A metabolic biomarker predicts Parkinson's disease at the early stages in patients and animal models

BackgroundCare management of Parkinson's disease (PD) patients currently remains symptomatic, mainly because diagnosis relying on the expression of the cardinal motor symptoms is made too late. Earlier detection of PD therefore represents a key step for developing therapies able to delay or slow down its progression.MethodsWe investigated metabolic markers in 3 different animal models of PD, mimicking different phases of the disease assessed by behavioral and histological evaluation, and in 3 cohorts of de novo PD patients and matched controls (n = 129). Serum and brain tissue samples were analyzed by nuclear magnetic resonance spectroscopy and data submitted to advanced multivariate statistics.ResultsOur translational strategy reveals common metabolic dysregulations in serum of the different animal models and PD patients. Some of them were mirrored in the tissue samples, possibly reflecting pathophysiological mechanisms associated with PD development. Interestingly, some metabolic dysregulations appeared before motor symptom emergence and could represent early biomarkers of PD. Finally, we built a composite biomarker with a combination of 6 metabolites. This biomarker discriminated animals mimicking PD from controls, even from the first, nonmotor signs and, very interestingly, also discriminated PD patients from healthy subjects.ConclusionFrom our translational study, which included 3 animal models and 3 de novo PD patient cohorts, we propose a promising biomarker exhibiting a high accuracy for de novo PD diagnosis that may possibly predict early PD development, before motor symptoms appear.FundingFrench National Research Agency (ANR), DOPALCOMP, Institut National de la Santé et de la Recherche Médicale, Université Grenoble Alpes, Association France Parkinson.

Involvement of the fecal amino acid profile in a clinical and anthropometric study of Mexican patients with insulin resistance and type 2 diabetes mellitus

The amino acids synthesized by the intestinal microbiota have been correlated with metabolic diseases, such as type 2 diabetes mellitus and insulin resistance; both are high incidence conditions in Mexico. However, the knowledge of the relationship of fecal amino acids with the development of both diseases in the Mexican population is scarce. The clinical study was descriptive; the study was carried out in the Antiguo Civil Hospital of Guadalajara. Samples were taken from a total of 48 participants with insulin resistance, diabetes, and a control group (n = 16 each). Anthropometric and biochemical measures were evaluated. HPLC carried out the quantification of fecal amino acids. A strong correlation between alanine and HOMA-IR (r = 0.5416) was found and between phenylalanine and HOMA-IR (r = 0.4258). Other interesting correlations were between alanine and glucose (r = 0.5854) and isoleucine and glucose (r = 0.5008). The diabetic group and the insulin-resistant group had increased fecal values of valine and isoleucine (branched-chain amino acids), which were positively correlated with the progression of both conditions. Likewise, alanine and phenylalanine can help predict the development of the disease in the Mexican population. Registry number: 037/19.

Importance of lipids for upper motor neuron health and disease

Building evidence reveals the importance of maintaining lipid homeostasis for the health and function of neurons, and upper motor neurons (UMNs) are no exception. UMNs are critically important for the initiation and modulation of voluntary movement as they are responsible for conveying cerebral cortex' input to spinal cord targets. To maintain their unique cytoarchitecture with a prominent apical dendrite and a very long axon, UMNs require a stable cell membrane, a lipid bilayer. Lipids can act as building blocks for many biomolecules, and they also contribute to the production of energy. Therefore, UMNs require sustained control over the production, utilization and homeostasis of lipids. Perturbations of lipid homeostasis lead to UMN vulnerability and progressive degeneration in diseases such as hereditary spastic paraplegia (HSP) and primary lateral sclerosis (PLS). Here, we discuss the importance of lipids, especially for UMNs.

Metabolomics: An emerging potential approach to decipher critical illnesses

Critical illnesses contribute to the maximum morbidity and mortality of hospitalized patients. Acute respiratory distress syndrome (ARDS) and sepsis/septic shock are the two most common acute illnesses associated with intensive care unit (ICU) admission. Once triggered, both have an identical underlying mechanism, portrayed by inflammation and endothelial dysfunction. The diagnosis of ARDS is based on clinical findings, laboratory tests, and radiological imaging. Blood cultures remain the gold standard for the diagnosis of sepsis, with the limitation of time delay and low positive yield. A combination of biomarkers has been proposed to diagnose and prognosticate these acute disorders with strengths and limitations, but still, the gold standard has been elusive to clinicians. In this review article, we illustrate the potential of metabolomics to unravel biomarkers that can be clinically utilized as a rapid prognostic and diagnostic tool associated with specific patient populations (ARDS and sepsis/septic shock) based on the available scientific data.

Serum fatty acid chain length associates with prevalent symptomatic end-stage osteoarthritis, independent of BMI

Higher body mass index (BMI) is associated with osteoarthritis (OA) in both weight-bearing and non-weight-bearing joints, suggesting a link between OA and poor metabolic health beyond mechanical loading. This risk may be influenced by systemic factors accompanying BMI. Fluctuations in concentrations of metabolites may mark or even contribute to development of OA. This study explores the association of metabolites with radiographic knee/hip OA prevalence and progression. A ¹H-NMR-metabolomics assay was performed on plasma samples of 1564 cases for prevalent OA and 2,125 controls collected from the Rotterdam Study, CHECK, GARP/NORREF and LUMC-arthroplasty cohorts. OA prevalence and 5 to 10 year progression was assessed by means of Kellgren-Lawrence (KL) score and the OARSI-atlas. End-stage knee/hip OA (TJA) was defined as indication for arthroplasty surgery. Controls did not have OA at baseline or follow-up. Principal component analysis of 227 metabolites demonstrated 23 factors, of which 19 remained interpretable after quality-control. Associations of factor scores with OA definitions were investigated with logistic regression. Fatty acids chain length (FALen), which was included in two factors which associated with TJA, was individually associated with both overall OA as well as TJA. Increased Fatty Acid chain Length is associated with OA.

