Relationship between a Weighted Multi-Gene Algorithm and Blood Pressure Control in Hypertension.
Pamela K PhelpsEli F KelleyDanielle M WallaJennifer K RossJerad J SimmonsEmma K BulockAudrie AyresMonica K AkreRyan SprisslerThomas P OlsonEric M SnyderPublished in: Journal of clinical medicine (2019)
Hypertension (HTN) is a complex disease with interactions among multiple organ systems, including the heart, vasculature, and kidney with a strong heritable component. Despite the multifactorial nature of HTN, no clinical guidelines utilize a multi-gene approach to guide blood pressure (BP) therapy. Non-smokers with a family history of HTN were included in the analysis (n = 384; age = 61.0 ± 0.9, 11% non-white). A total of 17 functional genotypes were weighted according to the previous effect size in the literature and entered into an algorithm. Pharmacotherapy was ranked from 1⁻4 as most to least likely to respond based on the algorithmic assessment of individual patient's genotypes. Three-years of data were assessed at six-month intervals for BP and medication history. There was no difference in BP at diagnosis between groups matching the top drug recommendation using the multi-gene weighted algorithm (n = 92) vs. those who did not match (n = 292). However, from diagnosis to nadir, patients who matched the primary recommendation had a significantly greater drop in BP when compared to patients who did not. Further, the difference between diagnosis to current 1-year average BP was lower in the group that matched the top recommendation. These data suggest an association between a weighted multi-gene algorithm on the BP response to pharmacotherapy.
Keyphrases
- blood pressure
- machine learning
- copy number
- deep learning
- genome wide
- magnetic resonance
- hypertensive patients
- contrast enhanced
- genome wide identification
- network analysis
- smoking cessation
- heart rate
- big data
- heart failure
- neural network
- emergency department
- electronic health record
- type diabetes
- dna methylation
- artificial intelligence
- blood glucose
- mesenchymal stem cells
- cell therapy
- data analysis
- replacement therapy
- drug induced
- arterial hypertension