded 30%. An operation interface on a webpage (https://cqmuxss.shinyapps.io/dr_tjj/) was built to improve the clinical utility of the nomogram.

Conclusions

The predictive model developed based on a minimal amount of clinical data available to diabetic patients with restricted medical resources could help primary healthcare practitioners promptly identify potential retinopathy.

A Nomogram for Predicting Vision-Threatening Diabetic Retinopathy Among Mild Diabetic Retinopathy Patients: A Case-Control and Prospective Study of Type 2 Diabetes

AIM

This study aims to develop a nomogram for predicting vision-threatening diabetic retinopathy (VTDR) in type 2 diabetes mellitus (T2DM) with mild non-proliferative diabetic retinopathy (NPDR) patients.

MATERIALS AND METHODS

In case-control analysis, 440 patients with mild NPDR or VTDR were enrolled to identify predictors and develop a nomogram. In the prospective cohort, 120 T2DM patients with mild NPDR were enrolled for external validation. Sensitivity, specificity, and area under the receiver operating characteristic (AUC) were calculated to evaluate the predictive performance of the nomogram.

RESULTS

In case-control analysis, 2-h C-peptide (OR = 0.85, 95% CI: 0.75 to 0.95, p = 0.006), sural nerve conduction impaired (SNCI) (mildly: OR = 2.18, 95% CI: 1.10 to 4.33, p = 0.026; moderately/severely: 3.66, 95% CI: 1.74 to 7.70, p < 0.001) and UACR (microalbuminuria: OR = 2.37, 95% CI: 1.25 to 4.48, p = 0.008; macroalbuminuria: 4.02, 95% CI: 1.61 to 10.06, p = 0.003) were identified as independent predictors. The concordance index of the prediction nomogram was 0.76 in the training set. In the test set, sensitivity, specificity, and AUC were 84.8%, 60.6%, and 0.73, respectively. In the prospective cohort, median follow-up period was 42 months, and 15 patients (12.5%) developed VTDR. Sensitivity, specificity, and AUC of prediction were 66.7%, 89.5%, and 0.75, respectively.

CONCLUSION

Introducing 2-h C-peptide, UACR, and SNCI, the nomogram demonstrated a good discriminatory power for predicting risk of VTDR in mild NPDR individuals.

Nomogram model predicts the risk of visual impairment in diabetic retinopathy: a retrospective study

BACKGROUND

To develop a model for predicting the risk of visual impairment in diabetic retinopathy (DR) by a nomogram.

METHODS

Patients with DR who underwent both optical coherence tomography angiography (OCTA) and fundus fluorescein angiography (FFA) were retrospectively enrolled. FFA was conducted for DR staging, swept-source optical coherence tomography (SS-OCT) of the macula and 3*3-mm blood flow imaging by OCTA to observe retinal structure and blood flow parameters. We defined a logarithm of the minimum angle of resolution visual acuity (LogMAR VA) ≥0.5 as visual impairment, and the characteristics correlated with VA were screened using binary logistic regression. The selected factors were then entered into a multivariate binary stepwise regression, and a nomogram was developed to predict visual impairment risk. Finally, the model was validated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration plots, decision curve analysis (DCA), and clinical impact curve (CIC).

RESULTS

A total of 29 parameters were included in the analysis, and 13 characteristics were used to develop a nomogram model. Finally, diabetic macular ischaemia (DMI) grading, disorganization of the retinal inner layers (DRIL), outer layer disruption, and the vessel density of choriocapillaris layer inferior (SubVD) were found to be statistically significant (P < 0.05). The model was found to have good accuracy based on the ROC (AUC = 0.931) and calibration curves (C-index = 0.930). The DCA showed that risk threshold probabilities in the (3-91%) interval models can be used to guide clinical practice, and the proportion of people at risk at each threshold probability is illustrated by the CIC.

CONCLUSION

The nomogram model for predicting visual impairment in DR patients demonstrated good accuracy and utility, and it can be used to guide clinical practice.

TRIAL REGISTRATION

Chinese Clinical Trial Registry, ChiCTR2200059835. Registered 12 May 2022, https://www.chictr.org.cn/edit.aspx?pid=169290&htm=4.

Prediction models of diabetes complications: a scoping review

BACKGROUND

Diabetes often places a large burden on people with diabetes (hereafter 'patients') and the society, that is, in part attributable to its complications. However, evidence from models predicting diabetes complications in patients remains unclear. With the collaboration of patient partners, we aimed to describe existing prediction models of physical and mental health complications of diabetes.

