Objective Post‐transplantation diabetes mellitus (PTDM) is a common complication in renal transplant recipients (RTRs). Gut microbiome plays important roles in a variety of chronic metabolic diseases, but its association with the occurrence and development of PTDM is still unknown. The present study integrates the analysis of gut microbiome and metabolites to further identify the characteristics of PTDM. Methods A total of 100 RTRs fecal samples were collected in our study. Among them, 55 samples were submitted to Hiseq sequencing, and 100 samples were used for non‐targeted metabolomics analysis. The gut microbiome and metabolomics of RTRs were comprehensively characterized. Results The species Dialister invisus was significantly associated with fasting plasma glucose (FPG). The functions of tryptophan and phenylalanine biosynthesis were enhanced in RTRs with PTDM, while the functions of fructose and butyric acid metabolism were reduced. Fecal metabolome analysis indicated that RTRs with PTDM had unique metabolite distribution characteristics, and two differentially expressed specific metabolites were significantly correlated with FPG. The correlation analysis of gut microbiome and metabolites showed that gut microbiome had an obvious effect on the metabolic characteristics of RTRs with PTDM. Moreover, the relative abundance of microbial function is associated with the expression of several specific gut microbiome and metabolites. Conclusions Our study identified the characteristics of gut microbiome and fecal metabolites in RTRs with PTDM, and we also found two important metabolites and a bacterium were significantly associated with PTDM, which might be used as novel targets in the research field of PTDM.

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Background: It has been reported that donor derived cell-free DNA(dd-cfDNA) accounts for less than 1.2% of total cell free DNA in stable kidney allograft recipients, and dd-cfDNA may be a non-invasive biomarker of acute rejection. However, the kinetics of plasma dd-cfDNA level is still unclear, which hinders the further application of dd-cfDNA in kidney transplantation (KTx). The purpose of this study was to explore the correlation between plasma dd-cfDNA and delayed graft function(DGF） and pulmonary infection after KTx, and to explore the diagnostic value of dd-cfDNA in DGF. In addition, we tried to find out the factors related to the rebound of dd-cfDNA level.Methods: A total of 183 kidney transplant recipients were enrolled in this study. Peripheral blood samples (10ml) were collected on the 1st, 7th, 14th and 21st day after KTx, and 546 plasma samples were collected. Droplet digital PCR (DDPCR) was used to detect the level of dd-cfDNA(%) and Mann Whitney U test was used to analyze the relationship between dd-cfDNA level and DGF and pulmonary infection. Logistic binary regression analysis was used to analyze the clinical factors related to the increase of dd-cfDNA.Results: There was no significant difference between DGF group and non-DGF group of dd-cfDNA level (P > 0.05). The mean value of dd-cfDNA on day 1 (6.97%) was significantly higher than that on day 7 (1.17%), day 14 (1.09%) and day 21 (1.18%). Logistic binary regression analysis was performed for dd-cfDNA level rebound group and non-rebound group. Pulmonary infection (OR = 2.11, P = 0.028) and DGF (OR = 1.37, P = 0.42) were significantly correlated with rebound of dd-cfDNA. At the same time, on the 1st, 7th and 14th day after KTx, the levels of dd-cfDNA in pulmonary infection group was significantly higher than non-infection group (P < 0.05).Conclusion: Our results indicate that dd-cfDNA (%) can’t be used as a biomarker for predicting DGF. The rebound of plasma dd-cfDNA (%) level was significantly correlated with the presence of pulmonary infection. However, further confirmatory studies are necessary.

Graft-derived cell-free DNA (GcfDNA) is a promising non-invasive biomarker for detecting allograft injury. In this study, we aimed to evaluate the efficacy of programmed monitoring of GcfDNA for identifying BK polyomavirus-associated nephropathy (BKPyVAN) in kidney transplant recipients. We recruited 158 kidney transplant recipients between November 2020 and December 2021. Plasma GcfDNA was collected on the tenth day, first month, third month, and sixth month for programmed monitoring and one day before biopsy. ΔGcfDNA (cp/mL) was obtained by subtracting the baseline GcfDNA (cp/mL) from GcfDNA (cp/mL) of the latest programmed monitoring before biopsy. The receiver operating characteristic curve showed the diagnostic performance of GcfDNA (cp/mL) at biopsy time and an optimal area under the curve (AUC) of 0.68 in distinguishing pathologically proven BKPyVAN from pathologically unconfirmed BKPyVAN. In contrast, ΔGcfDNA (cp/mL) had a sensitivity and specificity of 80% and 84.6%, respectively, and an AUC of 0.83. When distinguishing clinically diagnosed BKPyVAN from clinical excluded BKPyVAN, the AUC of GcfDNA (cp/mL) was 0.59 at biopsy time, and ΔGcfDNA (cp/mL) had a sensitivity and specificity of 81.0% and 76.5%, respectively, and an AUC of 0.81. Plasma ΔGcfDNA (cp/mL) was not significantly different between TCMR [0.15 (0.08, 0.24) cp/mL] and pathologically proven BKPyVAN[0.34 (0.20, 0.49) cp/mL]. In conclusion, we recommend programmed monitoring of plasma GcfDNA levels after a kidney transplant. Based on our findings from the programmed monitoring, we have developed a novel algorithm that shows promising results in identifying and predicting BKPyVAN.