Metabolic phenotyping in clinical and surgical environments

Metabonomic profiling of bladder cancer

Early diagnosis and life-long surveillance are clinically important to improve the long-term survival of bladder cancer patients. Currently, a noninvasive biomarker that is as sensitive and specific as cystoscopy in detecting bladder tumors is lacking. Metabonomics is a complementary approach for identifying perturbed metabolic pathways in bladder cancer. Significant progress has been made using modern metabonomic techniques to characterize and distinguish bladder cancer patients from control subjects, identify marker metabolites, and shed insights on the disease biology and potential therapeutic targets. With its rapid development, metabonomics has the potential to impact the clinical management of bladder cancer patients in the future by revolutionizing the diagnosis and life-long surveillance strategies and stratifying patients for diagnostic, surgical, and therapeutic clinical trials. An introduction to metabonomics, typical metabonomic workflow, and critical evaluation of metabonomic investigations in identifying biomarkers for the diagnosis of bladder cancer are presented.

Brain region mapping using global metabolomics

Historically, studies of brain metabolism have been based on targeted analyses of a limited number of metabolites. Here we present an untargeted mass spectrometry-based metabolomic strategy that has successfully uncovered differences in a broad array of metabolites across anatomical regions of the mouse brain. The NSG immunodeficient mouse model was chosen because of its ability to undergo humanization leading to numerous applications in oncology and infectious disease research. Metabolic phenotyping by hydrophilic interaction liquid chromatography and nanostructure imaging mass spectrometry revealed both water-soluble and lipid metabolite patterns across brain regions. Neurochemical differences in metabolic phenotypes were mainly defined by various phospholipids and several intriguing metabolites including carnosine, cholesterol sulfate, lipoamino acids, uric acid, and sialic acid, whose physiological roles in brain metabolism are poorly understood. This study helps define regional homeostasis for the normal mouse brain to give context to the reaction to pathological events.

Ion mobility-mass spectrometry strategies for untargeted systems, synthetic, and chemical biology

Contemporary strategies that concentrate on only one or a handful of molecular targets limits the utility of the information gained for diagnostic and predictive purposes. Recent advances in the sensitivity, speed, and precision of measurements obtained from ion mobility coupled to mass spectrometry (IM-MS) have accelerated the utility of IM-MS in untargeted, discovery-driven studies in biology. Perhaps most evident is the impact that such wide-scale discovery capabilities have yielded in the areas of systems, synthetic, and chemical biology, where the need for comprehensive, hypothesis-driving studies from multidimensional and unbiased data is required.

High resolution mass spectrometry based techniques at the crossroads of metabolic pathways

The metabolome is the set of small molecular mass compounds found in biological media, and metabolomics, which refers to as the analysis of metabolome in a given biological condition, deals with the large scale detection and quantification of metabolites in biological media. It is a data driven and multidisciplinary approach combining analytical chemistry for data acquisition, and biostatistics, informatics and biochemistry for mining and interpretation of these data. Since the middle of the 2000s, high resolution mass spectrometry is widely used in metabolomics, mainly because the detection and identification of metabolites are improved compared to low resolution instruments. As the field of HRMS is quickly and permanently evolving, the aim of this work is to review its use in different aspects of metabolomics, including data acquisition, metabolite annotation, identification and quantification. At last, we would like to show that, thanks to their versatility, HRMS instruments are the most appropriate to achieve optimal metabolome coverage, at the border of other omics fields such as lipidomics and glycomics.

1H NMR global metabolic phenotyping of acute pancreatitis in the emergency unit

We have investigated the urinary and plasma metabolic phenotype of acute pancreatitis (AP) patients presenting to the emergency room at a single center London teaching hospital with acute abdominal pain using (1)H NMR spectroscopy and multivariate modeling. Patients were allocated to either the AP (n = 15) or non-AP patients group (all other causes of abdominal pain, n = 21) on the basis of the national guidelines. Patients were assessed for three clinical outcomes: (1) diagnosis of AP, (2) etiology of AP caused by alcohol consumption and cholelithiasis, and (3) AP severity based on the Glasgow score. Samples from AP patients were characterized by high levels of urinary ketone bodies, glucose, plasma choline and lipid, and relatively low levels of urinary hippurate, creatine and plasma-branched chain amino acids. AP could be reliably identified with a high degree of sensitivity and specificity (OPLS-DA model R(2) = 0.76 and Q(2)Y = 0.59) using panel of discriminatory biomarkers consisting of guanine, hippurate and creatine (urine), and valine, alanine and lipoproteins (plasma). Metabolic phenotyping was also able to distinguish between cholelithiasis and colonic inflammation among the heterogeneous non-AP group. This work has demonstrated that combinatorial biomarkers have a strong diagnostic and prognostic potential in AP with relevance to clinical decision making in the emergency unit.

Discovery and validation of urinary metabotypes for the diagnosis of hepatocellular carcinoma in West Africans

There is no clinically applicable biomarker for surveillance of hepatocellular carcinoma (HCC), because the sensitivity of serum alpha-fetoprotein (AFP) is too low for this purpose. Here, we determined the diagnostic performance of a panel of urinary metabolites of HCC patients from West Africa. Urine samples were collected from Nigerian and Gambian patients recruited on the case-control platform of the Prevention of Liver Fibrosis and Cancer in Africa (PROLIFICA) program. Urinary proton nuclear magnetic resonance ((1) H-NMR) spectroscopy was used to metabolically phenotype 290 subjects: 63 with HCC; 32 with cirrhosis (Cir); 107 with noncirrhotic liver disease (DC); and 88 normal control (NC) healthy volunteers. Urine samples from a further cohort of 463 subjects (141 HCC, 56 Cir, 178 DC, and 88 NC) were analyzed, the results of which validated the initial cohort. The urinary metabotype of patients with HCC was distinct from those with Cir, DC, and NC with areas under the receiver operating characteristic (AUROC) curves of 0.86 (0.78-0.94), 0.93 (0.89-0.97), and 0.89 (0.80-0.98) in the training set and 0.81 (0.73-0.89), 0.96 (0.94-0.99), and 0.90 (0.85-0.96), respectively, in the validation cohort. A urinary metabolite panel, comprising inosine, indole-3-acetate, galactose, and an N-acetylated amino acid (NAA), showed a high sensitivity (86.9% [75.8-94.2]) and specificity (90.3% [74.2-98.0]) in the discrimination of HCC from cirrhosis, a finding that was corroborated in a validation cohort (AUROC: urinary panel = 0.72; AFP =  0.58). Metabolites that were significantly increased in urine of HCC patients, and which correlated with clinical stage of HCC, were NAA, dimethylglycine, 1-methylnicotinamide, methionine, acetylcarnitine, 2-oxoglutarate, choline, and creatine.