An NMR based panorama of the heterogeneous biology of acute respiratory distress syndrome (ARDS) from the standpoint of metabolic biomarkers

Acute respiratory distress syndrome (ARDS), manifested by intricate etiology and pathophysiology, demands careful clinical surveillance due to its high mortality and imminent life support measures. NMR based metabolomics provides an approach for ARDS which culminates from a wide spectrum of illness thereby confounding early manifestation and prognosis predictors. ¹ H NMR with its manifold applications in critical disease settings can unravel the biomarker of ARDS thus holding potent implications by providing surrogate endpoints of clinical utility. NMR metabolomics which is the current apogee platform of omics trilogy is contributing towards the possible panacea of ARDS by subsequent validation of biomarker credential on larger datasets. In the present review, the physiological derangements that jeopardize the whole metabolic functioning in ARDS are exploited and the biomarkers involved in progression are addressed and substantiated. The following sections of the review also outline the clinical spectrum of ARDS from the standpoint of NMR based metabolomics which is an emerging element of systems biology. ARDS is the main premise of intensivists textbook, which has been thoroughly reviewed along with its incidence, progressive stages of severity, new proposed diagnostic definition, and the preventive measures and the current pitfalls of clinical management. The advent of new therapies, the need for biomarkers, the methodology and the contemporary promising approaches needed to improve survival and address heterogeneity have also been evaluated. The review has been stepwise illustrated with potent biometrics employed to selectively pool out differential metabolites as diagnostic markers and outcome predictors. The following sections have been drafted with an objective to better understand ARDS mechanisms with predictive and precise biomarkers detected so far on the basis of underlying physiological parameters having close proximity to diseased phenotype. The aim of this review is to stimulate interest in conducting more studies to help resolve the complex heterogeneity of ARDS with biomarkers of clinical utility and relevance.

Noninvasive analysis and identification of an intramuscular fluid collection by postmortem 1H-MRS in a case of a fatal motor vehicle accident

In a case of a fatal traffic accident, a suspicious finding was identified in the muscular tissue of the left thigh by whole-body postmortem computed tomography. To better interpret the finding, the lower extremities were investigated by magnetic resonance imaging (MRI) and proton magnetic resonance spectroscopy (¹H-MRS). MRI revealed the presence of an evenly distributed intramuscular fluid and ¹H-MRS of a volume within the fluid detected concentrations of acetate and lactate. The fluid was assumed to be an extravasation of an intraosseous infusion, erroneously administered to the intermediate vastus of the left thigh during resuscitation, which was later confirmed when access to resuscitation protocols was granted. Further ex situ ¹H-MRS investigations of five different infusion fluids showed the possible discrimination of the fluids and further indicated the unknown fluid to be a Ringer's acetate solution. This paper presents the case-based application of postmortem intramuscular 1H-MRS and introduces the possibility of its use to differentiate exo- and endogenic fluids for forensic interpretation. Further research for this method regarding problems in forensic pathology is needed.

Metabolomics in early detection and prognosis of acute coronary syndrome

Acute coronary syndrome (ACS) is one of the most dangerous types of coronary heart disease (CHD) and contributes to significant mortality and morbidity worldwide. Outcomes in these patients remain a challenge despite improvements in diagnosis and treatment. Risk stratification continues to be problematic and the identification of novel predictors is crucial for improved outcomes. As such, there is a strong need for the development of novel analytical methods as well as the characterization of better predictive and prognostic biomarkers to enable more personalized treatment. Metabolite profile analysis may greatly assist in interpreting altered pathway dynamics, especially when combined with other 'omics' technologies such as transcriptomics and proteomics. In this review, we describe ACS pathophysiology and recent advances in the role of metabolomics in the diagnosis and the molecular pathogenesis of ACS. We briefly describe key technologies used in metabolomics research and statistical approaches for data reduction and pathway analysis and discuss their application to CHD.

The association between blood copper concentration and biomarkers related to cardiovascular disease risk - analysis of 206 individuals in the Northern Finland Birth Cohort 1966

BACKGROUND

Copper is an abundant trace element in humans where alterations in the circulating concentration could inform on chronic disease aetiology. To date, data are lacking to study how copper may associate with cardiovascular disease (CVD) risk factors in young and healthy population. Molecular evidence suggests an important role of copper in liver metabolism, an essential organ in maintaining cardiovascular health and inflammation, therefore supporting copper as an associated biomarker of the risk.

OBJECTIVE

We performed a cross-sectional analysis to examine the possible associations between blood copper levels and risk factors for CVD and pre-inflammatory process.

DESIGN

The data has been collected from a sub-sample set of the Northern Finland Birth Cohort 1966 (NFBC1966) at 31 years.

PARTICIPANTS

The study included 206 individuals, 116 men and 90 women. To reduce environmental individual variations affecting both copper and the metabolic profile in the study sample, the participants were selected as: i) being born in Finnish Lapland and ii) living in their birth place for the last five years preceding blood sampling.

MAIN OUTCOME MEASURES

Fasting blood copper concentration was measured by inductively coupled plasma mass spectrometer. The CVD risk factors included 6 metabolic clusters (30 cardiovascular and pro-inflammatory factors) assessed by nuclear magnetic resonance. Multivariate linear regression analysis was performed to test the linear association between blood copper and 6 metabolic clusters for CVD risk. Associations were assessed under correction for multiple testing.

RESULTS

Copper (Cu) levels were comparable in men and women, with no difference between sexes (p-value <0.60). In multiple regression models, sex adjusted, copper was associated with 9 metabolites from 4 metabolic clusters. After adjustment with BMI, copper was associated with 4 metabolites from 3 metabolic clusters: glutamine, beta-hydroxybutyrate, alpha-1-acid glycoprotein (AGP) and high-sensitive C-reactive protein (hs-CRP). After correction for multiple testing, Cu was found positively associated with only 2 biomarkers of inflammation including AGP [p = 0.04] and hs-CRP [p = 0.0001].

CONCLUSIONS

Considering the strength and limitation of the study design, the present study does not support evidence for an independent role of copper on biomarkers for CVD risk. Nevertheless, we are reporting a robust association of copper with the inflammatory load that is important to consider in light with the inflammatory component of chronic health. In addition, the association of copper with metabolites may be attributable to BMI or environmental factors associated to it, and warrants further research in large population samples.

Cognition, psychosis risk and metabolic measures in two adolescent birth cohorts

BACKGROUND

Psychoses, especially schizophrenia, are often preceded by cognitive deficits and psychosis risk states. Altered metabolic profiles have been found in schizophrenia. However, the associations between metabolic profiles and poorer cognitive performance and psychosis risk in the population remain to be determined.