METHODS

Building on existing frameworks, we systematically searched for studies in Ovid-Medline and Embase. We included studies describing prognostic prediction models that used data from patients with pre-diabetes or any type of diabetes, published between 2000 and 2020. Independent reviewers screened articles, extracted data and narratively synthesised findings using established reporting standards.

RESULTS

Overall, 78 studies reported 260 risk prediction models of cardiovascular complications (n=42 studies), mortality (n=16), kidney complications (n=14), eye complications (n=10), hypoglycaemia (n=8), nerve complications (n=3), cancer (n=2), fracture (n=2) and dementia (n=1). Prevalent complications deemed important by patients such as amputation and mental health were poorly or not at all represented. Studies primarily analysed data from older people with type 2 diabetes (n=54), with little focus on pre-diabetes (n=0), type 1 diabetes (n=8), younger (n=1) and racialised people (n=10). Per complication, predictors vary substantially between models. Studies with details of calibration and discrimination mostly exhibited good model performance.

CONCLUSION

This rigorous knowledge synthesis provides evidence of gaps in the landscape of diabetes complication prediction models. Future studies should address unmet needs for analyses of complications n> and among patient groups currently under-represented in the literature and should consistently report relevant statistics. SCOPING REVIEW REGISTRATION: https://osf.io/fjubt/.

Data Homogeneity Effect in Deep Learning-Based Prediction of Type 1 Diabetic Retinopathy

This study is aimed at evaluating a deep transfer learning-based model for identifying diabetic retinopathy (DR) that was trained using a dataset with high variability and predominant type 2 diabetes (T2D) and comparing model performance with that in patients with type 1 diabetes (T1D). The Kaggle dataset, which is a publicly available dataset, was divided into training and testing Kaggle datasets. In the comparison dataset, we collected retinal fundus images of T1D patients at Chang Gung Memorial Hospital in Taiwan from 2013 to 2020, and the images were divided into training and testing T1D datasets. The model was developed using 4 different convolutional neural networks (Inception-V3, DenseNet-121, VGG1, and Xception). The model performance in predicting DR was evaluated using testing images from each dataset, and area under the curve (AUC), sensitivity, and specificity were calculated. The model trained using the Kaggle dataset had an average (range) AUC of 0.74 (0.03) and 0.87 (0.01) in the testing Kaggle and T1D datasets, respectively. The model trained using the T1D dataset had an AUC of 0.88 (0.03), which decreased to 0.57 (0.02) in the testing Kaggle dataset. Heatmaps showed that the model focused on retinal hemorrhage, vessels, and exudation to predict DR. In wrong prediction images, artifacts and low-image quality affected model performance. The model developed with the high variability and T2D predominant dataset could be applied to T1D patients. Dataset homogeneity could affect the performance, trainability, and generalization of the model.

An online diabetic retinopathy screening tool for patients with type 2 diabetes

AIM

To develop a useful diabetic retinopathy (DR) screening tool for patients with type 2 diabetes mellitus (T2DM).

METHODS

A DR prediction model based on the Logistic regression algorithm was established on the development dataset containing 778 samples (randomly assigned to the training dataset and the internal validation dataset at a ratio of 7:3). The generalization capability of the model was assessed using an external validation dataset containing 128 samples. The DR risk calculator was developed through WeChat Developer Tools using JavaScript, which was embedded in the WeChat Mini Program.

RESULTS

The model revealed risk factors (duration of diabetes, diabetic nephropathy, and creatinine level) and protective factors (annual DR screening and hyperlipidemia) for DR. In the internal and external validation, the recall ratios of the model were 0.92 and 0.89, respectively, and the area under the curve values were 0.82 and 0.70, respectively.

CONCLUSION

The DR screening tool integrates education, risk prediction, and medical advice function, which could help clinicians in conducting DR risk assessments and providing recommendations for ophthalmic referral to increase the DR screening rate among patients with T2DM.

Clinical and Demographic Factors Associated With Diabetic Retinopathy Among Young Patients With Diabetes

IMPORTANCE

Diabetic retinopathy (DR) is a leading cause of vision loss worldwide. As the incidence of both type 1 and type 2 diabetes among youths continues to increase around the world, understanding the factors associated with the development of DR in this age group is important.

OBJECTIVE

To identify factors associated with DR among children, adolescents, and young adults with type 1 or type 2 diabetes in the US.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study pooled data from 2 large academic pediatric centers in the US (Baylor College of Medicine/Texas Children's Hospital [BCM/TCH] Diabetes and Endocrine Care Center and Johns Hopkins University [JHU] Pediatric Diabetes Center) to form a diverse population for analysis. Data were collected prospectively at the JHU center (via point-of-care screening using fundus photography) from December 3, 2018, to November 1, 2019, and retrospectively at the BCM/TCH center (via electronic health records of patients who received point-of-care screening using retinal cameras between June 1, 2016, and May 31, 2019). A total of 1640 individuals aged 5 to 21 years with type 1 or type 2 diabetes (308 participants from the JHU center and 1332 participants from the BCM/TCH center) completed DR screening and had gradable images.