CONCLUSION

The urinary metabotyping of this West African cohort identified and validated a metabolite panel that diagnostically outperforms serum AFP.

The emergence of proton nuclear magnetic resonance metabolomics in the cardiovascular arena as viewed from a clinical perspective

The ability to phenotype metabolic profiles in serum has increased substantially in recent years with the advent of metabolomics. Metabolomics is the study of the metabolome, defined as those molecules with an atomic mass less than 1.5 kDa. There are two main metabolomics methods: mass spectrometry (MS) and proton nuclear magnetic resonance (¹H NMR) spectroscopy, each with its respective benefits and limitations. MS has greater sensitivity and so can detect many more metabolites. However, its cost (especially when heavy labelled internal standards are required for absolute quantitation) and quality control is sub-optimal for large cohorts. ¹H NMR is less sensitive but sample preparation is generally faster and analysis times shorter, resulting in markedly lower analysis costs. ¹H NMR is robust, reproducible and can provide absolute quantitation of many metabolites. Of particular relevance to cardio-metabolic disease is the ability of ¹H NMR to provide detailed quantitative data on amino acids, fatty acids and other metabolites as well as lipoprotein subparticle concentrations and size. Early epidemiological studies suggest promise, however, this is an emerging field and more data is required before we can determine the clinical utility of these measures to improve disease prediction and treatment.

This review describes the theoretical basis of ¹H NMR; compares MS and ¹H NMR and provides a tabular overview of recent ¹H NMR-based research findings in the atherosclerosis field, describing the design and scope of studies conducted to date. ¹H NMR metabolomics-CVD related research is emerging, however further large, robustly conducted prospective, genetic and intervention studies are needed to advance research on CVD risk prediction and to identify causal pathways amenable to intervention.

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¹H NMR metabolomics is being increasingly applied to large cohort studies.

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Studies have identified potentially novel lipoprotein and metabolite predictors for CVD.

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Potential exists for the use of metabolomics in cardiovascular clinical practice.

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Current findings are too preliminary to translate into clinical recommendations.

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Further large scale studies are now needed to advance the field in a robust manner.

Metabolomics analysis and modeling suggest a lysophosphocholines-PAF receptor interaction in fibromyalgia

Fibromyalgia Syndrome (FMS) is a chronic disease characterized by widespread pain, and difficult to diagnose and treat. We analyzed the plasma metabolic profile of patients with FMS by using a metabolomics approach combining Liquid Chromatography-Quadrupole-Time Of Flight/Mass Spectrometry (LC-Q-TOF/MS) with multivariate statistical analysis, aiming to discriminate patients and controls. LC-Q-TOF/MS analysis of plasma (FMS patients: n = 22 and controls: n = 21) identified many lipid compounds, mainly lysophosphocholines (lysoPCs), phosphocholines and ceramides. Multivariate statistical analysis was performed to identify the discriminating metabolites. A protein docking and molecular dynamic (MD) study was then performed, using the most discriminating lysoPCs, to validate the binding to Platelet Activating Factor (1-alkyl-2-acetyl-sn-glycero-3-phosphocholine, PAF) Receptor (PAFr). Discriminating metabolites between FMS patients and controls were identified as 1-tetradecanoyl-sn-glycero-3-phosphocholine [PC(14∶0/0∶0)] and 1-hexadecanoyl-sn-glycero-3-phosphocholine [PC(16∶0/0∶0)]. MD and docking indicate that the ligands investigated have similar potentialities to activate the PAFr receptor. The application of a metabolomic approach discriminated FMS patients from controls, with an over-representation of PC(14∶0/0∶0) and PC(16∶0/0∶0) compounds in the metabolic profiles. These results and the modeling of metabolite-PAFr interaction, allowed us to hypothesize that lipids oxidative fragmentation might generate lysoPCs in abundance, that in turn will act as PAF-like bioactivators. Overall results suggest disease biomarkers and potential therapeutical targets for FMS.

Nontargeted modification-specific metabolomics study based on liquid chromatography-high-resolution mass spectrometry

Modifications of genes and proteins have been extensively studied in systems biology using comprehensive analytical strategies. Although metabolites are frequently modified, these modifications have not been studied using -omics approaches. Here a general strategy for the nontargeted profiling of modified metabolites, which we call "nontargeted modification-specific metabolomics", is reported. A key aspect of this strategy was the combination of in-source collision-induced dissociation liquid chromatography-mass spectrometry (LC-MS) and global nontargeted LC-MS-based metabolomics. Characteristic neutral loss fragments that are specific for acetylation, sulfation, glucuronidation, glucosidation, or ribose conjugation were reproducibly detected using human urine as a model specimen for method development. The practical application of this method was demonstrated by profiling urine samples from liver cirrhosis patients. Approximately 900 features were identified as modified endogenous metabolites and xenobiotics. Moreover, this strategy supports the identification of compounds not included in traditional metabolomics databases (HMDB, Metlin, and KEGG), which are currently referred to as "unknowns" in metabolomics projects. Nontargeted modification-specific metabolomics opens a new perspective in systems biology.

