Timing Is Everything: Age of Onset Influences Long-term Retinopathy Risk in Type 2 Diabetes, Independent of Traditional Risk Factors

Jencia Wong, MD; Lynda Molyneaux, RN; Maria Constantino; Stephen M. Twigg, MD, PHD; Dennis K. Yue, MD, PHD

Disclosures

Diabetes Care. 2008;31(10):1985-1990. 

In This Article

Research Design and Methods

Data from 8,301 patients with type 2 diabetes referred to the Royal Prince Alfred Hospital Diabetes Centre in Sydney, Australia, from 1989 to 2007 were available for study. These patients had a full complications assessment, and data were collected following a standardized protocol as described previously.[5] Specific information collected at each assessment includes demographic details, age of diagnosis, A1C, BMI, lipids, blood pressure, and albuminuria. Retinopathy status was assessed by direct fundoscopy through dilated pupils or from report by the treating ophthalmologist (in 10% of cases). Severity of retinopathy was scored as per a modified Early Treatment Diabetic Retinopathy Study (ETDRS) severity scale[6] into the following categories: 1) nil, 2) nonproliferative (minimal, mild-moderate, or severe), or 3) proliferative, each with or without 4) macular edema. Those with either of the last two categories were considered as having "vision-threatening retinopathy."

To assess the impact of age of onset on long-term retinopathy status, independent of duration, data from 624 patients with duration of 20-30 years of known type 2 diabetes at last follow-up, were analyzed (group A). To obviate possible bias due to a higher attrition from comorbidities in those with later-onset diabetes and retinopathy, 852 patients with type 2 diabetes of shorter duration (10-12 years, group B), and therefore younger in age, were similarly studied.

Data were analyzed by grouping patients according to the presence or absence of retinopathy at the last visit and then further stratified by age of onset and mean A1C over all visits (mean ± SD visits: group A 3.8 ± 3.2 and group B 2.8 ± 2.3). Other clinical data were taken from the patient's last visit. The presence of metabolic syndrome was defined by World Health Organization criteria.[7]

Statistical Analyses

Data were analyzed using NCSS 2004 software. Data for group A and group B were each grouped according to age of diagnosis of diabetes: <45, 45-55, and >55 years. A1C was also categorized: <7.0, 7.0-9.0, and >9.0%. These A1C categories were chosen to represent those with good, suboptimal, and very poor glycemic control. Continuous data were checked for normality and are presented as mean or median. Kruskal-Wallace ANOVA was used to compare means or medians. Categorical data were presented as percentage and 95% CI. X 2, Fisher's exact test, and odds ratios were used to compare the groups. To assess whether there was any increasing or decreasing trend between the groups, a trend test was performed. Logistic regression was used to determine the independent predictors for retinopathy both as continuous and categorical variables. Independent determinants used were age, age of diagnosis (as a categorical variable in model 1 and as a continuous variable in model 2), A1C, weight, metabolic syndrome (as individual factors and as a dichotomous variable), duration of diabetes, sex, ethnicity, and family history of diabetes. A stepwise forward method was used, and variables that were significant using the log likelihood method were included in the final model. Interactions were tested between the independent variables.

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