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

• Published in: Diabetologia Vol. 61; no. 8; pp. 1748 - 1757
• Main Authors: Nowak, Christoph, Carlsson, Axel C., Östgren, Carl Johan, Nyström, Fredrik H., Alam, Moudud, Feldreich, Tobias, Sundström, Johan, Carrero, Juan-Jesus, Leppert, Jerzy, Hedberg, Pär, Henriksen, Egil, Cordeiro, Antonio C., Giedraitis, Vilmantas, Lind, Lars, Ingelsson, Erik, Fall, Tove, Ärnlöv, Johan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2018; Springer Nature B.V
• Subjects: Adult; Aged; Apoptosis; Atherosclerosis - metabolism; Biomarkers; Biomarkers - metabolism; Cardiovascular diseases; Cardiovascular Diseases - complications; Cardiovascular Diseases - diagnosis; Diabetes; Diabetes mellitus; Diabetes mellitus (non-insulin dependent); Diabetes Mellitus, Type 2 - complications; Diabetes Mellitus, Type 2 - diagnosis; Epidemiology; Female; Fibroblast growth factors; Health and Welfare; Human Physiology; Humans; Hälsa och välfärd; Inflammation; Interleukin 27; Internal Medicine; Learning algorithms; Major adverse cardiovascular event; Male; Matrix metalloproteinase; Medicine; Medicine & Public Health; Metabolic Diseases; Metalloproteinase; Middle Aged; Proportional Hazards Models; Proteins; Proteomics; Proteomics - methods; Risk; Risk Factors; Sweden; TRAIL protein; Tumor necrosis factor; Tumor necrosis factor receptors; Type 2 diabetes; Sweden; Type 2 diabetes; Biomarkers; Risk; Major adverse cardiovascular event; Proteomics
• ISSN: 0012-186X; 1432-0428; 1432-0428
• DOI: 10.1007/s00125-018-4641-z

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 30–77-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 regularised 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 cardiovascular event.