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.

Relationship Between Erectile Dysfunction, Diabetes and Dyslipidemia in Hypertensive-Treated Men.

PURPOSE: PRESIDEN study is a large study to analyze the erectile dysfunction (ED) incidence in Spanish population. The present study is a pilot sub-analysis from PRESIDEN to determine if ED or plasma testosterone (TST) level in controlled hypertensive patients may be associated with comorbidities and/or plasma nitrite+nitrate and antioxidant capacity. MATERIALS AND METHODS: Forty-four hypertensive individuals were aleatory selected from PRESIDEN study, matching by age (28 showing ED and 16 without ED). RESULT: Diabetes was present in 28.57% of ED patients and in 18.75% of patients without ED. In patients with and without ED, increasing age showed tendency of higher frequency of an additional comorbidity (diabetes or dyslipemia) (P = .09). Apparently, plasma TST levels were lower in older ED patients compared to younger patients with and without ED, although it did not reach statistical significance (P = .69). Older ED patients also showed lower TST levels than older patients without ED, although it was not statistical significant (16.15 ± 2.84 vs 13.91± 2.77; P = .69). Dyslipidemia was showed by 52.17% with lower TST (? nmol/L) while 23.80% of patients with plasma TST levels > 15 nmol/L had dyslipidemia. The percentage of ED patients was similar between patients with low and high TST levels. CONCLUSION: More ED hypertensive patients seem to show two comorbidities (diabetes and dyslipidemia) than hypertensivepatients without ED. Younger patients with ED tended to show more commonly diabetes than older ED patients. Plasma TST levels were not associated with more prevalence of ED but lower plasma TST levels showed tendency to higher prevalence of dyslipidemia.