Analysis of renin-angiotensin-aldosterone system gene polymorphisms in resistant hypertension

Essential hypertension is a disease multifactorially triggered by genetic and environmental factors. The contribution of genetic polymorphisms of the renin-angiotensin-aldosterone system and clinical risk factors to the development of resistant hypertension was evaluated in 90 hypertensive patients and in 115 normotensive controls living in Southwestern Brazil. Genotyping for insertion/deletion of angiotensin-converting enzyme, angiotensinogen M235T, angiotensin II type 1 receptor A1166C, aldosterone synthase C344T, and mineralocorticoid receptor A4582C polymorphisms was performed by PCR, with further restriction analysis when required. The influence of genetic polymorphisms on blood pressure variation was assessed by analysis of the odds ratio, while clinical risk factors were evaluated by logistic regression. Our analysis indicated that individuals who carry alleles 235-T, 1166-A, 344-T, or 4582-C had a significant risk of developing resistant hypertension (P < 0.05). Surprisingly, when we tested individuals who carried the presumed risk genotypes A1166C, C344T, and A4582C we found that these genotypes were not associated with resistant hypertension. However, a gradual increase in the risk to develop resistant hypertension was detected when the 235-MT and TT genotypes were combined with one, two or three of the supposedly more vulnerable genotypes - A1166C (AC/AA), C344T (TC/TT) and A4582C (AC/CC). Analysis of clinical parameters indicated that age, body mass index and gender contribute to blood pressure increase (P < 0.05). These results suggest that unfavorable genetic renin-angiotensin-aldosterone system patterns and clinical risk variables may contribute to increasing the risk for the development of resistant hypertension in a sample of the Brazilian population.

Personalized diagnosis by cached solutions with hypertension as a study model

Statistical modeling of links between genetic profiles with environmental and clinical data to aid in medical diagnosis is a challenge. Here, we present a computational approach for rapidly selecting important clinical data to assist in medical decisions based on personalized genetic profiles. What could take hours or days of computing is available on-the-fly, making this strategy feasible to implement as a routine without demanding great computing power. The key to rapidly obtaining an optimal/nearly optimal mathematical function that can evaluate the "disease stage" by combining information of genetic profiles with personal clinical data is done by querying a precomputed solution database. The database is previously generated by a new hybrid feature selection method that makes use of support vector machines, recursive feature elimination and random sub-space search. Here, to evaluate the method, data from polymorphisms in the renin-angiotensin-aldosterone system genes together with clinical data were obtained from patients with hypertension and control subjects. The disease "risk" was determined by classifying the patients' data with a support vector machine model based on the optimized feature; then measuring the Euclidean distance to the hyperplane decision function. Our results showed the association of renin-angiotensin-aldosterone system gene haplotypes with hypertension. The association of polymorphism patterns with different ethnic groups was also tracked by the feature selection process. A demonstration of this method is also available online on the project's web site.