Systematic Review: Clinical Metabolomics to Forecast Outcomes in Liver Transplantation Surgery

Circulating metabolic markers after surgery identify patients at risk for severe postoperative complications: a prospective cohort study in colorectal cancer

BACKGROUND

Early detection of postoperative complications after colorectal cancer (CRC) surgery is associated with improved outcomes. The aim was to investigate early metabolomics signatures capable to detect patients at risk for severe postoperative complications after CRC surgery.

MATERIALS AND METHODS

Prospective cohort study of patients undergoing CRC surgery from 2015 to 2018. Plasma samples were collected before and after surgery, and analyzed by mass spectrometry obtaining 188 metabolites and 21 ratios. Postoperative complications were registered with Clavien-Dindo Classification and Comprehensive Complication Index.

RESULTS

One hundred forty-six patients were included. Surgery substantially modified metabolome and metabolic changes after surgery were quantitatively associated with the severity of postoperative complications. The strongest positive relationship with both Clavien-Dindo and Comprehensive Complication Index (β=4.09 and 63.05, P <0.001) corresponded to kynurenine/tryptophan, against an inverse relationship with lysophosphatidylcholines (LPCs) and phosphatidylcholines (PCs). Patients with LPC18:2/PCa36:2 below the cut-off 0.084 µM/µM resulted in a sevenfold higher risk of major complications (OR=7.38, 95% CI: 2.82-21.25, P <0.001), while kynurenine/tryptophan above 0.067 µM/µM a ninefold (OR=9.35, 95% CI: 3.03-32.66, P <0.001). Hexadecanoylcarnitine below 0.093 µM displayed a 12-fold higher risk of anastomotic leakage-related complications (OR=11.99, 95% CI: 2.62-80.79, P =0.004).

CONCLUSION

Surgery-induced phospholipids and amino acid dysregulation is associated with the severity of postoperative complications after CRC surgery, including anastomotic leakage-related outcomes. The authors provide quantitative insight on metabolic markers, measuring vulnerability to postoperative morbidity that might help guide early decision-making and improve surgical outcomes.

Quantitative plasma profiling by 1H NMR-based metabolomics: impact of sample treatment

Introduction: There is evidence that sample treatment of blood-based biosamples may affect integral signals in nuclear magnetic resonance-based metabolomics. The presence of macromolecules in plasma/serum samples makes investigating low-molecular-weight metabolites challenging. It is particularly relevant in the targeted approach, in which absolute concentrations of selected metabolites are often quantified based on the area of integral signals. Since there are a few treatments of plasma/serum samples for quantitative analysis without a universally accepted method, this topic remains of interest for future research. Methods: In this work, targeted metabolomic profiling of 43 metabolites was performed on pooled plasma to compare four methodologies consisting of Carr-Purcell-Meiboom-Gill (CPMG) editing, ultrafiltration, protein precipitation with methanol, and glycerophospholipid solid-phase extraction (g-SPE) for phospholipid removal; prior to NMR metabolomics analysis. The effect of the sample treatments on the metabolite concentrations was evaluated using a permutation test of multiclass and pairwise Fisher scores. Results: Results showed that methanol precipitation and ultrafiltration had a higher number of metabolites with coefficient of variation (CV) values above 20%. G-SPE and CPMG editing demonstrated better precision for most of the metabolites analyzed. However, differential quantification performance between procedures were metabolite-dependent. For example, pairwise comparisons showed that methanol precipitation and CPMG editing were suitable for quantifying citrate, while g-SPE showed better results for 2-hydroxybutyrate and tryptophan. Discussion: There are alterations in the absolute concentration of various metabolites that are dependent on the procedure. Considering these alterations is essential before proceeding with the quantification of treatment-sensitive metabolites in biological samples for improving biomarker discovery and biological interpretations. The study demonstrated that g-SPE and CPMG editing are effective methods for removing proteins and phospholipids from plasma samples for quantitative NMR analysis of metabolites. However, careful consideration should be given to the specific metabolites of interest and their susceptibility to the sample treatment procedures. These findings contribute to the development of optimized sample preparation protocols for metabolomics studies using NMR spectroscopy.

