糖化血红素
糖耐量受损
2型糖尿病
队列
医学
糖尿病
置信区间
内科学
金标准(测试)
糖耐量试验
葡萄糖稳态
队列研究
内分泌学
生物信息学
胰岛素抵抗
生物
作者
Julia Carrasco-Zanini,Maik Pietzner,Joni V Lindbohm,Eleanor Wheeler,Erin Oerton,Nicola D. Kerrison,Missy Simpson,Matthew J. Westacott,D. Drolet,Mika Kivimäki,Rachel Ostroff,Stephen Williams,Nick Wareham,Claudia Langenberg
出处
期刊:Nature Medicine
[Springer Nature]
日期:2022-11-01
卷期号:28 (11): 2293-2300
被引量:16
标识
DOI:10.1038/s41591-022-02055-z
摘要
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. 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.
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