Proteomic signatures for identification of impaired glucose tolerance - Nature Medicine

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A new study combines large-scale proteomics and machine-learning to identify proteins that can be used to identify individuals with impaired glucose tolerance, who would otherwise only be detectable with oral glucose tolerance tests MRC_Epid Julia_Czs

). The improvement was of similar magnitude than that observed in the Fenland study, although with overall lower AUROCs , and AUROC= 0.69 ). Significant differences between the AUROCs were asses by the Delong method. This might be best explained by differences in the characteristics of the study population, the design and the lack of HbA1c to define iIGT .

Extended Data Fig. 6 Internal validation of proposed 3-stage screening strategy in the test set only. Comparison is shown for proteins that were selected in more 80% of bootstrap samples for either IGT or iIGT .

 

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Julia_Czs Read our recent blog post on this research, including comments by our PhD student - and first author - Julia Carrasco Zanini and senior author Professor Claudia Langenberg.

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