Title |
Multiplex proteomics for prediction of major cardiovascular events in type 2 diabetes
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Published in |
Diabetologia, May 2018
|
DOI | 10.1007/s00125-018-4641-z |
Pubmed ID | |
Authors |
Christoph Nowak, Axel C. Carlsson, Carl Johan Östgren, Fredrik H. Nyström, Moudud Alam, Tobias Feldreich, Johan Sundström, Juan-Jesus Carrero, Jerzy Leppert, Pär Hedberg, Egil Henriksen, Antonio C. Cordeiro, Vilmantas Giedraitis, Lars Lind, Erik Ingelsson, Tove Fall, Johan Ärnlöv |
Abstract |
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. 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. 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. 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. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Chile | 1 | 10% |
United Kingdom | 1 | 10% |
Oman | 1 | 10% |
Germany | 1 | 10% |
Mexico | 1 | 10% |
Canada | 1 | 10% |
Unknown | 4 | 40% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 5 | 50% |
Scientists | 3 | 30% |
Practitioners (doctors, other healthcare professionals) | 1 | 10% |
Science communicators (journalists, bloggers, editors) | 1 | 10% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 102 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 18 | 18% |
Student > Master | 14 | 14% |
Researcher | 10 | 10% |
Student > Bachelor | 10 | 10% |
Student > Doctoral Student | 7 | 7% |
Other | 19 | 19% |
Unknown | 24 | 24% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 36 | 35% |
Biochemistry, Genetics and Molecular Biology | 9 | 9% |
Nursing and Health Professions | 6 | 6% |
Agricultural and Biological Sciences | 5 | 5% |
Computer Science | 5 | 5% |
Other | 9 | 9% |
Unknown | 32 | 31% |