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

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