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Proteomic signatures for identification of impaired glucose tolerance.

Accepted version
Peer-reviewed

Type

Article

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Authors

Carrasco-Zanini, Julia  ORCID logo  https://orcid.org/0000-0002-3988-7505
Oerton, Erin 

Abstract

The implementation of recommendations for type 2 diabetes (T2D) screening and diagnosis focuses on the measurement of glycated hemoglobin (HbA1c) and fasting glucose. This approach leaves a large number of individuals with isolated impaired glucose tolerance (iIGT), who are only detectable through oral glucose tolerance tests (OGTTs), at risk of diabetes and its severe complications. We applied machine learning to the proteomic profiles of a single fasted sample from 11,546 participants of the Fenland study to test discrimination of iIGT defined using the gold-standard OGTTs. We observed significantly improved discriminative performance by adding only three proteins (RTN4R, CBPM and GHR) to the best clinical model (AUROC = 0.80 (95% confidence interval: 0.79-0.86), P = 0.004), which we validated in an external cohort. Increased plasma levels of these candidate proteins were associated with an increased risk for future T2D in an independent cohort and were also increased in individuals genetically susceptible to impaired glucose homeostasis and T2D. Assessment of a limited number of proteins can identify individuals likely to be missed by current diagnostic strategies and at high risk of T2D and its complications.

Description

Keywords

Humans, Glucose Intolerance, Diabetes Mellitus, Type 2, Blood Glucose, Proteomics, Glucose Tolerance Test, Fasting

Journal Title

Nat Med

Conference Name

Journal ISSN

1078-8956
1546-170X

Volume Title

28

Publisher

Springer Science and Business Media LLC

Rights

All rights reserved
Sponsorship
Medical Research Council (MC_UU_12015/1)
MRC (MC_UU_00006/1)