Moores Cancer Center, University of California, San Diego, California, USA.
Institute of Molecular Systems Biology, ETH Zurich, Zurich, Switzerland.
Am J Nephrol. 2022;53(2-3):215-225. doi: 10.1159/000521940. Epub 2022 Feb 23.
Metabolomics could offer novel prognostic biomarkers and elucidate mechanisms of diabetic kidney disease (DKD) progression. Via metabolomic analysis of urine samples from 995 CRIC participants with diabetes and state-of-the-art statistical modeling, we aimed to identify metabolites prognostic to DKD progression.
Urine samples (N = 995) were assayed for relative metabolite abundance by untargeted flow-injection mass spectrometry, and stringent statistical criteria were used to eliminate noisy compounds, resulting in 698 annotated metabolite ions. Utilizing the 698 metabolites' ion abundance along with clinical data (demographics, blood pressure, HbA1c, eGFR, and albuminuria), we developed univariate and multivariate models for the eGFR slope using penalized (lasso) and random forest models. Final models were tested on time-to-ESKD (end-stage kidney disease) via cross-validated C-statistics. We also conducted pathway enrichment analysis and a targeted analysis of a subset of metabolites.
Six eGFR slope models selected 9-30 variables. In the adjusted ESKD model with highest C-statistic, valine (or betaine) and 3-(4-methyl-3-pentenyl)thiophene were associated (p < 0.05) with 44% and 65% higher hazard of ESKD per doubling of metabolite abundance, respectively. Also, 13 (of 15) prognostic amino acids, including valine and betaine, were confirmed in the targeted analysis. Enrichment analysis revealed pathways implicated in kidney and cardiometabolic disease.
Using the diverse CRIC sample, a high-throughput untargeted assay, followed by targeted analysis, and rigorous statistical analysis to reduce false discovery, we identified several novel metabolites implicated in DKD progression. If replicated in independent cohorts, our findings could inform risk stratification and treatment strategies for patients with DKD.
代谢组学可以提供新的预后生物标志物，并阐明糖尿病肾病（DKD）进展的机制。通过对 995 名患有糖尿病的 CRIC 参与者的尿液样本进行代谢组学分析和最先进的统计建模，我们旨在确定与 DKD 进展相关的代谢物。
通过非靶向流进质谱法对尿液样本（N = 995）进行相对代谢物丰度检测，并使用严格的统计标准消除嘈杂化合物，得到 698 个注释代谢物离子。利用 698 种代谢物离子的丰度以及临床数据（人口统计学、血压、HbA1c、eGFR 和蛋白尿），我们使用惩罚（lasso）和随机森林模型为 eGFR 斜率开发了单变量和多变量模型。通过交叉验证 C 统计量在时间到 ESKD（终末期肾病）上测试最终模型。我们还进行了途径富集分析和亚组代谢物的靶向分析。
六个 eGFR 斜率模型选择了 9-30 个变量。在具有最高 C 统计量的调整后的 ESKD 模型中，缬氨酸（或甜菜碱）和 3-（4-甲基-3-戊烯基）噻吩与代谢物丰度每增加一倍分别相关（p < 0.05），ESKD 的风险增加 44%和 65%。此外，在靶向分析中还证实了 13 种（15 种）有预后意义的氨基酸，包括缬氨酸和甜菜碱。富集分析显示了与肾脏和心脏代谢疾病相关的途径。
使用多样化的 CRIC 样本、高通量非靶向检测、随后进行靶向分析和严格的统计分析以减少假阳性发现，我们确定了几种与 DKD 进展相关的新代谢物。如果在独立队列中得到复制，我们的研究结果可以为 DKD 患者的风险分层和治疗策略提供信息。
