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Introduction <p>We present a simple test to assess whether a metabolomics dataset is fit-for-purpose. Current qualitycontrol approaches do not directly evaluate the ability to recover biologically meaningful perturbations.</p> Objectives <p>To evaluate whether known drug-induced metabolic perturbations can serve as internal benchmarks fordataset quality.</p> Methods <p>In a study (the TROMBOLOME study, unrelated to allopurinol therapy), 1,000 serum samples were analyzedwith one targeted and two untargeted metabo lomics panels. Samples were classified as allopurinol-positive (N=19)using detection of allopurinol analytical targets. Endogenous metabolite markers of allopurinol therapy wereevaluated based on hypotheses derived from the literature. Statistical evaluation was performed using Mann–Whitney U-tests.</p> Results <p>The hypothesis of upregulation was supported for xanthine, orotate, and orotidine (p &lt; 0.0001) inallopurinol-positive cases (N = 19). These findings demonstrate repro ducibility of well-characterized metabolicperturbations within the dataset.</p> Conclusion <p>In the absence of external quality assessment schemes for untargeted metabolomics, such benchmarkscould provide a practical way to evaluate whether datasets are suitable for downstream biological interpretation.The proposed targeted exposomics approach complements traditional QC metrics by assessing biologicalrecoverability.</p>

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Benchmarking untargeted metabolomics data quality with allopurinol-induced perturbations

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月16日

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  • Terje Vasskog,
  • Pia J. Heinsvig,
  • Ekaterina Sharashova,
  • Terkel Hansen,
  • Torbjørn N. Myhre,
  • Marie Mardal

摘要

Introduction

We present a simple test to assess whether a metabolomics dataset is fit-for-purpose. Current qualitycontrol approaches do not directly evaluate the ability to recover biologically meaningful perturbations.

Objectives

To evaluate whether known drug-induced metabolic perturbations can serve as internal benchmarks fordataset quality.

Methods

In a study (the TROMBOLOME study, unrelated to allopurinol therapy), 1,000 serum samples were analyzedwith one targeted and two untargeted metabo lomics panels. Samples were classified as allopurinol-positive (N=19)using detection of allopurinol analytical targets. Endogenous metabolite markers of allopurinol therapy wereevaluated based on hypotheses derived from the literature. Statistical evaluation was performed using Mann–Whitney U-tests.

Results

The hypothesis of upregulation was supported for xanthine, orotate, and orotidine (p < 0.0001) inallopurinol-positive cases (N = 19). These findings demonstrate repro ducibility of well-characterized metabolicperturbations within the dataset.

Conclusion

In the absence of external quality assessment schemes for untargeted metabolomics, such benchmarkscould provide a practical way to evaluate whether datasets are suitable for downstream biological interpretation.The proposed targeted exposomics approach complements traditional QC metrics by assessing biologicalrecoverability.