Identification of Plasma Metabolites Prognostic of Acute Kidney Injury after Cardiac Surgery with Cardiopulmonary Bypass

Bucket Fuser: Statistical Signal Extraction for 1D 1H NMR Metabolomic Data

Untargeted metabolomics is a promising tool for identifying novel disease biomarkers and unraveling underlying pathomechanisms. Nuclear magnetic resonance (NMR) spectroscopy is particularly suited for large-scale untargeted metabolomics studies due to its high reproducibility and cost effectiveness. Here, one-dimensional (1D) 1H NMR experiments offer good sensitivity at reasonable measurement times. Their subsequent data analysis requires sophisticated data preprocessing steps, including the extraction of NMR features corresponding to specific metabolites. We developed a novel 1D NMR feature extraction procedure, called Bucket Fuser (BF), which is based on a regularized regression framework with fused group LASSO terms. The performance of the BF procedure was demonstrated using three independent NMR datasets and was benchmarked against existing state-of-the-art NMR feature extraction methods. BF dynamically constructs NMR metabolite features, the widths of which can be adjusted via a regularization parameter. BF consistently improved metabolite signal extraction, as demonstrated by our correlation analyses with absolutely quantified metabolites. It also yielded a higher proportion of statistically significant metabolite features in our differential metabolite analyses. The BF algorithm is computationally efficient and it can deal with small sample sizes. In summary, the Bucket Fuser algorithm, which is available as a supplementary python code, facilitates the fast and dynamic extraction of 1D NMR signals for the improved detection of metabolic biomarkers.

Metabolomic profiling demonstrates evidence for kidney and urine metabolic dysregulation in a piglet model of cardiac surgery-induced acute kidney injury

Acute kidney injury (AKI) is a common cause of morbidity after congenital heart disease surgery. Progress on diagnosis and therapy remains limited, however, in part due to poor mechanistic understanding and a lack of relevant translational models. Metabolomic approaches could help identify novel mechanisms of injury and potential therapeutic targets. In the present study, we used a piglet model of cardiopulmonary bypass with deep hypothermic circulatory arrest (CPB/DHCA) and targeted metabolic profiling of kidney tissue, urine, and serum to evaluate metabolic changes specific to animals with histological acute kidney injury. CPB/DHCA animals with acute kidney injury were compared with those without acute kidney injury and mechanically ventilated controls. Acute kidney injury occurred in 10 of 20 CPB/DHCA animals 4 h after CPB/DHCA and 0 of 7 control animals. Injured kidneys showed a distinct tissue metabolic profile compared with uninjured kidneys (R² = 0.93, Q² = 0.53), with evidence of dysregulated tryptophan and purine metabolism. Nine urine metabolites differed significantly in animals with acute kidney injury with a pattern suggestive of increased aerobic glycolysis. Dysregulated metabolites in kidney tissue and urine did not overlap. CPB/DHCA strongly affected the serum metabolic profile, with only one metabolite that differed significantly with acute kidney injury (pyroglutamic acid, a marker of oxidative stress). In conclusion, based on these findings, kidney tryptophan and purine metabolism are candidates for further mechanistic and therapeutic investigation. Urine biomarkers of aerobic glycolysis could help diagnose early acute kidney injury after CPB/DHCA and warrant further evaluation. The serum metabolites measured at this early time point did not strongly differentiate based on acute kidney injury.

NEW & NOTEWORTHY This project explored the metabolic underpinnings of postoperative acute kidney injury (AKI) following pediatric cardiac surgery in a translationally relevant large animal model of cardiopulmonary bypass with deep hypothermic circulatory arrest. Here, we present novel evidence for dysregulated tryptophan catabolism and purine catabolism in kidney tissue and increased urinary glycolysis intermediates in animals who developed histological AKI. These pathways represent potential diagnostic and therapeutic targets for postoperative AKI in this high-risk population.

Use of Ultra High Performance Liquid Chromatography with High Resolution Mass Spectrometry to Analyze Urinary Metabolome Alterations Following Acute Kidney Injury in Post-Cardiac Surgery Patients

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Cardiac surgery-associated AKI results in dramatic changes in urinary metabolome.

