Global climate change has a main impact on the distribution of plants. Aralia chinensis L. is economically valuable, making it necessary to predict the impact of climate change on its distribution. It is important for researching the effects of climate change on A. chinensis distribution to achieve sustainable utilization. Based on 340 occurrence records of A. chinensis covering all known provinces and 58 environmental factor data, we used MaxEnt to simulate the potential distribution of A. chinensis under current and different future climate scenarios, analyzing the key environmental variables affecting its distribution. The results were as follows: (1) Suitable A. chinensis habitats under current and different future climate scenarios were mainly distributed in the southern region of China, east of the Hu Huanyong line. (2) Annual precipitation, minimum temperature during the coldest month, precipitation during the driest month, and slope were the key environmental variables affecting its potential distribution, and annual precipitation was more important. (3) Suitable habitat areas were projected to increase under different future climate scenarios and expand westward and northward while shrinking in the central regions, such as Hubei and Hunan. The results provide a theoretical reference for the conservation and cultivation of A. chinensis.

Background: Coronary heart disease (CHD) is a major cardiovascular disease. Cinnamomum migao volatile oil (CM-VO) is traditionally used for CHD, but its active basis and mechanisms remain unclear. Objective: To clarify the chemical basis and potential anti-CHD mechanisms of CM-VO. Methods: GC–MS was used to identify CM-VO constituents and serum-related metabolites. A mouse CHD model was established to evaluate pharmacodynamic effects. Network analysis was used for full-component mechanism prediction, while transcriptomics was used to screen treatment-responsive genes. UniProt standardization, deep learning, molecular docking, and H9c2 cell experiments were further performed for target prediction and validation. Results: A total of 68 CM-VO constituents and 8 serum-related metabolites were identified. CM-VO improved myocardial injury, inflammation, oxidative stress, endothelial dysfunction, and lipid-related abnormalities in CHD mice. Network analysis predicted 39 bioactive compounds and 58 key targets, while transcriptomics identified 44 DEGs and 101 CHD-related enriched genes. After UniProt standardization, 19 human genes were analyzed, and GADD45A, MTHFS, and ALAS2 were prioritized as high-affinity targets. In H9c2 cells, CM-VO-containing serum reduced lipid accumulation and injury markers, enhanced antioxidant capacity, and regulated GADD45A, MTHFS, and ALAS2 expression. Conclusion: CM-VO may protect against CHD mainly by regulating inflammation, oxidative stress, endothelial function, and lipid metabolism. This study provides integrated evidence for the pharmacological basis and molecular mechanisms of CM-VO against CHD.