METHODS

Detailed molecular profiles were measured for up to 8976 individuals from two general population-based prospective birth cohorts: the Northern Finland Birth Cohort 1986 (NFBC 1986) and the Avon Longitudinal Study of Parents and Children (ALSPAC). A high-throughput nuclear magnetic resonance spectroscopy platform was used to quantify 70 metabolic measures at age 15-16 years in the NFBC 1986 and at ages 15 and 17 years in ALSPAC. Psychosis risk was assessed using the PROD-screen questionnaire at age 15-16 years in the NFBC 1986 or the psychotic-like symptoms assessment at age 17 years in ALSPAC. Cognitive measures included academic performance at age 16 years in both cohorts and general intelligence and executive function in ALSPAC. Logistic regression measured cross-sectional and longitudinal associations between metabolic measures and psychosis risk and cognitive performance, controlling for important covariates.

RESULTS

Seven metabolic measures, primarily fatty acid (FA) measures, showed cross-sectional associations with general cognitive performance, four across both cohorts (low density lipoprotein diameter, monounsaturated FA ratio, omega-3 ratio and docosahexaenoic acid ratio), even after controlling for important mental and physical health covariates. Psychosis risk showed minimal metabolic associations.

CONCLUSIONS

FA ratios may be important in marking risk for cognitive deficits in adolescence. Further research is needed to clarify whether these biomarkers could be causal and thereby possible targets for intervention.

Combined systems pharmacology and fecal metabonomics to study the biomarkers and therapeutic mechanism of type 2 diabetic nephropathy treated with Astragalus and Leech

In our study, systems pharmacology was used to predict the molecular targets of Astragalus and Leech, and explore the therapeutic mechanism of type 2 diabetic nephropathy (T2DN) treated with Astragalus and Leech. Simultaneously, to reveal the systemic metabolic changes and biomarkers associated with T2DN, we performed ¹H NMR-based metabonomics and multivariate analysis to analyze fecal samples obtained from model T2DN rats. In addition, ELISA kits and histopathological studies were used to examine biochemical parameters and kidney tissue, respectively. Striking differences in the Pearson's correlation of 22 biomarkers and 9 biochemical parameters were also observed among control, T2DN and treated rats. Results of systems pharmacology analysis revealed that 9 active compounds (3,9-di-O-methylnissolin; (6aR,11aR)-9,10-dimethoxy-6a,11a-dihydro-6H-benzofurano[3,2-c]chromen-3-ol; hirudin; l-isoleucine; phenylalanine; valine; hirudinoidine A–C) and 9 target proteins (l-serine dehydratase; 3-hydroxyacyl-CoA dehydrogenase; tyrosyl-tRNA synthetase; tryptophanyl-tRNA synthetase; branched-chain amino acid aminotransferase; acetyl-CoA C-acetyltransferase; isovaleryl-CoA dehydrogenase; pyruvate dehydrogenase E1 component alpha subunit; hydroxyacylglutathione hydrolase) of Astragalus and Leech were closely associated with the treatment of T2DN. Using fecal metabonomics analysis, 22 biomarkers were eventually found to be closely associated with the occurrence of T2DN. Combined with systems pharmacology and fecal metabonomics, these biomarkers were found to be mainly associated with 6 pathways, involving amino acid metabolism (leucine, valine, isoleucine, alanine, lysine, glutamate, taurine, phenylalanine, tryptophan); energy metabolism (lactate, succinate, creatinine, α-glucose, glycerol); ketone body and fatty acid metabolism (3-hydroxybutyrate, acetate, n-butyrate, propionate); methylamine metabolism (dimethylamine, trimethylamine); and secondary bile acid metabolism and urea cycle (deoxycholate, citrulline). The underlying mechanisms of action included protection of the liver and kidney, enhancement of insulin sensitivity and antioxidant activity, and improvement of mitochondrial function. To the best of our knowledge, this is the first time that systems pharmacology combined with fecal metabonomics has been used to study T2DN. 6 metabolites (n-butyrate, deoxycholate, propionate, tryptophan, taurine and glycerol) associated with T2DN were newly discovered in fecal samples. These 6 metabolites were mainly derived from the intestinal flora, and related to amino acid metabolism, fatty acid metabolism, and secondary bile acid metabolism. We hope the results of this study could be inspirational and helpful for further exploration of T2DN treatment. Meanwhile, our results highlighted that exploring the biomarkers of T2DN and therapeutic mechanisms of Traditional Chinese Medicine (TCM) formulas on T2DN by combining systems pharmacology and fecal metabonomics methods was a promising strategy.

In our study, systems pharmacology was used to predict the molecular targets of Astragalus and Leech, and explore the therapeutic mechanism of type 2 diabetic nephropathy (T2DN) treated with Astragalus and Leech.

Specific Metabolome Profile of Exhaled Breath Condensate in Patients with Shock and Respiratory Failure: A Pilot Study

BACKGROUND

Shock includes different pathophysiological mechanisms not fully understood and remains a challenge to manage. Exhaled breath condensate (EBC) may contain relevant biomarkers that could help us make an early diagnosis or better understand the metabolic perturbations resulting from this pathological situation.

OBJECTIVE

we aimed to establish the metabolomics signature of EBC from patients in shock with acute respiratory failure in a pilot study.

MATERIAL AND METHODS

We explored the metabolic signature of EBC in 12 patients with shock compared to 14 controls using LC-HRMS. We used a non-targeted approach, and we performed a multivariate analysis based on Orthogonal Partial Least Square-Discriminant Analysis (OPLS-DA) to differentiate between the two groups of patients.

RESULTS

We optimized the procedure of EBC collection and LC-HRMS detected more than 1000 ions in this fluid. The optimization of multivariate models led to an excellent model of differentiation for both groups (Q2 > 0.4) after inclusion of only 6 ions.

DISCUSSION AND CONCLUSION

We validated the procedure of EBC collection and we showed that the metabolome profile of EBC may be relevant in characterizing patients with shock. We performed well in distinguishing these patients from controls, and the identification of relevant compounds may be promising for ICC patients.

