A groundbreaking study hasidentified new potential drug targets for coronary heart disease (CHD) by combining genetic, metabolic, and tissue-level data. The research, led by an international team, leverages advanced Mendelian randomization techniques to link urinary metabolites, plasma proteins, and atherosclerotic plaque tissue, offering fresh insights into the biological pathways driving CHD.
A Multi-Omics Approach to CHD

Researchers analyzed over 900 urinary metabolic breakdown products and more than 1,500 plasma proteins, seeking connections to CHD risk. By integrating these diverse data sources, the team aimed to uncover actionable biomarkers and therapeutic targets that could pave the way for new treatments.

Amino Acid Metabolism at the Heart of CHD

The study identified 29 urinary metabolites associated with CHD, with the majority originating from amino acid metabolism. This finding underscores the importance of metabolic processes in the development and progression of coronary heart disease.

From Metabolites to Proteins

The team further investigated plasma proteins linked to these metabolites and to CHD risk. By examining protein and mRNA expression in atherosclerotic plaque tissue, they prioritized 16 proteins that were found to play a significant role in plaque vulnerability.

Pinpointing Drug Targets

By linking specific proteins to increased plaque vulnerability, the study provides a deeper understanding of the mechanisms that make certain plaques more dangerous. This insight is crucial for developing therapies aimed at stabilizing plaques and preventing life-threatening cardiac events. “By integrating genetic and tissue-based evidence, we’ve been able to highlight the central role of amino acid metabolism in coronary heart disease and identify a set of proteins with strong potential as future therapeutic targets. This approach brings us closer to more precise and effective treatments for patients at risk,” says dr. Floriaan Schmidt, associate professor.

This approach brings us closer to more precise
and effective treatments for patients at risk
Floriaan Schmidt
Associate professor
The results were published today in Lancet eBioMedicine. The computational methods underpinning this work have been made publicly available as a Python package for computational genomics drug target discovery, lead by co-author Chris Finan: https://gitlab.com/cfinan/merit.

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