The association between plasma proteomics and incident cardiovascular disease identifies MMP-12 as a promising cardiovascular risk marker in patients with chronic kidney disease

BACKGROUND AND AIMS: Previous proteomics efforts in patients with chronic kidney disease (CKD) have predominantly evaluated urinary protein levels. Therefore, our aim was to investigate the association between plasma levels of 80 cardiovascular disease-related proteins and the risk of major adverse cardiovascular events (MACE) in patients with CKD. METHODS: Individuals with CKD stages 3-5 (eGFR below 60 ml min-1 [1.73 m]-2) from three community-based cohorts (PIVUS, ULSAM, SAVA), one diabetes cohort (CARDIPP) and one cohort with peripheral artery disease patients (PADVA) with information on 80 plasma protein biomarkers, assessed with a proximity extension assay, and follow-up data on incident MACE, were used as discovery sample. To validate findings and to asses generalizability to patients with CKD in clinical practice, an outpatient CKD-cohort (Malnutrition, Inflammation and Vascular Calcification (MIVC)) was used as replication sample. RESULTS: In the discovery sample (total n = 1316), 249 individuals experienced MACE during 7.0 ± 2.9 years (range 0.005-12.9) of follow-up, and in the replication sample, 71 MACE events in 283 individuals over a mean ± SD change of 2.9 ± 1.2 years (range 0.1-4.0) were documented. Applying Bonferroni correction, 18 proteins were significantly associated with risk of MACE in the discovery cohort, adjusting for age and sex in order of significance, GDF-15, FGF-23, REN, FABP4, IL6, TNF-R1, AGRP, MMP-12, AM, KIM-1, TRAILR2, TNFR2, CTSL1, CSF1, PlGF, CA-125, CCL20 and PAR-1 (p < 0.000625 for all). Only matrix metalloproteinase 12 (MMP-12) was significantly associated with an increased risk of MACE in the replication sample (hazard ratio (HR) per SD increase, 1.36, 95% CI (1.07-1.75), p = 0.013). CONCLUSIONS: Our proteomics analyses identified plasma MMP-12 as a promising cardiovascular risk marker in patients with CKD.

Multiplex proteomics for prediction of major cardiovascular events in type 2 diabetes

ABSTRACT: Aims/hypothesis Multiplex proteomics could improve understanding and risk prediction of major adverse cardiovascular events (MACE) in type 2 diabetes. This study assessed 80 cardiovascular and inflammatory proteins for biomarker discovery and prediction of MACE in type 2 diabetes. Methods We combined data from six prospective epidemiological studies of 30­77-year-old individuals with type 2 diabetes in whom 80 circulating proteins were measured by proximity extension assay. Multivariable-adjusted Cox regression was used in a discovery/replication design to identify biomarkers for incident MACE. We used gradient-boosted machine learning and lasso regularized Cox regression in a random 75% training subsample to assess whether adding proteins to risk factors included in the Swedish National Diabetes Register risk model would improve the prediction of MACE in the separate 25% test subsample. Results Of 1211 adults with type 2 diabetes (32% women), 211 experienced a MACE over a mean (±SD) of 6.4 ± 2.3 years. We replicated associations (<5% false discovery rate) between risk of MACE and eight proteins: matrix metalloproteinase (MMP)- 12, IL-27 subunit α (IL-27a), kidney injury molecule (KIM)-1, fibroblast growth factor (FGF)-23, protein S100-A12, TNF receptor (TNFR)-1, TNFR-2 and TNF-related apoptosis-inducing ligand receptor (TRAIL-R)2. Addition of the 80-protein assay to established risk factors improved discrimination in the separate test sample from 0.686 (95% CI 0.682, 0.689) to 0.748 (95% CI 0.746, 0.751). A sparse model of 20 added proteins achieved a C statistic of 0.747 (95% CI 0.653, 0.842) in the test sample. Conclusions/interpretation We identified eight protein biomarkers, four of which are novel, for risk of MACE in community residents with type 2 diabetes, and found improved risk prediction by combining multiplex proteomics with an established risk model. Multiprotein arrays could be useful in identifying individuals with type 2 diabetes who are at highest risk of a cardio vascular event.