Multiplex proteomics for prediction of major cardiovascular events in type 2 diabetes
Diabetologia, 2018
Nowak, C., Carlsson, A. C., Östgren, C. J., Nyström, F. H., Alam, M., Feldreich, T., Sundström, J., Carrero, J.-J., Leppert, J., Hedberg, P., Henriksen, E., Cordeiro, A. C., Giedraitis, V., Lind, L., Ingelsson, E., Fall, T., & Ärnlöv, J.
| Disease area | Application area | Sample type | Products |
|---|---|---|---|
Metabolic Diseases | Patient Stratification | Plasma |  Olink Target 96 |
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 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. 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.