Colorectal cancer detection using targeted serum metabolic profiling

Colorectal cancer (CRC) is one of the most prevalent and deadly cancers in the world. Despite an expanding knowledge of its molecular pathogenesis during the past two decades, robust biomarkers to enable screening, surveillance, and therapy monitoring of CRC are still lacking. In this study, we present a targeted liquid chromatography-tandem mass spectrometry-based metabolic profiling approach for identifying biomarker candidates that could enable highly sensitive and specific CRC detection using human serum samples. In this targeted approach, 158 metabolites from 25 metabolic pathways of potential significance were monitored in 234 serum samples from three groups of patients (66 CRC patients, 76 polyp patients, and 92 healthy controls). Partial least-squares-discriminant analysis (PLS-DA) models were established, which proved to be powerful for distinguishing CRC patients from both healthy controls and polyp patients. Receiver operating characteristic curves generated based on these PLS-DA models showed high sensitivities (0.96 and 0.89, respectively, for differentiating CRC patients from healthy controls or polyp patients), good specificities (0.80 and 0.88), and excellent areas under the curve (0.93 and 0.95). Monte Carlo cross validation was also applied, demonstrating the robust diagnostic power of this metabolic profiling approach.

The blood exposome and its role in discovering causes of disease

BACKGROUND

Since 2001, researchers have examined the human genome (G) mainly to discover causes of disease, despite evidence that G explains relatively little risk. We posit that unexplained disease risks are caused by the exposome (E; representing all exposures) and G × E interactions. Thus, etiologic research has been hampered by scientists' continuing reliance on low-tech methods to characterize E compared with high-tech omics for characterizing G.

OBJECTIVES

Because exposures are inherently chemical in nature and arise from both endogenous and exogenous sources, blood specimens can be used to characterize exposomes. To explore the "blood exposome" and its connection to disease, we sought human blood concentrations of many chemicals, along with their sources, evidence of chronic-disease risks, and numbers of metabolic pathways.

METHODS

From the literature we obtained human blood concentrations of 1,561 small molecules and metals derived from foods, drugs, pollutants, and endogenous processes. We mapped chemical similarities after weighting by blood concentrations, disease-risk citations, and numbers of human metabolic pathways.

RESULTS

Blood concentrations spanned 11 orders of magnitude and were indistinguishable for endogenous and food chemicals and drugs, whereas those of pollutants were 1,000 times lower. Chemical similarities mapped by disease risks were equally distributed by source categories, but those mapped by metabolic pathways were dominated by endogenous molecules and essential nutrients.

CONCLUSIONS

For studies of disease etiology, the complexity of human exposures motivates characterization of the blood exposome, which includes all biologically active chemicals. Because most small molecules in blood are not human metabolites, investigations of causal pathways should expand beyond the endogenous metabolome.

Design and analysis of metabolomics studies in epidemiologic research: a primer on -omic technologies

Metabolomics is the field of "-omics" research concerned with the comprehensive characterization of the small low-molecular-weight metabolites in biological samples. In epidemiology, it represents an emerging technology and an unprecedented opportunity to measure environmental and other exposures with improved precision and far less measurement error than with standard epidemiologic methods. Advances in the application of metabolomics in large-scale epidemiologic research are now being realized through a combination of improved sample preparation and handling, automated laboratory and processing methods, and reduction in costs. The number of epidemiologic studies that use metabolic profiling is still limited, but it is fast gaining popularity in this area. In the present article, we present a roadmap for metabolomic analyses in epidemiologic studies and discuss the various challenges these data pose to large-scale studies. We discuss the steps of data preprocessing, univariate and multivariate data analysis, correction for multiplicity of comparisons with correlated data, and finally the steps of cross-validation and external validation. As data from metabolomic studies accumulate in epidemiology, there is a need for large-scale replication and synthesis of findings, increased availability of raw data, and a focus on good study design, all of which will highlight the potential clinical impact of metabolomics in this field.

NMR-Based Screening for Inborn Errors of Metabolism: Initial Results from a Study on Turkish Neonates

Approximately 1 in 400 neonates in Turkey is affected by inherited metabolic diseases. This high prevalence is at least in part due to consanguineous marriages. Standard screening in Turkey now covers only three metabolic diseases (phenylketonuria, congenital hypothyroidism, and biotinidase deficiency). Once symptoms have developed, tandem-MS can be used, although this currently covers only up to 40 metabolites. NMR potentially offers a rapid and versatile alternative.We conducted a multi-center clinical study in 14 clinical centers in Turkey. Urine samples from 989 neonates were collected and investigated by using NMR spectroscopy in two different laboratories. The primary objective of the present study was to explore the range of variation of concentration and chemical shifts of specific metabolites without clinically relevant findings that can be detected in the urine of Turkish neonates. The secondary objective was the integration of the results from a healthy reference population of neonates into an NMR database, for routine and completely automatic screening of congenital metabolic diseases.Both targeted and untargeted analyses were performed on the data. Targeted analysis was aimed at 65 metabolites. Limits of detection and quantitation were determined by generating urine spectra, in which known concentrations of the analytes were added electronically as well as by real spiking. Untargeted analysis involved analysis of the whole spectrum for abnormal features, using statistical procedures, including principal component analysis. Outliers were eliminated by model building. Untargeted analysis was used to detect known and unknown compounds and jaundice, proteinuria, and acidemia. The results will be used to establish a database to detect pathological concentration ranges and for routine screening.

Promising molecular targets and biomarkers for male BPH and LUTS

Benign prostatic hyperplasia (BPH) is a major health concern for aging men. BPH is associated with urinary voiding dysfunction and lower urinary tract symptoms (LUTS), which negatively affects quality of life. Surgical resection and medical approaches have proven effective for improving urinary flow and relieving LUTS but are not effective for all men and can produce adverse effects that require termination of the therapeutic regimen. Thus, there is a need to explore other therapeutic targets to treat BPH/LUTS. Complicating the treatment of BPH/LUTS is the lack of biomarkers to effectively identify pathobiologies contributing to BPH/LUTS or to gauge successful response to therapy. This review will briefly discuss current knowledge and will highlight new studies that illuminate the pathobiologies contributing to BPH/LUTS, potential new therapeutic strategies for successfully treating BPH/LUTS, and new approaches for better defining these pathobiologies and response to therapeutics through the development of biomarkers and phenotyping strategies.

