Xu Yiming, Xiao Tian, Yang Junqing, Wang Jiali, Wang Bingting, Qiao Chen
School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China.
School of Pharmacy, China Pharmaceutical University, Nanjing, China.
J Diabetes Investig. 2025 Jul;16(7):1274-1283. doi: 10.1111/jdi.70051. Epub 2025 Apr 30.
Circulating cytokines were reported to be related to diabetic nephropathy (DN) in observational studies. However, the causal relationship between them remains unknown. This study aimed to investigate the causal relationship between DN and circulating cytokines with genetic data in the frame of Mendelian Randomization (MR).
We performed a two-sample MR analysis to investigate the causal relationship in individuals of European ancestry, utilizing publicly available genome-wide association study (GWAS) statistics. We selected eligible instrumental SNPs that were significantly related to the circulating cytokines. Multiple MR analysis approaches were employed, including inverse variance weighted (IVW), Weighted Median, MR-Egger, Weighted Mode, Simple Mode, and MR pleiotropy residual sum and outlier (MR-PRESSO) methods.
We found evidence supporting the causal role of genetically predicted circulating levels in the increased risk of DN. Specifically, we observed associations for interferon-gamma [OR = 1.352, 95% CI: 1.089-1.678, P = 0.006], stem cell factor [OR = 1.252, 95% CI: 1.028-1.525, P = 0.025], and stromal-cell-derived factor 1 alpha [OR = 1.326, 95% CI: 1.017-1.727, P = 0.037]. Additionally, MR analysis revealed a negative causal association between macrophage inflammatory protein 1b and DN [OR = 0.921, 95% CI: 0.858-0.988, P = 0.022]. The results obtained from MR-Egger, Weighted Median, Weighted Mode, and Simple Mode methods were consistent with the Inverse Variance Weighted (IVW) estimates. Sensitivity analyses showed no evidence of horizontal pleiotropy, suggesting that the causal estimates were not biased.
Our findings offer promising leads for developing novel therapeutic targets for DN. By identifying the role of inflammatory cytokines in this debilitating condition through a genetic epidemiological approach, our study made contributions to a better understanding of the underlying disease mechanisms.
在观察性研究中，循环细胞因子被报道与糖尿病肾病（DN）相关。然而，它们之间的因果关系仍不清楚。本研究旨在利用孟德尔随机化（MR）框架下的遗传数据，探讨DN与循环细胞因子之间的因果关系。
我们进行了一项两样本MR分析，以研究欧洲血统个体中的因果关系，利用公开可用的全基因组关联研究（GWAS）统计数据。我们选择了与循环细胞因子显著相关的合格工具单核苷酸多态性（SNP）。采用了多种MR分析方法，包括逆方差加权（IVW）、加权中位数、MR-Egger、加权模式、简单模式和MR多效性残差和异常值（MR-PRESSO）方法。
我们发现有证据支持遗传预测的循环水平在DN风险增加中的因果作用。具体而言，我们观察到干扰素-γ[比值比（OR）=1.352，95%置信区间（CI）：1.089-1.678，P=0.006]、干细胞因子[OR=1.252，95%CI：1.028-1.525，P=0.025]和基质细胞衍生因子1α[OR=1.326，95%CI：1.017-1.727，P=0.037]存在关联。此外，MR分析显示巨噬细胞炎性蛋白1β与DN之间存在负因果关联[OR=0.921，95%CI：0.858-0.988，P=0.022]。从MR-Egger、加权中位数、加权模式和简单模式方法获得的结果与逆方差加权（IVW）估计一致。敏感性分析未显示水平多效性的证据，表明因果估计无偏差。
我们的研究结果为开发DN的新型治疗靶点提供了有希望的线索。通过遗传流行病学方法确定炎症细胞因子在这种使人衰弱的疾病中的作用，我们的研究有助于更好地理解潜在的疾病机制。
