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.

2 型糖尿病 (T2D) 筛查和诊断建议的实施侧重于糖化血红蛋白 (HbA1c) 和空腹血糖的测量。这种方法使大量只有通过口服葡萄糖耐量试验 (OGTT) 才能检测到的孤立性葡萄糖耐量减低 (iIGT) 个体面临患糖尿病及其严重并发症的风险。我们将机器学习应用于来自 Fenland 研究的 11,546 名参与者的单个禁食样本的蛋白质组学概况，以测试使用黄金标准 OGTT 定义的 iIGT 的歧视。我们观察到通过将三种蛋白质（RTN4R、CBPM 和 GHR）添加到最佳临床模型（AUROC = 0.80（95% 置信区间：0.79–0.86），P = 0.004)，我们在外部队列中对此进行了验证。在一个独立队列中，这些候选蛋白的血浆水平升高与未来患 T2D 的风险增加相关，并且在遗传上易受葡萄糖稳态受损和 T2D 影响的个体中也会升高。对有限数量的蛋白质进行评估可以识别出可能被当前诊断策略遗漏的个体以及患 T2D 及其并发症的高风险个体。