While there is ample evidence indicating an increased occurrence of general neurological conditions among individuals with diabetes, there has been limited exploration into the cause-and-effect connection between type 2 diabetes (T2D) and specific neurological disorders, including conditions like carpal tunnel syndrome and Bell’s palsy. We used Mendelian randomization (MR) approach to investigate the causal effects of T2D on 67 neurological diseases. We primarily utilized the inverse-variance weighted method for the analysis, and also employed the weighted median and MR-Egger methods in our study. To detect and correct potential outliers, MR-PRESSO analysis was used. Heterogeneity was assessed using Cochrane Q-values. The MR analyses found a possible relationship between T2D and a risk increase of 8 diseases at suggestive level of evidence (P < .05). Notably, among the positive findings that met the false discovery rate threshold, nerve, nerve root, and plexus disorders (odds ratio [OR] = 1.11; 95% confidence interval [CI] = 1.08–1.15); neurological diseases (OR = 1.05; 95% CI = 1.03–1.07) and carpal tunnel syndrome (OR = 1.10; 95% CI = 1.05–1.16) were identified. Our findings affirm a cause-and-effect association between T2D and certain neurological disorders.

Lonicera japonica Thunb. and Lonicera hypoglauca are famous Chinese medicines used for hyperglycemia; however, the specific compounds that contributed to the hypoglycemic activity and mechanism are still unknown. In this study, the antidiabetic activity of L. japonica buds and L. hypoglauca buds, roots, stems, and leaves extracts was primarily evaluated, and the L. japonica buds and L. hypoglauca buds, roots, and stems extracts displayed significant hypoglycemic activity, especially for the buds of L. hypoglauca. A total of 72 high-level compounds, including 9 iridoid glycosides, 12 flavonoids, 34 organic acids, and 17 saponins, were identified by ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF-MS) combined with the fragmentation pathways of standards from different parts of L. japonica and L. hypoglauca extracts. Among them, 19 metabolites, including 13 saponins, were reported for the first time from both medicines. Seven high-content compounds identified from L. hypoglauca buds extract were further evaluated for hypoglycemic activity. The result indicated that neochlorogenic acid, chlorogenic acid, isochlorogenic acid A, isochlorogenic acid B, and isochlorogenic acid C displayed significant antidiabetic activity, especially for isochlorogenic acid A and isochlorogenic acid C, which demonstrated that the five chlorogenic-acid-type compounds were the active ingredients of hypoglycemic activity for L. japonica and L. hypoglauca. The potential mechanism of hypoglycemic activity for isochlorogenic acid A and isochlorogenic acid C was inhibiting the intestinal α-glucosidase activity to block the supply of glucose. This study was the first to clarify the hypoglycemic active ingredients and potential mechanism of L. japonica and L. hypoglauca, providing new insights for the comprehensive utilization of both resources and the development of hypoglycemic drugs.

American ginseng (Panax quinquefolius L.) is a rare and valuable plant utilized for medicinal and culinary purposes, with its geographic origin and cultivation significantly affecting its quality and efficacy. However, the metabolic differences between cultivated and wild American ginseng are not well understood. An accurate and reliable method for tracing the origin and evaluating the quality of American ginseng is therefore urgently required. This study introduces a UHPLC-Q/TOF-MS-based comparative metabolomics and machine learning strategy for the rapid identification of wild and cultivated American ginseng. Both principal component analysis and hierarchical cluster analysis revealed distinct metabolic phenotypes between wild and cultivated American ginseng. Furthermore, the integration of univariate and multivariate statistical analyses identified eight differential metabolites in the ESI⁺ mode and three in the ESI^- mode, including seven ginsenosides. A potential ginsenosides marker panel was used to construct five machine learning models to assist in diagnosing the metabolic phenotypes of American ginseng. The Random Forest model, based on the eight differential metabolites in the ESI⁺ mode, achieved a 100% classification rate in both test and validation sets for distinguishing between wild and cultivated American ginseng. This study highlights the feasibility and application of our artificial intelligence-driven comparative metabolomics strategy for cultivar identification and geographic tracing of American ginseng, offering new insights into the molecular basis of metabolic variation in cultivated American ginseng.