Predicting response to lisinopril in treating hypertension: a pilot study

Area of Science:

• Cardiovascular Medicine
• Pharmacogenomics
• Metabolomics

Background:

• Hypertension affects millions globally, with only about 50% responding to standard treatments.
• Identifying predictors of treatment response is crucial for personalized medicine in hypertension management.

Purpose of the Study:

• To identify clinical and metabolite markers that predict response to lisinopril, a common antihypertensive medication.
• To improve treatment efficacy by understanding individual patient variability in response.

Main Methods:

• A pilot study involving 45 hypertensive patients who received 10 mg of lisinopril.
• Blood pressure was measured at baseline and one week post-treatment.
• Responders were defined as those with a 10% decline in systolic blood pressure; plasma metabolites were analyzed using mass spectrometry.

Main Results:

• Body Mass Index (BMI) and Glomerular Filtration Rate (GFR) were significant clinical predictors of lisinopril response (p=0.009 and p=0.015, respectively).
• The metabolite 2-oxoglutarate also emerged as a potential predictor in the logistic regression model.
• These factors were integrated into a model to predict patient response to lisinopril.

Conclusions:

• Clinical factors (BMI, GFR) and the metabolite 2-oxoglutarate show promise in predicting lisinopril response in hypertensive patients.
• Further validation in larger cohorts is necessary to confirm the predictive utility of these markers.
• These findings could pave the way for more targeted hypertension treatment strategies.