Lipidomics reveals potential biomarkers and pathophysiological insights in the progression of diabetic kidney disease

Xiaozhen Guo , Zixuan Zhang , Cuina Li , Xueling Li , Yutang Cao , Yangyang Wang , Jiaqi Li , Yibin Wang , Kanglong Wang , Yameng Liu , Cen Xie , Yifei Zhong
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Abstract

Background

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease, affecting over 30 % of diabetes mellitus (DM) patients. Early detection of DKD in DM patients can enable timely preventive therapies, and potentially delay disease progression. Since the kidney relies on fatty acid oxidation for energy, dysregulated lipid metabolism has been implicated in proximal tubular cell damage and DKD pathogenesis. This study aimed to identify lipid alterations during DKD development and potential biomarkers differentiating DKD from DM.

Methods

lipidomics analysis was performed on serum collected from 55 patients with DM, 21 with early DKD stage and 32 with advanced DKD, and 22 healthy subjects. Associations between lipids and DKD risk were evaluated by logistic regression.

Results

Lipid profiling revealed elevated levels of certain lysophosphatidylethanolamines (LPEs), phosphatidylethanolamines (PEs), ceramides (Cers), and diacylglycerols (DAGs) in the DM-DKD transition, while most LPEs, lysophosphatidylcholines (LPCs), along with several monoacylglycerol (MAG) and triacylglycerols (TAGs), increased further from DKD-E to DKD-A. Logistic regression indicated positive associations between LPCs, LPEs, PEs, and DAGs with DKD risk, with most LPEs correlating significantly with urinary albumin-to-creatinine ratio (UACR) and inversely with estimated glomerular filtration rate (eGFR). A machine-learning-derived biomarker panel, Lipid9, consisting of LPC(18:2), LPC(20:5), LPE (16:0), LPE (18:0), LPE (18:1), LPE (24:0), PE (34:1), PE (34:2), and PE (36:2), accurately distinguished DKD (AUC: 0.78, 95 % CI 0.68–0.86) from DM. Incorporating two clinical indexes, serum creatinine and blood urea nitrogen, the Lipid9-SCB model further improved DKD detection (AUC: 0.83, 95 % CI 0.75–0.90) from DM, and was notably more sensitive for identifying DKD-E (AUC: 0.79, 95 % CI 0.67–0.91).

Conclusion

This study deciphers the lipid signature in DKD progression, and suggests the Lipid9-SCB panel as a promising tool for early DKD detection in DM patients.
脂质组学揭示了糖尿病肾病进展中的潜在生物标志物和病理生理学见解
背景:糖尿病肾病(DKD)是终末期肾脏疾病的主要原因,影响了超过30%的糖尿病(DM)患者。早期发现糖尿病患者的DKD可以及时预防治疗,并有可能延缓疾病进展。由于肾脏依赖脂肪酸氧化提供能量,脂质代谢失调与近端小管细胞损伤和DKD发病机制有关。本研究旨在确定DKD发展过程中的脂质改变以及区分DKD和DM的潜在生物标志物。方法对55名DM患者、21名早期DKD患者和32名晚期DKD患者以及22名健康受试者的血清进行滑质组学分析。通过逻辑回归评估脂质与DKD风险之间的关系。结果在DM-DKD转化过程中,某些溶血磷脂酰乙醇胺(LPEs)、磷脂酰乙醇胺(PEs)、神经酰胺(Cers)和二酰基甘油(dag)水平升高,而大多数LPEs、溶血磷脂酰胆碱(LPCs)以及一些单酰基甘油(MAG)和三酰基甘油(TAGs)在DKD-E到DKD-A之间进一步升高。Logistic回归显示LPCs、LPEs、PEs和dag与DKD风险呈正相关,大多数LPEs与尿白蛋白与肌酐比(UACR)显著相关,与肾小球滤过率(eGFR)估算呈负相关。由LPC(18:2)、LPC(20:5)、LPE(16:0)、LPE(18:0)、LPE(18:1)、LPE(24:0)、PE(34:1)、PE(34:2)和PE(36:2)组成的机器学习衍生的生物标志物面板Lipid9准确区分了DKD和DM (AUC: 0.78, 95% CI 0.68-0.86)。结合血清肌酐和血尿素氮两项临床指标,Lipid9- scb模型进一步提高了DKD和DM的检测(AUC: 0.83, 95% CI 0.75-0.90),对DKD- e的识别(AUC: 0.79, 95% CI 0.67-0.91)更为敏感。结论:本研究揭示了DKD进展中的脂质特征,并提示Lipid9-SCB面板是DM患者早期DKD检测的有希望的工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Metabolism open
Metabolism open Agricultural and Biological Sciences (General), Endocrinology, Endocrinology, Diabetes and Metabolism
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