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Thorand, B. ; Zierer, A. ; Büyüközkan, M. ; Krumsiek, J. ; Bauer, A. ; Schederecker, F. ; Sudduth-Klinger, J.* ; Meisinger, C. ; Grallert, H. ; Rathmann, W.* ; Roden, M. ; Peters, A. ; Koenig, W.* ; Herder, C. ; Huth, C.

A panel of six biomarkers significantly improves the prediction of type 2 diabetes in the MONICA/KORA study population.

J. Clin. Endocrinol. Metab. 106, e1647-e1659 (2021)
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CONTEXT: Improved strategies to identify persons at high risk of type 2 diabetes are important to target costly preventive efforts to those who will benefit most. OBJECTIVE: To assess whether novel biomarkers improve the prediction of type 2 diabetes beyond non-invasive standard clinical risk factors alone or in combination with HbA1c. DESIGN AND METHODS: We used a population-based case-cohort study for discovery (689 incident cases and 1,850 non-cases) and an independent cohort study (n=262 incident cases, 2,549 non-cases) for validation. An L1-penalized (lasso) Cox model was used to select the most predictive set among 47 serum biomarkers from multiple etiological pathways. All variables available from the non-invasive German Diabetes Risk Score (GDRSadapted) were forced into the models. The C-index and the category-free net reclassification index (cfNRI) were used to evaluate the predictive performance of the selected biomarkers beyond the GDRSadapted model (plus HbA1c). RESULTS: Interleukin-1 receptor antagonist, insulin growth factor binding protein-2, soluble E-selectin, decorin, adiponectin, and high density lipoprotein-cholesterol were selected as most relevant. The simultaneous addition of these six biomarkers significantly improved the predictive performance in both the discovery (C-index [95% CI]: 0.053 [0.039-0.066]; cfNRI [95% CI]: 67.4% [57.3%-79.5%]) and the validation study (0.034 [0.019-0.053]; 48.4% [35.6%-60.8%]). Significant improvements by these biomarkers were also seen on top of the GDRSadapted model plus HbA1c in both studies. CONCLUSION: The addition of six biomarkers significantly improved the prediction of type 2 diabetes when added to a non-invasive clinical model or to a clinical model plus HbA1c.
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Publication type Article: Journal article
Document type Scientific Article
Corresponding Author
Keywords Biomarkers ; Cohort Analysis ; Risk Prediction Model ; Type 2; Risk Prediction; Confidence-interval; Glucose-tolerance; Troponin-i; Validation; Mellitus; Model; Classification; Inflammation; Definition
ISSN (print) / ISBN 0021-972X
e-ISSN 1945-7197
Journal Journal of Clinical Endocrinology & Metabolism, The
Quellenangaben Volume: 106, Issue: 4, Pages: e1647-e1659 Article Number: , Supplement: ,
Publisher Endocrine Society
Publishing Place Bethesda, Md.
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Epidemiology (EPI)
Institute of Computational Biology (ICB)
Independent Research Group Clinical Epidemiology (KEPI)
Grants Singulex
German Research Foundation
German Federal Ministry of Education and Research (BMBF)
Helmholtz Alliance "Aging and Metabolic Programming, AMPro"
intramural funding for Translational & Clinical Projects of the Helmholtz Zentrum Munchen-German Research Center for Environmental Health, Germany - BMBF, Germany
State of Bavaria
Helmholtz Zentrum Munchen
Munich Center of Health Sciences (MC-Health), Ludwig-Maximilians-Universitat, as part of LMUinnovativ
Ministry of Science and Research of the State of North Rhine-Westphalia
German Federal Ministry of Health (BMG)
Tethys Bio-science Inc
Else Kroner-Fresenius-Stiftung