MAIN OUTCOME AND MEASURES

Prevalence of DR, as identified on fundus photography, and factors associated with DR.

RESULTS

Among 1640 participants (mean [SD] age, 15.7 [3.6] years; 867 female individuals [52.9%]), 1216 (74.1%) had type 1 diabetes, and 416 (25.4%) had type 2 diabetes. A total of 506 participants (30.9%) were Hispanic, 384 (23.4%) were non-Hispanic Black or African American, 647 (39.5%) were non-Hispanic White, and 103 (6.3%) were of other races or ethnicities (1 was American Indian or Alaska Native, 50 were Asian, 1 was Native Hawaiian or Pacific Islander, and 51 did not specify race or ethnicity, specified other race or ethnicity, or had unavailable data on race or ethnicity). Overall, 558 of 1216 patients (45.9%) with type 1 diabetes used an insulin pump, and 5 of 416 patients (1.2%) with type 2 diabetes used an insulin pump. Diabetic retinopathy was found in 57 of 1640 patients (3.5%). Patients with DR vs without DR had a greater duration of diabetes (mean [SD], 9.4 [4.4] years vs 6.6 [4.4] years; P < .001) and higher hemoglobin A1c (HbA1c) levels (mean [SD], 10.3% [2.4%] vs 9.2% [2.1%]; P < .001). Among those with type 1 diabetes, insulin pump use was associated with a lower likelihood of DR after adjusting for race and ethnicity, insurance status, diabetes duration, and HbA1c level (odds ratio [OR], 0.43; 95% CI, 0.20-0.93; P = .03). The likelihood of having DR was 2.1 times higher among Black or African American participants compared with White participants (OR, 2.12; 95% CI, 1.12-4.01; P = .02); this difference was no longer significant after adjusting for duration of diabetes, insurance status, insulin pump use (among patients with type 1 diabetes only), and mean HbA1c level (type 1 diabetes: OR, 1.79; 95% CI, 0.83-3.89; P = .14; type 2 diabetes: OR, 1.08; 95% CI, 0.30-3.85; P = .91).

CONCLUSIONS AND RELEVANCE

This study found that although the duration of diabetes and suboptimal glycemic control have long been associated with DR, insulin pump use (among those with type 1 diabetes) was independently associated with a lower likelihood of DR, which is likely owing to decreased glycemic variability and increased time in range (ie, the percentage of time blood glucose levels remain within the 70-180 mg/dL range). Black or African American race was found to be associated with DR in the univariable analysis but not in the multivariable analysis, which may represent disparities in access to diabetes technologies and care.

Nasal Methicillin-Resistant Staphylococcus aureus Colonization in Patients with Type 1 Diabetes in Taiwan

Nasal methicillin-resistant Staphylococcus aureus (MRSA) colonies are an essential reservoir of infection, especially for patients with diabetes. However, data on MRSA colonization in patients with type 1 diabetes are limited. We investigated the epidemiology of MRSA colonization in patients with type 1 diabetes. This prospective cross-sectional study was conducted in a medical center (Chang Gung Memorial Hospital) in Taiwan from 1 July to 31 December 2020. Nasal sampling and MRSA detection were performed. The molecular characteristics of MRSA isolates were tested, and factors associated with MRSA colonization were analyzed. We included 245 patients with type 1 diabetes; nasal MRSA colonization was identified in 13 (5.3%) patients. All isolates belonged to community-associated MRSA genetic strains; the most frequent strain was clonal complex 45 (53.8%), followed by ST59 (30.8%) (a local community strain). MRSA colonization was positively associated with age ≤ 10 years, body mass index < 18 kg/m², and diabetes duration < 10 years; moreover, it was negatively associated with serum low-density lipoprotein cholesterol ≥ 100 mg/dL. No independent factor was reported. The nasal MRSA colonization rate in type 1 diabetes is approximately 5% in Taiwan. Most of these colonizing strains are community strains, namely clonal complex 45 and ST59.

Higher HbA1c may reduce axial length elongation in myopic children: a comparison cohort study

AIMS

To compare the annual axial length (AL) changes in myopic children with type 1 diabetes mellitus (T1DM) and those without diabetes.

