Decision tree learning to predict overweight/obesity based on body mass index and gene polymporphisms.

The new technologies for data analysis, such as decision tree learning, may help to predict the risk of developing diseases. The aim of the present work was to develop a pilot decision tree learning to predict overweight/obesity based on the combination of six single nucleotide polymorphisms (SNP) located in feeding-associated genes. Genotype study was performed in 151 healthy individuals, who were anonymized and randomly selected from the TALAVERA study. The decision tree analysis was performed using the R package rpart. The learning process was stopped when 15 or less observation was found in a node. The participant group consisted of 78 men and 73 women, who 100 individuals showed body mass index (BMI) ≥ 25 kg/m2 and 51 BMI < 25 kg/m2. Chi-square analysis revealed that individuals with BMI ≥ 25 kg/m2 showed higher frequency of the allelic variation Ala67Ala in AgRP rs5030980 with respect to those with BMI <25 kg/m2. However, the variant Thr67Ala in AgRP rs5030980 was the most frequently found in individuals with BMI <25 kg/m2. There were no statistical differences in the other analyzed SNPs. Decision tree learning revealed that carriers of the allelic variants AgRP (rs5030980) Ala67Ala, ADRB2 (rs1042714) Gln27Glu or Glu27Glu, INSIG2 (rs7566605) 73 + 9802 with CC or GG genotypes and PPARG (rs1801282) with the allelic variants of Ala12Ala or Pro12Pro, will most likely develop overweight/obesity (BMI ≥ 25 kg/m2). Moreover, the decision tree learning indicated that age and gender may change the developed three decision learning associated with overweight/obesity development. The present work should be considered as a pilot demonstrative study to reinforce the broad field of application of new data analysis technologies, such as decision tree learning, as useful tools for diseases prediction. This technology may achieve a potential applicability in the design of early strategies to prevent overweight/obesity.