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Urinary metabolite disorder observed in patients with cardiac surgery-associated AKI.

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When metaboloite disorder was due to ischaemia and medical treatment, kidneys could return to normal.

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This work provides data about urinary metabolic profiles and resources for further research on AKI.

Background

Cardiac surgery-associated acute kidney injury (AKI) can increase the mortality and morbidity, and the incidence of chronic kidney disease, in critically ill survivors. The purpose of this research was to investigate possible links between urinary metabolic changes and cardiac surgery-associated AKI.

Methods

Using ultra-high-performance liquid chromatography coupled with Q-Exactive Orbitrap mass spectrometry, non-targeted metabolomics was performed on urinary samples collected from groups of patients with cardiac surgery-associated AKI at different time points, including Before_AKI (uninjured kidney), AKI_Day1 (injured kidney) and AKI_Day14 (recovered kidney) groups. The data among the three groups were analyzed by combining multivariate and univariate statistical methods, and urine metabolites related to AKI in patients after cardiac surgery were screened. Altered metabolic pathways associated with cardiac surgery-induced AKI were identified by examining the Kyoto Encyclopedia of Genes and Genomes database.

Results

The secreted urinary metabolome of the injured kidney can be well separated from the urine metabolomes of uninjured or recovered patients using multivariate and univariate statistical analyses. However, urine samples from the AKI_Day14 and Before_AKI groups cannot be distinguished using either of the two statistical analyses. Nearly 4000 urinary metabolites were identified through bioinformatics methods at Annotation Levels 1–4. Several of these differential metabolites may also perform essential biological functions. Differential analysis of the urinary metabolome among groups was also performed to provide potential prognostic indicators and changes in signalling pathways. Compared with the uninjured kidney group, the patients with cardiac surgery-associated AKI displayed dramatic changes in renal metabolism, including sulphur metabolism and amino acid metabolism.

Conclusions

Urinary metabolite disorder was observed in patients with cardiac surgery-associated AKI due to ischaemia and medical treatment, and the recovered patients’ kidneys were able to return to normal. This work provides data on urine metabolite markers and essential resources for further research on AKI.

Serum metabolic profile of postoperative acute kidney injury following infant cardiac surgery with cardiopulmonary bypass

BACKGROUND

We sought to determine differences in the circulating metabolic profile of infants with or without acute kidney injury (AKI) following cardiothoracic surgery with cardiopulmonary bypass (CPB).

METHODS

We performed a secondary analysis of preoperative and 24-h postoperative serum samples from infants ≤ 120 days old undergoing CPB. Metabolic profiling of the serum samples was performed by targeted analysis of 165 serum metabolites via tandem mass spectrometry. We then compared infants who did or did not develop AKI in the first 72 h postoperatively to determine global differences in the preoperative and 24-h metabolic profiles in addition to specific differences in individual metabolites.

RESULTS

A total of 57 infants were included in the study. Six infants (11%) developed KDIGO stage 2/3 AKI and 13 (23%) developed stage 1 AKI. The preoperative metabolic profile did not differentiate between infants with or without AKI. Infants with severe AKI could be moderately distinguished from infants without AKI by their 24-h metabolic profile, while infants with stage 1 AKI segregated into two groups, overlapping with either the no AKI or severe AKI groups. Differences in these 24-h metabolic profiles were driven by 21 metabolites significant at an adjusted false discovery rate of < 0.05. Prominently altered pathways include purine, methionine, and kynurenine/nicotinamide metabolism.

CONCLUSION

Moderate-to-severe AKI after infant cardiac surgery is associated with changes in the serum metabolome, including prominent changes to purine, methionine, and kynurenine/nicotinamide metabolism. A portion of infants with mild AKI demonstrated similar metabolic changes, suggesting a potential role for metabolic analysis in the evaluation of lower stage injury.

Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses

Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field.