METHODS

There are two groups of myopic children in this retrospective cohort study. Group 1 consisted of myopic children with T1DM (44 eyes of 22 patients). Group 2 comprised age-matched myopic children without diabetes (44 eyes of 22 children). These two groups were compared with regard to their baseline clinical characteristics. A generalized estimating equations (GEE) model was also used to determine the most likely factor that contributed to the results.

RESULTS

The average ages of group 1 and group 2 were 14.8 and 14.6 years, respectively. Children in group 1 had significantly slower annual AL changes (0.051 mm/year vs 0.103 mm/year; 50.5% slower, P = 0.011) and shorter baseline AL (23.97 vs 25.19 mm, P < 0.001) than those in group 2. GEE also showed that serum glycated hemoglobin (HbA1c) level (B = -0.023, P = 0.039) was the most important factor in reducing AL elongation in group 1 myopic children.

CONCLUSIONS

Long-term higher HbA1c level may reduce AL elongation. A strict blood sugar control strategy in clinical practice is warranted to axial myopia progression in T1DM children.

Nomogram Prediction Model for Diabetic Retinopathy Development in Type 2 Diabetes Mellitus Patients: A Retrospective Cohort Study

BACKGROUND

This study is aimed at investigating the systemic risk factors of diabetic retinopathy and further establishing a risk prediction model for DR development in T2DM patients.

METHODS

This is a retrospective cohort study including 330 type 2 diabetes mellitus (T2DM) patients who were followed up from December 2012 to November 2020. Multivariable cox regression analysis identifying factors associated with the hazard of developing diabetic retinopathy (DR) was used to construct the DR risk prediction model in the form of nomogram.

RESULTS

50.6% of participants (mean age: 58.60 ± 10.55) were female, and mean duration of diabetes was 7.09 ± 5.36 years. After multivariate cox regression, the risk factors for developing DR were age (HR 1.068, 95%Cl 1.021-1.118, P = 0.005), diabetes duration (HR 1.094, 95%Cl 1.018-1.177, P = 0.015), HbA1c (HR 1.411, 95%Cl 1.113-1.788, P = 0.004), albuminuria (HR 6.908, 95%Cl 1.794-26.599, P = 0.005), and triglyceride (HR 1.554, 95%Cl 1.037-2.330, P = 0.033). The AUC values of the nomogram for predicting developing DR at 3-, 4-, and 5-year were 0.854, 0.845, and 0.798.

CONCLUSION

Combining age, diabetes duration, HbA1c, albuminuria, and triglyceride, the nomogram model is effective for early recognition and intervention of individuals at high risk of DR development.

Foveal avascular zone analysis by optical coherence tomography angiography in patients with type 1 and 2 diabetes and without clinical signs of diabetic retinopathy

PURPOSE

To analyze the early macular microvascular alterations in patients with type 1 and 2 diabetes mellitus (DM) without diabetic retinopathy (DR), using optical coherence tomography angiography (OCT-A), and compare these with nondiabetic patients.

METHODS

This prospective study involved 93 patients with type 1 diabetes (DM1), 104 patients with type 2 diabetes (DM2) without signs of DR, and 71 healthy subjects for the control group. The foveal avascular zone (FAZ) area and the vessel density (VD) at the superficial capillary plexus (SCP) and deep capillary plexus (DCP) were evaluated.

RESULTS

The SCP and DCP FAZ areas were significantly larger in the DM1 group in comparison with the controls (p = .001), while no significant differences were observed between the DM2 group and the healthy control group (p = .12). Additionally, no significant differences in FAZ area were found between the DM1 and DM2 groups (p = .26). The VD was significantly reduced in DM1 and DM2 groups compared to controls. A direct correlation was found between the duration of diabetes and SCP FAZ area (r = 0.44; R² = 0.19; p = .0001). Statistically significant differences in the FAZ area at SCP and DCP were observed when comparing patients with a diabetes duration > 10 years and < 10 years in the DM2 group (p = .0001, respectively) and only in the FAZ area at the DCP in the DM1 group (p = .0001).

CONCLUSION

Diabetic patients without DR demonstrate early microvascular alteration in the macular area on OCT-A, which is more pronounced in type I DM, and correlates with the duration of the disease.

Foveal avascular zone area analysis in juvenile-onset type 1 diabetes using optical coherence tomography angiography

PURPOSE

Optical coherence tomography angiography (OCTA) was performed on patients with juvenile-onset type 1 diabetes (T1DM) but with no diabetic retinopathy to measure the foveal avascular zone (FAZ) area.

