NMR metabolomics of human blood and urine in disease research.

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Review

NMR metabolomics of human blood and urine in disease research Iola F. Duarte ∗ , Sílvia O. Diaz, Ana M. Gil CICECO, Department of Chemistry, University of Aveiro, Campus de Santiago, 3810-193 Aveiro, Portugal

a r t i c l e

i n f o

Article history: Received 1 July 2013 Received in revised form 16 September 2013 Accepted 24 September 2013 Available online xxx Keywords: NMR spectroscopy Metabolomics Blood Urine Disease

a b s t r a c t This paper reviews the main applications of NMR metabolomics of blood and urine in disease research, over the last 5 years. The broad range of disease types addressed attests the increasing interest within the academic and medical communities to explore the recognised potential of metabolomics to (1) provide insight into underlying disease pathogenesis and (2) unveil new metabolic markers for disease diagnosis and follow up. Importantly, most recent studies reveal an increasing awareness of possible limitations and pitfalls of the metabolomics approach, together with efforts for improved study design and statistical validation, which are crucial requisites for the sound development of NMR metabolomics and its progress into the clinical setting. © 2013 Elsevier B.V. All rights reserved.

Contents 1. 2.

3.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Diseases studied by NMR metabolomics of human blood and urine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Neoplasms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Digestive system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3. Endocrine, nutritional and metabolic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.4. Genitourinary system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.5. Circulatory system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.6. Nervous system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.7. Mental and behavioural . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.8. Respiratory system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.9. Pregnancy, childbirth and the puerperium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.10. Infectious and parasitic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Metabolomics (or metabonomics, a term often used in alternative) entails the comprehensive analysis of low molecular weight molecules (metabolites) in biological samples, usually through profiling techniques such as nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS)-based methods, in combination with multivariate statistical analysis [1,2]. The aim is to

∗ Corresponding author. Tel.: +351 234401424; fax: +351 234401470. E-mail address: [email protected] (I.F. Duarte).

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determine fluctuations on the levels of endogenous metabolites and to mine for consistent relationships between such variations and specific pathophysiological conditions or external perturbations, such as disease, diet or therapeutic intervention. As pathological conditions are expected to disrupt homeostasis, thus leading to altered metabolite levels and/or profiles, metabolomics holds great potential in disease diagnosis and monitoring, particularly if based on samples which may be collected non-invasively, such as blood and urine. The graph represented in Fig. 1 shows that, since 2008, just over 140 papers have been published on disease research using biofluids metabolomics (biofluids including mainly blood plasma and serum, but also urine and other biofluids more

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2

50

Table 1 Some recent review papers on specific disease metabolomic studies.

45

Number of papers

40 35 30 25 20

*

15 10 5 0

2008

2009

2010

2011

2012

2013

Year of publication Fig. 1. Number of papers published since 2008 on NMR metabolomics of biofluids, in relation to disease research. The asterisk indicates the number of papers published up to May 2013. Source: Web of Knowledge databases.

specific of the conditions under study). It is noticeable that 2012 has been a particularly productive year in this context, expressing the increasing awareness of the scientific and medical communities to the enticing potential of metabolomics strategies in pushing forward disease understanding and management. Fig. 2 illustrates the scientific investment made as a function of disease type, clearly indicating that a significantly higher emphasis has been given to neoplasms (cancer) research. This review will cover the main developments on 1 H NMR metabolomics of human blood (plasma and serum) and urine in disease research, which have taken place in the last 5 years (i.e. since and including 2008). Due to the large extension of the subject, 40 35

Number of papers

30 25 20 15 10 5 0

Disease type Fig. 2. Number of papers published since 2008 on NMR metabolomics of biofluids in relation to specific diseases. Source: Web of Knowledge databases.