The emergent role of metabolic phenotyping in dynamic patient stratification

The role that metabolic phenotyping can increasingly play in patient stratification and personalised medicine is discussed. The background to the general approach, comprehensive and simultaneous analysis of small-molecule metabolites in biofluids, tissues and tissue extracts combined with suitable multivariate statistical models, is summarised. The main techniques used (NMR and mass spectrometry) are cited, and the implementation of dedicated phenome centres is explained. Finally, the advantages and limitations, opportunities and drawbacks of the approach are discussed.

μHigh resolution-magic-angle spinning NMR spectroscopy for metabolic phenotyping of Caenorhabditis elegans

Analysis of model organisms, such as the submillimeter-size Caenorhabditis elegans, plays a central role in understanding biological functions across species and in characterizing phenotypes associated with genetic mutations. In recent years, metabolic phenotyping studies of C. elegans based on (1)H high-resolution magic-angle spinning (HR-MAS) nuclear magnetic resonance (NMR) spectroscopy have relied on the observation of large populations of nematodes, requiring labor-intensive sample preparation that considerably limits high-throughput characterization of C. elegans. In this work, we open new platforms for metabolic phenotyping of C. elegans mutants. We determine rich metabolic profiles (31 metabolites identified) from samples of 12 individuals using a (1)H NMR microprobe featuring high-resolution magic-angle coil spinning (HR-MACS), a simple conversion of a standard HR-MAS probe to μHR-MAS. In addition, we characterize the metabolic variations between two different strains of C. elegans (wild-type vs slcf-1 mutant). We also acquire a NMR spectrum of a single C. elegans worm at 23.5 T. This study represents the first example of a metabolomic investigation carried out on a small number of submillimeter-size organisms, demonstrating the potential of NMR microtechnologies for metabolomics screening of small model organisms.

Metabolomics approach in allergic and rheumatic diseases

Metabolomics is the analysis of the concentration profiles of low molecular weight compounds present in biological fluids. Metabolites are nonpeptide molecules representing the end products of cellular activity. Therefore, changes in metabolite concentrations reveal the range of biochemical effects induced by a disease or its therapeutic intervention. Metabolomics has recently become feasible with the accessibility of new technologies, including mass spectrometry and high-resolution proton nuclear magnetic resonance, and has already been applied to several disorders. Indeed, it has the advantage of being a nontargeted approach for identifying potential biomarkers, which means that it does not require a preliminary knowledge of the substances to be studied. In this review, we summarize the main studies in which metabolomic approach was used in some allergic (asthma, atopic dermatitis) and rheumatic diseases (rheumatoid arthritis, systemic lupus erythematosus) to explore the feasibility of this technique as a novel diagnostic tool in these complex disorders.

Customized metabolomics database for the analysis of NMR ¹H-¹H TOCSY and ¹³C-¹H HSQC-TOCSY spectra of complex mixtures

A customized metabolomics NMR database, termed ¹H(¹³C)-TOCCATA, is introduced, which contains complete ¹H and ¹³C chemical shift information on individual spin systems and isomeric states of common metabolites. Since this information directly corresponds to cross sections of 2D ¹H–¹H TOCSY and 2D ¹³C–¹H HSQC-TOCSY spectra, it allows the straightforward and unambiguous identification of metabolites of complex metabolic mixtures at ¹³C natural abundance from these types of experiments. The ¹H(¹³C)-TOCCATA database, which is complementary to the previously introduced TOCCATA database for the analysis of uniformly ¹³C-labeled compounds, currently contains 455 metabolites, and it can be used through a publicly accessible web portal. We demonstrate its performance by applying it to 2D ¹H–¹H TOCSY and 2D ¹³C–¹H HSQC-TOCSY spectra of a cell lysate from E. coli, which yields a substantial improvement over other databases, as well as 1D NMR-based approaches, in the number of compounds that can be correctly identified with high confidence.

Cerebrospinal fluid analysis in Alzheimer's disease: technical issues and future developments

Alzheimer's disease (AD) is a leading cause of morbidity, mortality, and a major epidemic worldwide. Although clinical assessment continues to remain the keystone for patient management and clinical trials, such evaluation has important limitations. In this context, cerebrospinal fluid (CSF) biomarkers are important tools to better identify high-risk individuals, to diagnose AD promptly and accurately, especially at the prodromal mild cognitive impairment stage of the disease, and to effectively prognosticate and treat AD patients. Recent advances in functional genomics, proteomics, metabolomics, and bioinformatics will hopefully revolutionize unbiased inquiries into several putative CSF markers of cerebral pathology that may be concisely informative with regard to the various stages of AD progression through years and decades. Moreover, the identification of efficient drug targets and development of optimal therapeutic strategies for AD will increasingly rely on a better understanding and integration of the systems biology paradigm, which will allow predicting the series of events and resulting responses of the biological network triggered by the introduction of new therapeutic compounds. In this scenario, unbiased systems biology-based diagnostic and prognostic models in AD will consist of relevant comprehensive panels of molecules and key branches of the disease-affected cellular neuronal network. Such characteristic and unbiased biomarkers will more accurately and comprehensively reflect pathophysiology from the early asymptomatic and presymptomatic to the final prodromal and symptomatic clinical stages in individual patients (and their individual genetic disease predisposition), ultimately increasing the chances of success of future disease modifying and preventive treatments.

Low invasive in vivo tissue sampling for monitoring biomarkers and drugs during surgery

The techniques currently used for drug, metabolite, and biomarker determination are based on sample collection, and therefore they are not suitable for repeated analysis because of the high invasiveness. Here, we present a novel method of biochemical analysis directly in organ during operation without need of a separate sample collection step: solid-phase microextraction (SPME). The approach is based on flexible microprobe coated with biocompatible extraction phase that is inserted to the tissue with no damage or disturbance of the organ. The method was evaluated during lung and liver transplantations using normothermic ex vivo liver perfusion (NEVLP) and ex vivo lung perfusion (EVLP). The study demonstrated feasibility of the method to extract wide range of endogenous compounds and drugs. Statistical analysis allowed observing metabolic changes of lung during cold ischemic time, perfusion, and reperfusion. It was also demonstrated that the level of drugs and their metabolites can be monitored over time. Based on the methylprednisolone as a selected example, the impairment of enzymatic properties of liver was detected in the injured organs but not in healthy control. This finding was supported by changes in pathways of endogenous metabolites. The SPME probe was also used for analysis of perfusion fluid using stopcock connection. The evaluation of biochemical profile of perfusates demonstrated potential of the approach for monitoring organ function during ex vivo perfusion. The simplicity of the device makes it convenient to use by medical personnel. With the microprobe, different areas of the organ or various organs can be sampled simultaneously. The technology allows assessment of organ function by biochemical profiling, determination of potential biomarkers, and drug monitoring. The use of this method for preintervention analysis could enhance the decision-making process for the best possible personalized approach, whereas post-transplantation monitoring would be used for graft assessments and fast response in case of organ failure.

