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It is well-known that obesity has a marked effect on many of the routinely used laboratory parameters. An obvious example is the serum level of various blood lipids: hyperlipidemia, hypercholesteremia are often observed in obese people. Our research aimed to provide a more thorough understanding of the effects of obesity on laboratory parameters, concentrating on every laboratory parameter, not just those that are already established as being related to obesity. We focused on adolescent population, as they are the most important from the public health point of view. To that end, we performed a cross-sectional clinical study that included the observation of n=148 male children (aged 12–16 year), consisting of healthy volunteers from four Hungarian secondary schools and obese patients treated with E66.9 “Obesity, unspecified” diagnosis (ICD-10). Observation included the recording of 27 laboratory parameter from a fasting blood sample. To explore this database, we performed a Principal Components Analysis (PCA) and Factor Analysis (FA) to ease the understanding of correlations of the laboratory parameters by identifying those groups of variables that have stochastic connection. Such connections between laboratory parameters were further analyzed by agglomerative hierarchical clustering of the variables. These all confirmed that stochastically connected laboratory parameters - with different physiological interpretation - can be in fact identified.

Obesity is a rapidly spreading endemic in almost every country of the developed world, of which Hungary is no exception. By a joint research project we aim to deepen our understanding of obesity-associated, and especially obesity-predicting changes of clinical markers (anthropometric indices, body composition, laboratory results etc.) in children, especially in teenage population. This paper presents the preliminary results of our investigations which pertain to obesity-related alterations in routine blood test parameters. For that end, we examined 340 healthy and obese children. Results show that there are differences between the routine laboratory parameters of obese and healthy subjects that are both statistically significant and medically interesting. We point out these differences in a statistically precise way, and show a method which can be efficiently used to classify children based on their laboratory parameters. This result can be used later to develop a more realistic model to predict the risk of obesity.