Ferraro Pietro Manuel, Li Yukun, Balasubramanian Raji, Curhan Gary C, Taylor Eric N
Section of Nephrology, Department of Medicine, Università degli Studi di Verona, Verona, Italy.
Department of Biostatistics and Epidemiology, University of Massachusetts Amherst, Amherst, Massachusetts.
J Am Soc Nephrol. 2024 Oct 1;35(10):1412-1421. doi: 10.1681/ASN.0000000000000421. Epub 2024 Jun 12.
Information on metabolomic profiles in kidney stone formers is limited. This article describes investigations of associations between plasma metabolomic profiles and the risk of incident, symptomatic kidney stones. Three novel metabolites had negative associations with kidney stones: -cryptoxanthin and two forms of sphingomyelin.
Information on metabolomic profiles in kidney stone formers is limited. To examine independent associations between plasma metabolomic profiles and the risk of incident, symptomatic kidney stones in adults, we conducted prospective nested case-control studies in two large cohorts.
We performed plasma metabolomics on 1758 participants, including 879 stone formers (346 from the Health Professionals Follow-Up Study [HPFS] cohort, 533 from the Nurses' Health Study [NHS] II cohort) and 879 non–stone formers (346 from HPFS, 533 from NHS II) matched for age, race, time of blood collection, fasting status, and (for NHS II) menopausal status and luteal day of menstrual cycle for premenopausal participants. Conditional logistic regression models were used to estimate the odds ratio (OR) of kidney stones adjusted for body mass index; hypertension; diabetes; thiazide use; and intake of potassium, animal protein, oxalate, dietary and supplemental calcium, caffeine, and alcohol. A plasma metabolite–based score was developed in each cohort in a conditional logistic regression model with a lasso penalty. The scores derived in the HPFS (“kidney stones metabolite score [KMS]_HPFS”) and the NHS II (“KMS_NHS”) were tested for their association with kidney stone risk in the other cohort.
A variety of individual metabolites were associated with incident kidney stone formation at prespecified levels of metabolome-wide statistical significance. We identified three metabolites associated with kidney stones in both HPFS and NHS II cohorts: -cryptoxanthin, sphingomyelin (d18:2/24:1, d18:1/24:2), and sphingomyelin (d18:2/24:2). The standardized KMS_HPFS yielded an OR of 1.23 (95% confidence interval, 1.05 to 1.44) for stones in the NHS II cohort. The standardized KMS_NHS was in the expected direction but did not reach statistical significance in HPFS (OR, 1.16; 95% confidence interval, 0.97 to 1.39).
The findings of specific metabolites associated with kidney stone status in two cohorts and a plasma metabolomic signature offer a novel approach to characterize stone formers.
关于肾结石患者代谢组学特征的信息有限。本文描述了血浆代谢组学特征与新发有症状肾结石风险之间关联的调查。三种新型代谢物与肾结石呈负相关：β-隐黄质和两种鞘磷脂。
关于肾结石患者代谢组学特征的信息有限。为了研究血浆代谢组学特征与成年人新发有症状肾结石风险之间的独立关联，我们在两个大型队列中进行了前瞻性巢式病例对照研究。
我们对1758名参与者进行了血浆代谢组学分析，其中包括879名结石患者（346名来自健康专业人员随访研究[HPFS]队列，533名来自护士健康研究[NHS]II队列）和879名非结石患者（346名来自HPFS，533名来自NHS II），这些患者在年龄、种族、采血时间、禁食状态以及（对于NHS II）绝经状态和绝经前参与者月经周期的黄体期方面进行了匹配。使用条件逻辑回归模型估计调整体重指数、高血压、糖尿病、噻嗪类药物使用以及钾、动物蛋白、草酸盐、膳食和补充钙、咖啡因和酒精摄入量后的肾结石比值比（OR）。在每个队列中，通过带有套索惩罚的条件逻辑回归模型开发了基于血浆代谢物的评分。对HPFS队列（“肾结石代谢物评分[KMS]_HPFS”）和NHS II队列（“KMS_NHS”）得出的评分在另一个队列中测试其与肾结石风险的关联。
在预先设定的全代谢组统计学显著性水平上，多种个体代谢物与新发肾结石形成相关。我们在HPFS和NHS II队列中均鉴定出三种与肾结石相关的代谢物：β-隐黄质、鞘磷脂（d18:二/24:一，d18:一/24:二）和鞘磷脂（d18:二/24:二）。标准化的KMS_HPFS在NHS II队列中结石的OR为一.二三（95%置信区间，一.零五至一.四四）。标准化的KMS_NHS方向符合预期，但在HPFS中未达到统计学显著性（OR，一.一六；95%置信区间，零.九七至一.三九）。
在两个队列中发现与肾结石状态相关的特定代谢物以及血浆代谢组学特征，为表征结石患者提供了一种新方法。