Managing customization in health care: a framework derived from the services sector literature

Organizations that provide health services are increasingly in need of systems and approaches that will enable them to be more responsive to the needs and wishes of their clients. Two recent trends, namely, patient-centered care (PCC) and personalized medicine, are first steps in the customization of care. PCC shifts the focus away from the disease to the patient. Personalized medicine, which relies heavily on genetics, promises significant improvements in the quality of healthcare through the development of tailored and targeted drugs. We need to understand how these two trends can be related to customization in healthcare delivery and, because customization often entails extra costs, to define new business models. This article analyze how customization of the care process can be developed and managed in healthcare. Drawing on relevant literature from various services sectors, we have developed a framework for the implementation of customization by the hospital managers and caregivers involved in care pathways.

Towards the disease biomarker in an individual patient using statistical health monitoring

In metabolomics, identification of complex diseases is often based on application of (multivariate) statistical techniques to the data. Commonly, each disease requires its own specific diagnostic model, separating healthy and diseased individuals, which is not very practical in a diagnostic setting. Additionally, for orphan diseases such models cannot be constructed due to a lack of available data. An alternative approach adapted from industrial process control is proposed in this study: statistical health monitoring (SHM). In SHM the metabolic profile of an individual is compared to that of healthy people in a multivariate manner. Abnormal metabolite concentrations, or abnormal patterns of concentrations, are indicated by the method. Subsequently, this biomarker can be used for diagnosis. A tremendous advantage here is that only data of healthy people is required to construct the model. The method is applicable in current–population based –clinical practice as well as in personalized health applications. In this study, SHM was successfully applied for diagnosis of several orphan diseases as well as detection of metabotypic abnormalities related to diet and drug intake.

A nano ultra-performance liquid chromatography-high resolution mass spectrometry approach for global metabolomic profiling and case study on drug-resistant multiple myeloma

Global metabolomics relies on highly reproducible and sensitive detection of a wide range of metabolites in biological samples. Here we report the optimization of metabolome analysis by nanoflow ultraperformance liquid chromatography coupled to high-resolution orbitrap mass spectrometry. Reliable peak features were extracted from the LC-MS runs based on mandatory detection in duplicates and additional noise filtering according to blank injections. The run-to-run variation in peak area showed a median of 14%, and the false discovery rate during a mock comparison was evaluated. To maximize the number of peak features identified, we systematically characterized the effect of sample loading amount, gradient length, and MS resolution. The number of features initially rose and later reached a plateau as a function of sample amount, fitting a hyperbolic curve. Longer gradients improved unique feature detection in part by time-resolving isobaric species. Increasing the MS resolution up to 120000 also aided in the differentiation of near isobaric metabolites, but higher MS resolution reduced the data acquisition rate and conferred no benefits, as predicted from a theoretical simulation of possible metabolites. Moreover, a biphasic LC gradient allowed even distribution of peak features across the elution, yielding markedly more peak features than the linear gradient. Using this robust nUPLC-HRMS platform, we were able to consistently analyze ~6500 metabolite features in a single 60 min gradient from 2 mg of yeast, equivalent to ~50 million cells. We applied this optimized method in a case study of drug (bortezomib) resistant and drug-sensitive multiple myeloma cells. Overall, 18% of metabolite features were matched to KEGG identifiers, enabling pathway enrichment analysis. Principal component analysis and heat map data correctly clustered isogenic phenotypes, highlighting the potential for hundreds of small molecule biomarkers of cancer drug resistance.

Imagine a world without cancer

Background

Since the “War on Cancer” was declared in 1971, the United States alone has expended some $300 billion on research, with a heavy focus on the role of genomics in anticancer therapy. Voluminous data have been collected and analyzed. However, in hindsight, any achievements made have not been realized in clinical practice in terms of overall survival or quality of life extended. This might be justified because cancer is not one disease but a conglomeration of multiple diseases, with widespread heterogeneity even within a single tumor type.

Discussion

Only a few types of cancer have been described that are associated with one major signaling pathway. This enabled the initial successful deployment of targeted therapy for such cancers. However, soon after this targeted approach was initiated, it was subverted as cancer cells learned and reacted to the initial treatments, oftentimes rendering the treatment less effective or even completely ineffective. During the past 30 plus years, the cancer classification used had, as its primary aim, the facilitation of communication and the exchange of information amongst those caring for cancer patients with the end goal of establishing a standardized approach for the diagnosis and treatment of cancers. This approach should be modified based on the recent research to affect a change from a service-based to an outcome-based approach. The vision of achieving long-term control and/or eradicating or curing cancer is far from being realized, but not impossible. In order to meet the challenges in getting there, any newly proposed anticancer strategy must integrate a personalized treatment outcome approach. This concept is predicated on tumor- and patient-associated variables, combined with an individualized response assessment strategy for therapy modification as suggested by the patient’s own results. As combined strategies may be outcome-orientated and integrate tumor-, patient- as well as cancer-preventive variables, this approach is likely to result in an optimized anticancer strategy.

Summary

Herein, we introduce such an anticancer strategy for all cancer patients, experts, and organizations: Imagine a World without Cancer.

Nutritional modulation of the metabonome: applications of metabolic phenotyping in translational nutritional research

PURPOSE OF REVIEW

Metabolic profiling technologies provide a global overview of complex dietary processes. Metabonomic analytical approaches have now been translated into multiple areas of clinical nutritional research based on the widespread adoption of high-throughput mass spectrometry and proton nuclear magnetic resonance spectroscopy. This has generated novel insights into the molecular mechanisms that shape the microbiome-dietary-chronic disease axis.

