Jeon You Hyun, Lee Sujin, Kim Da Woon, Kim Suhkmann, Bae Sun Sik, Han Miyeun, Seong Eun Young, Song Sang Heon
Department of Internal Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan, Republic of Korea.
Department of Chemistry and Chemistry Institute for Functional Materials, Pusan National University, Busan, Republic of Korea.
Kidney Res Clin Pract. 2023 Sep;42(5):591-605. doi: 10.23876/j.krcp.22.146. Epub 2023 May 18.
Immunoglobulin A nephropathy (IgAN) is the most prevalent form of glomerulonephritis worldwide. Prediction of disease progression in IgAN can help to provide individualized treatment based on accurate risk stratification.
We performed proton nuclear magnetic resonance-based metabolomics analyses of serum and urine samples from healthy controls, non-progressor (NP), and progressor (P) groups to identify metabolic profiles of IgAN disease progression. Metabolites that were significantly different between the NP and P groups were selected for pathway analysis. Subsequently, we analyzed multivariate area under the receiver operating characteristic (ROC) curves to evaluate the predictive power of metabolites associated with IgAN progression.
We observed several distinct metabolic fingerprints of the P group involving the following metabolic pathways: glycolipid metabolism; valine, leucine, and isoleucine biosynthesis; aminoacyl-transfer RNA biosynthesis; glycine, serine, and threonine metabolism; and glyoxylate and dicarboxylate metabolism. In multivariate ROC analyses, the combinations of serum glycerol, threonine, and proteinuria (area under the curve [AUC], 0.923; 95% confidence interval [CI], 0.667-1.000) and of urinary leucine, valine, and proteinuria (AUC, 0.912; 95% CI, 0.667-1.000) showed the highest discriminatory ability to predict IgAN disease progression.
This study identified serum and urine metabolites profiles that can aid in the identification of progressive IgAN and proposed perturbed metabolic pathways associated with the identified metabolites.
免疫球蛋白A肾病(IgAN)是全球最常见的肾小球肾炎形式。预测IgAN疾病进展有助于基于准确的风险分层提供个体化治疗。
我们对健康对照、非进展者(NP)和进展者(P)组的血清和尿液样本进行了基于质子核磁共振的代谢组学分析,以确定IgAN疾病进展的代谢谱。选择NP组和P组之间有显著差异的代谢物进行通路分析。随后,我们分析了多变量受试者工作特征(ROC)曲线下面积,以评估与IgAN进展相关的代谢物的预测能力。
我们观察到P组有几个独特的代谢指纹,涉及以下代谢途径:糖脂代谢;缬氨酸、亮氨酸和异亮氨酸生物合成;氨酰基转移RNA生物合成;甘氨酸、丝氨酸和苏氨酸代谢;以及乙醛酸和二羧酸代谢。在多变量ROC分析中,血清甘油、苏氨酸和蛋白尿的组合(曲线下面积[AUC],0.923;95%置信区间[CI],0.667-1.000)以及尿亮氨酸、缬氨酸和蛋白尿的组合(AUC,0.912;95%CI,0.667-1.000)对预测IgAN疾病进展具有最高的辨别能力。
本研究确定了有助于识别进展性IgAN的血清和尿液代谢物谱,并提出了与所识别代谢物相关的扰动代谢途径。