Recent Progress and Future Direction for the Application of Multiomics Data in Clinical Liver Transplantation

Omics data address key issues in liver transplantation (LT) as the most effective therapeutic means for end-stage liver disease. The purpose of this study was to review the current application and future direction for omics in LT. We reviewed the use of multiomics to elucidate the pathogenesis leading to LT and prognostication. Future directions with respect to the use of omics in LT are also described based on perspectives of surgeons with experience in omics. Significant molecules were identified and summarized based on omics, with a focus on post-transplant liver fibrosis, early allograft dysfunction, tumor recurrence, and graft failure. We emphasized the importance omics for clinicians who perform LTs and prioritized the directions that should be established. We also outlined the ideal workflow for omics in LT. In step with advances in technology, the quality of omics data can be guaranteed using an improved algorithm at a lower price. Concerns should be addressed on the translational value of omics for better therapeutic effects in patients undergoing LT.

Primary Nonfunction of the Liver Allograft

Severe allograft dysfunction, as opposed to the expected immediate function, following liver transplantation is a major complication, and the clinical manifestations of such that lead to either immediate retransplant or death are the catastrophic end of the spectrum. Primary nonfunction (PNF) has declined in incidence over the years, yet the impact on patient and healthcare teams, and the burden on the organ pool in case of the need for retransplant should not be underestimated. There is no universal test to define the diagnosis of PNF, and current criteria are based on various biochemical parameters surrogate of liver function; moreover, a disparity remains within different healthcare systems on selecting candidates eligible for urgent retransplantation. The impact on PNF from traditionally accepted risk factors has changed somewhat, mainly driven by the rising demand for organs, combined with the concerted approach by clinicians on the in-depth understanding of PNF, optimal graft recipient selection, mitigation of the clinical environment in which a marginal graft is reperfused, and postoperative management. Regardless of the mode, available data suggest machine perfusion strategies help reduce the incidence further but do not completely avert the risk of PNF. The mainstay of management relies on identifying severe allograft dysfunction at a very early stage and aggressive management, while excluding other identifiable causes that mimic severe organ dysfunction. This approach may help salvage some grafts by preventing total graft failure and also maintaining a patient in an optimal physiological state if retransplantation is considered the ultimate patient salvage strategy.

Measuring the human immune response to surgery: multiomics for the prediction of postoperative outcomes

PURPOSE OF REVIEW

Postoperative complications including infections, cognitive impairment, and protracted recovery occur in one-third of the 300 million surgeries performed annually worldwide. Complications cause personal suffering along with a significant economic burden on our healthcare system. However, the accurate prediction of postoperative complications and patient-targeted interventions for their prevention remain as major clinical challenges.

RECENT FINDINGS

Although multifactorial in origin, the dysregulation of immunological mechanisms that occur in response to surgical trauma is a key determinant of postoperative complications. Prior research, primarily focusing on inflammatory plasma markers, has provided important clues regarding their pathogenesis. However, the recent advent of high-content, single-cell transcriptomic, and proteomic technologies has considerably improved our ability to characterize the immune response to surgery, thereby providing new means to understand the immunological basis of postoperative complications and to identify prognostic biological signatures.

SUMMARY

The comprehensive and single-cell characterization of the human immune response to surgery has significantly advanced our ability to predict the risk of postoperative complications. Multiomic modeling of patients' immune states holds promise for the discovery of preoperative predictive biomarkers, ultimately providing patients and surgeons with actionable information to improve surgical outcomes. Although recent studies have generated a wealth of knowledge, laying the foundation for a single-cell atlas of the human immune response to surgery, larger-scale multiomic studies are required to derive robust, scalable, and sufficiently powerful models to accurately predict the risk of postoperative complications in individual patients.

Diagnosis Model of Hydrogen Sulfide Poisoning Based on Support Vector Machine

Introduction:

Hydrogen sulfide (H2S) is a lethal environmental and industrial poison. The mortality rate of occupational acute H2S poisoning reported in China is 23.1% ~ 50%. Due to the huge amount of information on metabolomics changes after body poisoning, it is important to use intelligent algorithms to mine multivariate interactions.

