High-calorie diet, circadian rhythms and metabolic features are intimately linked. However, the mediator(s) between nutritional status, circadian rhythms and metabolism remain largely unknown. This article aims to clarify the key metabolic pathways bridging nutritional status and circadian rhythms based on a combination of metabolomics and molecular biological techniques. A mouse model of high-fat diet-induced obesity was established and serum samples were collected in obese and normal mice at different zeitgeber times. Gas chromatography/mass spectrometry, multivariate/univariate data analyses and metabolic pathway analysis were used to reveal changes in metabolism. Metabolites involved in the metabolism of purines, carbohydrates, fatty acids and amino acids were markedly perturbed in accordance with circadian related variations, among which purine catabolism showed a typical oscillation. What’s more, the rhythmicity of purine catabolism dampened in the high-fat diet group. The expressions of clock genes and metabolic enzymes in the liver were measured. The mRNA expression of Xanthine oxidase (Xor) was highly correlated with the rhythmicity of Clock, Rev-erbα and Bmal1, as well as the metabolites involved in purine catabolism. These data showed that a high-fat diet altered the circadian rhythm of metabolic pathways, especially purine catabolism. It had an obvious circadian oscillation and a high-fat diet dampened its circadian rhythmicity. It was suggested that circadian rhythmicity of purine catabolism is related to circadian oscillations of expression of Xor, Uox and corresponding clock genes.

Wuzhi capsule (WZC), a commonly used Chinese patent medicine to treat various types of liver dysfunction in China, increases the exposure of tacrolimus (TAC) in liver transplant recipients. However, this interaction has inter-individual variability, and the underlying mechanism remains unclear. Current research indicates that CYP3A4/5 and drug transporters influence the disposal of both drugs. This study aims to evaluate the association between TAC dose-adjusted trough concentration (C/D) and specific genetic polymorphisms of CYP3A4/5, drug transporters and pregnane x receptor (PXR), and plasma levels of major WZC components, deoxyschisandrin and γ-schisandrin, in liver transplant patients receiving both TAC and WZC. Liquid chromatography-tandem-mass spectrometry was used to detect the plasma levels of deoxyschisandrin and γ-schisandrin, and nine polymorphisms related to metabolic enzymes, transporters and PXR were genotyped by sequencing. A linear mixed model was utilized to assess the impact of the interaction between genetic variations and WZC components on TAC lnC/D. Our results indicate a significant association of TAC lnC/D with the plasma levels of deoxyschisandrin and γ-schisandrin. Univariate analysis demonstrated three polymorphisms in the genes ABCB1 (rs2032582), ABCC2 (rs2273697), ABCC2 (rs3740066), and PXR (rs3842689) interact with both deoxyschisandrin and γ-schisandrin, influencing the TAC lnC/D. In multiple regression model analysis, the interactions between deoxyschisandrin and both ABCB1 (rs2032582) and ABCC2 (rs3740066), post-operative day (β < 0.001, p < 0.001), proton pump inhibitor use (β = -0.152, p = 0.008), body mass index (β = 0.057, p < 0.001), and ABCC2 (rs717620, β = -0.563, p = 0.041), were identified as significant factors of TAC lnC/D, accounting for 47.89% of the inter-individual variation. In summary, this study elucidates the influence of the interaction between ABCB1 and ABCC2 polymorphisms with WZC on TAC lnC/D. These findings offer a scientific basis for their clinical interaction, potentially aiding in the individualized management of TAC therapy in liver transplant patients.

Background and Objective: Liver cancer is the second highest cause of cancer-related deaths worldwide. It is commonly treated with liver transplantation, where tacrolimus is typically used as an antirejection immunosuppressant. The purpose of this study was to evaluate the effect of tacrolimus time in therapeutic range (TTR) on liver cancer recurrence in liver transplant recipients and to compare the performance of TTRs calculated according to the target ranges recommended in published guidelines. Methods: A total of 84 patients who underwent liver transplantation for liver cancer were retrospectively included. Tacrolimus TTR was calculated using linear interpolation from the date of transplantation until recurrence or the last follow-up according to target ranges recommended in the Chinese guideline and international expert consensus. Result: Twenty-four recipients developed liver cancer recurrence after liver transplantation. The CTTR (TTR calculated according to the Chinese guideline) for the recurrence group was significantly lower than that of the nonrecurrence group (26.39% vs. 50.27%, P < 0.001), whereas the ITTR (TTR calculated according to the international consensus) was not significantly different between the two groups (47.81% vs. 56.37%, P = 0.165). Multivariate survival analysis revealed that age, microvascular invasion, hepatocellular carcinoma, CTTR, and mean tacrolimus trough concentration were independent predictors of liver cancer recurrence after liver transplantation. Conclusions: TTR predicts liver cancer recurrence in liver transplant recipients. The range of tacrolimus concentrations recommended in the Chinese guideline was more beneficial than that recommended in the international consensus for Chinese patients undergoing liver transplantation for liver cancer.

This study aimed to evaluate the association between polymyxin B (PMB) exposure and acute kidney injury (AKI) and analyze the risk factors for PMB‐induced AKI in critically ill patients. Plasma concentrations of PMB were determined using an ultraperformance liquid chromatography–tandem mass spectrometer in intensive care unit patients who were administered PMB. Univariate and multivariate analyses were conducted to identify risk factors. A receiver operating characteristic curve was constructed to assess the discriminant power of the factors and to identify the cutoff value for AKI. The white blood cell count and estimated area under the concentration–time curve (AUC) of patients administered PMB were independent risk factors for PMB‐induced AKI, where AUC were calculated using a first‐order pharmacokinetic equation based on the mid‐dosing interval concentration (C_1/2t) and peak concentration. The area under the receiver operating characteristic curve of the final model was 0.805 (95% confidence interval, 0.690–0.921). The cutoff value for the combined predictor was 0.57. Alternatively, when using C_1/2t, which was strongly correlated with AUC, as the only independent risk factor, the analysis showed that the 3.47 μg/ml threshold provides favorable differentiation between the AKI and non‐AKI groups. These results provide insightful information for therapeutic drug monitoring–guiding PMB dosing in clinical practice.