Protein biomarkers for the prediction of cardiovascular disease in type 2 diabetes

Looker, Helen C.; Colombo, Marco; Agakov, Felix; Zeller, Tanja; Groop, Leif; Thorand, Barbara; Palmer, Colin N.A.; Hamsten, Anders; Fairé, Ulf dé; Nogoceke, Everson; Livingstone, Shona; Salomaa, Veikko; Leander, Karin; Barbarini, Nicola; Bellazzi, Riccardo; Zuydam, Natalie Van; McKeigue, Paul; Colhoun, Helen M.

doi:10.1007/s00125-015-3535-6

Cited by 64 publications

(63 citation statements)

References 34 publications

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“…NT-proBNP was also found to add some benefit in the CARDS and GoDARTS cohorts when added to B2M and KIM-1. NT-proBNP is a biomarker for heart failure [31] and may also be a good biomarker for CVD outcomes [32, 33]. However, it is also cleared renally and levels rise as renal function declines [34].…”

Section: Discussionmentioning

confidence: 99%

Serum kidney injury molecule 1 and β2-microglobulin perform as well as larger biomarker panels for prediction of rapid decline in renal function in type 2 diabetes

Colombo

Looker²,

Farran

et al. 2018

Diabetologia

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Aims/hypothesisAs part of the Surrogate Markers for Micro- and Macrovascular Hard Endpoints for Innovative Diabetes Tools (SUMMIT) programme we previously reported that large panels of biomarkers derived from three analytical platforms maximised prediction of progression of renal decline in type 2 diabetes. Here, we hypothesised that smaller (n ≤ 5), platform-specific combinations of biomarkers selected from these larger panels might achieve similar prediction performance when tested in three additional type 2 diabetes cohorts.MethodsWe used 657 serum samples, held under differing storage conditions, from the Scania Diabetes Registry (SDR) and Genetics of Diabetes Audit and Research Tayside (GoDARTS), and a further 183 nested case–control sample set from the Collaborative Atorvastatin in Diabetes Study (CARDS). We analysed 42 biomarkers measured on the SDR and GoDARTS samples by a variety of methods including standard ELISA, multiplexed ELISA (Luminex) and mass spectrometry. The subset of 21 Luminex biomarkers was also measured on the CARDS samples. We used the event definition of loss of >20% of baseline eGFR during follow-up from a baseline eGFR of 30–75 ml min−1 [1.73 m]−2. A total of 403 individuals experienced an event during a median follow-up of 7 years. We used discrete-time logistic regression models with tenfold cross-validation to assess association of biomarker panels with loss of kidney function.ResultsTwelve biomarkers showed significant association with eGFR decline adjusted for covariates in one or more of the sample sets when evaluated singly. Kidney injury molecule 1 (KIM-1) and β2-microglobulin (B2M) showed the most consistent effects, with standardised odds ratios for progression of at least 1.4 (p < 0.0003) in all cohorts. A combination of B2M and KIM-1 added to clinical covariates, including baseline eGFR and albuminuria, modestly improved prediction, increasing the area under the curve in the SDR, Go-DARTS and CARDS by 0.079, 0.073 and 0.239, respectively. Neither the inclusion of additional Luminex biomarkers on top of B2M and KIM-1 nor a sparse mass spectrometry panel, nor the larger multiplatform panels previously identified, consistently improved prediction further across all validation sets.Conclusions/interpretationSerum KIM-1 and B2M independently improve prediction of renal decline from an eGFR of 30–75 ml min−1 [1.73 m]−2 in type 2 diabetes beyond clinical factors and prior eGFR and are robust to varying sample storage conditions. Larger panels of biomarkers did not improve prediction beyond these two biomarkers.Electronic supplementary materialThe online version of this article (10.1007/s00125-018-4741-9) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

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Section: Discussionmentioning

confidence: 99%

Serum kidney injury molecule 1 and β2-microglobulin perform as well as larger biomarker panels for prediction of rapid decline in renal function in type 2 diabetes

Colombo

Looker²,

Farran

et al. 2018

Diabetologia

Self Cite

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“…One such technique, the proximity extension assay, has been shown to be useful for biomarker discovery in cardiometabolic disease [16–18]. Multiprotein assays have been used to discover new risk markers for cardiovascular disease in type 2 diabetes [19], but the proximity extension method has not been tested to predict risk of MACE in type 2 diabetes.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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Aims/hypothesisMultiplex 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.MethodsWe 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.ResultsOf 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/interpretationWe 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.Electronic supplementary materialThe online version of this article (10.1007/s00125-018-4641-z) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

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“…In addition to established risk factors, several biomarkers associated with atherosclerosis, inflammation and congestive heart failure [6][7][8][9][10][11] have recently been proposed to improve risk stratification in type 2 diabetes mellitus patients. Promising data have been obtained using biomarkers associated with the closely interlinked processes of atherosclerosis and low-grade systemic inflammation [12].…”

Section: Introductionmentioning

confidence: 99%

ANGPTL2 is associated with an increased risk of cardiovascular events and death in diabetic patients

et al. 2016

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Aims/hypothesis A high serum angiopoietin-like 2 (ANGPTL2) concentration is an independent risk factor for developing diabetes and is associated with insulin resistance and atherosclerosis. In this work, we have examined the impact of serum ANGPTL2 on improving cardiovascular (CV) risk stratification in patients with type 2 diabetes.Methods A prospective, monocentric cohort of consecutive type 2 diabetes patients (the SURDIAGENE cohort; total of 1353 type 2 diabetes patients; 58% men, mean ± SD age 64 ± 11 years) was followed for a median of 6.0 years for death as primary endpoint and major adverse CV events (MACE; i.e. CV death, myocardial infarction or stroke) as a secondary endpoint. Patients with end-stage renal disease, defined as a requirement for dialysis or a history of kidney transplantation, Eric Thorin and Samy Hadjadj contributed equally to this study. Electronic supplementary material

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Protein biomarkers for the prediction of cardiovascular disease in type 2 diabetes

Cited by 64 publications

References 34 publications

Serum kidney injury molecule 1 and β2-microglobulin perform as well as larger biomarker panels for prediction of rapid decline in renal function in type 2 diabetes

Serum kidney injury molecule 1 and β2-microglobulin perform as well as larger biomarker panels for prediction of rapid decline in renal function in type 2 diabetes

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

ANGPTL2 is associated with an increased risk of cardiovascular events and death in diabetic patients

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