A network analysis of biomarkers for type 2 diabetes

Tianyi Huang, Kimberly Glass, Oana A. Zeleznik, Jae H. Kang, Kerry Ivey, Abhijeet R. Sonawane, Brenda M. Birmann, Craig P. Hersh, Frank B. Hu, Shelley S. Tworoger

Research output: Contribution to journalArticle

Abstract

Numerous studies have investigated individual biomarkers in relation to risk of type 2 diabetes. However, few have considered the interconnectivity of these biomarkers in the etiology of diabetes as well as the potential changes in the biomarker correlation network during diabetes development. We conducted a secondary analysis of 27 plasma biomarkers representing glucose metabolism, inflammation, adipokines, endothelial dysfunction, IGF axis, and iron store plus age and BMI at blood collection from an existing case-control study nested in the Nurses’ Health Study (NHS), including 1,303 incident diabetes case subjects and 1,627 healthy women. A correlation network was constructed based on pairwise Spearman correlations of the above factors that were statistically different between case and noncase subjects using permutation tests (P < 0.0005). We further evaluated the network structure separately among diabetes case subjects diagnosed <5, 5–10, and >10 years after blood collection versus noncase subjects. Although pairwise biomarker correlations tended to have similar directions comparing diabetes case subjects to noncase subjects, most correlations were stronger in noncase than in case subjects, with the largest differences observed for the insulin/HbA 1c and leptin/adiponectin correlations. Leptin and soluble leptin receptor were two hubs of the network, with large numbers of different correlations with other biomarkers in case versus noncase subjects. When examining the correlation network by timing of diabetes onset, there were more perturbations in the network for case subjects diagnosed >10 years versus <5 years after blood collection, with consistent differential correlations of insulin and HbA 1c . C-peptide was the most highly connected node in the early-stage network, whereas leptin was the hub for mid- or late-stage networks. Our results suggest that perturbations of the diabetes-related biomarker network may occur decades prior to clinical recognition. In addition to the persistent dysregulation between insulin and HbA 1c , our results highlight the central role of the leptin system in diabetes development.

LanguageEnglish
Pages281-290
Number of pages10
JournalDiabetes
Volume68
Issue number2
DOIs
Publication statusPublished - 1 Feb 2019

ASJC Scopus subject areas

  • Internal Medicine
  • Endocrinology, Diabetes and Metabolism

Cite this

Huang, T., Glass, K., Zeleznik, O. A., Kang, J. H., Ivey, K., Sonawane, A. R., ... Tworoger, S. S. (2019). A network analysis of biomarkers for type 2 diabetes. Diabetes, 68(2), 281-290. https://doi.org/10.2337/db18-0892
Huang, Tianyi ; Glass, Kimberly ; Zeleznik, Oana A. ; Kang, Jae H. ; Ivey, Kerry ; Sonawane, Abhijeet R. ; Birmann, Brenda M. ; Hersh, Craig P. ; Hu, Frank B. ; Tworoger, Shelley S. / A network analysis of biomarkers for type 2 diabetes. In: Diabetes. 2019 ; Vol. 68, No. 2. pp. 281-290.
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Huang, T, Glass, K, Zeleznik, OA, Kang, JH, Ivey, K, Sonawane, AR, Birmann, BM, Hersh, CP, Hu, FB & Tworoger, SS 2019, 'A network analysis of biomarkers for type 2 diabetes', Diabetes, vol. 68, no. 2, pp. 281-290. https://doi.org/10.2337/db18-0892

A network analysis of biomarkers for type 2 diabetes. / Huang, Tianyi; Glass, Kimberly; Zeleznik, Oana A.; Kang, Jae H.; Ivey, Kerry; Sonawane, Abhijeet R.; Birmann, Brenda M.; Hersh, Craig P.; Hu, Frank B.; Tworoger, Shelley S.

In: Diabetes, Vol. 68, No. 2, 01.02.2019, p. 281-290.

Research output: Contribution to journalArticle

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Huang T, Glass K, Zeleznik OA, Kang JH, Ivey K, Sonawane AR et al. A network analysis of biomarkers for type 2 diabetes. Diabetes. 2019 Feb 1;68(2):281-290. https://doi.org/10.2337/db18-0892