Methods:

This paper first uses GC-MS metabolomics to detect changes in the urine components of the poisoned group and control rats to form a metabolic dataset, and then uses the SVM classification algorithm in machine learning to train the hydrogen sulfide poisoning training dataset to obtain a classification recognition model. A batch of rats (n = 15) was randomly selected and exposed to 20 ppm H2S gas for 40 days (twice morning and evening, 1 hour each exposure) to prepare a chronic H2S rat poisoning model. The other rats (n = 15) were exposed to the same volume of air and 0 ppm hydrogen sulfide gas as the control group. The treated urine samples were tested using a GC-MS.

Results:

The method locates the optimal parameters of SVM, which improves the accuracy of SVM classification to 100%. This paper uses the information to gain an attribute evaluation method to screen out the top 6 biomarkers that contribute to the predicted category (Glycerol, -Hydroxybutyric acid, arabinofuranose, Pentitol, L-Tyrosine, L-Proline).

Conclusion:

The SVM diagnostic model of hydrogen sulfide poisoning constructed in this work has training time and prediction accuracy; it has achieved excellent results and provided an intelligent decision-making method for the diagnosis of hydrogen sulfide poisoning.

Metabolomics in Bariatric Surgery: Towards Identification of Mechanisms and Biomarkers of Metabolic Outcomes

Bariatric surgery has been widely performed for the treatment of obesity and type 2 diabetes. Efforts have been made to investigate the mechanisms underlying the metabolic effects achieved by bariatric surgery and to identify candidates who will benefit from this surgery. Metabolomics, which includes comprehensive profiling of metabolites in biological samples, has been utilized for various disease entities to discover pathophysiological metabolic pathways and biomarkers predicting disease progression or prognosis. Over the last decade, metabolomic studies on patients undergoing bariatric surgery have identified significant biomarkers related to metabolic effects. This review describes the significance, progress, and challenges for the future of metabolomics in the area of bariatric surgery.

Viability Assessment in Liver Transplantation-What Is the Impact of Dynamic Organ Preservation?

Based on the continuous increase of donor risk, with a majority of organs classified as marginal, quality assessment and prediction of liver function is of utmost importance. This is also caused by the notoriously lack of effective replacement of a failing liver by a device or intensive care treatment. While various parameters of liver function and injury are well-known from clinical practice, the majority of specific tests require prolonged diagnostic time and are more difficult to assess ex situ. In addition, viability assessment of procured organs needs time, because the development of the full picture of cellular injury and the initiation of repair processes depends on metabolic active tissue and reoxygenation with full blood over several hours or days. Measuring injury during cold storage preservation is therefore unlikely to predict the viability after transplantation. In contrast, dynamic organ preservation strategies offer a great opportunity to assess organs before implantation through analysis of recirculating perfusates, bile and perfused liver tissue. Accordingly, several parameters targeting hepatocyte or cholangiocyte function or metabolism have been recently suggested as potential viability tests before organ transplantation. We summarize here a current status of respective machine perfusion tests, and report their clinical relevance.

Toward Rapid Screening of Liver Grafts at the Operating Room Using Mid-infrared Spectroscopy

The estimation of steatosis in a liver graft is mandatory prior to liver transplantation, as the risk of graft failure increases with the level of infiltrated fat. However, the assessment of liver steatosis before transplantation is typically based on a qualitative or semiquantitative characterization by visual inspection and palpation and histological analysis. Thus, there is an unmet need for transplantation surgeons to have access to a diagnostic tool enabling an in situ fast classification of grafts prior to extraction. In this study, we have assessed an attenuated total reflection-Fourier transform infrared (ATR-FTIR) spectroscopic method compatible with the requirements of an operation room for the evaluation of the lipid contents in human livers. A set of 20 human liver biopsies obtained from organs intended for transplantation were analyzed by expert pathologists, ATR-FTIR spectroscopy, lipid biochemical analysis, and UPLC-ESI(+/-)TOFMS for lipidomic profiling. Comparative analysis of multisource data showed strong correlations between ATR-FTIR, clinical, and lipidomic information. Results show that ATR-FTIR captures a global picture of the lipid composition of the liver, along with information for the quantification of the triradylglycerol content in liver biopsies. Although the methodology performance needs to be further validated, results support the applicability of ATR-FTIR for the in situ determination of the grade of liver steatosis at the operation room as a fast, quantitative method, as an alternative to the qualitative and subjective pathological examination.

