Abstract
ObjectiveTo explore the association between serum leucine (leu) and diabetic retinopathy (DR) in patients with type 2 diabetes (T2D) and then to analyze the influence of gender on the association.MethodThe electronic medical records of 1,149 T2D patients who met inclusion and exclusion criteria were retrieved from the Second Affiliated Hospital of Dalian Medical University and the First Affiliated Hospital of Jinzhou Medical University. Serum leu levels of all subjects were measured by liquid chromatography–mass spectrometry. Logistic regression was used to obtain the odds ratio (OR) and CI of leu–DR risk in multiple models. When using these models, restricted cubic spline (RCS) was used to test the potential non-linear relationship between multiple continuous independent variables, such as leu and DR (classification), and dependent variables. We also used the additive interaction method to evaluate the interaction effect between leu and gender on DR.ResultsLeu was a protective factor of DR [0.78 (0.66, 0.92)]. When gender was divided into male and female, the above relationship was statistically significant only in men [0.73 (0.58, 0.94)]. Three indicators of additive interaction—RERI, AP, and S—suggested that there is no interaction between gender and leu on the risk of DR.ConclusionsMale T2D patients with high leu levels may have a lower risk of DR.
Highlights
Diabetes is a typical metabolic disease with high prevalence and serious complications [1], and the number of people suffering from the disease is increasing at an accelerated rate
Leu was a protective factor of diabetic retinopathy (DR) [0.78 (0.66, 0.92)]
Three indicators of additive interaction—risk due to interaction (RERI), AP, and S—suggested that there is no interaction between gender and leu on the risk of DR
Summary
To explore the association between serum leucine (leu) and diabetic retinopathy (DR) in patients with type 2 diabetes (T2D) and to analyze the influence of gender on the association. Serum leu levels of all subjects were measured by liquid chromatography–mass spectrometry. Logistic regression was used to obtain the odds ratio (OR) and CI of leu–DR risk in multiple models. When using these models, restricted cubic spline (RCS) was used to test the potential non-linear relationship between multiple continuous independent variables, such as leu and DR (classification), and dependent variables.
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