Lipidomic Signature of Progression of Chronic Kidney Disease in the Chronic Renal Insufficiency Cohort.
Lipidomic Signature of Progression of Chronic Kidney Disease in the Chronic Renal Insufficiency Cohort.
复制标题
慢性肾功能不全队列中慢性肾脏病进展的脂质组学特征
DOI：
10.1016/j.ekir.2016.08.007
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发表时间：
2016-11
影响因子：
6
通讯作者：
Pennathur S
中科院分区：
文献类型：
--
作者：
Afshinnia F;Rajendiran TM;Karnovsky A;Soni T;Wang X;Xie D;Yang W;Shafi T;Weir MR;He J;Brecklin CS;Rhee EP;Schelling JR;Ojo A;Feldman H;Michailidis G;Pennathur S
人体研究报告了关于血脂对慢性肾脏疾病进展的预测能力的相互矛盾的结果。我们的目的是系统地确定预测进展为终末期肾病的脂质。从慢性肾功能不全队列中，选择了79例随访6年以上进展为终末期肾病的2 - 3期慢性肾脏疾病患者，并按年龄、性别、种族和糖尿病进行频率匹配，其中121例为非进展者，随访期间估计肾小球滤过率下降小于25%。患者被随机分为训练集和测试集。我们应用基于液相色谱-质谱法的脂质组学对第1年访视样本进行分析。我们鉴定了510种脂质，其中前10种与训练集中0.058的错误发现阈值一致。从前10种脂质中，二酰基甘油和胆固醇酯的丰度较低，但磷脂酸44：4和单酰基甘油16：0的丰度在进展者中显著较高。使用逻辑回归模型，由二酰基甘油和单酰基甘油组成的多标志物组独立预测进展。与基础模型相比，添加到基础模型（包括估计的肾小球滤过率和尿蛋白/肌酐比值）的多标记物组的c统计量为0.92（95%置信区间：0.88-0.97）和0.83（95%置信区间：0.76-0.90，P < 0.01），这是在测试子集中验证的观察结果。我们的结论是，一个独特的面板脂质可能会改善预测慢性肾脏疾病的进展超过估计肾小球滤过率和尿蛋白肌酐比时，添加到基础模型。
Human studies report conflicting results on the predictive power of serum lipids on the progression of chronic kidney disease. We aimed to systematically identify the lipids that predict progression to end-stage kidney disease. From the Chronic Renal Insufficiency Cohort, 79 patients with chronic kidney disease stages 2 to 3 who progressed to end-stage kidney disease over 6 years of follow-up were selected and frequency matched by age, sex, race, and diabetes with 121 nonprogressors with less than 25% decline in estimated glomerular filtration rate during the follow-up. The patients were randomly divided into training and test sets. We applied liquid chromatography-mass spectrometry-based lipidomics on visit year 1 samples. We identified 510 lipids, of which the top 10 coincided with false discovery threshold of 0.058 in the training set. From the top 10 lipids, the abundance of diacylglycerols and cholesteryl esters was lower, but that of phosphatidic acid 44:4 and monoacylglycerol 16:0 was significantly higher in progressors. Using logistic regression models, a multimarker panel consisting of diacylglycerols and monoacylglycerol independently predicted progression. The c-statistic of the multimarker panel added to the base model consisting of estimated glomerular filtration rate and urine protein-to-creatinine ratio as compared with that of the base model was 0.92 (95% confidence interval: 0.88–0.97) and 0.83 (95% confidence interval: 0.76–0.90, P < 0.01), respectively, an observation that was validated in the test subset. We conclude that a distinct panel of lipids may improve prediction of progression of chronic kidney disease beyond estimated glomerular filtration rate and urine protein-to-creatinine ratio when added to the base model.