Insulin resistance syndrome revisited: application of self-organizing maps.

Most common chronic diseases have a multifaceted aetiological background. Because currently used statistical methods have severe limitations in describing complex non-linear processes, the authors evaluated the usefulness of a multivariate method which is able to describe non-linear phenomena, the self-organizing map (SOM). The study subjects were the 1650 participants of the Kuopio Ischemic Heart Disease Risk Factor Study (KIHD). The SOM model was constructed using 25 continuous biochemical and physiological variables. The aim of the SOM algorithm, together with Sammon's mapping, is to group the data into reduced but representative format and divide the study population into homogeneous subgroups. The study population consisted of four groups (clusters) according to the method used. In the clusters C1 to C4 were 637, 445, 275 and 121 men, respectively. There were eight neurons (n = 172) which were not included to the four main clusters. The mean values of the variables related to insulin resistance syndrome in the identified SOM map were 32.1 (kg/m(2)) for body mass index (BMI), 1.01 for waist-to-hip ratio (WHR), 158.7 mmHg and 103.8 mmHg for systolic (SBP) and diastolic blood pressure (DBP), 2.8 mmol/l for triglycerides, 6.2 mmol/l for blood glucose and 22.4 mU/l for serum insulin. There was a statistically significant difference in the mean values of BMI, WHR, SBP, DBP, HDL, triglycerides and blood glucose between the cluster representing the insulin resistance syndrome and the normal cluster. This study shows that the multidimensional structures of insulin resistance syndrome can be visualized and identified at qualitative and quantitative level using the SOM algorithm.