Circulating Metabolites as Potential Biomarkers for Neurological Disorders-Metabolites in Neurological Disorders

There are, still, limitations to predicting the occurrence and prognosis of neurological disorders. Biomarkers are molecules that can change in different conditions, a feature that makes them potential tools to improve the diagnosis of disease, establish a prognosis, and monitor treatments. Metabolites can be used as biomarkers, and are small molecules derived from the metabolic process found in different biological media, such as tissue samples, cells, or biofluids. They can be identified using various strategies, targeted or untargeted experiments, and by different techniques, such as high-performance liquid chromatography, mass spectrometry, or nuclear magnetic resonance. In this review, we aim to discuss the current knowledge about metabolites as biomarkers for neurological disorders. We will present recent developments that show the need and the feasibility of identifying such biomarkers in different neurological disorders, as well as discuss relevant research findings in the field of metabolomics that are helping to unravel the mechanisms underlying neurological disorders. Although several relevant results have been reported in metabolomic studies in patients with neurological diseases, there is still a long way to go for the clinical use of metabolites as potential biomarkers in these disorders, and more research in the field is needed.

Prognostic value of endogenous and exogenous metabolites in liver transplantation

Liver transplantation has been widely accepted as an effective intervention for end-stage liver diseases and early hepatocellular carcinomas. However, a variety of postoperative complications and adverse reactions have baffled medical staff and patients. Currently, transplantation monitoring relies primarily on nonspecific biochemical tests, whereas diagnosis of multiple complications depends on invasive pathological examination. Therefore, a noninvasive monitoring method with high selectivity and specificity is desperately needed. This review summarized the potential of endogenous small-molecule metabolites as biomarkers for assessing graft function, ischemia-reperfusion injury and liver rejection. Exogenous metabolites, mainly those immunosuppressive agents with high intra- and inter-individual variability, were also discussed for transplantation monitoring.

The Role of Metabolomics in Current Concepts of Organ Preservation

In solid organ transplantation (Tx), both survival rates and quality of life have improved dramatically over the last few decades. Each year, the number of people on the wait list continues to increase, widening the gap between organ supply and demand. Therefore, the use of extended criteria donor grafts is growing, despite higher susceptibility to ischemia-reperfusion injury (IRI) and consecutive inferior Tx outcomes. Thus, tools to characterize organ quality prior to Tx are crucial components for Tx success. Innovative techniques of metabolic profiling revealed key pathways and mechanisms involved in IRI occurring during organ preservation. Although large-scale trials are needed, metabolomics appears to be a promising tool to characterize potential biomarkers, for the assessment of graft quality before Tx and evaluate graft-related outcomes. In this comprehensive review, we summarize the currently available literature on the use of metabolomics in solid organ Tx, with a special focus on metabolic profiling during graft preservation to assess organ quality prior to Tx.

Deregulation of the Purine Pathway in Pre-Transplant Liver Biopsies Is Associated with Graft Function and Survival after Transplantation

The current shortage of livers for transplantation has increased the use of marginal organs sourced from donation after circulatory death (DCD). However, these organs have a higher incidence of graft failure, and pre-transplant biomarkers which predict graft function and survival remain limited. Here, we aimed to find biomarkers of liver function before transplantation to allow better clinical evaluation. Matched pre- and post-transplant liver biopsies from DCD (n = 24) and donation after brain death (DBD, n = 70) were collected. Liver biopsies were analysed using mass spectroscopy molecular phenotyping. Discrimination analysis was used to parse metabolites differentiated between the two groups. Five metabolites in the purine pathway were investigated. Of these, the ratios of the levels of four metabolites to those of urate differed between DBD and DCD biopsies at the pre-transplantation stage (q < 0.05). The ratios of Adenosine monophosphate (AMP) and adenine levels to those of urate also differed in biopsies from recipients experiencing early graft function (EGF) (q < 0.05) compared to those of recipients experiencing early allograft dysfunction (EAD). Using random forest, a panel consisting of alanine aminotransferase (ALT) and the ratios of AMP, adenine, and hypoxanthine levels to urate levels predicted EGF with area under the curve (AUC) of 0.84 (95% CI (0.71, 0.97)). Survival analysis revealed that the metabolite classifier could stratify six-year survival outcomes (p = 0.0073). At the pre-transplantation stage, a panel composed of purine metabolites and ALT could improve the prediction of EGF and survival.