omarkers, characterizing biochemical pathophysiology, and developing targeted interventions. There are a limited number of studies of longitudinal metabolomics and virtually none in pediatric CKD. Methods The CKD in Children study is a multi-institutional, prospective cohort that enrolled children aged 6 months to 16 years with eGFR 30–90 ml/min per 1.73 m2. Untargeted metabolomics profiling was performed on plasma samples from the baseline, 2-, and 4-year study visits. There were technologic updates in the metabolomic profiling platform used between the baseline and follow-up assays. Statistical approaches were adopted to avoid direct comparison of baseline and follow-up measurements. To identify metabolite associations with eGFR or urine protein-creatinine ratio (UPCR) among all three time points, we applied linear mixed-effects (LME) models. To identify metabolites associated with time, we applied LME models to the 2- and 4-year follow-up data. We applied linear regression analysis to examine associations between change in metabolite level over time (∆level) and change in eGFR (∆eGFR) and UPCR (∆UPCR). We reported significance on the basis of both the false discovery rate (FDR) <0.05 and P < 0.05. Results There were 1156 person-visits (N: baseline=626, 2-year=254, 4-year=276) included. There were 622 metabolites with standardized measurements at all three time points. In LME modeling, 406 and 343 metabolites associated with eGFR and UPCR at FDR <0.05, respectively. Among 530 follow-up person-visits, 158 metabolites showed differences over time at FDR <0.05. For participants with complete data at both follow-up visits (n=123), we report 35 metabolites with ∆level–∆eGFR associations significant at FDR <0.05. There were no metabolites with significant ∆level–∆UPCR associations at FDR <0.05. We report 16 metabolites with ∆level–∆UPCR associations at P < 0.05 and associations with UPCR in LME modeling at FDR <0.05. Conclusions We characterized longitudinal plasma metabolomic patterns associated with eGFR and UPCR in a large pediatric CKD population. Many of these metabolite signals have been associated with CKD progression, etiology, and proteinuria in previous CKD Biomarkers Consortium studies. There were also novel metabolite associations with eGFR and proteinuria detected....

中文翻译：

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儿童 CKD 的纵向血浆代谢组模式及其与肾功能和蛋白尿的关系


标记物，表征生化病理生理学，并制定有针对性的干预措施。纵向代谢组学的研究数量有限，而且几乎没有针对儿科 CKD 的研究。方法 儿童 CKD 研究是一项多机构前瞻性队列研究，纳入 eGFR 30-90 ml/min/1.73 m2 的 6 个月至 16 岁儿童。对基线、2 年和 4 年研究访视的血浆样本进行了非靶向代谢组学分析。基线和后续检测之间使用的代谢组学分析平台进行了技术更新。采用统计方法来避免直接比较基线和后续测量结果。为了确定所有三个时间点中代谢物与 eGFR 或尿蛋白肌酐比 (UPCR) 的关联，我们应用了线性混合效应 (LME) 模型。为了识别与时间相关的代谢物，我们将 LME 模型应用于 2 年和 4 年的随访数据。我们应用线性回归分析来检查代谢物水平随时间的变化 (Δlevel) 与 eGFR (ΔeGFR) 和 UPCR (ΔUPCR) 的变化之间的关联。我们根据错误发现率 (FDR) <0.05 和 P < 0.05 报告显着性。结果 共纳入 1156 人次就诊（N：基线 = 626，2 年 = 254，4 年 = 276）。在所有三个时间点都有 622 种代谢物进行了标准化测量。在 LME 模型中，FDR <0.05 时，分别有 406 种和 343 种代谢物与 eGFR 和 UPCR 相关。在 530 名随访人员中，158 种代谢物随着时间的推移显示出差异，FDR <0.05。对于两次随访时均具有完整数据的参与者 (n=123)，我们报告了 35 种代谢物，其 Δlevel-ΔeGFR 关联在 FDR <0.05 时显着。 在 FDR <0.05 时，没有代谢物具有显着的 Δlevel-ΔUPCR 关联。我们报告了 16 种代谢物与 Δlevel–ΔUPCR 关联的 P < 0.05，以及与 LME 模型中 UPCR 关联的 FDR <0.05。结论 我们对大量儿童 CKD 人群中与 eGFR 和 UPCR 相关的纵向血浆代谢组模式进行了表征。在之前的 CKD 生物标志物联盟研究中，许多代谢信号与 CKD 进展、病因和蛋白尿有关。还检测到与 eGFR 和蛋白尿相关的新代谢物......