Disease

Reference

Cancer Cardiovascular Endocrine Infectious Kidney injury/urinary infections Neonatal Neurological Respiratory

[5–10] [11–14] [15,16] [17,18] [19–21] [22,23] [24–26] [27–30]

studies involving other sample types (other biofluids, tissues, cells) or animal models are considered to be out of the scope of this text, as are the developments based on MS metabolomics, the latter subject justifying a review of its own. In addition, specific reading about multivariate analysis tools and methodologies can be found elsewhere [3,4]. 2. Diseases studied by NMR metabolomics of human blood and urine For a generally interested reader, Table 1 lists some recent review papers on metabolomics of different disease types [5–30]. For a more specific reading and support to the present text, Table 2 identifies the main research papers on NMR metabolomics of human blood and urine, aimed at investigating several types of diseases (classified according to the International Classification of Diseases proposed by the World Health Organisation, ICD-10 Version: 2010, http://www.who.int/classifications/icd/en/). In the text that follows, the different diseases will be addressed approximately in decreasing order of work volume carried out, briefly discussing the most relevant and/or recent studies for each disease type. 2.1. Neoplasms The term neoplasm describes an abnormal tissue growth or division of cells, which may develop into benign, pre-malignant or malignant (cancer) conditions. From Table 2, it may be seen that the neoplasms most extensively studied (four or more reports) through biofluids metabolomics have comprised breast cancer (blood serum and urine), colorectal cancer (blood plasma and serum), liver cancer (blood serum and urine) and pancreatic cancer (blood serum/plasma and urine). Studies of the remaining neoplasm types listed in Table 2 have, in the last 5 years, amounted to up to three different reports. Generally, most studies on neoplasms have addressed blood analysis (either through plasma or serum), followed by urine and, in some cases (not discussed here), specific biofluids of particular relevance to each neoplasm type (e.g. bile, faecal extracts, prostatic secretions). Regarding breast cancer biofluid metabolomics, a first report [31] described a study on urine obtained from early- and late-stage breast cancer patients (in parallel with ovarian cancer patients), compared to a random collection from females with no known cancer. The noted changes in Krebs cycle intermediates and in metabolites related to energetic, amino acid and gut microbial metabolisms, suggested the usefulness of non-invasive urine analysis for detecting early-stage breast (and ovarian) cancer. In a subsequent study [32], blood serum was analysed by a combination of NMR and MS methods, giving rise to more robust principal component analysis (PCA) and partial least squares (PLS) analysis models of cancer differentiation from healthy controls, compared to those obtained with basis on a single analytical technique. The authors advanced that the approach enables disease classification to be expressed on a continuum, instead of on a binary scale, thus achieving a better representation of the disease state [32]. Pre- and



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Table 2 Main research papers on NMR metabolomics of blood serum/plasma and urine in relation to disease, from 2008 up to May 2013. Disease Neoplasms Bladder Breast Cervical Colorectal Gallbladder Kidney Liver Lung Myeloma Oesophageal

Oral Ovarian Pancreatic

Prostate Digestive system Celiac disease Cirrhosis Functional dyspepsia Inflammatory bowel disease

Pancreatitis Endocrine, nutritional and metabolic Diabetes Obesity Polycystic ovary syndrome Genitourinary system Chronic kidney disease Endometriosis Nephropaties

Ureteral obstruction Urinary tract infection Circulatory system Abdominal aortic aneurysm Cardiovascular risk

Carotid atherosclerosis Cerebral infarction Heart failure Ischaemia

Biofluid

Reference

Urine Serum Urine Plasma Serum Plasma Serum Serum Serum Urine Plasma Urine Serum Urine Serum Plasma Urine Serum Plasma Serum Urine Serum Plasma Urine Serum

[61] [32–35] [31] [50] [36–39] [40] [49] [60] [41,42] [43,44] [54] [62] [55] [55] [51,52] [53] [53] [56] [57] [58] [31] [45,46] [47] [48] [59]

Serum Urine Serum Plasma Serum Plasma Urine Urine

[71,72] [71,72] [68–70] [73] [63,64] [64] [64–67] [74]

Serum Urine Urine Serum Plasma

[75–79,81] [80,81] [85] [84] [82,83]

Serum Urine Serum Serum Urine Urine Urine Urine

[89] [90] [93] [86] [87] [88] [92] [91]

