Su Xiaojuan, Ren Ruru, Yang Lingling, Su Chao, Wang Yingli, Lu Jun, Liu Jing, Zong Rong, Lu Fangfang, Wilson Gidion, Ding Shuqin, Ma Xueqin
Department of Pharmaceutical Analysis, School of Pharmacy, Ningxia Medical University, 1160 Shenli Street, Yinchuan 750004, China.
Yinchuan Weikang Nephrology Hospital, Intersection of Lijing North Street and Agricultural Materials Lane, Yinchuan 750004, China.
Evid Based Complement Alternat Med. 2022 Jul 30;2022:7450977. doi: 10.1155/2022/7450977. eCollection 2022.
Chronic kidney disease, including renal failure (RF), is a global public health problem. The clinical diagnosis mainly depends on the change of estimated glomerular filtration rate, which usually lags behind disease progression and likely has limited clinical utility for the early detection of this health problem. Now, we employed Q-Exactive HFX Orbitrap LC-MS/MS based metabolomics to reveal the metabolic profile and potential biomarkers for RF screening. 27 RF patients and 27 healthy controls were included as the testing groups, and comparative analysis of results using different techniques, such as multivariate pattern recognition and univariate statistical analysis, was applied to screen and elucidate the differential metabolites. The dot plots and receiver operating characteristics curves of identified different metabolites were established to discover the potential biomarkers of RF. The results exhibited a clear separation between the two groups, and a total of 216 different metabolites corresponding to 13 metabolic pathways were discovered to be associated with RF; and 44 metabolites showed high levels of sensitivity and specificity under curve values of close to 1, thus might be used as serum biomarkers for RF. In summary, for the first time, our untargeted metabolomics study revealed the distinct metabolic profile of RF, and 44 metabolites with high sensitivity and specificity were discovered, 3 of which have been reported and were consistent with our observations. The other metabolites were first reported by us. Our findings might provide a feasible diagnostic tool for identifying populations at risk for RF through detection of serum metabolites.
慢性肾脏病,包括肾衰竭(RF),是一个全球性的公共卫生问题。临床诊断主要依赖于估算肾小球滤过率的变化,而这通常滞后于疾病进展,对于该健康问题的早期检测,其临床效用可能有限。现在,我们采用基于Q-Exactive HFX Orbitrap液相色谱-串联质谱的代谢组学技术来揭示肾衰竭的代谢谱及潜在生物标志物。将27例肾衰竭患者和27例健康对照纳入测试组,并运用多元模式识别和单变量统计分析等不同技术对结果进行比较分析,以筛选和阐明差异代谢物。建立已鉴定的不同代谢物的点图和受试者工作特征曲线,以发现肾衰竭的潜在生物标志物。结果显示两组之间有明显区分,共发现216种与13条代谢途径相对应的不同代谢物与肾衰竭相关;44种代谢物在曲线下值接近1时表现出高灵敏度和特异性,因此可能用作肾衰竭的血清生物标志物。总之,我们的非靶向代谢组学研究首次揭示了肾衰竭独特的代谢谱,发现了44种具有高灵敏度和特异性的代谢物,其中3种已被报道且与我们的观察结果一致。其他代谢物是我们首次报道。我们的研究结果可能为通过检测血清代谢物来识别肾衰竭风险人群提供一种可行的诊断工具。