Clinical Proteomics: Spotting Cardiomyopathy Risk Early

Genetic cardiomyopathy can hide in plain sight. A carrier of the phospholamban (PLN) p.Arg14del variant, written R14del, may look healthy on an echocardiogram for years, then slide into end-stage heart failure with little warning. Clinical proteomics offers a way to read that hidden trajectory from a single tube of plasma. A study in Cardiovascular Research profiled 87 R14del carriers across the disease spectrum, pairing targeted plasma proteomics with metabolomics and lipidomics to sort patients into molecular subgroups that routine cardiac imaging does not separate.

What Clinical Proteomics Revealed in PLN Cardiomyopathy

Working in the DECIPHER-PLN cohort, the team ran Olink Explore proximity extension assays and quantified 2,611 plasma proteins per carrier. Unsupervised clustering split the carriers into five groups with distinct protein profiles. Three clusters showed higher N-terminal pro-B-type natriuretic peptide (NT-proBNP) and lower left ventricular ejection fraction, the usual marks of advancing failure; two looked clinically quiet. Targeted metabolomics added depth, with 96 of 148 metabolites separating the clusters and symmetric dimethylarginine, cis-aconitic acid, and succinate rising in the sickest groups. Building on van der Zwaag and colleagues (2012), who first linked this variant to inherited cardiomyopathy, the work moves past a single genetic label toward a portrait of who is progressing.

Key Findings

• Five subgroups from one plasma draw: clustering of 2,611 proteins resolved R14del carriers into five clusters, three tracking heart-failure severity and two appearing clinically silent.

• Silent did not mean safe. Cluster 1 carriers looked asymptomatic yet showed elevated apoptosis markers and energy-metabolism metabolites, a signature of ongoing cardiac damage.

• Metabolites mirrored the protein story: symmetric dimethylarginine, cis-aconitic acid, and succinate climbed in the groups with the worst cardiac function.

• Outcomes confirmed the call: across a median 916 days of follow-up, Cluster 1 carriers had the most heart-failure hospitalizations, device implantations, and deaths.

Figure 1. Unsupervised clustering of plasma proteomics separates phospholamban R14del carriers into five subgroups. Panel A outlines the workflow, from plasma collection across the disease spectrum to targeted proteomics, metabolomics, and lipidomics. Panel B shows the protein clustering and Panel C its principal component analysis; Panels D and E compare NT-proBNP and ejection fraction across clusters. Adapted from Deiman et al. (2026), Cardiovascular Research.

Reading Risk Before Symptoms Appear

The most useful result sits in Cluster 1. These carriers passed as asymptomatic, with preserved ejection fraction and unremarkable NT-proBNP, yet their plasma told a different story. Pathway enrichment of their proteome pointed to Rho GTPase signalling, cell cycle activity, and apoptosis, and energy-related metabolites tracked closely with those apoptosis markers. Over a median follow-up near 2.5 years, this group accrued the most adverse events. A molecular flag that precedes the echocardiogram is what early intervention in genetic cardiomyopathy has lacked.

Why Three Omics Layers Beat One

Proteins set the scaffold, but the metabolite and lipid layers gave the clusters their meaning. Symmetric dimethylarginine ties to endothelial and renal stress, while succinate and cis-aconitic acid point to a strained citric acid cycle, signals invisible from proteins alone. This multi-omics integration is what lets the authors argue Cluster 1 reflects active damage rather than benign variation, on a cohort single-layer profiling would have flattened.

From Single-Sample Integration to Decisions

Pulling proteins, metabolites, and lipids from one plasma aliquot is what lets a study like this hold together; split those layers across separate draws or extra freeze-thaw cycles and the cluster boundaries blur. Running all three from a single injection, the way the Omni-MS workflow at Dalton is built to do, keeps sample volume and batch effects from masquerading as biology. For a heterogeneous carrier population, that consistency often decides whether you see five clean clusters or noise.

Frequently Asked Questions

What is clinical proteomics?

Clinical proteomics is the large-scale measurement of proteins in patient samples, usually blood or tissue, to find molecular signatures of disease. It supports biomarker discovery and patient stratification, as it did here by grouping cardiomyopathy carriers by molecular state.

How did clinical proteomics identify high-risk cardiomyopathy carriers?

Clustering of 2,611 plasma proteins split 87 R14del carriers into five subgroups. One subgroup looked healthy on imaging but carried apoptosis-related protein and metabolite signals, and on follow-up it had the most heart-failure events, flagging risk that standard measures missed.

Is targeted proteomics or mass spectrometry better for plasma biomarkers?

It depends on the question. Affinity panels like the proximity extension assay scale to thousands of proteins from small volumes, while mass spectrometry gives sequence-level specificity and pairs naturally with LC-MS metabolomics. Many biomarker programs run both, then validate on the platform headed for the clinic.

Conclusion

What holds up: plasma molecular profiles capture risk structure that ejection fraction and NT-proBNP alone miss, even in carriers who look healthy. Causality is not settled, since this is a cross-sectional snapshot, and the authors rightly flag the need for longitudinal sampling before these signatures guide therapy. For trial designers, the practical pull is patient selection; enrolling Cluster 1 carriers before overt failure could sharpen any test of early treatment.

Citation

Deiman, F. E., Arslan, T., de Brouwer, R., Henry, I., Lofgren, L., Cavallin, A., Michopoulos, F., Nilsson, R., Henricsson, M., Ahlstrom, C., Davidsson, P., Bomer, N., Grote Beverborg, N., & van der Meer, P. (2026). Plasma proteomics stratification identifies phospholamban R14del carriers at risk for disease progression. Cardiovascular Research, 122(8), 1104-1118. https://doi.org/10.1093/cvr/cvag089

Note

This blog post summarizes findings from the above-cited research. Figures are adapted from the original publication. For full details, please refer to the source article.

By Seungjun Yeo, CEO at Dalton Bioanalytics. Specializing in multi-omics mass spectrometry for drug discovery and biomarker research.