# Model for Predicting the Effect of Sibutramine Therapy in Obesity
## 2 Materials and Methods  
For the study, a sample of very well-phenotyped patients was used, collected on the basis of the Research Laboratory of Diabetology of the Institute of Endocrinology of Almazov National Medical Research Centre, who evaluated not only standard anthropometric and metabolic parameters but also the concentration of hormones involved in the regulation of energy balance and appetite (leptin, ghrelin, increting (GPP1 and GIP)), their postprandial dynamics, biomarkers of inflammation in the bloodstream (CRP, myeloperoxidase, paraoxonase \(-1\)), and a number of molecular genetic markers, the expression of which was altered in subcutaneous adipose tissue during obesity (leptin, ADIPOQ, HIF1a, CCL2, miR142, miR155, miR378). Our previous studies demonstrated the existence of a relationship between these indicators and the response to treatment in obesity [24], which allowed us to assume their usefulness in constructing a predictive model. The study was performed in accordance with the standards of Good Clinical Practice and the principles of the Helsinki Declaration. The research protocol was approved by the local Ethics Committee of the Almazov National Medical Research Centre (extract from Protocol No. 022018-14d dated 12 February 2018). Before inclusion in the study, written informed consent was obtained from all participants. The inclusion/non-inclusion criteria are presented in Table 1. The study group of patients received sibutramine therapy (REDUXIN) at a dose of 10 mg/day (1 capsule) in the morning 30 min before meals, for a period of 6 months. Nutrition standardization was performed for patients included in the study: moderate hypocaloric intake of 15 kcal/kg (carbohydrates 45-55%, proteins 15-20%, fats 20-35%, fiber 35 g/day) or based on the ideal body weight according to the Mifflin-St. Jeor formula with adjustments for physical activity levels. To exclude patients with liver pathology, a blood test was performed for ALT, AST, total bilirubin, renal pathology--creatinine, glomerular filtration rate (GFR; Cockcroft-Gault calculation), and pathology of carbohydrate metabolism--fasting plasma glucose concentration. The exclusion of pathology from the cardiovascular system was performed anamnnetically (the presence of a history of myocardial infarction, stroke, chronic heart failure III-IV FC (NYHA), hypertension with inappropriate blood pressure levels). Having collected the dataset, we continue research by pre-processing this data. As it was mentioned, the main goal of our decision system is to predict the most possible outcome of obesity treatment. So, we set binary labels as targets for the model corresponding to the treatment success thresholds at 5% for the 3-month period and 7% for the 6-month period. Additionally, we appended the result of 3 months of treatment in the model for the second visit after completing the first part of therapy. One of the key features of the system, as stated before, is the utilization of the most available and low-cost parameters in the clinical practice set. So, the model building process is divided into two stages: the described parameters are applied to the models, and then, to make the decision more stable, the expert appends scientific parameters (hormones, mRNA, and other tests). The data processing part included several parts. To mitigate the influence of outliers on the model, parameter values that fall outside the interval bounded by Q3 + 1.5 IQR (third quartile) from above and by Q1-1.5 IQR from below were replaced with the mean value of the parameter. For experiments with classifiers, apart from gradient boosting, missing values were filled with the mean for continuous variables and the mode for discrete variables. The next step involves feature engineering, which includes various methods. We identified the predictors that have the most significant impact on the target variable according to statistical tests.