Metabolic Dynamics and Prediction of Gestational Age and Time to Delivery in Pregnant Women

Metabolism during pregnancy is a dynamic and precisely programmed process, the failure of which can bring devastating consequences to the mother and fetus. To define a high-resolution temporal profile of metabolites during healthy pregnancy, we analyzed the untargeted metabolome of 784 weekly blood samples from 30 pregnant women. Using linear models, we built a metabolic clock with five metabolites that time gestational age in high accordance with ultrasound (R = 0.92). Furthermore, two to threemetabolites can identify when labor occurs (time to delivery within two, four, and eight weeks, AUROCR0.85). Our study represents a weekly characterization of the human pregnancy metabolome, providing ahigh-resolution landscape for understanding pregnancy with potential clinical utilities.


MetNormalizer is used to normalize large scale metabolomics data.


The deepPseudoMSI project is the first method that convert LC-MS raw data to “images” and then process them using deep learning method for diagnosis.


metID is a R packge which is used for metabolite identification based on in-house database and public database based on accurate mass, rentention time and/or MS2 spectra.


TidyMass project is a comprehensive computational framework that can process the whole workflow of data processing and analysis for LC-MS-based untargeted metabolomics using tidyverse principles.