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 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.

Fig. 1

Study flowchart showing (a) cohorts and (b) further details of the analysis. The combined analysis was adjusted for: sex, current smoking, duration of type 2 diabetes (T2D), BMI, systolic BP, HbA_1c, LDL-cholesterol, microalbuminuria, statin use, previous cardiovascular disease (CVD), atrial fibrillation and eGFR. NDR predictors were: age of onset of T2D, T2D duration, total cholesterol/HDL-cholesterol, HbA_1c, systolic BP, BMI, sex, current smoking, microalbuminuria, macroalbuminuria, atrial fibrillation and previous CVD. PH, proportional hazards

Fig. 2

Associations between replicated biomarkers and risk of MACE. Cox regression results in the total sample (n = 1211) are given as HR per SD increase in baseline protein levels (error bars denote 95% CIs), and plotted on a log scale. Adjustment for age and sex (black symbols and numbers) is compared with additional adjustment for atrial fibrillation, BMI, HbA_1c, LDL-cholesterol, microalbuminuria, systolic blood pressure, smoking, statin use, duration of type 2 diabetes, history of cardiovascular disease and eGFR (grey symbols and numbers)