Objective Meta-analysis was used in order to systematically evaluate the evidence for multi-omics technology in treating cardiovascular disease (CVD) aiming to provide new treatment avenues for CVD patients. Methods A systematic search was conducted across Chinese databases (such as China Biology Medicine disc and China National Knowledge Infrastructure) and international databases (including PubMed and Web of Science) for literature published between April 2015 and April 2025 on multi-omics-based interventions for CVD. Research indicators such as interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), systolic blood pressure (SBP), diastolic blood pressure (DBP) were observed to explore the impact of multi-omics technology on cardiovascular diseases, and Review Manager 5.4 software was selected for Meta-analysis, and Egger's test was used to Publication bias was analyzed. Results Six papers with a total of 588 patients were finally included. Meta-analysis results showed that the TNF-α level in the treatment group based on multi-omics technology was significantly lower than the TNF-α level before the application of multi-omics technology (SMD=-0.78, 95% CI: -1.10 to -0.46, P<0.05). Moreover, the funnel plot exhibits symmetry, indicating a low likelihood of publication bias. And after the treatment based on multi-omics technology, the other traditional clinical indexes in patients with cardiovascular diseases did not show significant changes, and some of the indexes were not statistically significant. Conclusion In the treatment of cardiovascular diseases, multi-omics technology can be used to regulate vascular function-related molecular networks and down-regulate the expression of pro-inflammatory factors by modulating the vascular function-related molecular networks, while possibly targeting more non-traditional pathways.
Key words
multi-omics technology /
cardiovascular disease /
meta-analysis /
inflammatory factors /
lipid metabolism
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