Plasma Serum Serum Plasma Urine Plasma Plasma Urine Urine Serum Serum

[97] [101] [102] [100] [103] [96] [98] [98] [99] [94] [95]

Table 2 (Continued ) Disease

Biofluid

Reference

Major depressive disorder

Plasma Urine Serum Plasma Urine

[109] [110] [112] [111] [111,112]

Serum Urine Serum Plasma Urine

[115] [116] [113] [114] [117]

Plasma Urine Urine Urine Plasma Urine Serum Plasma

[123] [125] [124] [120] [121,122] [121,122] [118] [119]

Plasma Urine Plasma Urine Urine

[127] [126,127] [128] [129] [130]

Schizophrenia

Nervous system Idiopathic intracranial hypertension (IIH), multiple sclerosis (MS) and cerebrovascular disease (CVD) Mild cognitive impairment Multiple sclerosis Parkinson’s disease

Serum

[106]

Serum Serum Plasma

[104] [107] [105]

Mental and behavioural disorders Bipolar disorder

Serum

[108]

3

Respiratory system Asthma Chronic obstructive pulmonary disease Pneumonia Pregnancy, childbirth and the puerperium Foetal growth/maturity/weight

Gestational diabetes mellitus Several prenatal disorders Preeclampsia Infectious and parasitic Hepatitis HIV-1 Malaria Schistosomiasis

post-operative serum samples collected from early breast cancer patients were studied by NMR metabolomics [33], having enabled the metabolic discrimination of early and metastatic patients and, thus, offering interesting potential as a complementary prognostic tool. A subsequent pilot study of pre-treatment and serial ontreatment serum samples from women with metastatic breast cancer [34] aimed at exploring the effects of different outcomes and treatments on sera metabolome. The authors concluded that serum metabolomics may aid in understanding the sensitivity of HER2-positive subjects (those that test positive for human epidermal growth factor receptor 2) to paclitaxel and lapatinib treatment. Another study [35] was based on the observation that breast cancer patients suffering from metabolic syndrome usually have poor outcomes. The relationship between serum metabolite levels, together with metabolic syndrome indications, and the response to cancer treatment was investigated, having suggested that high blood glucose, as part of metabolic syndrome, may be associated with a poor response. Research carried out on colorectal cancer (CRC) has included several studies on blood serum [36–39] and one on plasma [40]. An initial account of blood serum NMR analysis [36] proposed the use of a faster variant of the standard total correlation spectroscopy (TOCSY) experiment, named Hadamard-encoded TOCSY, so that multiple lines from the serum NMR spectra are simultaneously selected and used to acquire pseudo-2D spectra in minutes. This significantly reduces signal degeneracy and improves the discrimination ability of PCA models, as shown for a set of CRC patients compared to healthy controls. A couple of years later [37], a pharmacometabolomics study was carried out to attempt the prediction of capecitabine toxicity in treated patients with inoperable CRC. Serum was collected prior to capecitabine treatment and samples showing higher levels of polyunsaturated fatty acids and choline phospholipids correlated well with higher grade toxicity values, thus showing the potential for the predictive assessment of treatment viability for CRC patients. Two more recent studies have addressed serum metabolic profile as a means for distinguishing CRC stage [38] and identifying and predicting CRC patient survival [39]. In the first study, both NMR and gas chromatography mass spectrometry (GC–MS) were used, having revealed that


