Gooding Jessica R, Agrawal Shipra, McRitchie Susan, Acuff Zach, Merchant Michael L, Klein Jon B, Smoyer William E, Sumner Susan J
National Institutes of Health Eastern Regional Comprehensive Metabolomics Resource Core (ERCMRC) at University of North Carolina, Chapel Hill, North Carolina, USA.
Discovery, Science and Technology, RTI International, Research Triangle Park, North Carolina, USA.
Kidney Int Rep. 2019 Sep 19;5(1):81-93. doi: 10.1016/j.ekir.2019.09.010. eCollection 2020 Jan.
Nephrotic syndrome (NS) is a kidney disease that affects both children and adults. Glucocorticoids have been the primary therapy for >60 years but are ineffective in approximately 20% of children and approximately 50% of adult patients. Unfortunately, patients with steroid-resistant NS (SRNS; vs. steroid-sensitive NS [SSNS]) are at high risk for both glucocorticoid-induced side effects and disease progression.
We performed proton nuclear magnetic resonance (H NMR) metabolomic analyses on plasma samples ( = 86) from 45 patients with NS (30 SSNS and 15 SRNS) obtained at initial disease presentation before glucocorticoid initiation and after approximately 7 weeks of glucocorticoid therapy to identify candidate biomarkers able to either predict SRNS before treatment or define critical molecular pathways/targets regulating steroid resistance.
Stepwise logistic regression models identified creatinine concentration and glutamine concentration (odds ratio [OR]: 1.01; 95% confidence interval [CI]: 0.99-1.02) as 2 candidate biomarkers predictive of SRNS, and malonate concentration (OR: 0.94; 95% CI: 0.89-1.00) as a third candidate predictive biomarker using a similar model (only in children >3 years). In addition, paired-sample analyses identified several candidate biomarkers with the potential to identify mechanistic molecular pathways/targets that regulate clinical steroid resistance, including lipoproteins, adipate, pyruvate, creatine, glucose, tyrosine, valine, glutamine, and sn-glycero-3-phosphcholine.
Metabolomic analyses of serial plasma samples from children with SSNS and SRNS identified elevated creatinine and glutamine concentrations, and reduced malonate concentrations, as auspicious candidate biomarkers to predict SRNS at disease onset in pediatric NS, as well as additional candidate biomarkers with the potential to identify mechanistic molecular pathways that may regulate clinical steroid resistance.
肾病综合征（NS）是一种影响儿童和成人的肾脏疾病。糖皮质激素作为主要治疗方法已超过60年，但约20%的儿童患者和约50%的成人患者对其无效。不幸的是，激素抵抗性肾病综合征（SRNS；与激素敏感性肾病综合征[SSNS]相比）患者面临糖皮质激素诱导的副作用和疾病进展的高风险。
我们对45例肾病综合征患者（30例SSNS和15例SRNS）在糖皮质激素治疗开始前的疾病初发时以及糖皮质激素治疗约7周后采集的血浆样本（n = 86）进行了质子核磁共振（1H NMR）代谢组学分析，以确定能够在治疗前预测SRNS或定义调节激素抵抗的关键分子途径/靶点的候选生物标志物。
逐步逻辑回归模型确定肌酐浓度和谷氨酰胺浓度（优势比[OR]：1.01；95%置信区间[CI]：0.99 - 1.02）为预测SRNS的2种候选生物标志物，丙二酸浓度（OR：0.94；95% CI：0.89 - 1.00）为使用类似模型（仅在3岁以上儿童中）预测SRNS的第3种候选生物标志物。此外，配对样本分析确定了几种有潜力识别调节临床激素抵抗的机制性分子途径/靶点的候选生物标志物，包括脂蛋白、己二酸、丙酮酸、肌酸、葡萄糖、酪氨酸、缬氨酸、谷氨酰胺和sn -甘油-3 -磷酸胆碱。
对SSNS和SRNS患儿的系列血浆样本进行代谢组学分析，确定肌酐和谷氨酰胺浓度升高以及丙二酸浓度降低是预测小儿肾病综合征发病时SRNS的有前景的候选生物标志物，以及有潜力识别可能调节临床激素抵抗的机制性分子途径的其他候选生物标志物。