RECENT FINDINGS

Metabolome-wide association studies have created a new paradigm in nutritional molecular epidemiology and they have highlighted the importance of gut microbial cometabolic processes in the development of cardiovascular disease and diabetes. Targeted analyses are helping to explain the mechanisms by which high-risk diets (such as red meat) modulate disease risk and they are generating novel biomarkers that will serve to re-define how the efficacy of nutritional interventions is assessed. Nutritional metabonome-microbiome interactions have also been defined in extreme dietary states such as obesity and starvation, and they also serve as important models for understanding how the gut microbiome modifies disease risk. Finally, nutritional systems medicine approaches are creating novel insights into the functional components of our diet, and the mechanisms by which they cause disease.

SUMMARY

Diet is an important modulator of the human metabolic phenotype and the analysis of the nutritional metabolome will drive future development of personalized nutritional interventions.

Biomarker profiling by nuclear magnetic resonance spectroscopy for the prediction of all-cause mortality: an observational study of 17,345 persons

BACKGROUND

Early identification of ambulatory persons at high short-term risk of death could benefit targeted prevention. To identify biomarkers for all-cause mortality and enhance risk prediction, we conducted high-throughput profiling of blood specimens in two large population-based cohorts.

METHODS AND FINDINGS

106 candidate biomarkers were quantified by nuclear magnetic resonance spectroscopy of non-fasting plasma samples from a random subset of the Estonian Biobank (n = 9,842; age range 18-103 y; 508 deaths during a median of 5.4 y of follow-up). Biomarkers for all-cause mortality were examined using stepwise proportional hazards models. Significant biomarkers were validated and incremental predictive utility assessed in a population-based cohort from Finland (n = 7,503; 176 deaths during 5 y of follow-up). Four circulating biomarkers predicted the risk of all-cause mortality among participants from the Estonian Biobank after adjusting for conventional risk factors: alpha-1-acid glycoprotein (hazard ratio [HR] 1.67 per 1-standard deviation increment, 95% CI 1.53-1.82, p = 5×10⁻³¹), albumin (HR 0.70, 95% CI 0.65-0.76, p = 2×10⁻¹⁸), very-low-density lipoprotein particle size (HR 0.69, 95% CI 0.62-0.77, p = 3×10⁻¹²), and citrate (HR 1.33, 95% CI 1.21-1.45, p = 5×10⁻¹⁰). All four biomarkers were predictive of cardiovascular mortality, as well as death from cancer and other nonvascular diseases. One in five participants in the Estonian Biobank cohort with a biomarker summary score within the highest percentile died during the first year of follow-up, indicating prominent systemic reflections of frailty. The biomarker associations all replicated in the Finnish validation cohort. Including the four biomarkers in a risk prediction score improved risk assessment for 5-y mortality (increase in C-statistics 0.031, p = 0.01; continuous reclassification improvement 26.3%, p = 0.001).

CONCLUSIONS

Biomarker associations with cardiovascular, nonvascular, and cancer mortality suggest novel systemic connectivities across seemingly disparate morbidities. The biomarker profiling improved prediction of the short-term risk of death from all causes above established risk factors. Further investigations are needed to clarify the biological mechanisms and the utility of these biomarkers for guiding screening and prevention.

Chemo-informatic strategy for imaging mass spectrometry-based hyperspectral profiling of lipid signatures in colorectal cancer

Mass spectrometry imaging (MSI) provides the opportunity to investigate tumor biology from an entirely novel biochemical perspective and could lead to the identification of a new pool of cancer biomarkers. Effective clinical translation of histology-driven MSI in systems oncology requires precise colocalization of morphological and biochemical features as well as advanced methods for data treatment and interrogation. Currently proposed MSI workflows are subject to several limitations, including nonoptimized raw data preprocessing, imprecise image coregistration, and limited pattern recognition capabilities. Here we outline a comprehensive strategy for histology-driven MSI, using desorption electrospray ionization that covers (i) optimized data preprocessing for improved information recovery; (ii) precise image coregistration; and (iii) efficient extraction of tissue-specific molecular ion signatures for enhanced biochemical distinction of different tissue types. The proposed workflow has been used to investigate region-specific lipid signatures in colorectal cancer tissue. Unique lipid patterns were observed using this approach according to tissue type, and a tissue recognition system using multivariate molecular ion patterns allowed highly accurate (>98%) identification of pixels according to morphology (cancer, healthy mucosa, smooth muscle, and microvasculature). This strategy offers unique insights into tumor microenvironmental biochemistry and should facilitate compilation of a large-scale tissue morphology-specific MSI spectral database with which to pursue next-generation, fully automated histological approaches.

Metabolic characterization of all-trans-retinoic acid (ATRA)-induced craniofacial development of murine embryos using in vivo proton magnetic resonance spectroscopy

AIM

To characterize the abnormal metabolic profile of all-trans-retinoic acid (ATRA)-induced craniofacial development in mouse embryos using proton magnetic resonance spectroscopy (1H-MRS).

METHODS

Timed-pregnant mice were treated by oral gavage on the morning of embryonic gestation day 11 (E11) with all-trans-retinoic acid (ATRA). Dosing solutions were adjusted by maternal body weight to provide 30, 70, or 100 mg/kg RA. The control group was given an equivalent volume of the carrier alone. Using an Agilent 7.0 T MR system and a combination of surface coil coils, a 3 mm×3 mm×3 mm 1H-MRS voxel was selected along the embryonic craniofacial tissue. 1H-MRS was performed with a single-voxel method using PRESS sequence and analyzed using LCModel software. Hematoxylin and eosin was used to detect and confirm cleft palate.

RESULT

1H-MRS revealed elevated choline levels in embryonic craniofacial tissue in the RA70 and RA100 groups compared to controls (P<0.05). Increased choline levels were also found in the RA70 and RA100 groups compared with the RA30 group (P<0.01). High intra-myocellular lipids at 1.30 ppm (IMCL13) in the RA100 group compared to the RA30 group were found (P<0.01). There were no significant changes in taurine, intra-myocellular lipids at 2.10 ppm (IMCL21), and extra-myocellular lipids at 2.30 ppm (EMCL23). Cleft palate formation was observed in all fetuses carried by mice administered 70 and 100 mg/kg RA.