Further development of biomarkers in amyotrophic lateral sclerosis

INTRODUCTION

Amyotrophic lateral sclerosis (ALS) is an idiopathic neurodegenerative disease usually fatal in less than three years. Even if standard guidelines are available to diagnose ALS, the mean diagnosis delay is more than one year. In this context, biomarker discovery is a priority. Research has to focus on new diagnostic tools, based on combined explorations.

AREAS COVERED

In this review, we specifically focus on biology and imaging markers. We detail the innovative field of 'omics' approach and imaging and explain their limits to be useful in routine practice. We describe the most relevant biomarkers and suggest some perspectives for biomarker research. Expert commentary: The successive failures of clinical trials in ALS underline the need for new strategy based on innovative tools to stratify patients and to evaluate their responses to treatment. Biomarker data may be useful to improve the designs of clinical trials. Biomarkers are also needed to better investigate disease pathophysiology, to identify new therapeutic targets, and to improve the performance of clinical assessments for diagnosis and prognosis in the clinical setting. A consensus on the best management of neuroimaging and 'omics' methods is necessary and a systematic independent validation of findings may add robustness to future studies.

Quantitative serum nuclear magnetic resonance metabolomics in cardiovascular epidemiology and genetics

Metabolomics is becoming common in epidemiology due to recent developments in quantitative profiling technologies and appealing results from their applications for understanding health and disease. Our team has developed an automated high-throughput serum NMR metabolomics platform that provides quantitative molecular data on 14 lipoprotein subclasses, their lipid concentrations and composition, apolipoprotein A-I and B, multiple cholesterol and triglyceride measures, albumin, various fatty acids as well as on numerous low-molecular-weight metabolites, including amino acids, glycolysis related measures and ketone bodies. The molar concentrations of these measures are obtained from a single serum sample with costs comparable to standard lipid measurements. We have analyzed almost 250 000 samples from around 100 epidemiological cohorts and biobanks and the new international set-up of multiple platforms will allow an annual throughput of more than 250 000 samples. The molecular data have been used to study type 1 and type 2 diabetes etiology as well as to characterize the molecular reflections of the metabolic syndrome, long-term physical activity, diet and lipoprotein metabolism. The results have revealed new biomarkers for early atherosclerosis, type 2 diabetes, diabetic nephropathy, cardiovascular disease and all-cause mortality. We have also combined genomics and metabolomics in diverse studies. We envision that quantitative high-throughput NMR metabolomics will be incorporated as a routine in large biobanks; this would make perfect sense both from the biological research and cost point of view - the standard output of over 200 molecular measures would vastly extend the relevance of the sample collections and make many separate clinical chemistry assays redundant.

The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research

Mass spectrometry (MS) and nuclear magnetic resonance (NMR) have evolved as the most common techniques in metabolomics studies, and each brings its own advantages and limitations. Unlike MS spectrometry, NMR spectroscopy is quantitative and does not require extra steps for sample preparation, such as separation or derivatization. Although the sensitivity of NMR spectroscopy has increased enormously and improvements continue to emerge steadily, this remains a weak point for NMR compared with MS. MS-based metabolomics provides an excellent approach that can offer a combined sensitivity and selectivity platform for metabolomics research. Moreover, different MS approaches such as different ionization techniques and mass analyzer technology can be used in order to increase the number of metabolites that can be detected. In this chapter, the advantages, limitations, strengths, and weaknesses of NMR and MS as tools applicable to metabolomics research are highlighted.

Standardizing the experimental conditions for using urine in NMR-based metabolomic studies with a particular focus on diagnostic studies: a review

The metabolic composition of human biofluids can provide important diagnostic and prognostic information. Among the biofluids most commonly analyzed in metabolomic studies, urine appears to be particularly useful. It is abundant, readily available, easily stored and can be collected by simple, noninvasive techniques. Moreover, given its chemical complexity, urine is particularly rich in potential disease biomarkers. This makes it an ideal biofluid for detecting or monitoring disease processes. Among the metabolomic tools available for urine analysis, NMR spectroscopy has proven to be particularly well-suited, because the technique is highly reproducible and requires minimal sample handling. As it permits the identification and quantification of a wide range of compounds, independent of their chemical properties, NMR spectroscopy has been frequently used to detect or discover disease fingerprints and biomarkers in urine. Although protocols for NMR data acquisition and processing have been standardized, no consensus on protocols for urine sample selection, collection, storage and preparation in NMR-based metabolomic studies have been developed. This lack of consensus may be leading to spurious biomarkers being reported and may account for a general lack of reproducibility between laboratories. Here, we review a large number of published studies on NMR-based urine metabolic profiling with the aim of identifying key variables that may affect the results of metabolomics studies. From this survey, we identify a number of issues that require either standardization or careful accounting in experimental design and provide some recommendations for urine collection, sample preparation and data acquisition.

Metabolomic analysis of long-term spontaneous exercise in mice suggests increased lipolysis and altered glucose metabolism when animals are at rest

Exercise has been associated with several beneficial effects and is one of the major modulators of metabolism. The working muscle produces and releases substances during exercise that mediate the adaptation of the muscle but also improve the metabolic flexibility of the complete organism, leading to adjustable substrate utilization. Metabolomic studies on physical exercise are scarce and most of them have been focused on the effects of intense exercise in professional sportsmen. The aim of our study was to determine plasma metabolomic adaptations in mice after a long-term spontaneous exercise intervention study (18 mo). The metabolic changes induced by long-term spontaneous exercise were sufficient to achieve complete discrimination between groups in the principal component analysis scores plot. We identified plasma indicators of an increase in lipolysis (elevated unsaturated fatty acids and glycerol), a decrease in glucose and insulin plasma levels and in heart glucose consumption (by PET), and altered glucose metabolism (decreased alanine and lactate) in the wheel running group. Collectively these data are compatible with an increase in skeletal muscle insulin sensitivity in the active mice. We also found an increase in amino acids involved in catecholamine synthesis (tyrosine and phenylalanine), in the skeletal muscle pool of creatine phosphate and taurine, and changes in phospholipid metabolism (phosphocholine and choline in lipids) between the sedentary and the active mice. In conclusion, long-term spontaneous wheel running induces significant plasma and tissue (heart) metabolic responses that remain even when the animal is at rest.

