Institute of Pharmaceutical Sciences of Western Switzerland, University of Geneva, Geneva, Switzerland.
Department of Genetic and Laboratory Medicine, Geneva University Hospitals (HUG), Geneva, Switzerland.
Anal Chim Acta. 2020 Feb 22;1099:26-38. doi: 10.1016/j.aca.2019.11.050. Epub 2019 Nov 23.
Kidney transplantation is one of the renal replacement options in patients suffering from end-stage renal disease (ESRD). After a transplant, patient follow-up is essential and is mostly based on immunosuppressive drug levels control, creatinine measurement and kidney biopsy in case of a rejection suspicion. The extensive analysis of metabolite levels offered by metabolomics might improve patient monitoring, help in the surveillance of the restoration of a "normal" renal function and possibly also predict rejection. The longitudinal follow-up of those patients with repeated measurements is useful to understand changes and decide whether an intervention is necessary. The time modality, therefore, constitutes a specific dimension in the data structure, requiring dedicated consideration for proper statistical analysis. The handling of specific data structures in metabolomics has received strong interest in recent years. In this work, we demonstrated the recently developed ANOVA multiblock OPLS (AMOPLS) to efficiently analyse longitudinal metabolomic data by considering the intrinsic experimental design. Indeed, AMOPLS combines the advantages of multilevel approaches and OPLS by separating between and within individual variations using dedicated predictive components, while removing most uncorrelated variations in the orthogonal component(s), thus facilitating interpretation. This modelling approach was applied to a clinical cohort study aiming to evaluate the impact of kidney transplantation over time on the plasma metabolic profile of graft patients and donor volunteers. A dataset of 266 plasma metabolites was identified using an LC-MS multiplatform analytical setup. Two separate AMOPLS models were computed: one for the recipient group and one for the donor group. The results highlighted the benefits of transplantation for recipients and the relatively low impacts on blood metabolites of donor volunteers.
肾移植是终末期肾病（ESRD）患者的肾脏替代选择之一。移植后，患者的随访至关重要，主要基于免疫抑制药物水平控制、肌酐测量和肾活检，以怀疑排斥反应。代谢组学提供的代谢物水平的广泛分析可能会改善患者监测，有助于监测“正常”肾功能的恢复，并可能预测排斥反应。对那些进行重复测量的患者进行纵向随访有助于了解变化并决定是否需要干预。因此，时间模式构成了数据结构的特定维度，需要专门考虑适当的统计分析。代谢组学中特殊数据结构的处理近年来受到了广泛关注。在这项工作中，我们展示了最近开发的方差分析多块 OPLS（AMOPLS），通过考虑内在的实验设计，有效地分析纵向代谢组学数据。实际上，AMOPLS 通过使用专门的预测成分在个体间和个体内变化之间进行分离，同时在正交成分中去除大多数不相关的变化，从而结合了多层次方法和 OPLS 的优点，从而便于解释。这种建模方法应用于一项临床队列研究，旨在评估随着时间的推移，肾移植对移植物患者和供体志愿者血浆代谢谱的影响。使用 LC-MS 多平台分析设置确定了 266 个血浆代谢物的数据集。计算了两个单独的 AMOPLS 模型：一个用于受体组，一个用于供体组。结果突出了移植对受体的益处，以及对供体志愿者血液代谢物的相对低影响。
