[Correlation between drug-induced liver injury in rats caused by Xianling Gubao oral preparation and extraction process].

In light of the liver injury risk associated with the oral administration of Xianlin Gubao oral preparation, this study compared the differences in liver injury induced by two different extraction processes in rats and explored the correlation between hepatotoxicity and extraction process from the perspective of the differences in the content of the relevant components. Thirty male Sprague-Dawley(SD) rats were randomly divided into a normal group, tablet extract groups of different doses, and capsule extract groups of different doses, with 6 rats in each group. Each group received continuous oral administration for 4 weeks. The assessment of liver injury caused by different extracts was conducted by examining rat body weight, liver function blood biochemical indicators, liver coefficient, and liver pathological changes. In addition, a high-performance liquid chromatography(HPLC) method was established to simultaneously determine the content of icariin, baohuoside I, and bakuchiol in the extracts to compare the differences in the content of these three components under the two extraction processes. The results showed that both extracts caused liver injury in rats. Compared with the normal group, the tablet extract groups, at the studied dose, led to slow growth in body weight, a significant increase in triglyceride levels(P<0.05), a significant decrease in liver-to-brain ratio(P<0.05), and the appearance of hepatic steatosis. The capsule extract groups, at the studied dose, resulted in slow growth in body weight, a significant increase in aspartate aminotransferase levels(P<0.05), a significant decrease in body weight, liver weight, and liver-to-brain ratio(P<0.05), and the presence of hepatic steatosis and inflammatory cell infiltration. In comparison, the capsule extraction process had a higher risk of liver injury. Furthermore, based on the completion of the liquid chromatography method, the content of icariin and baohuoside Ⅰ in the capsule extract groups was 0.83 and 0.81 times that in the tablet extract groups, respectively, while the bakuchiol content in the capsule extract group was 29.80 times that in the tablet extract groups, suggesting that the higher risk of liver injury associated with the capsule extraction process may be due to its higher bakuchiol content. In summary, the differences in rat liver injury caused by the two extracts are closely related to the extraction process. This should be taken into consideration in the formulation production and clinical application.

The characteristics of postprandial glycemic response patterns to white rice and glucose in healthy adults: Identifying subgroups by clustering analysis.

Objectives: Large interpersonal variability in postprandial glycemic response (PGR) to white rice has been reported, and differences in the PGR patterns during the oral glucose tolerance test (OGTT) have been documented. However, there is scant study on the PGR patterns of white rice. We examined the typical PGR patterns of white rice and glucose and the association between them. Materials and methods: We analyzed the data of 3-h PGRs to white rice (WR) and glucose (G) of 114 normoglycemic female subjects of similar age, weight status, and same ethnic group. Diverse glycemic parameters, based on the discrete blood glucose values, were calculated over 120 and 180 min. K-means clustering based on glycemic parameters calculated over 180 min was applied to identify subgroups and representative PGR patterns. Principal factor analysis based on the parameters used in the cluster analysis was applied to characterize PGR patterns. Simple correspondence analysis was performed on the clustering categories of WR and G. Results: More distinct differences were found in glycemic parameters calculated over 180 min compared with that calculated over 120 min, especially in the negative area under the curve and Nadir. We identified four distinct PGR patterns to WR (WR1, WR2, WR3, and WR4) and G (G1, G2, G3, and G4), respectively. There were significant differences among the patterns regard to postprandial hyperglycemia, hypoglycemic, and glycemic variability. The WR1 clusters had significantly lower glycemic index (59 ± 19), while no difference was found among the glycemic index based on the other three clusters. Each given G subgroup presented multiple patterns of PGR to WR, especially in the largest G subgroup (G1), and in subgroup with the greatest glycemic variability (G3). Conclusion: Multiple subgroups could be classified based on the PGR patterns to white rice and glucose even in seemingly homogeneous subjects. Extending the monitoring time to 180 min was conducive to more effective discrimination of PGR patterns. It may not be reliable to extrapolate the patterns of PGR to rice from that to glucose, suggesting a need of combining OGTT and meal tolerance test for individualized glycemic management.