Biological and neuroimaging biomarkers for amyotrophic lateral sclerosis: 2013 and beyond

SUMMARY Amyotrophic lateral sclerosis is an idiopathic, incurable neurodegenerative disease that is fatal for most patients in less than 3 years from the time weakness first appears. Alongside identification of etiologies and stronger neuroprotective agents, the development of biomarkers is a main research priority. Since the original description, diagnosis and progression measurement in amyotrophic lateral sclerosis has been clinical. The time from symptom onset to diagnosis is usually more than a year, and clinical research studies utilize clinical end points that have low sensitivity. Few eligible patients and inefficient trials mean that just one or a few new therapies can be tested each year. Biological markers are needed not only to improve the sensitivity of clinical assessments, but also to better examine disease pathophysiology in vivo.

Metabolomics in cerebrospinal fluid of patients with amyotrophic lateral sclerosis: an untargeted approach via high-resolution mass spectrometry

Amyotrophic lateral sclerosis (ALS) is characterized by the absence of reliable diagnostic biomarkers. The aim of the study was to (i) devise an untargeted metabolomics methodology that reliably compares cerebrospinal fluid (CSF) from ALS patients and controls by liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS); (ii) ascertain a metabolic signature of ALS by use of the LC-HRMS platform; (iii) identify metabolites for use as diagnostic or pathophysiologic markers. We developed a method to analyze CSF components by UPLC coupled with a Q-Exactive mass spectrometer that uses electrospray ionization. Metabolomic profiles were created from the CSF obtained at diagnosis from ALS patients and patients with other neurological conditions. We performed multivariate analyses (OPLS-DA) and univariate analyses to assess the contribution of individual metabolites as well as compounds identified in other studies. Sixty-six CSF samples from ALS patients and 128 from controls were analyzed. Metabolome analysis correctly predicted the diagnosis of ALS in more than 80% of cases. OPLS-DA identified four features that discriminated diagnostic group (p < 0.004). Our data demonstrate that untargeted metabolomics with LC-HRMS is a robust procedure to generate a specific metabolic profile for ALS from CSF and could be an important aid to the development of biomarkers for the disease.

Integration of datasets from different analytical techniques to assess the impact of nutrition on human metabolome

Bacteria colonizing the human intestinal tract exhibit a high phylogenetic diversity that reflects their immense metabolic potentials. The catalytic activity of gut microbes has an important impact on gastrointestinal (GI) functions and host health. The microbial conversion of carbohydrates and other food components leads to the formation of a large number of compounds that affect the host metabolome and have beneficial or adverse effects on human health. Metabolomics is a metabolic-biology system approach focused on the metabolic responses understanding of living systems to physio-pathological stimuli by using multivariate statistical data on human body fluids obtained by different instrumental techniques. A metabolomic approach based on an analytical platform could be able to separate, detect, characterize and quantify a wide range of metabolites and its metabolic pathways. This approach has been recently applied to study the metabolic changes triggered in the gut microbiota by specific diet components and diet variations, specific diseases, probiotic and synbiotic food intake. This review describes the metabolomic data obtained by analyzing human fluids by using different techniques and particularly Gas Chromatography Mass Spectrometry Solid-phase Micro Extraction (GC-MS/SPME), Proton Nuclear Magnetic Resonance (¹H-NMR) Spectroscopy and Fourier Transform Infrared (FTIR) Spectroscopy. This instrumental approach has a good potential in the identification and detection of specific food intake and diseases biomarkers.

Study of high-resolution H1 nuclear magnetic resonance spectra of the serum and its albumin faction in patients with the first schizophrenia episode

We studied high-resolution (1)H nuclear magnetic resonance spectra of the serum and serum albumin from patients with the first episode of schizophrenia and healthy individuals. A relative increase in signal intensities of CH(2) protons in serum LDL and VLDL in schizophrenia was demonstrated. Higher intensities of CH(2) and CH(3) protons of non-esterified fatty acids were found in (1)H nuclear magnetic resonance spectra of serum albumin. These data attest to an essential role of changes in lipid metabolism and changed ligand load of albumin in schizophrenia.

Sphingomyelin is associated with kidney disease in type 1 diabetes (The FinnDiane Study)

Diabetic kidney disease, diagnosed by urinary albumin excretion rate (AER), is a critical symptom of chronic vascular injury in diabetes, and is associated with dyslipidemia and increased mortality. We investigated serum lipids in 326 subjects with type 1 diabetes: 56% of patients had normal AER, 17% had microalbuminuria (20 ≤ AER < 200 μg/min or 30 ≤ AER < 300 mg/24 h) and 26% had overt kidney disease (macroalbuminuria AER ≥ 200 μg/min or AER ≥ 300 mg/24 h). Lipoprotein subclass lipids and low-molecular-weight metabolites were quantified from native serum, and individual lipid species from the lipid extract of the native sample, using a proton NMR metabonomics platform. Sphingomyelin (odds ratio 2.53, P < 10(-7)), large VLDL cholesterol (odds ratio 2.36, P < 10(-10)), total triglycerides (odds ratio 1.88, P < 10(-6)), omega-9 and saturated fatty acids (odds ratio 1.82, P < 10(-5)), glucose disposal rate (odds ratio 0.44, P < 10(-9)), large HDL cholesterol (odds ratio 0.39, P < 10(-9)) and glomerular filtration rate (odds ratio 0.19, P < 10(-10)) were associated with kidney disease. No associations were found for polyunsaturated fatty acids or phospholipids. Sphingomyelin was a significant regressor of urinary albumin (P < 0.0001) in multivariate analysis with kidney function, glycemic control, body mass, blood pressure, triglycerides and HDL cholesterol. Kidney injury, sphingolipids and excess fatty acids have been linked in animal models-our exploratory approach provides independent support for this relationship in human patients with diabetes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-011-0343-y) contains supplementary material, which is available to authorized users.

