Key result
High-throughput affinity proteomics advances CV risk prediction, diagnosis, patient stratification, and drug target discovery.
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Proteomics holds significant potential to transform precision cardiovascular medicine by improving risk prediction, diagnosis, and therapeutic target identification.
Despite recent advances in prevention and treatment, cardiovascular disease (CVD) remains a leading cause of premature death and disability globally, with a rising burden in many low- and middle-income countries. Several modifiable determinants of CVD are well-established (eg, smoking, hypertension, obesity, dyslipidaemia), but they do not fully explain temporal trends and large variations in disease rates between different populations. Moreover, the causal relevance of certain CVD risk factors and/or their associated biological mechanisms is still incompletely understood. High-throughput affinity-based proteomic assays now enable quantification of several thousand protein markers in the blood, and their application in large epidemiological and clinical studies will facilitate the development of precision cardiovascular medicine. This review describes recent findings from large population-based studies to illustrate the value of proteomics in cardiology for improved risk prediction, diagnosis and patient stratification; better understanding of disease aetiology and pathophysiology; and identification of repurposing and novel therapeutic targets. To overcome the current limitations, future studies should aim to further increase the sample size, number of proteins measured reliably (eg, via multiple assay platforms) and longitudinally, and ancestry population diversity, to expedite clinical translation of key research findings that will help to transform development of precision medicine in cardiology globally.
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Fry et al. (2026) conducted a review in Cardiovascular disease. Proteomics was evaluated. High-throughput affinity-based proteomic assays offer significant value in cardiology for improved risk prediction, diagnosis, patient stratification, and identification of novel therapeutic targets.
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