A proteomic surrogate for cardiovascular outcomes that is sensitive to multiple mechanisms of change in risk

Abstract

A reliable, individualized, and dynamic surrogate of cardiovascular risk, synoptic for key biologic mechanisms, could shorten the path for drug development, enhance drug cost-effectiveness and improve patient outcomes. We used highly multiplexed proteomics to address these objectives, measuring about 5000 proteins in each of 32,130 archived plasma samples from 22,849 participants in nine clinical studies. We used machine learning to derive a 27-protein model predicting 4-year likelihood of myocardial infarction, stroke, heart failure, or death. The 27 proteins encompassed 10 biologic systems, and 12 were associated with relevant causal genetic traits. We independently validated results in 11,609 participants. Compared to a clinical model, the ratio of observed events in quintile 5 to quintile 1 was 6.7 for proteins versus 2.9 for the clinical model, AUCs (95% CI) were 0.73 (0.72 to 0.74) versus 0.64 (0.62 to 0.65), c -statistics were 0.71 (0.69 to 0.72) versus 0.62 (0.60 to 0.63), and the net reclassification index was +0.43. Adding the clinical model to the proteins only improved discrimination metrics by 0.01 to 0.02. Event rates in four predefined protein risk categories were 5.6, 11.2, 20.0, and 43.4% within 4 years; median time to event was 1.71 years. Protein predictions were directionally concordant with changed outcomes. Adverse risks were predicted for aging, approaching an event, anthracycline chemotherapy, diabetes, smoking, rheumatoid arthritis, cancer history, cardiovascular disease, high systolic blood pressure, and lipids. Reduced risks were predicted for weight loss and exenatide. The 27-protein model has potential as a “universal” surrogate end point for cardiovascular risk.

Robust and High-Throughput Analytical Flow Proteomics Analysis of Cynomolgus Monkey and Human Matrices with Zeno SWATH Data Independent Acquisition

“…With omics technologies becoming more mature, they are being increasingly used in epidemiological studies generating high content quality datasets for disease risk prediction and prevention as well as disease mechanism investigation. Focusing on circulating blood markers, metabolomics and proteomics are the two most popular approaches and successfully generated predictive models for a wide range of disorders including metabolic, cardiovascular and neurological diseases [35][36][37][38]. Even though proteomics has been successful in capturing important aspects of human disease pathophysiology, adding information from complementary omics layers has improved sensitivity and specificity of predictive models in studies of complex diseases (i.e.…”

Human influenza virus infection elicits distinct patterns of monocyte and dendritic cell mobilization in blood and the nasopharynx

“…Briefly, the platform utilizes DNA-based binding reagents (slow off-rate modified aptamers) to quantify by fluorescence the availability of binding epitopes (which represents protein abundance, shape, and charge) for over 7000 protein targets with high specificity and limits of detection comparable to antibody-based assays. The assay captures both high- and low-abundance proteins over a dynamic range of detection of approximately 10 logs ( Williams et al, 2022 ). Normalization of SomaScan data was performed on plasma and NPA samples separately for all statistical analysis except PCA visualization.…”

From ‘Omics to Multi-omics Technologies: the Discovery of Novel Causal Mediators

“…Current proteomic technologies can be broadly characterised as mass spectrometry-based, antibody-based, or aptamer-based, but the broad dynamic range of proteins is often a hurdle for comprehensive and scalable characterisation of the proteome that is under active development [46]. Still, proteins have the advantage of being functionally closer to phenotypes and reflecting some environmental factors, such as smoking [47] or diet [48,49], making them well-suited for predicting disease risk [50][51][52], and yielding insights into mechanistic pathways [53,54]. In addition, proteins are translatable to a broad range of therapeutic modalities for drug discovery but such applications require the additional burden of establishing a causal role in disease.…”