Quantitative 1H NMR. Development and potential of an analytical method: an update

Covering the literature from mid-2004 until the end of 2011, this review continues a previous literature overview on quantitative (1)H NMR (qHNMR) methodology and its applications in the analysis of natural products. Among the foremost advantages of qHNMR is its accurate function with external calibration, the lack of any requirement for identical reference materials, a high precision and accuracy when properly validated, and an ability to quantitate multiple analytes simultaneously. As a result of the inclusion of over 170 new references, this updated review summarizes a wealth of detailed experiential evidence and newly developed methodology that supports qHNMR as a valuable and unbiased analytical tool for natural product and other areas of research.

A proton nuclear magnetic resonance-based metabonomics study of metabolic profiling in immunoglobulin a nephropathy

OBJECTIVES

Immunoglobulin A nephropathy is the most common cause of chronic renal failure among primary glomerulonephritis patients. The ability to diagnose immunoglobulin A nephropathy remains poor. However, renal biopsy is an inconvenient, invasive, and painful examination, and no reliable biomarkers have been developed for use in routine patient evaluations. The aims of the present study were to identify immunoglobulin A nephropathy patients, to identify useful biomarkers of immunoglobulin A nephropathy and to establish a human immunoglobulin A nephropathy metabolic profile.

METHODS

Serum samples were collected from immunoglobulin A nephropathy patients who were not using immunosuppressants. A pilot study was undertaken to determine disease-specific metabolite biomarker profiles in three groups: healthy controls (N = 23), low-risk patients in whom immunoglobulin A nephropathy was confirmed as grades I-II by renal biopsy (N = 23), and high-risk patients with nephropathies of grades IV-V (N = 12). Serum samples were analyzed using proton nuclear magnetic resonance spectroscopy and by applying multivariate pattern recognition analysis for disease classification.

RESULTS

Compared with the healthy controls, both the low-risk and high-risk patients had higher levels of phenylalanine, myo-Inositol, lactate, L6 lipids ( = CH-CH2-CH = O), L5 lipids (-CH2-C = O), and L3 lipids (-CH2-CH2-C = O) as well as lower levels of β -glucose, α-glucose, valine, tyrosine, phosphocholine, lysine, isoleucine, glycerolphosphocholine, glycine, glutamine, glutamate, alanine, acetate, 3-hydroxybutyrate, and 1-methylhistidine.

CONCLUSIONS

These metabolites investigated in this study may serve as potential biomarkers of immunoglobulin A nephropathy. Point scoring of pattern recognition analysis was able to distinguish immunoglobulin A nephropathy patients from healthy controls. However, there were no obvious differences between the low-risk and high-risk groups in our research. These results offer new, sensitive and specific, noninvasive approaches that may be of great benefit to immunoglobulin A nephropathy patients by enabling earlier diagnosis.

NMR-based plant metabolomics: where do we stand, where do we go?

NMR-based metabolomics is an important tool for studying biological systems and has been applied in various organisms, including animals, plants and microbes. NMR is able to provide a 'holistic view' of the metabolites under certain conditions, and thus is advantageous for metabolomic studies. To maximize the use of the information obtained, it is also important to create a platform to measure, store and share data. Public databases for storing and sharing information are still lacking for NMR-based metabolomic analysis in plants. Such databases are urgently needed to make metabolic profiling a real omics technology. In addition, to understand metabolic processes in depth, single-cell analysis and the turnover of metabolites in pathways (fluxomics) should be measured.

Metabolomic profiling for identification of novel potential biomarkers in cardiovascular diseases

Metabolomics involves the identification and quantification of metabolites present in a biological system. Three different approaches can be used: metabolomic fingerprinting, metabolic profiling, and metabolic footprinting, in order to evaluate the clinical course of a disease, patient recovery, changes in response to surgical intervention or pharmacological treatment, as well as other associated features. Characteristic patterns of metabolites can be revealed that broaden our understanding of a particular disorder. In the present paper, common strategies and analytical techniques used in metabolomic studies are reviewed, particularly with reference to the cardiovascular field.

NMR methods for unravelling the spectra of complex mixtures

The main methods for the simplification of the NMR of complex mixtures by selective attenuation/suppression of the signals of certain components are presented. The application of relaxation, diffusion and PSR filters and other techniques to biological samples, pharmaceuticals, foods, living organisms and natural products are illustrated with examples.

1H-NMR-based metabolomic profiling of CSF in early amyotrophic lateral sclerosis

Background

Pathophysiological mechanisms involved in amyotrophic lateral sclerosis (ALS) are complex and none has identified reliable markers useful in routine patient evaluation. The aim of this study was to analyze the CSF of patients with ALS by ¹H NMR (Nuclear Magnetic Resonance) spectroscopy in order to identify biomarkers in the early stages of the disease, and to evaluate the biochemical factors involved in ALS.

Methodology

CSF samples were collected from patients with ALS at the time of diagnosis and from patients without neurodegenerative diseases. One and two-dimensional ¹H NMR analyses were performed and metabolites were quantified by the ERETIC method. We compared the concentrations of CSF metabolites between both groups. Finally, we performed principal component (PCA) and discriminant analyses.

Principal Findings

Fifty CSF samples from ALS patients and 44 from controls were analyzed. We quantified 17 metabolites including amino-acids, organic acids, and ketone bodies. Quantitative analysis revealed significantly lower acetate concentrations (p = 0.0002) in ALS patients compared to controls. Concentration of acetone trended higher (p = 0.015), and those of pyruvate (p = 0.002) and ascorbate (p = 0.003) were higher in the ALS group. PCA demonstrated that the pattern of analyzed metabolites discriminated between groups. Discriminant analysis using an algorithm of 17 metabolites revealed that patients were accurately classified 81.6% of the time.

Conclusion/Significance

CSF screening by NMR spectroscopy could be a useful, simple and low cost tool to improve the early diagnosis of ALS. The results indicate a perturbation of glucose metabolism, and the need to further explore cerebral energetic metabolism.

High throughput prediction of chylomicron triglycerides in human plasma by nuclear magnetic resonance and chemometrics

Background

The lipid content of the chylomicrons is a key biomarker and risk factor of cardiovascular diseases and for the understanding of obesity. A high throughput determination of chylomicrons in human blood plasma is outlined.

