Using Metabolomics to Monitor Kidney Transplantation Patients by Means of Clustering to Spot Anomalous Patient Behavior

Circulating Metabolites in Relation to the Kidney Allograft Function in Posttransplant Patients

End-stage kidney disease is preferably treated by kidney transplantation. The suboptimal function of the allograft often results in misbalances in kidney-controlled processes and requires long-term monitoring of allograft function and viability. As the kidneys are organs with a very high metabolomic rate, a metabolomics approach is suitable to describe systematic changes in post-transplant patients and has great potential for monitoring allograft function, which has not been described yet. In this study, we used blood plasma samples from 55 patients after primary kidney transplantation identically treated with immunosuppressants with follow-up 50 months in the mean after surgery and evaluated relative levels of basal plasma metabolites detectable by NMR spectroscopy. We were looking for the correlations between circulating metabolites levels and allograft performance and allograft rejection features. Our results imply a quantitative relationship between restricted renal function, insufficient hydroxylation of phenylalanine to tyrosine, lowered renal glutamine utilization, shifted nitrogen balance, and other alterations that are not related exclusively to the metabolism of the kidney. No link between allograft function and energy metabolism can be concluded, as no changes were found for glucose, glycolytic intermediates, and 3-hydroxybutyrate as a ketone body representative. The observed changes are to be seen as a superposition of changes in the comprehensive inter-organ metabolic exchange, when the restricted function of one organ may induce compensatory effects or cause secondary alterations. Particular differences in plasma metabolite levels in patients with acute cellular and antibody-mediated allograft rejection were considered rather to be related to the loss of kidney function than to the molecular mechanism of graft rejection since they largely follow the alterations observed by restricted allograft function. In the end, we showed using a simple mathematical model, multilinear regression, that the basal plasmatic metabolites correlated with allograft function expressed by the level of glomerular filtration rate (with creatinine: p-value = 4.0 × 10⁻²⁶ and r = 0.94, without creatinine: p-value = 3.2 × 10⁻²² and r = 0.91) make the noninvasive estimation of the allograft function feasible.

Estimated Renal Metabolomics at Reperfusion Predicts One-Year Kidney Graft Function

Renal transplantation is the gold-standard procedure for end-stage renal disease patients, improving quality of life and life expectancy. Despite continuous advancement in the management of post-transplant complications, progress is still needed to increase the graft lifespan. Early identification of patients at risk of rapid graft failure is critical to optimize their management and slow the progression of the disease. In 42 kidney grafts undergoing protocol biopsies at reperfusion, we estimated the renal metabolome from RNAseq data. The estimated metabolites' abundance was further used to predict the renal function within the first year of transplantation through a random forest machine learning algorithm. Using repeated K-fold cross-validation we first built and then tuned our model on a training dataset. The optimal model accurately predicted the one-year eGFR, with an out-of-bag root mean square root error (RMSE) that was 11.8 ± 7.2 mL/min/1.73 m2. The performance was similar in the test dataset, with a RMSE of 12.2 ± 3.2 mL/min/1.73 m2. This model outperformed classic statistical models. Reperfusion renal metabolome may be used to predict renal function one year after allograft kidney recipients.

Serum metabolomics approach to monitor the changes in metabolite profiles following renal transplantation

Systemic metabolic changes after renal transplantation reflect the key processes that are related to graft accommodation. In order to describe and better understand these changes, the ¹HNMR based metabolomics approach was used. The changes of 47 metabolites in the serum samples of 19 individuals were interpreted over time with respect to their levels prior to transplantation. Considering the specific repeated measures design of the experiments, data analysis was mainly focused on the multiple analyses of variance (ANOVA) methods such as ANOVA simultaneous component analysis and ANOVA-target projection. We also propose here the combined use of ANOVA and classification and regression trees (ANOVA-CART) under the assumption that a small set of metabolites the binary splits on which may better describe the graft accommodation processes over time. This assumption is very important for developing a medical protocol for evaluating a patient's health state. The results showed that besides creatinine, which is routinely used to monitor renal activity, the changes in levels of hippurate, mannitol and alanine may be associated with the changes in renal function during the post-transplantation recovery period. Specifically, the level of hippurate (or histidine) is more sensitive to any short-term changes in renal activity than creatinine.

Advances in Detection of Kidney Transplant Injury

Early detection of graft injury after kidney transplantation is key to maintaining long-term good graft function. Graft injury could be due to a multitude of factors including ischaemia reperfusion injury, cell or antibody-mediated rejection, progressive interstitial fibrosis and tubular atrophy, infections and toxicity from the immunosuppressive drugs themselves. The current gold standard for assessing renal graft dysfunction is renal biopsy. However, biopsy is usually late when triggered by a change in serum creatinine and of limited utility in diagnosis of early injury when histological changes are equivocal. Therefore, there is a need for timely, objective and non-invasive diagnostic techniques with good early predictive value to determine graft injury and provide precision in titrating immunosuppression. We review potential novel plasma and urine biomarkers that offer sensitive new strategies for early detection and provide major insights into mechanisms of graft injury. This is a rapidly expanding field, but it is likely that a combination of biomarkers will be required to provide adequate sensitivity and specificity for detecting graft injury.