STUDY DESIGN

Retrospective single-facility study METHODS: Twenty-nine patients (58 eyes) with juvenile-onset T1DM were studied. Images (3 mm x 3 mm cube centered on the fovea) were acquired using an OCTA device. Age at examination was 16.1 ± 8.7 years; onset age was 6.4 ± 3.5 years; duration of diabetes was 9.7 ± 8.3 years. Twenty-four age-matched healthy individuals were studied as controls.

RESULTS

FAZ area was significantly larger in T1DM patients than in controls (0.29 ± 0.09 vs. 0.25 ± 0.08 mm², P = 0.0234). Parafoveal vessel density was not significantly different between patients and controls (50.43 ± 4.24 vs. 50.07 ± 4.64, P = 0.8842). By generalized linear model analysis, annual HbA1c (P = 0.0190), number of serious hypoglycemic attacks (P = 0.0210), and onset age (P = 0.0447) were identified as variables significantly associated with FAZ area. Age, gender, duration of disease, total cholesterol, high or low-density lipoprotein, triglycerides, and body mass index were not significantly associated with FAZ area.

CONCLUSION

Patients with juvenile-onset T1DM and no diabetic retinopathy had increased FAZ, but no significant difference in parafoveal vessel density compared to healthy controls. Larger FAZ area was associated with higher annual HbA1c, more episodes of severe hypoglycemic attacks, and older onset age.

Glycosylated Hemoglobin A1c Improves the Performance of the Nomogram for Predicting the 5-Year Incidence of Type 2 Diabetes

AIM

To develop and validate a model, which combines traditional risk factors and glycosylated hemoglobin A1c (HbA1c) for predicting the risk of type 2 diabetes (T2DM).

MATERIALS AND METHODS

This is a historical cohort study from a collected database, which included 8419 males and 7034 females without diabetes at baseline with a median follow-up of 5.8-years and 5.1-years, respectively. Multivariate cox regression analysis was used to select significant prognostic factors of T2DM. Two nomograms were constructed to predict the 5-year incidence of T2DM based on traditional risk factors (Model 1) and traditional risk factors plus HbA1c (Model 2). C-index, calibration curve, and time-dependent receiver-operating characteristic (ROC) curve were conducted in the training sets and validation sets.

RESULTS

In males, the C-index was 0.824 (95% CI: 0.795-0.853) in Model 1 and 0.867 (95% CI: 0.840-0.894) in Model 2; in females, the C-index was 0.830 (95% CI: 0.770-0.890) in Model 1 and 0.856 (95% CI: 0.795-0.917) in Model 2. The areas under curve (AUC) in Model 2 for prediction of T2DM development were higher than in Model 1 at each time point. The calibration curves showed excellent agreement between the predicted possibility and the actual observation in both models. The results of validation sets were similar to the results of training sets.

CONCLUSION

The proposed nomogram can be used to accurately predict the risk of T2DM. Compared with the traditional nomogram, HbA1c can improve the performance of nomograms for predicting the 5-year incidence of T2DM.

Risk factors for prevalent diabetic retinopathy and proliferative diabetic retinopathy in type 1 diabetes

PURPOSE

Age at diagnosis of type 1 diabetes (DM1) has been implied as an important factor associated with the development of the microvascular complications. Our aim was to identify factors associated with prevalent diabetic retinopathy (DR) and proliferative diabetic retinopathy (PDR) in DM1 people with early and late-onset.

METHODS

We reviewed medical records from all DM1 people from the reference area of a tertiary center (about 340,000 persons). Univariate and multivariate logistic regression were used to assess the relationship between potential risk factors (sociodemographic, diabetes-related, co-morbidities, and laboratory parameters) and prevalent DR/PDR. We performed an analysis comparing patients diagnosed before (early-onset) and after (late-onset) 18 years of age.

RESULTS

We included 140 patients in early-onset DM1 group and 169 in late-onset DM1 group. Longer duration of diabetes and HbA1c remained associated with prevalent DR in both groups after adjusting for potential risk factors. Nephropathy was associated with prevalent DR in the late-onset group but not in the early-onset group. Peripheral neuropathy remained associated with prevalent PDR when modeled together in the multivariate model. High BMI demonstrated a significative association with PDR in early but not in the late-onset DM1 group.

CONCLUSIONS

Although previous reports suggest that age at DM1 diagnosis may have a role in DR prevalence, the risk factors for DR in early and late-onset DM1 were similar for both groups. Duration of disease and lifelong metabolic control were the major predictors for DR in both groups. Nephropathy was associated with DR in patients with late-onset disease. Neuropathy was associated with PDR occurrence in both groups. BMI was associated with PDR early-onset DM1 group.