Methods

The new method, which uses a combination of Nuclear Magnetic Resonance (NMR) analysis and multivariate calibration analysis (chemometrics), is based on a correlation analysis towards the established standard method (ultracentrifugation and colorimetric test kit) and enables extraordinarily fast, inexpensive, and robust prediction of triglyceride (TG) content in chylomicrons. It is the position and shape of the complex lipid methylene resonance band that determines the chylomicron TG status and this information is extracted by the multivariate regression method.

Results

The resulting method is a relatively simple multivariate model that facilitates parsimonious and accurate prediction of chylomicron lipids from NMR spectra of blood. The chemometric model predicts the chylomicron TG content with a correlation coefficient (R) of 0.96 when plotted against density gradient ultracentrifugation data.

Conclusions

The new rapid method facilitates large scale clinical and nutritional trials with inclusion of diagnostics of chylomicron status and thus creates new opportunities for research in lifestyle diseases and obesity.

Integration of metabolomics and transcriptomics data to aid biomarker discovery in type 2 diabetes

Type 2 diabetes (T2D), one of the most common diseases in the western world, is characterized by insulin resistance and impaired beta-cell function but currently it is difficult to determine the precise pathophysiology in individual T2D patients. Non-targeted metabolomics technologies have the potential for providing novel biomarkers of disease and drug efficacy, and are increasingly being incorporated into biomarker exploration studies. Contextualization of metabolomics results is enhanced by integration of study data from other platforms, such as transcriptomics, thus linking known metabolites and genes to relevant biochemical pathways. In the current study, urinary NMR-based metabolomic and liver, adipose, and muscle transcriptomic results from the db/db diabetic mouse model are described. To assist with cross-platform integration, integrative pathway analysis was used. Sixty-six metabolites were identified in urine that discriminate between the diabetic db/db and control db/+ mice. The combined analysis of metabolite and gene expression changes revealed 24 distinct pathways that were altered in the diabetic model. Several of these pathways are related to expected diabetes-related changes including changes in lipid metabolism, gluconeogenesis, mitochondrial dysfunction and oxidative stress, as well as protein and amino acid metabolism. Novel findings were also observed, particularly related to the metabolism of branched chain amino acids (BCAAs), nicotinamide metabolites, and pantothenic acid. In particular, the observed decrease in urinary BCAA catabolites provides direct corroboration of previous reports that have inferred that elevated BCAAs in diabetic patients are caused, in part, by reduced catabolism. In summary, the integration of metabolomics and transcriptomics data via integrative pathway mapping has facilitated the identification and contextualization of biomarkers that, presuming further analytical and biological validation, may be useful in future T2D clinical studies by identifying patient populations that share common disease pathophysiology and therefore may identify those patients that may respond better to a particular class of anti-diabetic drugs.

Improving metabolite knowledge in stable atherosclerosis patients by association and correlation of GC-MS and 1H NMR fingerprints

The plasma of patients with stable carotid atherosclerosis (n = 9), and healthy subjects (n = 10) have been fingerprinted with both GC-MS and (1)H NMR. Principal component analysis (PCA), partial least-squares-discriminant analysis (PLS-DA) and orthogonal partial least-squares-discriminant analysis (OPLS-DA) have been applied to the profiles from each technique both separately and in combination. These techniques complement each other and enable a clearer picture of the biological samples to be interpreted not only for classification purposes, but also more importantly to define the metabolic state of patients with carotid atherosclerosis. The results showed at least 24 metabolites that were significantly modified in the group of atherosclerotic patients by this nontargeted procedure. Most of the changes can be associated to alterations of the metabolism characteristics of insulin resistance that can be strongly related to the metabolic syndrome. In addition, correlations among variables accounting for the classification show amino acids as variables whose changes showed a high degree of correlation. GC-MS and (1)H NMR fingerprints can provide complementary information in the identification of altered metabolic pathways in patients with carotid atherosclerosis. Moreover, correlations among the results with both techniques, instead of a single study, can provide a deeper insight into the patient state.

NMR metabolic analysis of samples using fuzzy K-means clustering

The global analysis of metabolites can be used to define the phenotypes of cells, tissues or organisms. Classifying groups of samples based on their metabolic profile is one of the main topics of metabolomics research. Crisp clustering methods assign each feature to one cluster, thereby omitting information about the multiplicity of sample subtypes. Here, we present the application of fuzzy K-means clustering method for the classification of samples based on metabolomics 1D (1)H NMR fingerprints. The sample classification was performed on NMR spectra of cancer cell line extracts and of urine samples of type 2 diabetes patients and animal models. The cell line dataset included NMR spectra of lipophilic cell extracts for two normal and three cancer cell lines with cancer cell lines including two invasive and one non-invasive cancers. The second dataset included previously published NMR spectra of urine samples of human type 2 diabetics and healthy controls, mouse wild type and diabetes model and rat obese and lean phenotypes. The fuzzy K-means clustering method allowed more accurate sample classification in both datasets relative to the other tested methods including principal component analysis (PCA), hierarchical clustering (HCL) and K-means clustering. In the cell line samples, fuzzy clustering provided a clear separation of individual cell lines, groups of cancer and normal cell lines as well as non-invasive and invasive tumour cell lines. In the diabetes dataset, clear separation of healthy controls and diabetics in all three models was possible only by using the fuzzy clustering method.

Pharmacometabonomic identification of a significant host-microbiome metabolic interaction affecting human drug metabolism

We provide a demonstration in humans of the principle of pharmacometabonomics by showing a clear connection between an individual's metabolic phenotype, in the form of a predose urinary metabolite profile, and the metabolic fate of a standard dose of the widely used analgesic acetaminophen. Predose and postdose urinary metabolite profiles were determined by (1)H NMR spectroscopy. The predose spectra were statistically analyzed in relation to drug metabolite excretion to detect predose biomarkers of drug fate and a human-gut microbiome cometabolite predictor was identified. Thus, we found that individuals having high predose urinary levels of p-cresol sulfate had low postdose urinary ratios of acetaminophen sulfate to acetaminophen glucuronide. We conclude that, in individuals with high bacterially mediated p-cresol generation, competitive O-sulfonation of p-cresol reduces the effective systemic capacity to sulfonate acetaminophen. Given that acetaminophen is such a widely used and seemingly well-understood drug, this finding provides a clear demonstration of the immense potential and power of the pharmacometabonomic approach. However, we expect many other sulfonation reactions to be similarly affected by competition with p-cresol and our finding also has important implications for certain diseases as well as for the variable responses induced by many different drugs and xenobiotics. We propose that assessing the effects of microbiome activity should be an integral part of pharmaceutical development and of personalized health care. Furthermore, we envisage that gut bacterial populations might be deliberately manipulated to improve drug efficacy and to reduce adverse drug reactions.