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serum metabolic profile changes with metastasis and also with site of disease (locoregional, liver-only and extrahepatic metastases), information which may aid enhancing CRC staging accuracy. In the second study [39], a strong serum metabolic NMR signature was identified for metastatic CRC patients, using separate training and validation sample sets. It was also suggested that the serum metabolic profile may reflect an inflammatory status, which apparently correlates to patient survival, thus suggesting the possibility of predicting survival rate with basis on serum profile. In a blood plasma study [40] of CRC patients, plasma metabolic signatures associated with CRC and dietary fibre intake (a possible protective factor) were identified, the authors having proposed a strategy for the identification of intermediated biomarkers linking exposure and disease. In the case of liver cancer, two studies have involved blood serum analysis [41,42], along with other two on urine [43,44]. The first study in this context addressed serum samples from liver cirrhosis (LC) and hepatocellular carcinoma (HCC) patients, both groups compared to healthy controls [41]. The sera of LC and HCC patients showed changes in endogenous metabolites related to energy metabolism, a distinction between LC and HCC having also been suggested. A subsequent study involved the metabolomic study of sera from LC patients with and without HCC [42]. The results showed discrimination between LC and patients affected by large HCC, putting forward a serum fingerprint that could be specific of large HCC in cirrhotic livers. Patients with small HCC defined a more heterogeneous group spanning from LC to large HCC profiles, suggesting the possibility of defining subgroups related to different prognosis. In relation to urine analysis, an initial report described an NMR study of urine of HCC patients, healthy controls and LC patients from a Nigerian population [43]. Changes in creatinine, carnitine, creatine and acetone were identified as mostly responsible for differentiating HCC patients from controls and the LC cohort. A follow-up study applied a similar strategy to patients from Nigerian and Egyptian populations, results suggesting that altered metabolism related to tumorogenesis seems to be independent of etiologically and ethnically distinct populations [44]. Pancreatic cancer is a malignant tumour with the worst prognosis among all cancers. Usually, surgical cure is no longer feasible at the time of diagnosis, so that early detection is of crucial importance. The initial biofluid studies on pancreatic cancer involved blood serum analysis [45,46], having been followed by recent studies on blood plasma [47] and urine [48]. NMR metabolomics of serum from patients with benign hepatobiliary disease and patients with pancreatic cancer showed glutamate and glucose elevated levels in the cancer group, along with elevated creatine and glutamine in benign cases [45]. In addition, diabetes mellitus was identified as a possible confounder in the cancer group. Another study detected additional metabolite changes in the cancer group, compared to healthy controls, for instance comprising decreased levels of 3hydroxybutyrate and 3-hydroxyisovalerate and elevated levels of triglycerides and creatinine (among other variations) [46]. A more recent study [47] addressed the specificity of blood plasma changes in association with pancreatic cancer, by comparing a cancer group with a group of patients with chronic pancreatitis (CP), as well as with healthy controls. Some of the observations made in previous sera studies were confirmed, along with several other metabolite changes suggesting a plasma signature capable of distinguishing cancer patients from those with CP, as well as from controls. A recent study addressed the urine collected for pancreatic ductal adenocarcinoma (PDAC) and healthy controls [48], having unveiled metabolite changes not only between the general group of cancer patients and healthy controls, but also for individuals with intermediate and advanced pathological staging as well as with different tumour localisation.

