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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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Open Access Hybrid
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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
Keywords Biomarkers ; Cohort Analysis ; Risk Prediction Model ; Type 2; Risk Prediction; Confidence-interval; Glucose-tolerance; Troponin-i; Validation; Mellitus; Model; Classification; Inflammation; Definition
Language english
Publication Year 2021
Prepublished in Year 2020
HGF-reported in Year 2020
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.
Reviewing status Peer reviewed
Institute(s) Institute of Epidemiology (EPI)
Institute of Computational Biology (ICB)
Independent Research Group Clinical Epidemiology (KEPI)
POF-Topic(s) 30202 - Environmental Health
30205 - Bioengineering and Digital Health
Research field(s) Genetics and Epidemiology
Enabling and Novel Technologies
PSP Element(s) G-504000-006
G-504000-002
G-504090-001
G-554100-001
G-504000-010
G-502900-001
G-504091-002
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
DOI 10.1210/clinem/dgaa953
WOS ID WOS:000648919200014
Scopus ID 85103606619
PubMed ID 33382400
Erfassungsdatum 2021-01-12
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