# Global Lipidomics Analysis Techniques for Novel Biomarker Discovery of Environmental Enteropathy

> **NIH NIH F31** · CINCINNATI CHILDRENS HOSP MED CTR · 2022 · $46,752

## Abstract

Project Summary/Abstract
As a condition of the small intestine, environmental enteropathy causes to chronic inflammation and
malabsorption of nutrients leading it to present itself phenotypically similar to malnutrition. Separation and
identification of patients with environmental enteropathy from malnutrition can only be done via an invasive upper
gastrointestinal endoscopy. In an effort to better characterize and understand environmental enteropathy, global
lipidomics profiling was performed on plasma, urine, and duodenal aspirate samples of a large cohort clinical
study of 415 Pakistani children. Larger sample sizes introduces technical challenges, such as the need to run
samples in batches, which in turn presents computational challenges of analysis. This proposal is focused on
the development of data-dependent methodologies for untargeted lipidomics analysis to identify robust lipid
profiles unique to each environmental enteropathy and malnutrition.
The first aim of this proposed research is to design a multi-batch analysis pipeline to efficiently and accurately
aggregate data across the various batches. This pipeline will address 3 computational challenges of multi-
batched data: 1) chromatographic retention time alignment, 2) missing data imputation, and 3) batch effect
correction. Each of these challenges have been analyzed individually, but in data analysis each step is
dependent on and influenced by the prior one. Development of an integrated, data-dependent pipeline specific
to mass spectrometry data will allow for reproducible results. The pipeline will be evaluated for accuracy via
testing on various sample matrices and by comparison to existing algorithms.
The proposed second aim is the development of a pathway-based data-dependent tool for putative lipid
identification. A bottleneck of untargeted analysis is the rapid identification of compounds. Due to the volume of
data, the current approach of only identifying those features which are statistically significant creates gaps in
downstream work such as pathway analysis. Introducing pathway knowledge earlier in the workflow will yield in
more meaningful results. This approach uses an initial input of lipids unique to the study and builds a networks
of additional connected lipids. These new lipids are stored in a database and a search is performed for them in
the user’s data. Identification of lipids with this methods will lead to a more complete network profile of results.
This project will identify distinctive lipidome profiles of environmental enteropathy patients and separate them
from a larger malnutrition disease control cohort. This initial step will lay the foundation for future validation
studies and ultimately the utilization of non-invasive diagnostics markers of environmental enteropathy, leading
to improved health of these children. As large-scale studies steadily become more common and to answer the
resulting computational challenges, this project will produce data-depe...

## Key facts

- **NIH application ID:** 10537670
- **Project number:** 1F31DK131885-01A1
- **Recipient organization:** CINCINNATI CHILDRENS HOSP MED CTR
- **Principal Investigator:** Khyati Mehta
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $46,752
- **Award type:** 1
- **Project period:** 2022-07-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10537670

## Citation

> US National Institutes of Health, RePORTER application 10537670, Global Lipidomics Analysis Techniques for Novel Biomarker Discovery of Environmental Enteropathy (1F31DK131885-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10537670. Licensed CC0.

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