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综合分析确定了糖代谢异常参与者中与慢性肾病相关的新蛋白质。

Integrative analysis identifies novel proteins associated with chronic kidney disease in participants with abnormal glucose metabolism.

作者信息

Li Ning, Liu Jingyang, Wu Guangheng, Zhang Jie, Liu Long, Zheng Manqi, Li Haibin, Li Changwei, Wen Yalu, Ji Jianguang, Yu Yang, Zhao Kun, Zheng Deqiang

机构信息

Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China; Beijing Key Laboratory of environment and aging, Capital Medical University, Beijing, China; Department of Statistics, University of Auckland, Auckland, New Zealand.

Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China; Beijing Key Laboratory of environment and aging, Capital Medical University, Beijing, China.

出版信息

Diabetes Res Clin Pract. 2025 Sep 9;229:112474. doi: 10.1016/j.diabres.2025.112474.

Abstract

AIM

Chronic kidney disease (CKD) is highly prevalent among individuals with abnormal glucose metabolism. However, limited research has specifically investigated CKD-associated proteins within this high-risk population. To address this gap, our study aimed to identify proteins associated with CKD in participants with abnormal glucose metabolism, potentially informing early detection and targeted therapeutic strategies.

METHODS

We first employed orthogonal partial least squares discriminant analysis (OPLS-DA) to select important proteins and further used Cox proportional hazards models using proteomic data from the UK Biobank to identify candidate proteins associated with CKD in participants with abnormal glucose metabolism. Subsequently, we performed one-sample Mendelian randomization (MR) using individual-level genomic data from the UK Biobank and pQTL summary statistics from the UKB-PPP, applying a two-stage least squares approach. For two-sample MR, we utilized pQTL data from deCODE and CKD GWAS summary statistics derived from the UK Biobank, applying either the Wald ratio or inverse variance weighted (IVW) method. Proteins supported by both observational analyses and at least one MR approach were further evaluated using publicly available databases to determine their novelty. Finally, for proteins consistently identified across all approaches, we assessed tissue specificity, gene expression, and conducted sensitivity analyses to strengthen the robustness of our findings.

RESULTS

Through integrated observational and MR analyses, we identified a total of 45 proteins significantly associated with CKD in participants with abnormal glucose metabolism, among which 11 represent novel discoveries: CD300C, CD300LG, CDNF, CDSN, CHRDL1, ENPP6, LEFTY2, MOG, RSPO3, TNFRSF13B, and MYLPF. Notably, ENPP6 emerged with consistent evidence across all analytic approaches. Observational analyses demonstrated a hazard ratio (HR) of 0.75 (95% CI: 0.63-0.89), while one-sample MR revealed an odds ratio (OR) of 0.32 (95% CI: 0.14-0.73), and two-sample MR produced an OR of 0.60 (95% CI: 0.37-0.98), supporting a protective role of ENPP6 in CKD development. Furthermore, ENPP6 displayed kidney-specific expression, particularly within peritubular and proximal tubular cells. These findings were robustly validated through comprehensive sensitivity analyses.

CONCLUSION

In conclusion, we identified 11 novel proteins associated with CKD in individuals with abnormal glucose metabolism, with ENPP6 emerging as a particularly compelling candidate due to its consistent protective association across multiple analytical approaches. These findings offer promising insights into CKD pathophysiology and highlight ENPP6 as a potential biomarker or therapeutic target. Further research is warranted to elucidate the mechanistic roles of these novel proteins in CKD development and progression.

摘要

目的

慢性肾脏病(CKD)在糖代谢异常个体中高度流行。然而,针对这一高危人群中与CKD相关蛋白质的研究有限。为填补这一空白,我们的研究旨在识别糖代谢异常参与者中与CKD相关的蛋白质,为早期检测和靶向治疗策略提供依据。

方法

我们首先采用正交偏最小二乘判别分析(OPLS-DA)来选择重要蛋白质,并进一步使用Cox比例风险模型,利用英国生物银行的蛋白质组学数据,识别糖代谢异常参与者中与CKD相关的候选蛋白质。随后,我们使用来自英国生物银行的个体水平基因组数据和来自UKB-PPP的pQTL汇总统计数据,采用两阶段最小二乘法进行单样本孟德尔随机化(MR)。对于两样本MR,我们利用来自deCODE的pQTL数据和源自英国生物银行的CKD全基因组关联研究(GWAS)汇总统计数据,采用Wald比率或逆方差加权(IVW)方法。通过公开可用数据库进一步评估经观察性分析和至少一种MR方法支持的蛋白质,以确定其新颖性。最后,对于所有方法一致鉴定出的蛋白质,我们评估了组织特异性、基因表达,并进行了敏感性分析,以加强我们研究结果的稳健性。

结果

通过综合观察性分析和MR分析,我们在糖代谢异常参与者中总共鉴定出45种与CKD显著相关的蛋白质,其中11种代表新发现:CD300C、CD300LG丶CDNF、CDSN、CHRDL1、ENPP6、LEFTY2、MOG、RSPO3、TNFRSF13B和MYLPF。值得注意的是,ENPP6在所有分析方法中都有一致的证据。观察性分析显示风险比(HR)为0.75(95%置信区间:0.63-0.89),而单样本MR显示比值比(OR)为0.32(95%置信区间:0.14-0.73),两样本MR产生的OR为0.60(95%置信区间:0.37-0.98),支持ENPP6在CKD发生发展中的保护作用。此外,ENPP6表现出肾脏特异性表达,特别是在肾小管周围和近端肾小管细胞中。这些发现通过全面敏感性分析得到了有力验证。

结论

总之,我们在糖代谢异常个体中鉴定出11种与CKD相关新蛋白质,其中ENPP6因其在多种分析方法中一致的保护关联而成为特别有说服力的候选者。这些发现为CKD病理生理学提供了有前景的见解,并突出ENPP6作为潜在生物标志物或治疗靶点。有必要进一步研究以阐明这些新蛋白质在CKD发生发展中的机制作用。

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