Machine-learning-based integrative –‘omics analyses reveal immunologic and metabolic dysregulation in environmental enteric dysfunction

Fatima Zulqarnain, Xueheng Zhao, Kenneth D.R. Setchell, Yash Sharma, Phillip Fernandes, Sanjana Srivastava, Aman Shrivastava, Lubaina Ehsan, Varun Jain, Shyam Raghavan, Christopher Moskaluk, Yael Haberman, Lee A. Denson, Khyati Mehta, Najeeha T. Iqbal, Najeeb Rahman, Kamran Sadiq, Zubair Ahmad, Romana Idress, Junaid IqbalSheraz Ahmed, Aneeta Hotwani, Fayyaz Umrani, Beatrice Amadi, Paul Kelly, Donald E. Brown, Sean R. Moore, Syed Asad Ali, Sana Syed*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


Environmental enteric dysfunction (EED) is a subclinical enteropathy challenging to diagnose due to an overlap of tissue features with other inflammatory enteropathies. EED subjects (n = 52) from Pakistan, controls (n = 25), and a validation EED cohort (n = 30) from Zambia were used to develop a machine-learning-based image analysis classification model. We extracted histologic feature representations from the Pakistan EED model and correlated them to transcriptomics and clinical biomarkers. In-silico metabolic network modeling was used to characterize alterations in metabolic flux between EED and controls and validated using untargeted lipidomics. Genes encoding beta-ureidopropionase, CYP4F3, and epoxide hydrolase 1 correlated to numerous tissue feature representations. Fatty acid and glycerophospholipid metabolism-related reactions showed altered flux. Increased phosphatidylcholine, lysophosphatidylcholine (LPC), and ether-linked LPCs, and decreased ester-linked LPCs were observed in the duodenal lipidome of Pakistan EED subjects, while plasma levels of glycine-conjugated bile acids were significantly increased. Together, these findings elucidate a multi-omic signature of EED.

Original languageEnglish
Article number110013
Issue number6
StatePublished - 21 Jun 2024
Externally publishedYes


  • Gastroenterology
  • Lipidomics
  • Machine learning
  • Medical imaging
  • Metabolic flux analysis
  • Transcriptomics


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