期刊
JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM
卷 106, 期 4, 页码 E1647-E1659出版社
ENDOCRINE SOC
DOI: 10.1210/clinem/dgaa953
关键词
type 2 diabetes; risk prediction model; biomarkers; cohort analysis
资金
- Else Kroner-Fresenius-Stiftung [2015_ A130]
- German Research Foundation [TH-784/2-1, TH-784/2-2]
- 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
- Singulex
The addition of 6 biomarkers significantly improved the prediction of type 2 diabetes when added to a noninvasive clinical model or to a clinical model plus HbA(1c). This improvement was observed in both the discovery and validation studies. Additionally, these biomarkers showed significant enhancements on top of the existing GDRS(adapted) model plus HbA(1c) in both studies.
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: This work aimed to assess whether novel biomarkers improve the prediction of type 2 diabetes beyond noninvasive standard clinical risk factors alone or in combination with glycated hemoglobin A(1c) (HbA(1c)). Methods: We used a population-based case-cohort study for discovery (689 incident cases and 1850 noncases) and an independent cohort study (262 incident cases, 2549 noncases) 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 noninvasive German Diabetes Risk Score (GDRS(adapted)) 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 GDRS(adapted) model (plus HbA(1c)). Results: Interleukin-1 receptor antagonist, insulin-like growth factor binding protein 2, soluble E-selectin, decorin, adiponectin, and high-density lipoprotein cholesterol were selected as the most relevant biomarkers. The simultaneous addition of these 6 biomarkers significantly improved the predictive performance both in 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 GDRS(adapted) model plus HbA(1c) in both studies. Conclusion: The addition of 6 biomarkers significantly improved the prediction of type 2 diabetes when added to a noninvasive clinical model or to a clinical model plus HbA(1c).
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