Applicability of Precision Medicine Approaches to Managing Hypertension in Rural Populations

Abstract

As part of the Heart Healthy Lenoir Project, we developed a practice level intervention to improve blood pressure control. The goal of this study was: (i) to determine if single nucleotide polymorphisms (SNPs) that associate with blood pressure variation, identified in large studies, are applicable to blood pressure control in subjects from a rural population; (ii) to measure the association of these SNPs with subjects’ responsiveness to the hypertension intervention; and (iii) to identify other SNPs that may help understand patient-specific responses to an intervention. We used a combination of candidate SNPs and genome-wide analyses to test associations with either baseline systolic blood pressure (SBP) or change in systolic blood pressure one year after the intervention in two genetically defined ancestral groups: African Americans (AA) and Caucasian Americans (CAU). Of the 48 candidate SNPs, 13 SNPs associated with baseline SBP in our study; however, one candidate SNP, rs592582, also associated with a change in SBP after one year. Using our study data, we identified 4 and 15 additional loci that associated with a change in SBP in the AA and CAU groups, respectively. Our analysis of gene-age interactions identified genotypes associated with SBP improvement within different age groups of our populations. Moreover, our integrative analysis identified AQP4-AS1 and PADI2 as genes whose expression levels may contribute to the pleiotropy of complex traits involved in cardiovascular health and blood pressure regulation in response to an intervention targeting hypertension. In conclusion, the identification of SNPs associated with the success of a hypertension treatment intervention suggests that genetic factors in combination with age may contribute to an individual’s success in lowering SBP. If these findings prove to be applicable to other populations, the use of this genetic variation in making patient-specific interventions may help providers with making decisions to improve patient outcomes. Further investigation is required to determine the role of this genetic variance with respect to the management of hypertension such that more precise treatment recommendations may be made in the future as part of personalized medicine.

Targeted Metabolic Profiling and PRM Analysis of Proteins Revealed Impaired Polyunsaturated Fatty Acid Metabolism and GTP Metabolism in the Brainstem of Spontaneously Hypertensive Rats

“…17,22 Another androgen-dependent gene, the PADI2 gene, was associated with blood pressure regulation in hypertension patients. 10 The expression level of PADI2 was significantly reduced in the brainstem of SHR, similar to the reduced DECR1 protein. The correlation of PADI2, DECR1, dysregulated fatty acids, and energy metabolites is shown in Figure 4.…”

“…Among them, DECR1 was a critical rate-limiting enzyme in PUFA oxidation and RHOA, GNB1, and RHOB were GTPases. The PADI2 gene was reported to be mutated in hypertension patients . However, these differentially expressed proteins have not been validated, and the differential metabolites were scattered within various metabolic pathways, which makes it hard to explain the underlying mechanism of hypertension in the brainstem of SHR.…”

Long non-coding RNA-associated competing endogenous RNA axes in the olfactory epithelium in schizophrenia: a bioinformatics analysis

“…Earlier studies have found that structural variant within FLG‐AS1 may play an important role in attention-deficit hyperactivity disorder (ADHD) development 48 . The AQP4-AS1 gene transcribes an antisense lncRNA with an unknown function 49 . AQP4-AS1 was identified as a ceRNA in gastric cancer via bioinformatics analysis 50 .…”

Evaluating the Potential of Younger Cases and Older Controls Cohorts to Improve Discovery Power in Genome-Wide Association Studies of Late-Onset Diseases

“…Research is ongoing into mechanisms like methylation with age [77] and modulation with age in transcriptome and exon splicing [78] that may influence DNA configuration and gene expression, and as a result, influence the causal for LODs SNP effect sizes. An interesting approach to finding SNPs associated with hypertension treatment (reminiscent of this study design, except studying a trait quantitatively rather than an LOD diagnosis), where participants were grouped into 10-year cohorts between 20 and 80 years of age, allowed the discovery of mildly protective SNPs [79,80]. As was reviewed in the Introduction, there is a strong theoretical and experimental consensus regarding multiplicative gene–environment genetic interaction explaining the incidence of the diseases analyzed here.…”