The Use of Neural Networks in Evaluation of the Direction and Dynamics of Changes in Lipid Parameters in Kidney Transplant Patients on the Mediterranean Diet

Objective

The objective of the study was to assess whether neural networks can be a tool useful in the evaluation of the effect of the Mediterranean diet (MD) on the direction and dynamics of selected parameters.

Design

Randomized, prospective study.

Setting

Outpatient Clinic of the Department of Nephrology, Transplantology, and Internal Medicine.

Patients and Intervention

The study group consisted of 21 patients after kidney transplantation whose diet complied with the MD; the control group included 16 patients (also after transplantation) on a low-fat diet, isocaloric with the study diet.

Main Outcome Measures

Anthropometry, plasma lipids, chromatography of triacylglycerols and fatty acids, and activity of superoxide dismutase and catalase were measured in both groups. Statistical analysis was done with the SNN (Statistica Neural Networks) StatSoft software package.

Results

The advantage of neural networks is the possibility of the dynamic presentation of a process taking place in a biological system. In the MD group in the first months of use of the diet, the cholesterol level was reduced only in the group of young and middle-aged patients. This tendency was not observed among elderly patients, among whom a small reduction of the total cholesterol level was noted only at the end of the observation period. In control group at the beginning of the observation, the plasma total cholesterol level was proportional to the patient’s age. After 6 months, the total cholesterol increased in young patients and redacted in the group of elderly patients.

Conclusions

We concluded that the MD diet would be ideal for posttransplantation patients without serious pathologic dyslipidemia. In the case of patients with substantial dyslipidemia, appropriate pharmacologic treatment lowering proatherosclerotic lipid levels should be used in combination with the MD. Artificial neural networks (ANNs) were a useful tool in modeling biological parameters, showing dynamics of the studied interactions in a very detailed way. ANN is the most suitable method for investigations with many variables, interconnected nonlinearly; therefore, this method allows for a more general approach to biological problems. However, it should be noted that considerable data sets are required to obtain a satisfactory fit to the data. Moreover, to ensure the predictive power of this method for new cases, the representative database is indispensable. In spite of these demands, ANN is a prospective tool for reliable, quick assessments and predictions.