Urinary Spermidine Predicts and Associates with In-Hospital Acute Kidney Injury after Cardiac Surgery

Acute Kidney Injury (AKI) affects up to 30% of the patients who undergo cardiac surgery (CVS) and is related to higher mortality. We aim to investigate molecular features associated with in-hospital AKI development and determine the predictive value of these features when analyzed preoperatively. This is a case-control study. From an initial cohort of 110 recruited subjects, a total of 60 patients undergoing cardiac surgery were included: 20 (33%) developed in-hospital AKI (CVS-AKI) and 40 did not (controls, CVS-C). Pre- and post-surgery samples were collected and a prospective study was carried out. A total of 312 serum samples and 258 urine samples were analyzed by nuclear magnetic resonance, mass spectrometry and ELISA. Six features predicted AKI development in pre-surgery samples: urinary kidney functional loss marker kidney injury molecule-1 (uKIM-1), 2-hydroxybutyric acid, 2-hydroxyphenylacetic acid, hippuric acid, phosphoethanolamine and spermidine. Two of them stood out as powerful predictors. Pre-surgery uKIM-1 levels were increased in CVS-AKI vs. CVS-C (AUC = 0.721, p-value = 0.0392) and associated strongly with the outcome (OR = 5.333, p-value = 0.0264). Spermidine showed higher concentration in CVS-AKI (p-value < 0.0001, AUC = 0.970) and had a strong association with the outcome (OR = 69.75, p-value < 0.0001). uKIM-1 and particularly spermidine predict in-hospital AKI associated with CVS in preoperative samples. These findings may aid in preventing postoperative AKI and improve prognosis of CVS.

Metabolomic Profiling for Diagnosis and Prognostication in Surgery: A Scoping Review

OBJECTIVE

This review assimilates and critically evaluates available literature regarding the use of metabolomic profiling in surgical decision-making.

BACKGROUND

Metabolomic profiling is performed by nuclear magnetic resonance spectroscopy or mass spectrometry of biofluids and tissues to quantify biomarkers (ie, sugars, amino acids, and lipids), producing diagnostic and prognostic information that has been applied among patients with cardiovascular disease, inflammatory bowel disease, cancer, and solid organ transplants.

METHODS

PubMed was searched from 1995 to 2019 to identify studies investigating metabolomic profiling of surgical patients. Articles were included and assimilated into relevant categories per PRISMA-ScR guidelines. Results were summarized with descriptive analytical methods.

RESULTS

Forty-seven studies were included, most of which were retrospective studies with small sample sizes using various combinations of analytic techniques and types of biofluids and tissues. Results suggest that metabolomic profiling has the potential to effectively screen for surgical diseases, suggest diagnoses, and predict outcomes such as postoperative complications and disease recurrence. Major barriers to clinical adoption include a lack of high-level evidence from prospective studies, heterogeneity in study design regarding tissue and biofluid procurement and analytical methods, and the absence of large, multicenter metabolome databases to facilitate systematic investigation of the efficacy, reproducibility, and generalizability of metabolomic profiling diagnoses and prognoses.

CONCLUSIONS

Metabolomic profiling research would benefit from standardization of study design and analytic approaches. As technologies improve and knowledge garnered from research accumulates, metabolomic profiling has the potential to provide personalized diagnostic and prognostic information to support surgical decision-making from preoperative to postdischarge phases of care.

Urinary NMR Profiling in Pediatric Acute Kidney Injury-A Pilot Study

Acute kidney injury (AKI) in critically ill children and adults is associated with significant short- and long-term morbidity and mortality. As serum creatinine- and urine output-based definitions of AKI have relevant limitations, there is a persistent need for better diagnostics of AKI. Nuclear magnetic resonance (NMR) spectroscopy allows for analysis of metabolic profiles without extensive sample manipulations. In the study reported here, we examined the diagnostic accuracy of NMR urine metabolite patterns for the diagnosis of neonatal and pediatric AKI according to the Kidney Disease: Improving Global Outcomes (KDIGO) definition. A cohort of 65 neonatal and pediatric patients (0–18 years) with established AKI of heterogeneous etiology was compared to both a group of apparently healthy children (n = 53) and a group of critically ill children without AKI (n = 31). Multivariate analysis identified a panel of four metabolites that allowed diagnosis of AKI with an area under the receiver operating characteristics curve (AUC-ROC) of 0.95 (95% confidence interval 0.86–1.00). Especially urinary citrate levels were significantly reduced whereas leucine and valine levels were elevated. Metabolomic differentiation of AKI causes appeared promising but these results need to be validated in larger studies. In conclusion, this study shows that NMR spectroscopy yields high diagnostic accuracy for AKI in pediatric patients.

