| Title |
Multiplex proteomics for prediction of major cardiovascular events in type 2 diabetes
|
|---|---|
| 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
The data shown below were collected from the profiles of 10 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| 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
The data shown below were compiled from readership statistics for 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 February 2019.
All research outputs
#5,761,411
of 23,079,238 outputs
Outputs from Diabetologia
#2,460
of 5,094 outputs
Outputs of similar age
#100,231
of 330,368 outputs
Outputs of similar age from Diabetologia
#41
of 57 outputs
Altmetric has tracked 23,079,238 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 5,094 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 22.7. This one has gotten more attention than average, scoring higher than 51% of its peers.
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We're also able to compare this research output to 57 others from the same source and published within six weeks on either side of this one. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.