# Mayo Clinic Undiagnosed Disease Network Metabolomics Core

> **NIH NIH U01** · MAYO CLINIC ROCHESTER · 2022 · $311,640

## Abstract

As the Phase II Metabolomics Core for the Undiagnosed Disease Network (UDN), our main goal is to apply
state-of-the-art, untargeted and targeted/quantitative metabolomics approaches, bioinformatics, and expert
clinical interpretation to biological samples from patients enrolled in the UDN clinical pipeline. To date, our
activities have centered around an “N=1” approach, where a single proband’s samples are analyzed and
compared with appropriate controls or familial trio. While this approach generates biochemical insights at an
individual patient level, a drawback is inherent biological variability that is unaddressed by technical replicates.
Nevertheless, the abundance of banked Phase I samples in the UDN biorepository alongside extensive clinical
information, and metadata, provides a unique opportunity to move beyond an “N=1” status quo by interrogating
metabolomic profiles in carefully defined clusters of cases alongside a large number of specimens from a
reference population. The objective of this administrative supplement is to continue an initiative that began at
the start of Phase II with the goal of analyzing a large number of banked UDN biospecimens to perform
metabolomics analyses in cohorts logically grouped by phenotype, symptom, or related HPO terms and
compare these results against a reference cohort.
The main goals of this proposal will be:
 1. CDG testing of serum samples from the UDN biorepository and samples from clinical sites that have not
 yet been sent to biorepository.
 2. Broad metabolomic profiling in expanded cohorts of clustered phenotypes that did not undergo this type
 of metabolomics analysis previously.
 3. Analysis of a reference cohort of control samples that will allow us to generate appropriate reference
 ranges for specific research platforms where current reference ranges do not typically include children.
 4. Implement our bioinformatics approach by applying additional tools for informatics and pattern detection.
We believe that the data generated as a result of this supplement will lead to identification of CDG in undiagnosed
UDN individuals and new fundamental understanding of how metabolomics may help aid in rare disease
diagnosis, and also serve as a reference dataset for the future.

## Key facts

- **NIH application ID:** 10600558
- **Project number:** 3U01TR002471-04S1
- **Recipient organization:** MAYO CLINIC ROCHESTER
- **Principal Investigator:** IAN R LANZA
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $311,640
- **Award type:** 3
- **Project period:** 2022-07-25 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10600558

## Citation

> US National Institutes of Health, RePORTER application 10600558, Mayo Clinic Undiagnosed Disease Network Metabolomics Core (3U01TR002471-04S1). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/10600558. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*