Protein biomarkers for the prediction of cardiovascular disease in type 2 diabetes
Open accessAbstract
Aims/hypothesis We selected the most informative protein biomarkers for the prediction of incident cardiovascular disease (CVD) in people with type 2 diabetes. Methods In this nested case-control study we measured 42 candidate CVD biomarkers in 1,123 incident CVD cases and 1,187 controls with type 2 diabetes selected from five European centres. Combinations of biomarkers were selected using cross-validated logistic regression models. Model prediction was assessed using the area under the receiver operating characteristic curve (AUROC). Results Sixteen biomarkers showed univariate associations with incident CVD. The most predictive subset selected by forward selection methods contained six biomarkers: N-terminal pro-B-type natriuretic peptide (OR 1.69 per 1 SD, 95% CI 1.47, 1.95), high-sensitivity troponin T (OR 1.29, 95% CI 1.11, 1.51), IL-6 (OR 1.13, 95% CI 1.02, 1.25), IL-15 (OR 1.15, 95% CI 1.01, 1.31), apolipoprotein C-III (OR 0.79, 95% CI 0.70, 0.88) and soluble receptor for AGE (OR 0.84, 95% CI 0.76, 0.94). The prediction of CVD beyond clinical covariates improved from an AUROC of 0.66 to 0.72 (AUROC for Framingham Risk Score covariates 0.59). In addition to the biomarkers, the most important clinical covariates for improving prediction beyond the Framingham covariates were estimated GFR, insulin therapy and HbA1c. Conclusions/interpretation We identified six protein biomarkers that in combination with clinical covariates improved the prediction of our model beyond the Framingham Score covariates. Biomarkers can contribute to improved prediction of CVD in diabetes but clinical data including measures of renal function and diabetes-specific factors not included in the Framingham Risk Score are also needed.
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Looker et al. demonstrated that the inclusion of six circulating protein biomarkers increased the AUC from 0.66 to 0.72, with a net reclassification improvement (NRI) of 8.1%.
“Looker et al. [105] demonstrated that the inclusion of six circulating protein biomarkers increased the AUC from 0.66 to 0.72, with a net reclassification improvement (NRI) of 8.1%.”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [105]
Looker et al. demonstrated that combining protein biomarkers with clinical covariates improved cardiovascular risk stratification in individuals with type 2 diabetes.
“For example, Shen et al. [104] showed that integrating metabolomic information with clinical variables substantially enhanced predictive performance of CVD, while Looker et al. [105] demonstrated that combining protein biomarkers with clinical covariates improved cardiovascular risk stratification in individuals with T2D.”
From Artificial intelligence-powered prediction of diabetic complications: from clinical data to molecular omics (2026) · cites this paper as [105]
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References 33 · Cited by 66
Research works printed but not linked: 4
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- Authors
- on behalf of the SUMMIT Investigators, Looker, H. C., Colombo, M., Agakov, F., Zeller, T., Groop, L., Thorand, B., Palmer, C. N., Hamsten, A., de Faire, U., Nogoceke, E., Livingstone, S. J., Salomaa, V., Leander, K., Barbarini, N., Bellazzi, R., van Zuydam, N., McKeigue, P. M., & Colhoun, H. M.
- Affiliations
- University of Dundee (GB)
- University of Edinburgh (GB)
- Universität Hamburg (DE)
- Lund University (SE)
- Karolinska Institutet (SE)
- F. Hoffmann-La Roche AG (CH)
- University of Pavia (IT)
- NHS Fife (GB)
- Corresponding author
- Looker, Helen C.
- ORCID
- Venue
- Diabetologia
- Published
- 2015-03-05
- Volume
- 58
- Issue
- 6
- Pages
- 1363-1371
- ISSN
- 0012-186X (ISSN-L)
- 1432-0428
- OpenAlex
- W2063795068
- Cited by
- 66
- References
- 33
- Citations by year
- 2026: 2 · 2025: 2 · 2024: 3 · 2023: 7 · 2022: 4 · 2021: 8 · 2020: 6 · 2019: 6 · 2018: 10 · 2017: 7 · 2016: 7 · 2015: 4
- FWCI
- 8.10
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Diabetologia (publishedVersion)
- PubMed (publishedVersion)
- Discovery Research Portal (University of Dundee) (submittedVersion)
- Lund University Publications (Lund University) (submittedVersion)
- Site cant be reached (submittedVersion)
- PuSH - Publication Server of Helmholtz Zentrum München (submittedVersion)
- STM:n Hallinnonalan avoin julkaisuarkisto (Julkari) (submittedVersion)
- Funders
- European Federation of Pharmaceutical Industries and Associations (FP7/20072013)
- European Commission (115006)
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Health Sciences › Medicine › Epidemiology › Adipokines, Inflammation, and Metabolic Diseases
- Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
- Keywords
- cardiovascular disease, type 2 diabetes, protein biomarkers, cardiovascular risk prediction, N-terminal pro-brain natriuretic peptide, high-sensitivity troponin T, interleukin-6, IL-15, apolipoprotein C-III, Framingham risk score, renal function, insulin therapy, HbA1c
- MeSH terms
- Aged, Cardiovascular Diseases, Diabetes Mellitus, Type 2, Europe, Female, Glomerular Filtration Rate, Glycated Hemoglobin, Humans, Insulin, Male, Middle Aged, Peptide Fragments, Risk Factors, ROC Curve, Biomarkers, Interleukin-6, Logistic Models, Case-Control Studies, Interleukin-15, Area Under Curve, Natriuretic Peptide, Brain, Troponin T, Diabetes Complications, Apolipoprotein C-III
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