Application of NMR metabolomics to search for human disease biomarkers in blood

Recently, nuclear magnetic resonance spectroscopy (NMR)-based metabolomics analysis and multivariate statistical techniques have been incorporated into a multidisciplinary approach to profile changes in small molecules associated with the onset and progression of human diseases. The purpose of these efforts is to identify unique metabolite biomarkers in a specific human disease so as to (1) accurately predict and diagnose diseases, including separating distinct disease stages; (2) provide insights into underlying pathways in the pathogenesis and progression of the malady and (3) aid in disease treatment and evaluate the efficacy of drugs. In this review we discuss recent developments in the application of NMR-based metabolomics in searching disease biomarkers in human blood samples in the last 5 years.

A Novel Metabolic Signature To Predict the Requirement of Dialysis or Renal Transplantation in Patients with Chronic Kidney Disease

Identification of chronic kidney disease patients at risk of progressing to end-stage renal disease (ESRD) is essential for treatment decision-making and clinical trial design. Here, we explored whether proton nuclear magnetic resonance (NMR) spectroscopy of blood plasma improves the currently best performing kidney failure risk equation, the so-called Tangri score. Our study cohort comprised 4640 participants from the German Chronic Kidney Disease (GCKD) study, of whom 185 (3.99%) progressed over a mean observation time of 3.70 ± 0.88 years to ESRD requiring either dialysis or transplantation. The original four-variable Tangri risk equation yielded a C statistic of 0.863 (95% CI, 0.831-0.900). Upon inclusion of NMR features by state-of-the-art machine learning methods, the C statistic improved to 0.875 (95% CI, 0.850-0.911), thereby outperforming the Tangri score in 94 out of 100 subsampling rounds. Of the 24 NMR features included in the model, creatinine, high-density lipoprotein, valine, acetyl groups of glycoproteins, and Ca²⁺-EDTA carried the highest weights. In conclusion, proton NMR-based plasma fingerprinting improved markedly the detection of patients at risk of developing ESRD, thus enabling enhanced patient treatment.

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.

Bretschneider solution-induced alterations in the urine metabolome in cardiac surgery patients

The development of Bretschneider’s histidine-tryptophan-ketoglutarate (HTK) cardioplegia solution represented a major advancement in cardiac surgery, offering significant myocardial protection. However, metabolic changes induced by this additive in the whole body have not been systematically investigated. Using an untargeted mass spectrometry-based method to deeply explore the urine metabolome, we sought to provide a holistic and systematic view of metabolic perturbations occurred in patients receiving HTK. Prospective urine samples were collected from 100 patients who had undergone cardiac surgery, and metabolomic changes were profiled using a high-performance chemical isotope labeling liquid chromatography-mass spectrometry (LC-MS) method. A total of 14,642 peak pairs or metabolites were quantified using differential ¹³C-/¹²C-dansyl labeling LC-MS, which targets the amine/phenol submetabolome from urine specimens. We identified 223 metabolites that showed significant concentration change (fold change > 5) and assembled several potential metabolic pathway maps derived from these dysregulated metabolites. Our data indicated upregulated histidine metabolism with subsequently increased glutamine/glutamate metabolism, altered purine and pyrimidine metabolism, and enhanced vitamin B₆ metabolism in patients receiving HTK. Our findings provide solid evidence that HTK solution causes significant perturbations in several metabolic pathways and establish a basis for further study of key mechanisms underlying its organ-protective or potential harmful effects.

