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Novel biomarkers and emerging tools to identify causal molecular pathways in hypertension and associated
Ewelina Józefczuk1,2, Tomasz J Guzik3,4,5, Mateusz Siedlinski3,4
1Department of Internal and Agricultural Medicine, Jagiellonian University Medical College, Kraków, Poland. mateusz.siedlinski@uj.edu.pl.
Insights
Novel biomarkers causally linked to hypertension (HT) and cardiovascular diseases (CVDs) are needed. Mendelian randomization analysis of large-scale biobank data can identify new drug targets for HT and related CVDs.
Area of Science:
- Cardiovascular research
- Genetics
- Biomarker discovery
Background:
- Hypertension (HT) is a major modifiable risk factor for cardiovascular diseases (CVDs).
- Current HT treatments target largely unchanged molecular pathways, necessitating novel therapeutic strategies.
- Identifying causal biomarkers for HT is crucial for developing new pharmacological interventions.
Purpose of the Study:
- To explore novel biomarkers causally linked to hypertension (HT).
- To identify new pharmacologically targetable pathways for HT and associated cardiovascular diseases (CVDs).
- To leverage high-throughput data and genetic causal inference for therapeutic target discovery.
Main Methods:
- Utilized Mendelian randomization (MR) for genetic causal inference.
- Analyzed large-scale biobank data, including proteomics (OLINK, SomaScan) and metabolomics (NMR).
- Applied MR to nominate potentially causal biomarkers and assess genetic proxies for existing drug targets.
Main Results:
- MR analysis identified potential causal biomarkers for HT and CVDs, including glycine, branched-chain amino acids, lipoprotein(a), IGF-1, and fibronectin 1.
- Genetic proxies for drug targets (statins, PCSK9, ACE inhibitors) provided insights into potential side effects and personalized medicine.
- Genetic causal inference helped disentangle effects of correlated traits on cardiovascular outcomes.
Conclusions:
- High-throughput omics data combined with MR analysis can reveal novel druggable molecular targets for HT and CVDs.
- This approach supports the selection of drug targets for clinical testing and advances personalized medicine.
- Further research may lead to the development of innovative treatments for hypertension and cardiovascular conditions.
Abstract:
Hypertension (HT) is a modifiable risk factor for life-threatening cardiovascular diseases (CVDs) including coronary artery disease, heart failure, or stroke. Despite significant progress in understanding the pathophysiological mechanisms of the disease, the molecular pathways targeted by HT treatment remain largely unchanged. This warrants the need for finding novel biomarkers, which are causally related to persistent high blood pressure (BP) and may be pharmacologically targeted. Analytical output derived from large-scale biobanks, containing high-throughput genetic and biochemical data, such as OLINK and SomaScan-based proteomics or Nuclear Magnetic Resonance-based metabolomics, as well as novel analytical tools including the Mendelian randomization (MR) approach, enabling genetic causal inference, may create new treatment opportunities for HT and related CVDs. MR analysis may constitute additional evidence for observational studies and facilitate selection of drug targets for clinical testing and has been already used to nominate potentially causal biomarkers for HT and CVDs such as circulating glycine, branched-chain amino acids, lipoprotein(a), insulin-like growth factor 1, or fibronectin 1. Using the MR framework, genetic proxies for targets of already known drugs, such as statins, PCSK9, and ACE inhibitors, may additionally be informative about potential side effects and eventually contribute to more personalized medicine. Finally, genetic causal inference may disentangle independent direct effects of correlated traits such as lipid classes or markers of inflammation on cardiovascular clinical outcomes such as atherosclerosis and HT. While several novel HT-targeting drugs are currently under clinical investigation (e.g. brain renin-angiotensin-aldosterone system inhibitors or endothelin-1 receptor antagonists), analysis of high-throughput proteomic and metabolomic data from well-powered studies may deliver novel druggable molecular targets for HT and associated CVDs.
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