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Introduction <p>Metabolomics analysis shows great promise in identifying non-invasive biomarkers for interstitial lung diseases (ILDs). However, the relevant data are scattered across numerous disparate publications, hindering their full utilization.</p> Objectives <p>To comprehensively leverage the metabolomic data disseminated throughout the literature, we manually curated and integrated them into the ILDMDB database (<a href="https://ildmdb.shinyapps.io/ILDMDB/">https://ildmdb.shinyapps.io/ILDMDB/</a>). This database will be regularly updated and maintained.</p> Methods <p>We conducted a systematic literature search and extracted key metabolomics data, including changes in metabolites, relevant clinical parameters, and predictive model performance metrics etc. These data were then manually integrated into the ILDMDB database.</p> Results <p>The current version of ILDMDB contains 3,969 entries, representing 20 ILD types and over 1,000 metabolites derived from Homo sapiens, animal models, and cell line experiments. Each entry comprises detailed information, including the metabolite name, disease type, and original reference. In addition, we have incorporated model data on metabolites used for ILD diagnosis, disease severity, and prognosis, along with information on metabolites associated with clinical parameters. Users can search for target metabolites freely, view their expression patterns and detailed information, and manage metabolite collections in the database.</p> Conclusion <p>ILDMDB serves as an exploratory platform designed to assist researchers in swiftly and conveniently accessing the metabolic landscape of ILDs, thereby advancing research into the diagnosis, prognosis, and treatment of ILDs from a metabolic perspective.</p>

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ILDMDB: a manually curated database of metabolite-disease associations in interstitial lung diseases

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月29日

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  • Yaowu He,
  • Yupeng Li,
  • Jing Geng,
  • Hong Chen,
  • Huaping Dai

摘要

Introduction

Metabolomics analysis shows great promise in identifying non-invasive biomarkers for interstitial lung diseases (ILDs). However, the relevant data are scattered across numerous disparate publications, hindering their full utilization.

Objectives

To comprehensively leverage the metabolomic data disseminated throughout the literature, we manually curated and integrated them into the ILDMDB database (https://ildmdb.shinyapps.io/ILDMDB/). This database will be regularly updated and maintained.

Methods

We conducted a systematic literature search and extracted key metabolomics data, including changes in metabolites, relevant clinical parameters, and predictive model performance metrics etc. These data were then manually integrated into the ILDMDB database.

Results

The current version of ILDMDB contains 3,969 entries, representing 20 ILD types and over 1,000 metabolites derived from Homo sapiens, animal models, and cell line experiments. Each entry comprises detailed information, including the metabolite name, disease type, and original reference. In addition, we have incorporated model data on metabolites used for ILD diagnosis, disease severity, and prognosis, along with information on metabolites associated with clinical parameters. Users can search for target metabolites freely, view their expression patterns and detailed information, and manage metabolite collections in the database.

Conclusion

ILDMDB serves as an exploratory platform designed to assist researchers in swiftly and conveniently accessing the metabolic landscape of ILDs, thereby advancing research into the diagnosis, prognosis, and treatment of ILDs from a metabolic perspective.