期刊
NATURE MEDICINE
卷 28, 期 11, 页码 2293-+出版社
NATURE PORTFOLIO
DOI: 10.1038/s41591-022-02055-z
关键词
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资金
- Medical Research Council [MR/N003284/1 MC-UU_12015/1, MC_UU_00006/1, MC_UU_12015/1, MC_PC_13046, R01HL36310, R01AG013196]
- University of Cambridge [MC_UU_12015/1, MC_PC_13046]
- Cancer Research UK [C864/A14136]
- UK Medical Research Council [221854/Z/20/Z, MR/K013351/1, G0902037, R024227]
- British Heart Foundation [MR/K013351/1, G0902037, RG/13/2/30098]
- US National Institutes of Health [R01HL36310, R01AG013196, RG/13/2/30098]
- Wellcome Trust [MC_UU_12015/1, 221854/Z/20/Z]
- NIA, NIH [R01AG056477]
- Academy of Finland [311492, 339568]
- Helsinki Institute of Life Science [H970]
- Paivikki and Sakari Sohlberg foundation
- Cambridge Trust
This study combines large-scale proteomics and machine learning to identify proteins that can be used to identify individuals with isolated impaired glucose tolerance. By adding only three proteins, the discrimination of impaired glucose tolerance improved significantly.
A new study combines large-scale proteomics and machine learning to identify proteins that can be used to identify individuals with isolated impaired glucose tolerance, who would otherwise only be detectable with oral glucose tolerance tests. 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.
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