Format

Send to

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

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

Author information

1
Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society (NVS), Karolinska Institutet, Alfred Nobels Allé 23, SE 14183, Huddinge, Sweden.
2
Department of Medical Sciences, Uppsala University, Uppsala, Sweden.
3
Department of Medical and Health Sciences, Linköping University, Linköping, Sweden.
4
School of Technology and Business Studies/Statistics, Dalarna University, Falun, Sweden.
5
School of Health and Social Studies, Dalarna University, Falun, Sweden.
6
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
7
Centre for Clinical Research, Uppsala University, Västerås, Sweden.
8
Department of Hypertension and Nephrology, Dante Pazzanese Institute of Cardiology, São Paulo, Brazil.
9
Department of Public Health and Caring Sciences, Geriatrics, Uppsala University, Uppsala, Sweden.
10
Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA.
11
Division of Family Medicine and Primary Care, Department of Neurobiology, Care Sciences and Society (NVS), Karolinska Institutet, Alfred Nobels Allé 23, SE 14183, Huddinge, Sweden. johan.arnlov@ki.se.
12
School of Health and Social Studies, Dalarna University, Falun, Sweden. johan.arnlov@ki.se.

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

Supplemental Content

Full text links

Icon for Springer Icon for PubMed Central
Loading ...
Support Center