Other neoplasms have mainly been investigated though blood metabolomics, namely for the study of malignant gallstone disease [49], cervical [50], oesophageal [51–53], lung [54], myeloma [55], oral [56,57], ovarian [58], prostate [59] and renal cell carcinoma [60]. In terms of urine metabolomics, reports have addressed bladder [61], oesophageal [53], lung [62], myeloma [55] and ovarian [31] neoplasms. 2.2. Digestive system Several NMR metabolomic studies have investigated altered systemic metabolism characterising inflammatory bowel diseases (IBD), namely ulcerative colitis (UC) and Crohn’s disease (CD). Williams et al. have shown that NMR serum profile could accurately discriminate IBD patients from healthy controls, especially based on the levels of lipoproteins, choline, N-acetylglycoproteins and amino acids [63]. Furthermore, UC and CD subjects were found to differ mainly in terms of lipid and choline metabolism. Another study considering a similar set of subjects (UC, CD and healthy controls), extended the metabolic profiling of IBD to serum, plasma and urine [64]. Multivariate modelling of NMR data provided equivalent discrimination accuracy between IBD patients and controls for all three biofluids. However, UC and CD patients showed no significant differences in their urinary metabolites and only weak differences in their serum or plasma composition. The relationship between IBD and urinary NMR profile has also been explored in a few other studies [65–67]. Disturbance of gut microflora metabolites, such as hippurate and formate, was one of the most commonly reported findings. Additionally, Stephens et al. described urinary changes in TCA cycle intermediates and amino acids [67]. In regard to UC vs CD distinction, some studies were unable to produce reliable separations between the two diseases [64,67], whereas Williams et al. reported good predictive models with hippurate, citrate, methylhistidine and 4-cresol sulphate playing key roles [65]. Liver cirrhosis is a life-threatening disease consisting of liver tissue scarring and function loss. The potential of serum NMR metabolomics for assessing the severity of chronic liver failure (CLF) in alcoholic cirrhotic patients has been recently demonstrated [68]. Several significant differences were noted in the serum metabolome of patients with low, mild or severe CLF, reflecting a multi-parametric response to the degree of hepatic function impairment. The serum profile of hepatitis B virus (HBV)-infected cirrhosis patients has also been characterised and shown to enable the distinction between compensated (early stage) and decompensated cirrhosis [69]. In another study of the same research group, the serum metabolic features discriminating alcoholic cirrhosis from HBV-caused cirrhosis were identified, creatine, acetotacetate, glutamine and glutamate having been reported as the most influential metabolites [70]. The metabolic signature of celiac disease (CD) has been characterised by NMR analysis of both serum and urine [71]. Based mainly on serum levels of lipids, glucose and ketone bodies, as well as on urinary microbial-related metabolites, malabsorption, disturbed energy metabolism and altered gut microflora and/or intestinal permeability were underlined as the major CD effects. Moreover, the re-examination of a subset of patients under a 12month gluten-free diet suggested the reversion of metabolism to normality. In a later work, the same authors addressed potential CD cases, who lacked a jejunal biopsy consistent with clear CD but had immunological abnormalities similar to those of overt CD patients [72]. While the urinary profiles showed several differences between potential and overt patients, the serum metabolic pattern was quite similar, allowing the authors to postulate that serum metabolomics could detect CD before its clinical manifestations were fully evident. Two other diseases of the digestive system, namely functional dyspepsia [73] and pancreatitis [74], have been investigated in




recent years by NMR metabolomics. Although considering a small number of subjects, plasma profiling suggested dyspepsia-related perturbations of metabolism, as well as changes upon acupuncture treatment [73]. In regard to the urinary profiling of pancreatitis, decreased citrate and increased adenosine were highlighted as the main changes relatively to healthy controls [74]. 2.3. Endocrine, nutritional and metabolic The metabolic features of several diabetic complications have been investigated through NMR analysis of serum from over 600 patients with type 1 diabetes mellitus (T1DM) [75]. Using selforganizing maps (SOM), the metabolic information derived from two NMR windows, one selecting (lipo)proteins and the other showing low molecular weight metabolites, was related to the incidence of diabetic kidney disease (DKD), the metabolic syndrome, macrovascular diseases (MVD), and mortality. For instance, albumin, creatinine and urea showed a strong association with DKD, while triglycerides, lactate and acetate showed patterns related to the metabolic syndrome. By unveiling complex relationships between T1DM vascular complications, these results were suggested to potentially aid the monitoring of patients’ progress in the diabetic disease continuum [75]. A subsequent work focused on the metabolic phenotypes characterising T1DM patients with varying severity and progression of DKD, by combining baseline NMR serum data and prospective clinical information [76]. Distinct profiles were associated with early and late phases of DKD, with phospholipids, unsaturated fatty acids, IDL and LDL lipids being increased in the early phase and high saturated fatty acids and low HDL suggesting accelerated progression. Serum lipid composition of T1DM patients was further investigated in another study, where sphingomyelin showed a strong correlation with urinary albumin excretion [77], thus supporting the previous suggestion of a role of the sphingolipid pathway in end-stage kidney injury [76]. The response of T1DM patients to short-term intense exercise was addressed in a metabolomics work employing NMR and GC–MS of serum samples collected at rest and after exercise training [78]. Compared to healthy controls, diabetic patients showed similar metabolic alterations, but an overall attenuated response to acute exercise, particularly regarding lipolysis, proteolysis, glycogenolysis and oxidative metabolism. Type 2 diabetes mellitus (T2DM) has also been the focus of a few recent metabolomic studies. The multivariate comparison between serum NMR profiles of subjects with normal glucose tolerance (NGT), impaired glucose regulation (IGR) and T2DM revealed disturbances of glucose, choline, lipid and amino acid metabolisms, as well as disruption of TCA cycle, in IGR and T2DM groups compared to NGT participants [79]. Moreover, these disturbances appeared to define a metabolic trajectory closely associated with insulin resistance status and glucose intolerance severity. The potential of urine NMR profiling for T2DM diagnosis has also been addressed in an exploratory study, where a classifier, based on non-glucose regions of the spectra selected through a genetic algorithm, showed relatively high accuracy (>80%) in discriminating T2DM samples from controls [80]. In another study, the potential of serum and urine metabolic profiles measured by NMR (and also by LC/GC–MS for serum) for predicting the response of T2DM patients to three commonly used antidiabetic agents was explored [81]. This pharmacometabolomics study, which included samples collected prior to and after 4 and 8 weeks of treatment, showed that the baseline measurement of serum 1-methylhistidine, urinary citrate and seric IL-8 (an important cytokine in the inflammatory process) was associated with the response to thiazolidinedione. Polycystic ovary syndrome (PCOS) is an endocrine disorder affecting women at reproductive age. Two plasma NMR metabolomic studies have revealed multiple metabolic alterations