Statistical Analysis of NMR Metabolic Fingerprints: Established Methods and Recent Advances

In this review, we summarize established and recent bioinformatic and statistical methods for the analysis of NMR-based metabolomics. Data analysis of NMR metabolic fingerprints exhibits several challenges, including unwanted biases, high dimensionality, and typically low sample numbers. Common analysis tasks comprise the identification of differential metabolites and the classification of specimens. However, analysis results strongly depend on the preprocessing of the data, and there is no consensus yet on how to remove unwanted biases and experimental variance prior to statistical analysis. Here, we first review established and new preprocessing protocols and illustrate their pros and cons, including different data normalizations and transformations. Second, we give a brief overview of state-of-the-art statistical analysis in NMR-based metabolomics. Finally, we discuss a recent development in statistical data analysis, where data normalization becomes obsolete. This method, called zero-sum regression, builds metabolite signatures whose estimation as well as predictions are independent of prior normalization.

Scale-Invariant Biomarker Discovery in Urine and Plasma Metabolite Fingerprints

Metabolomics data is typically scaled to a common reference like a constant volume of body fluid, a constant creatinine level, or a constant area under the spectrum. Such scaling of the data, however, may affect the selection of biomarkers and the biological interpretation of results in unforeseen ways. Here, we studied how both the outcome of hypothesis tests for differential metabolite concentration and the screening for multivariate metabolite signatures are affected by the choice of scale. To overcome this problem for metabolite signatures and to establish a scale-invariant biomarker discovery algorithm, we extended linear zero-sum regression to the logistic regression framework and showed in two applications to ¹H NMR-based metabolomics data how this approach overcomes the scaling problem. Logistic zero-sum regression is available as an R package as well as a high-performance computing implementation that can be downloaded at https://github.com/rehbergT/zeroSum .

Lactate Elevation During and After Major Cardiac Surgery in Adults: A Review of Etiology, Prognostic Value, and Management

Elevated lactate is a common occurrence after cardiac surgery. This review summarizes the literature on the complex etiology of lactate elevation during and after cardiac surgery, including considerations of oxygen delivery, oxygen utilization, increased metabolism, lactate clearance, medications and fluids, and postoperative complications. Second, the association between lactate and a variety of outcomes are described, and the prognostic role of lactate is critically assessed. Despite the fact that elevated lactate is strongly associated with many important outcomes, including postoperative complications, length of stay, and mortality, little is known about the optimal management of postoperative patients with lactate elevations. This review ends with an assessment of the limited literature on this subject.

Biomarkers for the Early Detection and Prognosis of Acute Kidney Injury

AKI is an increasingly common disorder that is strongly linked to short- and long-term morbidity and mortality. Despite a growing heterogeneity in its causes, providing a timely and certain diagnosis of AKI remains challenging. In this review, we summarize the evolution of AKI biomarker studies over the past few years, focusing on two major areas of investigation: the early detection and prognosis of AKI. We highlight some of the lessons learned in conducting AKI biomarker studies, including ongoing attempts to address the limitations of creatinine as a reference standard and the recent shift toward evaluating the prognostic potential of these markers. Lastly, we suggest current gaps in knowledge and barriers that may be hindering their incorporation into care and a full ascertainment of their value.

An overview of renal metabolomics

The high-throughput, high-resolution phenotyping enabled by metabolomics has been applied increasingly to a variety of questions in nephrology research. This article provides an overview of current metabolomics methodologies and nomenclature, citing specific considerations in sample preparation, metabolite measurement, and data analysis that investigators should understand when examining the literature or designing a study. Furthermore, we review several notable findings that have emerged in the literature that both highlight some of the limitations of current profiling approaches, as well as outline specific strengths unique to metabolomics. More specifically, we review data on the following: (i) tryptophan metabolites and chronic kidney disease onset, illustrating the interpretation of metabolite data in the context of established biochemical pathways; (ii) trimethylamine-N-oxide and cardiovascular disease in chronic kidney disease, illustrating the integration of exogenous and endogenous inputs to the blood metabolome; and (iii) renal mitochondrial function in diabetic kidney disease and acute kidney injury, illustrating the potential for rapid translation of metabolite data for diagnostic or therapeutic aims. Finally, we review future directions, including the need to better characterize interperson and intraperson variation in the metabolome, pool existing data sets to identify the most robust signals, and capitalize on the discovery potential of emerging nontargeted methods.