Abstract
Relevance: There are evidences of the participation of adipose tissue hormones (ATH) in the development of diabetic microangiopathy and retinal neovascularization. The disign of methods for the mathematical evaluation of the prognosis of the development of diabetic retinopathy (DR) with the participation of ATH is an actual problem in modern diabetology. Goal: Elaboration of a mathematical model for assessing the prognostic significance of ATH to study the likelihood of developing and progressing DR in patients with type 2 diabetes mellitus (T2DM). Materials and Methods: An open observational single-center one-stage selective study was conducted. The study was approved by the Local Ethics Committee. 59 patients (187 eyes) with T2DM and DR (men and women; mean age - 58.20±0.18 years; mean T2DM duration - 9.19±0.46 years; mean HbA1C - 9.10±0.17 %), were assigned to 3 groups, based on the stage of DR (according to fundus instrumental examination), and underwent the study. The diagnostic predictive value was assessed by discriminant analysis. Models with linear combinations of the serum leptin, adiponectin and resistin, triglyceride (TG), also HbA1C, type of antidiabetic therapy (ADT) were developed, and, subsequently, formulas for classification-relevant discriminant functions were derived. ADT included metformin, either alone (type 1 ADT), or in combination with oral anti-hyperglycemic medication (type 2 ADT) or insulin therapy (type 3 ADT). The classification functions (CF) computed based on the variables found from the above developed models provided the basis for predicting the development of DR. Results: The formulas for CF from model are as follows: CF1 = 0.29 * TG + 1.55 * HbA1 + 1.81 * ADT_Type + 0.04 * Leptin + 0,34* Adiponectin + 0,91* Resistin – 13,82; CF2 = 0.05*TG + 1.36 * HbA1 + 3.01 * ADT_Type + 0.08 * Leptin + 0,35* Adiponectin + 1,01 * Resistin – 15.95. A step-by-step approach to diagnostic decision making should be used. First, blood samples are tested for serum leptin, adiponectin and resistin, TG, blood HbA1, and the patient is assigned a code for ADT_Type (1, 2 or 3). Second, CF1 and CF2 values are calculated. Finally, the two values are compared to determine which is greater. The predictive decision is made by selecting the classification function with the greater value. Thus, if CF1>CF2, the process can be stabilized at this stage given an adequate glycemic control (through compensation of carbohydrate metabolism) and body-mass control as well as patient compliance. If CF1<CF2, the pathological process may progress to the next stage or even within stage 3, and there is an urgent need to reduce BMI, and to correct the ADT and the blood lipid profile. Conclusion: Informativeness and statistical significance of model is 71.4 % and p=0.040, respectively.
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