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in PCOS patients compared to healthy controls [82,83]. Although the findings of both studies were not fully coincident, common disturbances in amino acid metabolism, TCA cycle and lipid metabolism were reported. Moreover, based on increased glycoprotein levels detected in the diffusion-edited plasma spectra, Sun et al. underlined the relationship to inflammation in PCOS [82]. In a longitudinal study of non-obese PCOS women who provided serum samples prior to and at the end of a 30-month polytherapy period, multivariate modelling of data from three analytical platforms (NMR, GC–MS and LC–MS) revealed multiple alterations in the serum metabolome upon treatment [84]. Overall, the results showed a reduction in oxidised lipoproteins and downstream oxidative metabolites, reflecting the treatment-related improvement in the condition of PCOS patients. Urine NMR metabolomics has been applied to assess the predisposition and metabolismrelated ability of obese adolescents to loose weight during a 3 weeks immersion healthy lifestyle programme [85]. Some differences between responders and non-responders were apparent in the baseline urinary profile while others emerged after the 3-week period. Fatty acid and amino acid metabolisms were proposed as key factors in the response to the programme, with levels of 2oxoisocaproate, glycine, taurine and tyrosine differing significantly between the two groups. Furthermore, integration with psychosocial data revealed subsets of metabolites associated with impaired self-esteem and depression.

2.4. Genitourinary system NMR metabolomics has been applied to study different nephropaties. The metabolic profile of serum has been shown to contain diagnostic information about immunoglobulin A nephropathy (IgAN) [86]. Multiple changes in both lipids and small metabolites seemed to define a complex response to IgAN, although the different grades of the disease (representing low and high risk for chronic renal failure) could not be discriminated based on the sera profiles. Two other studies used urine metabolomics to address the discrimination of different nephropaties from healthy controls in a paediatric population [87] and to characterise the metabolic effects of Balkan endemic nephropathy (BEN) in patients from two Balkan countries [88]. A number of metabolic variations characterising each BEN group and distinguishing it from respective controls were identified and discussed. Chronic kidney disease (CKD) has been the subject of a serum metabolomics work [89]. Different disease stages were considered and the impact of renal dysfunction and progression of CKD on the serum profile was shown, with glycolytic products, amino acids and organic osmolytes highlighted as the main players in that response. The same disease was also addressed by NMR of urine in a specific population of adult subjects born with extremely low-birth weight (ELBW) and age-matched controls born at term appropriate for gestational age (AGA) [90]. Besides being distinct from controls, ELBW metabolic profiles were significantly correlated with the urinary biomarker of kidney injury, neutrophil gelatinase-associated lipocalin (NGAL). Other urinary system disorders addressed by urine NMR metabolomics included urinary tract infection (UTI) [91] and ureteral obstruction [92]. In the case of UTI patients, followed over a recovery period, the urine metabolic profile was shown to reflect not only the bacterial load in urine (through acetate and trimethylamine), but also patient morbidity, with para-aminohippurate and scyllo-inositol suggested as main markers [91]. Renal function recoverability has also been addressed in cases of bilateral/unilateral ureteral obstruction (BUO/UUO) by analysing urine at different time points before and after obstruction relief [92]. The metabolic variations detected in BUO patients defined a trajectory which could be used to predict the recovery of UUO patients after treatment.




