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Updated: Jul 25, 2026

The MPLEx Protocol for Multi-omic Analyses of Soil Samples
Published on: May 30, 2018
Multiomics analysis demonstrated a strong correlation between lipid-mediated chronic kidney disease and stroke:
Yan-Song Liu1, Jian-Hang Zhang1, Jia-Yue Han1
1Provincial 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.
Insights
This study reveals that lipid metabolism, including LDL-C, HDL-C, TC, and TG, acts as a key link between chronic kidney disease (CKD) and stroke. These findings support targeted lipid-lowering therapies for stroke prevention in CKD patients.
Area of Science:
- Nephrology
- Neurology
- Metabolic Research
Background:
- Chronic kidney disease (CKD) significantly increases stroke risk.
- Traditional Chinese Medicine's 'kidney-brain axis' theory suggests a link between kidney health and neurological outcomes.
- Understanding the mechanisms connecting CKD and stroke is crucial for effective prevention and treatment.
Purpose of the Study:
- To explore the potential association between chronic kidney disease (CKD) and stroke.
- To identify mediating factors, particularly those related to lipid metabolism, that link CKD and stroke.
- To verify the causal relationship and mediating effects using genetic and clinical data.
Main Methods:
- Utilized data from GEO, NHANES, and GWAS databases.
- Performed gene data enrichment analysis (GEO) and logistic regression (NHANES).
- Employed Mendelian randomization to confirm causality and mediating effects of lipid factors.
Main Results:
- Lipid-related pathways, including LDL-C, HDL-C, TC, and TG, were identified as potential mediators between CKD and stroke.
- Changes in these lipid markers significantly affect stroke occurrence.
- Confirmed a causal relationship between CKD and stroke, mediated by lipid metabolism.
Conclusions:
- Lipid metabolism plays a critical mediating role in the relationship between CKD and stroke.
- Findings provide a scientific basis for stroke treatment guided by the 'kidney-brain correlation' concept.
- Highlights potential for AI-driven precision medicine and tailored interventions for high-risk CKD patients.
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.
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