Mosterd Charlotte M, Verhaar Barbara J H, van den Born Bert-Jan H, Nieuwdorp Max, van Raalte Daniel H
Department of Internal and Vascular medicine, Amsterdam University Medical Centers, AMC, Amsterdam, The Netherlands.
Diabetes Center, Department of Internal Medicine, Amsterdam University Medical Centers, VUmc, Amsterdam, The Netherlands.
Kidney Med. 2025 Apr 17;7(7):101009. doi: 10.1016/j.xkme.2025.101009. eCollection 2025 Jul.
RATIONALE & OBJECTIVE: We aimed to find predictive plasma and urine metabolites for nondiabetic chronic kidney disease (CKD), and to validate these biomarkers in a diabetic kidney disease (DKD) population, using data of the population-based multiethnic Healthy Life in an Urban Setting study.
Cross-sectional metabolome study.
SETTING & PARTICIPANTS: From the Healthy Life in an Urban Setting population-based cohort, we included 124 participants with nondiabetic CKD, 45 with DKD and 200 healthy controls.
Plasma and urine metabolites were measured using ultra-high-performance liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) with an untargeted approach.
(Nondiabetic) CKD.
We used machine learning models to predict nondiabetic CKD from metabolite profiles and used logistic regression models with adjustment for potential confounders to verify our results in the best predicting metabolites. In addition, we assessed the associations between the best predicting metabolites and DKD.
Urine metabolites were more predictive of nondiabetic kidney disease than plasma metabolites. In plasma, the best predicting metabolites for nondiabetic CKD included many amino acids, including N-acetylated amino acids, histidine, and indolepropionate. In urine, the highest-ranked metabolites were predominantly lipids, including sphingomyelins and phosphatidylcholines. There was limited overlap among the top-ranked metabolites in predicting nondiabetic CKD between plasma and urine. Almost all associations with nondiabetic CKD could be translated to DKD. No interactions were observed with ethnicity.
The cross-sectional design limits causal inference.
Our analyses revealed that urine metabolites were strongly associated with CKD than plasma metabolites in this multiethnic population. The finding that specific associations of plasma and urine metabolites could be translated to subjects with DKD suggests a shared pathophysiological background.
我们旨在利用基于人群的多民族城市健康生活研究数据，寻找非糖尿病慢性肾脏病（CKD）的预测性血浆和尿液代谢物，并在糖尿病肾病（DKD）人群中验证这些生物标志物。
横断面代谢组学研究。
从基于人群的城市健康生活队列中，我们纳入了124例非糖尿病CKD患者、45例DKD患者和200例健康对照。
采用非靶向方法，通过超高效液相色谱-串联质谱联用（LC-MS/MS）测定血浆和尿液代谢物。
（非糖尿病）CKD。
我们使用机器学习模型根据代谢物谱预测非糖尿病CKD，并使用对潜在混杂因素进行调整的逻辑回归模型在最佳预测代谢物中验证我们的结果。此外，我们评估了最佳预测代谢物与DKD之间的关联。
尿液代谢物比血浆代谢物更能预测非糖尿病肾病。在血浆中，非糖尿病CKD的最佳预测代谢物包括许多氨基酸，包括N-乙酰化氨基酸、组氨酸和吲哚丙酸。在尿液中，排名最高的代谢物主要是脂质，包括鞘磷脂和磷脂酰胆碱。血浆和尿液中预测非糖尿病CKD的排名靠前的代谢物之间重叠有限。几乎所有与非糖尿病CKD的关联都可转化为DKD。未观察到与种族的相互作用。
横断面设计限制了因果推断。
我们的分析表明，在这个多民族人群中，尿液代谢物比血浆代谢物与CKD的关联更强。血浆和尿液代谢物的特定关联可转化为DKD患者的这一发现提示了共同的病理生理背景。
