Metabolic landscape of inborn errors of metabolism

Abstract

Metabolomics is a powerful tool to identify the complex metabolic characteristics of human diseases. Inborn errors of metabolism (IEMs) are Mendelian diseases caused by inherited defects in metabolic enzymes, nutrient transporters, and other components of the human metabolic network. Using metabolomics to assess metabolic anomalies in patients with IEMs can help characterize these diseases and clarify the impact of genomic variants. Here we present a cohort of 474 subjects recruited from Mendelian disease clinics, including 242 patients with known or suspected IEMs and their family members. Through metabolomics of plasma and patient-derived fibroblast lines, we uncovered genotype-specific metabolic alterations in 57 different IEMs, providing a detailed view of the impact of single metabolic defects in humans. We report widespread metabolite-metabolite associations and show how departures from inter-metabolite congruence help pinpoint metabolic defects in IEMs. By comparing metabolic anomalies between plasma and fibroblasts from the same patient, we identify marked differences in the systemic and cell-autonomous impact of IEMs. We show how shared patterns of metabolomic anomalies can help categorize patients with suspected IEMs, providing diagnostic clarity to pinpoint causative genetic defects. This study provides resources including a database of metabolomic features in blood and cells from dozens of rare diseases, and analytical tools to assist in data analysis and advance understanding of genetically dictated metabolic aberrations in humans.

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BibTeX RIS

BibTeX

@misc{ni2026metabolic,
  title = {{Metabolic landscape of inborn errors of metabolism}},
  author = {M. Ni and L. Cai and H. S. Vu and H. Chen and M. B. Cervantes and W. Gu and L. Abou Haidar and Z. Wu and C. Pan and F. Cai and P. T. Nguyen and Y. Jin and J. Wang and B. Ko and C. Yang and L. G. Zacharias and J. Sudderth and S. Montgomery and C. Uhles and H. Fisher and J. Hudnall and C. Hornbuckle and C. Quinn and D. Michel and L. Umana and A. Scheuerle and M. C. McNutt and G. K. Gotway and J. A. Phillips and B. Afroze and R. J. DeBerardinis},
  howpublished = {medRxiv},
  year = {2026},
  month = oct,
  doi = {10.64898/2026.10.28.26366400},
  url = {https://www.medrxiv.org/content/10.64898/2026.10.28.26366400v1},
  note = {CC BY},
}

RIS

TY  - UNPB
TI  - Metabolic landscape of inborn errors of metabolism
AU  - M. Ni
AU  - L. Cai
AU  - H. S. Vu
AU  - H. Chen
AU  - M. B. Cervantes
AU  - W. Gu
AU  - L. Abou Haidar
AU  - Z. Wu
AU  - C. Pan
AU  - F. Cai
AU  - P. T. Nguyen
AU  - Y. Jin
AU  - J. Wang
AU  - B. Ko
AU  - C. Yang
AU  - L. G. Zacharias
AU  - J. Sudderth
AU  - S. Montgomery
AU  - C. Uhles
AU  - H. Fisher
AU  - J. Hudnall
AU  - C. Hornbuckle
AU  - C. Quinn
AU  - D. Michel
AU  - L. Umana
AU  - A. Scheuerle
AU  - M. C. McNutt
AU  - G. K. Gotway
AU  - J. A. Phillips
AU  - B. Afroze
AU  - R. J. DeBerardinis
PY  - 2026
DA  - 2026/10/28
PB  - medRxiv
DO  - 10.64898/2026.10.28.26366400
UR  - https://www.medrxiv.org/content/10.64898/2026.10.28.26366400v1
AB  - Metabolomics is a powerful tool to identify the complex metabolic characteristics of human diseases. Inborn errors of metabolism (IEMs) are Mendelian diseases caused by inherited defects in metabolic enzymes, nutrient transporters, and other components of the human metabolic network. Using metabolomics to assess metabolic anomalies in patients with IEMs can help characterize these diseases and clarify the impact of genomic variants. Here we present a cohort of 474 subjects recruited from Mendelian disease clinics, including 242 patients with known or suspected IEMs and their family members. Through metabolomics of plasma and patient-derived fibroblast lines, we uncovered genotype-specific metabolic alterations in 57 different IEMs, providing a detailed view of the impact of single metabolic defects in humans. We report widespread metabolite-metabolite associations and show how departures from inter-metabolite congruence help pinpoint metabolic defects in IEMs. By comparing metabolic anomalies between plasma and fibroblasts from the same patient, we identify marked differences in the systemic and cell-autonomous impact of IEMs. We show how shared patterns of metabolomic anomalies can help categorize patients with suspected IEMs, providing diagnostic clarity to pinpoint causative genetic defects. This study provides resources including a database of metabolomic features in blood and cells from dozens of rare diseases, and analytical tools to assist in data analysis and advance understanding of genetically dictated metabolic aberrations in humans.
ER  -

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