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多组学分析表明脂质介导的慢性肾病与中风之间存在密切关联:对受影响患者队列的潜在益处。

Multiomics analysis demonstrated a strong correlation between lipid-mediated chronic kidney disease and stroke: Potential benefits of affected patient cohorts.

作者信息

Liu Yan-Song, Zhang Jian-Hang, Han Jia-Yue, Long Yu-Yan, Liu Yu-Chen, Mao Kai-Ni, Feng Yu-Jing, Song Zhi-Guang, Peng Si-Min, Tan Shi-Min, Cai Si-le, Yang Jing-Yi, Lin Li-Hua, Song Wan-Ying, Li Hua, Liu Wang-Hua

机构信息

Provincial Key Laboratory of TCM Diagnostics, Hunan University of Chinese Medicine, Changsha 410208, Hunan, China; Key Laboratory of TCM Heart and Lung Syndrome Differentiation & Medicated Diet and Dietotherapy, Hunan University of Chinese Medicine, Changsha 410208, Hunan, China; Hunan Engineering Technology Research Center for Medicinal and Functional Food, Hunan University of Chinese Medicine, Changsha 410208, Hunan, China.

Provincial Key Laboratory of TCM Diagnostics, Hunan University of Chinese Medicine, Changsha 410208, Hunan, China; Key Laboratory of TCM Heart and Lung Syndrome Differentiation & Medicated Diet and Dietotherapy, Hunan University of Chinese Medicine, Changsha 410208, Hunan, China.

出版信息

J Stroke Cerebrovasc Dis. 2025 Jun;34(6):108285. doi: 10.1016/j.jstrokecerebrovasdis.2025.108285. Epub 2025 Mar 11.

DOI:10.1016/j.jstrokecerebrovasdis.2025.108285
PMID:40081514
Abstract

OBJECTIVE

Patients with chronic kidney disease (CKD) exhibit a disproportionately elevated risk of stroke, frequently compounded by renal impairment. Therapeutic strategies for stroke based on Traditional Chinese Medicine's 'kidney-brain axis' theory demonstrate clinical efficacy, indicating that there may be a potential association between chronic kidney disease and stroke, which needs further exploration and verification.

METHODS

In this study, databases such as GEO, NHANES, and GWAS were used to collect data related to CKD and stroke. GEO gene data enrichment analysis was used to explore possible mediating factors between CKD and stroke. NHANES clinical data were used to verify the GEO data analysis results. Mendelian randomization was used to confirm the causal relationship between CKD and stroke and verify the association effect of mediating factors in these two diseases.

RESULTS

Cross-gene analysis and transcription factor analysis of GEO data revealed that lipid-related pathways may have a mediating effect on the relationship between CKD and stroke. Logistic regression analysis based on NHANES data revealed that changes in LDL-C, HDL-C, TC, and TG can affect the occurrence of stroke. Mendelian randomization analysis was used to determine the causal relationship between CKD and stroke and verified the mediating effects of lipid factors, such as LDL-C, HDL-C, TC, and TG, indicating that LDL-C, HDL-C, TC, and TG may be potential mediating factors for these two diseases. Our findings highlight the clinical relevance of lipid pathways in bridging CKD and stroke. By integrating predictive biomarkers and multi-level diagnostics, this study paves the way for AI-driven precision medicine in stroke prevention. Specifically, machine learning approaches could enhance risk stratification of high-risk CKD cohorts, enabling tailored interventions such as lipid-lowering therapies and personalized monitoring protocols. These strategies align with emerging paradigms in healthcare benefits and population-specific management.

CONCLUSION

This study provides new insights into the interactive relationship between CKD and stroke and provides a scientific basis for the process of syndrome differentiation and the treatment of stroke under the guidance of the "kidney-brain correlation". Moreover, the influence of mediating factors related to lipid metabolism on the occurrence of these two diseases was investigated, which deepened researchers' understanding of the potential association mechanism between the two diseases.

摘要

目的

慢性肾脏病(CKD)患者中风风险异常升高,且常因肾功能损害而加重。基于中医“肾脑轴”理论的中风治疗策略显示出临床疗效,这表明慢性肾脏病与中风之间可能存在潜在关联,有待进一步探索和验证。

方法

本研究利用GEO、NHANES和GWAS等数据库收集与CKD和中风相关的数据。采用GEO基因数据富集分析来探索CKD与中风之间可能的中介因素。利用NHANES临床数据验证GEO数据分析结果。采用孟德尔随机化来确定CKD与中风之间的因果关系,并验证这两种疾病中介因素的关联效应。

结果

对GEO数据进行交叉基因分析和转录因子分析发现,脂质相关途径可能在CKD与中风的关系中起中介作用。基于NHANES数据的逻辑回归分析表明,低密度脂蛋白胆固醇(LDL-C)、高密度脂蛋白胆固醇(HDL-C)、总胆固醇(TC)和甘油三酯(TG)的变化会影响中风的发生。采用孟德尔随机化分析确定CKD与中风之间的因果关系,并验证了LDL-C、HDL-C、TC和TG等脂质因素的中介作用,表明LDL-C、HDL-C、TC和TG可能是这两种疾病的潜在中介因素。我们的研究结果突出了脂质途径在连接CKD和中风方面的临床相关性。通过整合预测生物标志物和多层次诊断,本研究为人工智能驱动的中风预防精准医学铺平了道路。具体而言,机器学习方法可加强对高危CKD队列的风险分层,从而实现如降脂治疗和个性化监测方案等针对性干预。这些策略与医疗保健效益和特定人群管理的新兴模式相一致。

结论

本研究为CKD与中风的交互关系提供了新见解,并为“肾脑相关”指导下的中风辨证论治过程提供了科学依据。此外,研究了脂质代谢相关中介因素对这两种疾病发生的影响,加深了研究人员对这两种疾病潜在关联机制的理解。

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