Systematic review and meta-analysis of biological variation data of urine albumin, albumin to creatinine ratio and other markers in urine

IF 3.2 3区医学 Q2 MEDICAL LABORATORY TECHNOLOGY Clinica Chimica Acta Pub Date : 2024-11-07 DOI:10.1016/j.cca.2024.120032

Berna Aslan , Anna Carobene , Niels Jonker , Kornelia Galior , Beatriz Boned , Fernando Marqués-García , Carmen Ricós , William Bartlett , Abdurrahman Coskun , Jorge Diaz-Garzon , Pilar Fernández-Calle , Elisabet Gonzalez-Lao , Margarida Simon , Sverre Sandberg , Aasne K. Aarsand , on behalf of the European Federation of Clinical Chemistry, Laboratory Medicine Task Group for the Biological Variation Database

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Abstract

Introduction

Significant variations in Biological Variation (BV) estimates have been reported for urine markers. This study aimed to systematically review and critically appraise BV studies for albumin, creatinine, albumin-to-creatinine ratio (ACR), and other urine markers to perform a meta-analysis of eligible studies.

Methods

Publications were identified through a systematic search and evaluated using the Biological Variation Data Critical Appraisal Checklist (BIVAC). BIVAC-compliant studies (grades A-C; A being fully compliant) conducted in healthy individuals were included in the meta-analysis, providing within-subject (CV_I) and between-subject (CV_G) BV estimates with 95% confidence intervals for various sample collection types.

Results

Out of 37 studies evaluated, 16 were included (one grade A, three B, twelve C). No eligible publications were identified for meta-analysis of albumin and ACR. Limited data were available for first-morning urine specimens. A CV_I between 15% and 30% was found for most measurands in 24-hour urine samples, while CV_I estimates for random urine appeared higher.

Conclusion

Published BV studies on urine markers utilized different sample collections and reporting units. Most were considered unfit for use or ineligible for meta-analysis. Given the critical role of urine albumin and ACR in chronic kidney disease risk assessment, there is a need for more BIVAC-compliant studies.

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尿液白蛋白、白蛋白与肌酐比值以及尿液中其他标记物的生物变异数据的系统回顾和荟萃分析。

导言：据报道，尿液指标的生物变异（BV）估计值存在显著差异。本研究旨在对白蛋白、肌酐、白蛋白肌酐比值（ACR）和其他尿液指标的生物变异研究进行系统回顾和批判性评估，并对符合条件的研究进行荟萃分析：方法：通过系统性检索确定文献，并使用生物变异数据关键评估核对表（BIVAC）进行评估。符合 BIVAC 标准的研究（A-C 级；A 级为完全符合标准）被纳入荟萃分析，这些研究针对不同样本采集类型提供了受试者内（CVI）和受试者间（CVG）BV 估计值及 95% 置信区间：在评估的 37 项研究中，有 16 项被纳入（1 项 A 级，3 项 B 级，12 项 C 级）。在对白蛋白和 ACR 进行荟萃分析时，未发现符合条件的出版物。关于晨尿标本的数据有限。在 24 小时尿液样本中发现，大多数测量值的 CVI 在 15%至 30%之间，而随机尿液的 CVI 估计值似乎更高：已发表的有关尿液标记物的 BV 研究采用了不同的样本采集和报告单位。结论：已发表的有关尿液标志物的 BV 研究采用了不同的样本采集和报告单位，其中大多数被认为不适合使用或不符合荟萃分析的要求。鉴于尿白蛋白和 ACR 在慢性肾脏病风险评估中的关键作用，需要进行更多符合 BIVAC 标准的研究。

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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.