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

Diabetologia. 2018 Aug;61(8):1748-1757. doi: 10.1007/s00125-018-4641-z. Epub 2018 May 24.


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.

Keywords: Biomarkers; Major adverse cardiovascular event; Proteomics; Risk; Type 2 diabetes.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Aged
  • Atherosclerosis / metabolism
  • Biomarkers / metabolism
  • Cardiovascular Diseases / complications*
  • Cardiovascular Diseases / diagnosis*
  • Diabetes Mellitus, Type 2 / complications*
  • Diabetes Mellitus, Type 2 / diagnosis*
  • Female
  • Fibroblast Growth Factor-23
  • Humans
  • Inflammation
  • Male
  • Middle Aged
  • Proportional Hazards Models
  • Proteomics / methods*
  • Risk Factors
  • Sweden


  • Biomarkers
  • FGF23 protein, human
  • Fibroblast Growth Factor-23