Background Untargeted metabolomics has shown promise in expanding screening and diagnostic capabilities for inborn errors of metabolism (IEMs). However, inter-batch variability remains a major barrier to its implementation in the clinical laboratory, despite attempts to address this through normalization techniques. We have developed a sustainable, matrix-matched reference material (RM) using the iterative batch averaging method (IBAT) to correct inter-batch variability in liquid chromatography-high-resolution mass spectrometry-based untargeted metabolomics for IEM screening. Methods The RM was created using pooled batches of remnant plasma specimens. The batch size, number of batch iterations per RM, and stability compared to a conventional pool of specimens were determined. The effectiveness of the RM for correcting inter-batch variability in routine screening was evaluated using plasma collected from a cohort of phenylketonuria (PKU) patients. Results The RM exhibited lower metabolite variability between iterations over time compared to metabolites from individual batches or individual specimens used for its creation. In addition, the mean variation across amino acid (n = 19) concentrations over 12 weeks was lower for the RM (CVtotal = 8.8%; range 4.7%–25.3%) compared to the specimen pool (CVtotal = 24.6%; range 9.0%–108.3%). When utilized in IEM screening, RM normalization minimized unwanted inter-batch variation and enabled the correct classification of 30 PKU patients analyzed 1 month apart from 146 non-PKU controls. Conclusions Our RM minimizes inter-batch variability in untargeted metabolomics and demonstrated its potential for routine IEM screening in a cohort of PKU patients. It provides a practical and sustainable solution for data normalization in untargeted metabolomics for clinical laboratories.

中文翻译：

---

先天性代谢缺陷的非靶向代谢组学：用于纠正批次间变异性的可持续参考物质的开发和评估


背景 非靶向代谢组学在扩大先天性代谢缺陷 （IEM） 的筛查和诊断能力方面显示出前景。然而，尽管试图通过标准化技术来解决这个问题，但批次间的差异仍然是其在临床实验室中实施的主要障碍。我们使用迭代批次平均法 （IBAT） 开发了一种可持续的基质匹配参考物质 （RM），以校正用于 IEM 筛选的液相色谱-高分辨率质谱非靶向代谢组学的批次间差异。方法 RM 是使用混合批次的残余血浆标本创建的。确定了批次大小、每 RM 的批次迭代次数以及与常规样本库相比的稳定性。使用从苯丙酮尿症 （PKU） 患者队列收集的血浆评估 RM 纠正常规筛查中批次间差异的有效性。结果 与用于其创建的单个批次或单个样本的代谢物相比，RM 在迭代之间随时间的代谢物变异性较低。此外，与样本库（CVtotal = 24.6%;范围9.0%–108.3%）相比，RM （CVtotal = 8.8%;范围 4.7%–25.3%）在 12 周内氨基酸 （n = 19） 浓度的平均变化较低。当用于 IEM 筛查时，RM 标准化最大限度地减少了不需要的批次间差异，并能够对 1 个月分析的 30 名 PKU 患者与 146 名非 PKU 对照进行正确分类。结论我们的 RM 最大限度地减少了非靶向代谢组学的批次间变异性，并证明了它在 PKU 患者队列中常规 IEM 筛查的潜力。 它为临床实验室非靶向代谢组学中的数据归一化提供了一种实用且可持续的解决方案。