CONCLUSIONS

This novel study suggests that the elevated choline and lipid levels found by 1H-MRS may represent early biomarkers of craniofacial defects. Further studies will determine performance of this test and pathogenetic mechanisms of craniofacial malformation.

Effects of intra- and post-operative ischemia on the metabolic profile of clinical liver tissue specimens monitored by NMR

Metabolomic profiles of tissues could greatly contribute to advancements in personalized medicine but are influenced by differences in adopted preanalytical procedures; nonhomogeneous pre- and post-excision ischemia times are potential sources of variability. In this study, we monitored the impact of ischemia on the metabolic profiles, acquired with high-resolution magic-angle-spinning (1)H NMR, of 162 human liver samples collected during and up to 6 h after routine surgery. The profiles changed significantly as a function of intraoperative warm ischemia (WI) and postresection cold ischemia (CI) time, with significant variations in the concentration of the same 16 metabolites. Therefore, a tight control of the preanalytical phase is essential for reliable metabolomic analyses of liver diseases. The NMR profiles provide a reliable "fingerprint" of ischemia and have predictive value: the best-performing predictive models are found to discriminate extreme time points of CI (0' vs 360 ') in the training set with cross-validation accuracy of ~90%; samples in the validation cohort can discriminate short (≤60') from long (≥180') CI with an accuracy of ~80%. For WI, the corresponding figures are 95.6 and 92%, respectively. Therefore, ischemia NMR profiles might become a tool for tissue quality control in biobanks.

RAMSY: ratio analysis of mass spectrometry to improve compound identification

The complexity of biological samples poses a major challenge for reliable compound identification in mass spectrometry (MS). The presence of interfering compounds that cause additional peaks in the spectrum can make interpretation and assignment difficult. To overcome this issue, new approaches are needed to reduce complexity and simplify spectral interpretation. Recently, focused on unknown metabolite identification, we presented a new approach, RANSY (ratio analysis of nuclear magnetic resonance spectroscopy; Anal. Chem. 2011, 83, 7616-7623), which extracts the (1)H signals related to the same metabolite based on peak intensity ratios. On the basis of this concept, we present the ratio analysis of mass spectrometry (RAMSY) method, which facilitates improved compound identification in complex MS spectra. RAMSY works on the principle that, under a given set of experimental conditions, the abundance/intensity ratios between the mass fragments from the same metabolite are relatively constant. Therefore, the quotients of average peak ratios and their standard deviations, generated using a small set of MS spectra from the same ion chromatogram, efficiently allow the statistical recovery of the metabolite peaks and facilitate reliable identification. RAMSY was applied to both gas chromatography/MS and liquid chromatography tandem MS (LC-MS/MS) data to demonstrate its utility. The performance of RAMSY is typically better than the results from correlation methods. RAMSY promises to improve unknown metabolite identification for MS users in metabolomics or other fields.

The impact of inflammation on metabolomic profiles in patients with arthritis

Objective. Inflammatory arthritis is associated with systemic manifestations including alterations in metabolism. We used nuclear magnetic resonance (NMR) spectroscopy–based metabolomics to assess metabolic fingerprints in serum from patients with established rheumatoid arthritis (RA) and those with early arthritis.

Methods. Serum samples were collected from newly presenting patients with established RA who were naive for disease-modifying antirheumatic drugs, matched healthy controls, and 2 groups of patients with synovitis of ≤3 months' duration whose outcomes were determined at clinical followup. Serum metabolomic profiles were assessed using 1-dimensional ¹H-NMR spectroscopy. Discriminating metabolites were identified, and the relationships between metabolomic profiles and clinical variables including outcomes were examined.

Results. The serum metabolic fingerprint in established RA was clearly distinct from that of healthy controls. In early arthritis, we were able to stratify the patients according to the level of current inflammation, with C-reactive protein correlating with metabolic differences in 2 separate groups (P < 0.001). Lactate and lipids were important discriminators of inflammatory burden in both early arthritis patient groups. The sensitivities and specificities of models to predict the development of either RA or persistent arthritis in patients with early arthritis were low.

Conclusion. The metabolic fingerprint reflects inflammatory disease activity in patients with synovitis, demonstrating that underlying inflammatory processes drive significant changes in metabolism that can be measured in the peripheral blood. The identification of metabolic alterations may provide insights into disease mechanisms operating in patients with inflammatory arthritis.

1H-NMR metabolic profiling of cerebrospinal fluid in patients with complex regional pain syndrome-related dystonia

In complex regional pain syndrome (CRPS)-related dystonia, compelling evidence points to the involvement of the central nervous system, but the underpinning pathobiology is still unclear. Thus, to enable a hypothesis-free, unbiased view of the problem and to obtain new insight into the pathobiology of dystonia in CRPS, we applied an exploratory metabolomics analysis of cerebrospinal fluid (CSF) of patients with CRPS-related dystonia. (1)H-NMR spectroscopy in combination with multivariate modeling were used to investigate metabolic profiles of a total of 105 CSF samples collected from patients with CRPS-related dystonia and controls. We found a significantly different metabolic profile of CSF in CRPS patients compared to controls. The differences were already reflected in the first two principal components of the principal component analysis model, which is an indication that the variance associated with CRPS is stronger than variance caused by such classical confounders as gender, age, or individual differences. A supervised analysis generated a strong model pinpointing the most important metabolites contributed to the metabolic signature of patients with CRPS-related dystonia. From the set of identified discriminators, the most relevant metabolites were 2-keto-isovalerate, glucose, glutamine, and lactate, which all showed increased concentrations, and urea, which showed decreased concentration in CRPS subjects. Our findings point at a catabolic state in chronic CRPS patients with dystonia that is likely associated with inflammation.

Metabolomics in the developing infant

PURPOSE OF REVIEW

The aim of this review is to update readers on the most recent publications concerning clinical metabolomics in developing infants.

