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Multiplex proteomics for prediction of major cardiovascular events in type 2 diabetes
Karolinska Inst, Sweden.
Karolinska Inst, Sweden; Uppsala Univ, Sweden.
Linköping University, Department of Medical and Health Sciences, Division of Community Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Primary Care Center, Primary Health Care Center Ödeshög.
Linköping University, Department of Medical and Health Sciences, Division of Cardiovascular Medicine. Linköping University, Faculty of Medicine and Health Sciences. Region Östergötland, Heart and Medicine Center, Department of Endocrinology.ORCID iD: 0000-0002-1680-1000
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2018 (English)In: Diabetologia, ISSN 0012-186X, E-ISSN 1432-0428, Vol. 61, no 8, p. 1748-1757Article in journal (Refereed) Published
Abstract [en]

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 (amp;lt; 5% false discovery rate) between risk of MACE and eight proteins: matrix metalloproteinase (MMP)-12, IL-27 subunit alpha (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.

Place, publisher, year, edition, pages
SPRINGER , 2018. Vol. 61, no 8, p. 1748-1757
Keywords [en]
Biomarkers; Major adverse cardiovascular event; Proteomics; Risk; Type 2 diabetes
National Category
Endocrinology and Diabetes
Identifiers
URN: urn:nbn:se:liu:diva-149837DOI: 10.1007/s00125-018-4641-zISI: 000437432200006PubMedID: 29796748OAI: oai:DiVA.org:liu-149837DiVA, id: diva2:1236460
Note

Funding Agencies|European Union Horizon 2020 project [634869]; Swedish Research Council [2012-2215, 2015-03477]; Landstinget Dalarna (Falun, Sweden); Dalarna University (Falun, Sweden); Sparbanksstiftelsen Nya [552, 693, 932, 2297]; Region Vastmanland (Vasteras, Sweden); Swedish Medical Association; Swedish Heart-Lung Foundation [20150429]

Available from: 2018-08-02 Created: 2018-08-02 Last updated: 2019-05-01

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Östgren, Carl JohanNyström, Fredrik

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Division of Community MedicineFaculty of Medicine and Health SciencesPrimary Health Care Center ÖdeshögDivision of Cardiovascular MedicineDepartment of Endocrinology
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