A Panel of 6 Biomarkers Significantly Improves the Prediction of Type 2 Diabetes in the MONICA/KORA Study Population
| Autor: | Astrid Zierer, Christian Herder, Annette Peters, Florian Schederecker, Mustafa Buyukozkan, Jan Krumsiek, Christa Meisinger, Cornelia Huth, Barbara Thorand, Julie Sudduth-Klinger, Wolfgang Rathmann, Michael Roden, Wolfgang Koenig, Alina Bauer, Harald Grallert |
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| Rok vydání: | 2019 |
| Předmět: |
Oncology
Adult Male medicine.medical_specialty Diabetes risk Endocrinology Diabetes and Metabolism Clinical Biochemistry Population 030209 endocrinology & metabolism Type 2 diabetes Biochemistry Cohort Studies 03 medical and health sciences 0302 clinical medicine Endocrinology Internal medicine medicine Humans ddc:610 education Online Only Articles Clinical Research Articles 030304 developmental biology Aged 0303 health sciences education.field_of_study Adiponectin Proportional hazards model business.industry Biochemistry (medical) biomarkers Middle Aged medicine.disease cohort analysis Confidence interval ddc Diabetes Mellitus Type 2 Case-Control Studies Population study Female type 2 diabetes business AcademicSubjects/MED00250 risk prediction model Cohort study |
| Zdroj: | The Journal of Clinical Endocrinology and Metabolism |
| Popis: | ContextImproved strategies to identify persons at high risk of type 2 diabetes are important to target costly preventive efforts to those who will benefit most.ObjectiveThis 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 A1c (HbA1c).MethodsWe 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 (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).ResultsInterleukin-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 GDRSadapted model plus HbA1c in both studies.ConclusionThe 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 HbA1c. |
| Databáze: | OpenAIRE |
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