RECENT FINDINGS

Only a limited number of neonatal and pediatric metabolomic studies have been published, in comparison to the adult. However, this number of pediatric and neonatal papers is constantly increasing. The latest papers are related to intrauterine growth restricted and small for gestational age neonates, prematurity, mode of delivery, hypoxic ischemic encephalopathy, persistent ductus arteriosus, respiratory syndrome and surfactant therapy, cytomegalovirus infection, nephrouropathy, inborn errors of metabolism, pharmametabolomics, and nutrimetabolomics (including study of maternal milk and formula). Also numerous papers have been presented in experimental neonatology. In particular, the fluids most frequently used were as follows: urine, cord blood plasma, but also milk and stools. Each condition or disease presents a specific discriminating set of metabolites, which can be considered like a 'bar code'.

SUMMARY

In the near future, improved tools for metabolomic analysis (like simplified 'dipsticks' for urine) and its integration with other 'omics' will make this technology available in the clinical setting, leading to better or easier clinical decision making. Urinary metabolomics will probably be one of the most used tools in pediatrics and the metabolome will be 'our world'.

The human urine metabolome

Urine has long been a "favored" biofluid among metabolomics researchers. It is sterile, easy-to-obtain in large volumes, largely free from interfering proteins or lipids and chemically complex. However, this chemical complexity has also made urine a particularly difficult substrate to fully understand. As a biological waste material, urine typically contains metabolic breakdown products from a wide range of foods, drinks, drugs, environmental contaminants, endogenous waste metabolites and bacterial by-products. Many of these compounds are poorly characterized and poorly understood. In an effort to improve our understanding of this biofluid we have undertaken a comprehensive, quantitative, metabolome-wide characterization of human urine. This involved both computer-aided literature mining and comprehensive, quantitative experimental assessment/validation. The experimental portion employed NMR spectroscopy, gas chromatography mass spectrometry (GC-MS), direct flow injection mass spectrometry (DFI/LC-MS/MS), inductively coupled plasma mass spectrometry (ICP-MS) and high performance liquid chromatography (HPLC) experiments performed on multiple human urine samples. This multi-platform metabolomic analysis allowed us to identify 445 and quantify 378 unique urine metabolites or metabolite species. The different analytical platforms were able to identify (quantify) a total of: 209 (209) by NMR, 179 (85) by GC-MS, 127 (127) by DFI/LC-MS/MS, 40 (40) by ICP-MS and 10 (10) by HPLC. Our use of multiple metabolomics platforms and technologies allowed us to identify several previously unknown urine metabolites and to substantially enhance the level of metabolome coverage. It also allowed us to critically assess the relative strengths and weaknesses of different platforms or technologies. The literature review led to the identification and annotation of another 2206 urinary compounds and was used to help guide the subsequent experimental studies. An online database containing the complete set of 2651 confirmed human urine metabolite species, their structures (3079 in total), concentrations, related literature references and links to their known disease associations are freely available at http://www.urinemetabolome.ca.

Metabolomics in nutrition research: current status and perspectives

Metabolomics is the study of metabolites present in biological samples such as biofluids, tissue/cellular extracts and culture media. Combining metabolomic data with multivariate data analysis tools allows us to study alterations in metabolic pathways following different perturbations. Examples of perturbations can be disease state, drug or nutritional interventions with successful applications in the fields of drug toxicology, biomarker development and nutrition research. Application of metabolomics to nutrition research is increasing and applications range from assessing novel biomarkers of dietary intake to application of metabolomics in intervention studies. The present review highlights the use of metabolomics in nutrition research.

Biomarker Discovery and Translation in Metabolomics

The multifaceted field of metabolomics has witnessed exponential growth in both methods development and applications. Owing to the urgent need, a significant fraction of research investigations in the field is focused on understanding, diagnosing and preventing human diseases; hence, the field of biomedicine has been the major beneficiary of metabolomics research. A large body of literature now documents the discovery of numerous potential biomarkers and provides greater insights into pathogeneses of numerous human diseases. A sizable number of findings have been tested for translational applications focusing on disease diagnostics ranging from early detection, to therapy prediction and prognosis, monitoring treatment and recurrence detection, as well as the important area of therapeutic target discovery. Current advances in analytical technologies promise quantitation of biomarkers from even small amounts of bio-specimens using non-invasive or minimally invasive approaches, and facilitate high-throughput analysis required for real time applications in clinical settings. Nevertheless, a number of challenges exist that have thus far delayed the translation of a majority of promising biomarker discoveries to the clinic. This article presents advances in the field of metabolomics with emphasis on biomarker discovery and translational efforts, highlighting the current status, challenges and future directions.

Pharmacometabonomics and personalized medicine

BACKGROUND

Pharmacometabonomics is a new branch of science, first described in 2006 and defined as 'the prediction of the effects of a drug on the basis of a mathematical model of pre-dose metabolite profiles'. Pharmacometabonomics has been used to predict drug metabolism, pharmacokinetics (PK), drug safety and drug efficacy in both animals and humans and is complementary to both pharmacogenomics (PGx) and pharmacoproteomics.

METHODS

A literature review using the search terms pharmacometabonomics, pharmacometabolomics, pharmaco-metabonomics, pharmaco-metabolomics and the singular form of all those terms was conducted in October 2012 using PubMed and Web of Science. The review was updated until mid April 2013.

RESULTS

Since the original description of pharmacometabonomics in 2006, 21 original publications and eight reviews have emerged, covering a broad range of applications from the prediction of PK to the prediction of drug metabolism, efficacy and safety in humans and animals.

CONCLUSIONS

Pharmacometabonomics promises to be an important new approach to the delivery of personalized medicine to improve both drug efficacy and safety for patients in the future. Pharmacometabonomics is particularly powerful as it is sensitive to both genetic and environmental factors such as diet, drug intake and most importantly, a person's microbiome. PGx is now over 50 years old and although it has not achieved as much as some hoped, it is starting to have important applications in personalized medicine. We predict that pharmacometabonomics will be equally important in the next few decades and will be both valuable in its own right and complementary to pharmacoproteomics and PGx.