five

Storage Time and Urine Biomarker Levels in the ASSESS-AKI Study

收藏
NIAID Data Ecosystem2026-03-09 收录
下载链接:
https://figshare.com/articles/dataset/Storage_Time_and_Urine_Biomarker_Levels_in_the_ASSESS-AKI_Study/4124343
下载链接
链接失效反馈
官方服务:
资源简介:
Background Although stored urine samples are often used in biomarker studies focused on acute and chronic kidney disease, how storage time impacts biomarker levels is not well understood. Methods 866 subjects enrolled in the NIDDK-sponsored ASsessment, Serial Evaluation, and Subsequent Sequelae in Acute Kidney Injury (ASSESS-AKI) Study were included. Samples were processed under standard conditions and stored at -70°C until analyzed. Kidney injury molecule-1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL), interleukin-18 (IL-18), and liver fatty acid binding protein (L-FABP) were measured in urine samples collected during the index hospitalization or an outpatient visit 3 months later. Mixed effects models were used to determine the effect of storage time on biomarker levels and stratified by visit. Results Median storage was 17.8 months (25–75% IQR 10.6–23.7) for samples from the index hospitalization and 14.6 months (IQR 7.3–20.4) for outpatient samples. In the mixed effects models, the only significant association between storage time and biomarker concentration was for KIM-1 in outpatient samples, where each month of storage was associated with a 1.7% decrease (95% CI -3% to -0.3%). There was no relationship between storage time and KIM-1 levels in samples from the index hospitalization. Conclusion There was no significant impact of storage time over a median of 18 months on urine KIM-1, NGAL, IL-18 or L-FABP in hospitalized samples; a statistically significant effect towards a decrease over time was noted for KIM-1 in outpatient samples. Additional studies are needed to determine whether longer periods of storage at -70°C systematically impact levels of these analytes.
创建时间:
2016-10-28
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作