High-throughput serum NMR metabonomics for cost-effective holistic studies on systemic metabolism

A high-throughput proton (1H) nuclear magnetic resonance (NMR) metabonomics approach is introduced to characterise systemic metabolic phenotypes. The methodology combines two molecular windows that contain the majority of the metabolic information available by 1H NMR from native serum, e.g. serum lipids, lipoprotein subclasses as well as various low-molecular-weight metabolites. The experimentation is robotics-controlled and fully automated with a capacity of about 150-180 samples in 24 h. To the best of our knowledge, the presented set-up is unique in the sense of experimental high-throughput, cost-effectiveness, and automated multi-metabolic data analyses. As an example, we demonstrate that the NMR data as such reveal associations between systemic metabolic phenotypes and the metabolic syndrome (n = 4407). The high-throughput of up to 50,000 serum samples per year is also paving the way for this technology in large-scale clinical and epidemiological studies. In contradiction to single 'biomarkers', the application of this holistic NMR approach and the integrated computational methods provides a data-driven systems biology approach to biomedical research.

Metabonomics research of diabetic nephropathy and type 2 diabetes mellitus based on UPLC-oaTOF-MS system

Ultra performance liquid chromatography (UPLC) coupled with orthogonal acceleration time-of-flight (oaTOF) mass spectrometry has showed great potential in diabetes research. In this paper, a UPLC-oaTOF-MS system was employed to distinguish the global serum profiles of 8 diabetic nephropathy (DN) patients, 33 type 2 diabetes mellitus (T2DM) patients and 25 healthy volunteers, and tried to find potential biomarkers. The UPLC system produced information-rich chromatograms with typical measured peak widths of 4 s, generating peak capacities of 225 in 15 min. Furthermore, principal component analysis (PCA) was used for group differentiation and marker selection. As shown in the scores plot, the distinct clustering between the patients and controls was observed, and DN and T2DM patients were also separated into two individual groups. Several compounds were tentatively identified based on accurate mass, isotopic pattern and MS/MS information. In addition, significant changes in the serum level of leucine, dihydrosphingosine and phytosphingosine were noted, indicating the perturbations of amino acid metabolism and phospholipid metabolism in diabetic diseases, which having implications in clinical diagnosis and treatment.

Plasma amino acid analysis for diagnosis and amino acid-based metabolic networks

PURPOSE OF REVIEW

To highlight the usefulness of amino acid profiling in clinical diagnosis and current developments in analysis revealing underlying metabolic relationships.

RECENT FINDINGS

Recent innovations in metabolomics and systems biology enable high throughput measurement of diverse amino acids and the subsequent data mining for various uses. Recent studies show new possibilities of using plasma amino acid analysis as biomarker discovery tools by generating diagnostic indices through systematic computation. Such studies show that amino acid-based clinical diagnostic indices for hepatic fibrosis in type C hepatitis patients can be generated. In addition, several studies show the potential of treating amino acid profile data as a metabolomic subset, which can be integrated through the analysis of correlation with different types of 'omics' data for describing metabolite-to-metabolite or metabolite-to-gene interaction networks.

CONCLUSION

Amino acid profiling of biological samples could be used to generate indices that could be used for clinical diagnosis and is a useful tool for understanding metabolic implications under various physiological conditions. Although further improvements in analytical methods are needed, amino acids could be useful indicators for facilitating nutritional management of specific physiological and pathological states.

Metabolic profiling reveals distinct variations linked to nicotine consumption in humans--first results from the KORA study

Exposure to nicotine during smoking causes a multitude of metabolic changes that are poorly understood. We quantified and analyzed 198 metabolites in 283 serum samples from the human cohort KORA (Cooperative Health Research in the Region of Augsburg). Multivariate analysis of metabolic profiles revealed that the group of smokers could be clearly differentiated from the groups of former smokers and non-smokers. Moreover, 23 lipid metabolites were identified as nicotine-dependent biomarkers. The levels of these biomarkers are all up-regulated in smokers compared to those in former and non-smokers, except for three acyl-alkyl-phosphatidylcholines (e.g. plasmalogens). Consistently significant results were further found for the ratios of plasmalogens to diacyl-phosphatidylcolines, which are reduced in smokers and regulated by the enzyme alkylglycerone phosphate synthase (alkyl-DHAP) in both ether lipid and glycerophospholipid pathways. Notably, our metabolite profiles are consistent with the strong down-regulation of the gene for alkyl-DHAP (AGPS) in smokers that has been found in a study analyzing gene expression in human lung tissues. Our data suggest that smoking is associated with plasmalogen-deficiency disorders, caused by reduced or lack of activity of the peroxisomal enzyme alkyl-DHAP. Our findings provide new insight into the pathophysiology of smoking addiction. Activation of the enzyme alkyl-DHAP by small molecules may provide novel routes for therapy.

1H NMR metabonomics approach to the disease continuum of diabetic complications and premature death

Subtle metabolic changes precede and accompany chronic vascular complications, which are the primary causes of premature death in diabetes. To obtain a multimetabolite characterization of these high-risk individuals, we measured proton nuclear magnetic resonance (1H NMR) data from the serum of 613 patients with type I diabetes and a diverse spread of complications. We developed a new metabonomics framework to visualize and interpret the data and to link the metabolic profiles to the underlying diagnostic and biochemical variables. Our results indicate complex interactions between diabetic kidney disease, insulin resistance and the metabolic syndrome. We illustrate how a single 1H NMR protocol is able to identify the polydiagnostic metabolite manifold of type I diabetes and how its alterations translate to clinical phenotypes, clustering of micro- and macrovascular complications, and mortality during several years of follow-up. This work demonstrates the diffuse nature of complex vascular diseases and the limitations of single diagnostic biomarkers. However, it also promises cost-effective solutions through high-throughput analytics and advanced computational methods, as applied here in a case that is representative of the real clinical situation.