Urinary metabolites predict prolonged duration of delayed graft function in DCD kidney transplant recipients

Extending kidney donor criteria, including donation after circulatory death (DCD), has resulted in increased rates of delayed graft function (DGF) and primary nonfunction. Here, we used Nuclear Magnetic Resonance (NMR) spectroscopy to analyze the urinary metabolome of DCD transplant recipients at multiple time points (days 10, 42, 180, and 360 after transplantation). The aim was to identify markers that predict prolonged duration of functional DGF (fDGF). Forty-seven metabolites were quantified and their levels were evaluated in relation to fDGF. Samples obtained at day 10 had a different profile than samples obtained at the other time points. Furthermore, at day 10 there was a statistically significant increase in eight metabolites and a decrease in six metabolites in the group with fDGF (N = 53) vis-à-vis the group without fDGF (N = 22). In those with prolonged fDGF (≥21 days) (N = 17) urine lactate was significantly higher and pyroglutamate lower than in those with limited fDGF (<21 days) (N = 36). In order to further distinguish prolonged fDGF from limited fDGF, the ratios of all metabolites were analyzed. In a logistic regression analysis, the sum of branched-chain amino acids (BCAAs) over pyroglutamate and lactate over fumarate, predicted prolonged fDGF with an AUC of 0.85. In conclusion, kidney transplant recipients with fDGF can be identified based on their altered urinary metabolome. Furthermore, two ratios of urinary metabolites, lactate/fumarate and BCAAs/pyroglutamate, adequately predict prolonged duration of fDGF.

Elucidating time-dependent changes in the urinary metabolome of renal transplant patients by a combined (1)H NMR and GC-MS approach

Urine metabolomic profiling can identify biochemical alterations resulting from various injuries affecting the graft outcome after renal transplantation. Here, we aimed to describe in depth the metabolite content of urines of renal transplant patients and to link it with the major injury factors acting at critical stages following transplantation. Morning urine samples were prospectively collected from 38 kidney transplant patients at 7 days (D7), 3 months (M3) and 12 months (M12) after transplantation. Twenty-five patients were treated with tacrolimus (Tac) and thirteen patients with cyclosporine (CsA). (1)H-NMR (proton nuclear magnetic resonance) and gas chromatography-mass spectrometry (GC-MS) were used to examine the overall metabolomic signature of each sample. Multivariate analysis was performed to study the changes in the metabolic profile over time and their dependency on the type of calcineurin inhibitor (CNI) administered to patients. Biological pathways affected by transplantation were identified using a metabolomics pathway analysis (MetPA) web-tool. The metabolic profile of urine samples clearly varied with time. Markers of medullary injury, tubule cell oxidative metabolism and impaired tubular reabsorption or secretion were present at D7. Differences in metabolic profiles became less marked as time passed on, urine content being quite similar at M3 and M12. The metabolite profile tended to differ between patients receiving Tac and those receiving CsA but no clear discriminating profiles can be found. The combination of (1)H-NMR and GC-MS for the analysis of urine metabolomic profiles is a very useful method to study patho-physiological alterations in kidney transplant patients over time.

Urinary metabolomics for noninvasive detection of borderline and acute T cell-mediated rejection in children after kidney transplantation

The goal of this study was to evaluate the utility of urinary metabolomics for noninvasive diagnosis of T cell-mediated rejection (TCMR) in pediatric kidney transplant recipients. Urine samples (n = 277) from 57 patients with surveillance or indication kidney biopsies were assayed for 134 unique metabolites by quantitative mass spectrometry. Samples without TCMR (n = 183) were compared to borderline tubulitis (n = 54) and TCMR (n = 30). Partial least squares discriminant analysis identified distinct classifiers for TCMR (area under receiver operating characteristic curve [AUC] = 0.892; 95% confidence interval [CI] 0.827-0.957) and borderline tubulitis (AUC = 0.836; 95% CI 0.781-0.892), respectively. Application of the TCMR classifier to borderline tubulitis samples yielded a discriminant score (-0.47 ± 0.33) mid-way between TCMR (-0.20 ± 0.34) and No TCMR (-0.80 ± 0.32) (p < 0.001 for all comparisons). Discriminant scoring for combined borderline/TCMR versus No TCMR (AUC = 0.900; 95% CI 0.859-0.940) applied to a validation cohort robustly distinguished between samples with (-0.08 ± 0.52) and without (-0.65 ± 0.54, p < 0.001) borderline/TCMR (p < 0.001). The TCMR discriminant score was driven by histological t-score, ct-score, donor-specific antibody and biopsy indication, and was unaffected by renal function, interstitial or microcirculatory inflammation, interstitial fibrosis or pyuria. These preliminary findings suggest that urinary metabolomics is a sensitive, specific and noninvasive tool for TCMR identification that is superior to serum creatinine, with minimal confounding by other allograft injury processes.