Yeo Wan-Jin, Surapaneni Aditya L, Hasson Denise C, Schmidt Insa M, Sekula Peggy, Köttgen Anna, Eckardt Kai-Uwe, Rebholz Casey M, Yu Bing, Waikar Sushrut S, Rhee Eugene P, Schrauben Sarah J, Feldman Harold I, Vasan Ramachandran S, Kimmel Paul L, Coresh Josef, Grams Morgan E, Schlosser Pascal
Division of Precision Medicine, Department of Medicine, NYU Langone Health, New York, New York.
Division of Pediatric Critical Care Medicine, Hassenfeld Children's Hospital, NYU Langone Health, New York, New York.
J Am Soc Nephrol. 2024 Jun 6;35(9):1252-65. doi: 10.1681/ASN.0000000000000403.
We provide an atlas of cross-sectional and longitudinal serum and urine metabolite associations with eGFR and urine albumin-creatinine ratio in an older community-based cohort. Metabolic profiling in serum and urine provides distinct and complementary insights into disease.
Metabolites represent a read-out of cellular processes underlying states of health and disease.
We evaluated cross-sectional and longitudinal associations between 1255 serum and 1398 urine known and unknown (denoted with “X” in name) metabolites (Metabolon HD4, 721 detected in both biofluids) and kidney function in 1612 participants of the Atherosclerosis Risk in Communities study. All analyses were adjusted for clinical and demographic covariates, including for baseline eGFR and urine albumin-creatinine ratio (UACR) in longitudinal analyses.
At visit 5 of the Atherosclerosis Risk in Communities study, the mean age of participants was 76 years (SD 6); 56% were women, mean eGFR was 62 ml/min per 1.73 m (SD 20), and median UACR level was 13 mg/g (interquartile range, 25). In cross-sectional analysis, 675 serum and 542 urine metabolites were associated with eGFR (Bonferroni-corrected < 4.0E-5 for serum analyses and < 3.6E-5 for urine analyses), including 248 metabolites shared across biofluids. Fewer metabolites (75 serum and 91 urine metabolites, including seven metabolites shared across biofluids) were cross-sectionally associated with albuminuria. Guanidinosuccinate; N2,N2-dimethylguanosine; hydroxy-N6,N6,N6-trimethyllysine; X-13844; and X-25422 were significantly associated with both eGFR and albuminuria. Over a mean follow-up of 6.6 years, serum mannose (hazard ratio [HR], 2.3 [1.6–3.2], = 2.7E-5) and urine X-12117 (HR, 1.7 [1.3–2.2], = 1.9E-5) were risk factors of UACR doubling, whereas urine sebacate (HR, 0.86 [0.80–0.92], = 1.9E-5) was inversely associated. Compared with clinical characteristics alone, including the top five endogenous metabolites in serum and urine associated with longitudinal outcomes improved the outcome prediction (area under the receiver operating characteristic curves for eGFR decline: clinical model=0.79, clinical+metabolites model=0.87, = 8.1E-6; for UACR doubling: clinical model=0.66, clinical+metabolites model=0.73, = 2.9E-5).
Metabolomic profiling in different biofluids provided distinct and potentially complementary insights into the biology and prognosis of kidney diseases.
我们在一个以社区为基础的老年队列中，提供了一份横断面和纵向的血清及尿液代谢物与估算肾小球滤过率（eGFR）及尿白蛋白肌酐比值的关联图谱。血清和尿液中的代谢谱分析为疾病提供了独特且互补的见解。
代谢物代表了健康和疾病状态下细胞过程的一种表现形式。
我们评估了社区动脉粥样硬化风险研究中1612名参与者的1255种血清和1398种尿液中已知及未知（名称中用“X”表示）代谢物（Metabolon HD4，在两种生物流体中均检测到721种）与肾功能之间的横断面和纵向关联。所有分析均针对临床和人口统计学协变量进行了调整，在纵向分析中包括基线eGFR和尿白蛋白肌酐比值（UACR）。
在社区动脉粥样硬化风险研究的第5次访视时，参与者的平均年龄为76岁（标准差6）；56%为女性，平均eGFR为62 ml/min/1.73 m²（标准差20），UACR水平中位数为13 mg/g（四分位间距，25）。在横断面分析中，675种血清代谢物和542种尿液代谢物与eGFR相关（血清分析经Bonferroni校正P<4.0E - 5，尿液分析P<3.6E - 5），其中包括248种在两种生物流体中均存在的代谢物。较少的代谢物（75种血清代谢物和91种尿液代谢物，包括7种在两种生物流体中均存在的代谢物）与蛋白尿呈横断面相关。胍基琥珀酸、N2,N2 - 二甲基鸟苷、羟基 - N6,N6,N6 - 三甲基赖氨酸、X - 13844和X - 25422与eGFR和蛋白尿均显著相关。在平均6.6年的随访中，血清甘露糖（风险比[HR]，2.3[1.6 - 3.2]，P = 2.7E - 5）和尿液X - 12117（HR，1.7[1.3 - 2.2]，P = 1.9E - 5）是UACR翻倍的危险因素，而尿液癸二酸（HR，0.86[0.80 - 0.92]，P = 1.9E - 5）呈负相关。与仅使用临床特征相比，将血清和尿液中与纵向结局相关的前五种内源性代谢物纳入后改善了结局预测（eGFR下降的受试者工作特征曲线下面积：临床模型 = 0.79，临床 + 代谢物模型 = 0.87，P = 8.1E - ⑥；UACR翻倍：临床模型 = 0.66，临床 + 代谢物模型 = 0.73，P = 2.9E - 5）。
不同生物流体中的代谢组学分析为肾脏疾病的生物学机制和预后提供了独特且可能互补的见解。
