Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Mar 25;106(4):e1647-e1659.
doi: 10.1210/clinem/dgaa953.

A Panel of 6 Biomarkers Significantly Improves the Prediction of Type 2 Diabetes in the MONICA/KORA Study Population

Barbara Thorand  1   2 , Astrid Zierer  1 , Mustafa Büyüközkan  3   4 , Jan Krumsiek  3   4 , Alina Bauer  1 , Florian Schederecker  1 , Julie Sudduth-Klinger  5 , Christa Meisinger  2   6   7 , Harald Grallert  1   2 , Wolfgang Rathmann  2   8 , Michael Roden  2   9   10 , Annette Peters  1   2   11 , Wolfgang Koenig  11   12   13 , Christian Herder  2   9   10 , Cornelia Huth  1   2
Affiliations

A Panel of 6 Biomarkers Significantly Improves the Prediction of Type 2 Diabetes in the MONICA/KORA Study Population

Barbara Thorand et al. J Clin Endocrinol Metab. .

Abstract

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 A1c (HbA1c).

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 (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-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.

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 HbA1c.

Keywords: biomarkers; cohort analysis; risk prediction model; type 2 diabetes.

PubMed Disclaimer

Figures

Figure 1.
Figure 1.
Black squares: Monitoring of Trends and Determinants in Cardiovascular Diseases/Cooperative Health Research in the Region of Augsburg (MONICA/KORA) case-cohort study. White squares: KORA S4 cohort study. Hazard ratios (HRs) are adjusted for the adapted German Diabetes Risk Score variables (age, sex, body mass index, survey, hypertension, physical activity, smoking status, coffee consumption, red meat consumption, whole-grain bread/muesli consumption, parental history of diabetes). The biomarkers are sorted by size of HR estimates in the MONICA/KORA case-cohort study.
Figure 2.
Figure 2.
The adapted German Diabetes Risk Score (GDRSadapted) model includes the covariates age, sex, body mass index, survey, hypertension, physical activity, smoking status, coffee consumption, red meat consumption, whole-grain bread/muesli consumption, and parental history of diabetes.
See this image and copyright information in PMC

References

    1. Gillies CL, Abrams KR, Lambert PC, et al. . Pharmacological and lifestyle interventions to prevent or delay type 2 diabetes in people with impaired glucose tolerance: systematic review and meta-analysis. BMJ. 2007;334(7588):299. - PMC - PubMed
    1. Abbasi A, Peelen LM, Corpeleijn E, et al. . Prediction models for risk of developing type 2 diabetes: systematic literature search and independent external validation study. BMJ. 2012;345:e5900. - PMC - PubMed
    1. Paprott R, Mühlenbruch K, Mensink GB, et al. . Validation of the German diabetes risk score among the general adult population: findings from the German Health Interview and Examination Surveys. BMJ Open Diabetes Res Care. 2016;4(1):e000280. - PMC - PubMed
    1. Petersmann A, Müller-Wieland D, Müller UA, et al. . Definition, classification and diagnosis of diabetes mellitus. Exp Clin Endocrinol Diabetes. 2019;127(S 01):S1-S7. - PubMed
    1. Buijsse B, Simmons RK, Griffin SJ, Schulze MB. Risk assessment tools for identifying individuals at risk of developing type 2 diabetes. Epidemiol Rev. 2011;33:46-62. - PMC - PubMed

Publication types