Biological variation of methylmalonic acid in urine in spanish population

IF 3.2 3区医学 Q2 MEDICAL LABORATORY TECHNOLOGY Clinica Chimica Acta Pub Date : 2024-08-25 DOI:10.1016/j.cca.2024.119943

Maria Santes Berto , Sara Sanchez Asis , Juan Robles Bauza , Ana Rubio Alaejos , Josep Miquel Bauça , Jose Antonio Delgado

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Abstract

Background-Aim

Methylmalonic acid (MMA) is currently the best biomarker of functional vitamin B12 deficiency. However, for a correct interpretation of the patient’s results it is necessary to know its biological variation (BV).

No BV data are available for urine MMA values, as measured by mass spectrometry. Hence, the aim of this study was to estimate the within- and between-person coefficients of variation (CV_w, CV_g) for MMA in a healthy population, and the associated index of individuality (II), as well as to define quality specifications based on BV and the reference change value (RCV).

Methods

Random urine samples from 34 healthy volunteers were collected over four consecutive weeks. Samples were stored at –80 °C until analysis in a single analytical run. MMA excretion was quantified by tandem liquid chromatography coupled to mass spectrometry (HPLC-MS/MS). Results were normalized to urine creatinine.

The coefficients of variation were estimated by CV-ANOVA. Confidence intervals (95 %) were calculated. Quality specifications were defined according to international recommendations.

Results

A total of 128 samples were included. The coefficients of variation were CV_w = 35.7 % (26.1–45.3) and CV_g = 67.7 % (58.3–77.0). The associated II was 0.5 and the RCV was 88.1 %.

Conclusion

Considering the II obtained, MMA in urine has high individuality, therefore, RCV is better to evaluate serial clinical results. Our results will contribute to a better clinical interpretation of this biomarker and will represent a great aid when defining analytical performance specifications for this magnitude.

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西班牙人口尿液中甲基丙二酸的生物变异。

背景-目的：甲基丙二酸（MMA）是目前功能性维生素 B12 缺乏症的最佳生物标志物。然而，为了正确解释患者的结果，有必要了解其生物变异（BV）。目前还没有通过质谱法测量尿液中 MMA 值的生物变异数据。因此，本研究旨在估算健康人群中 MMA 的人内和人际变异系数（CVw、CVg）以及相关的个体化指数（II），并根据 BV 和参考变化值（RCV）确定质量规格：方法：连续四周收集 34 名健康志愿者的随机尿样。样品储存在 -80 °C，直到在一次分析运行中进行分析。采用串联液相色谱耦合质谱法（HPLC-MS/MS）对 MMA 的排泄量进行量化。结果以尿肌酐为标准。变异系数由 CV-ANOVA 估算。计算置信区间（95 %）。质量标准根据国际建议确定：结果：共纳入 128 份样本。变异系数为 CVw = 35.7 % (26.1-45.3) 和 CVg = 67.7 % (58.3-77.0)。相关的 II 值为 0.5，RCV 为 88.1%：考虑到所获得的 II，尿液中的 MMA 具有较高的个体差异性，因此 RCV 更适合用于评估连续的临床结果。我们的研究结果将有助于更好地在临床上解释这一生物标志物，并将为确定这一量级的分析性能指标提供极大的帮助。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Clinica Chimica Acta 医学-医学实验技术

CiteScore

10.10

自引率

2.00%

发文量

1268

审稿时长

23 days

期刊介绍： The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells. The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.