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Concerning the reproductive system, a recent study has employed serum NMR metabolomics to develop a classification model for endometriosis [93]. Ten metabolites were found to be up-regulated and three down-regulated in women with confirmed diagnosis of early stage endometriosis, compared to age/BMImatched control women. 2.5. Circulatory system Aiming at predicting exercise-induced ischaemia in patients with suspected coronary artery disease, Barba et al. have performed a serum NMR metabolomics study where lactate, glucose, lipids and long-chain amino acids were highlighted as the main metabolites changing upon the stress test [94]. A later study has used animal and human models of controlled coronary ischaemia, in the setting of planned angioplasty, and found striking alterations in the serum profiles of both swine and patients immediately after myocardial ischaemia (MIS) [95]. Moreover, the serum metabolomic signature detected showed high accuracy in predicting MIS within a validation group of spontaneous chest pain patients. Carotid atherosclerosis has been investigated by NMR and GC–MS of plasma from patients compared to healthy subjects [96]. Although sample numbers were low (≤10 each group), several metabolites were found to be significantly altered in patients, most of the changes relating to insulin resistance. NMR and GC–MS of plasma have also been applied to the study of abdominal aortic aneurysm (AAA) [97]. Multivariate analysis of separate and combined analytical datasets revealed changes related to carbohydrate, lipid and amino acid metabolism. In particular, aminomalonate, potentially associated with oxidative stress and proteolysis, was highlighted as a promising biomarker in AAA. In addition to plasma, Jung et al. have included urine NMR analysis in their study of cerebral infarction (stroke) and found a clear discrimination from healthy volunteers, based on both biofluids [98]. Overall, the strokerelated metabolic features were suggested to comprise anaerobic glycolysis, folic acid deficiency and hyperhomocysteinemia. The potential of urine analysis has also been shown in a metabolomics study addressing chronic heart failure, where changes relating to TCA cycle, fatty acid, methylmalonate and nucleotide metabolism were proposed [99]. Several recent studies have attempted to identify a metabolic pattern that correlates with cardiovascular disease (CVD) risk, using large epidemiological sample cohorts of apparently healthy subjects for which detailed clinical and biochemical indicators of CVD risk were available. Metabolomics of serum/plasma has unveiled important correlations between those indicators and the NMR-detected blood composition, comprising not only expected changes in lipoproteins, but also in low molecular weight metabolites [100,101]. Another work performed multivariate genome-wide association analysis (GWAS) using single nucleotide polymorphisms (SNP) data and 11 metabolic networks, identified with basis on the observed correlation structure of 130 serum metabolites measured by NMR [102]. Candidate genes for atherosclerosis and associated up-regulated metabolites were highlighted. Urine NMR metabolomics has also been applied to CVD risk assessment, in particular for helping explaining geographical differences between two regions of China [103]. The authors found multiple urinary metabolites to discriminate between northern and southern participants differing in CVD risk, which were suggested to reflect dietary and gut microbial differences. 2.6. Nervous system Mild cognitive impairment (MCI), a transitional state representing increased risk for Alzheimer’s disease, has been investigated within an elderly population followed for 6 years, through serum NMR metabolomics [104]. Self-organising maps (SOM)

analysis performed on three spectra types (lipoprotein profile, lowmolecular-weight metabolites profile and lipid extract) suggested significant associations between MCI, the metabolic syndrome and inflammation, thus supporting the relationship between these conditions and cognitive decline. In a targeted metabolite profiling work, pattern recognition methods have been applied to 22 NMR-detected plasma metabolites, aiming at discriminating between Parkinson’s disease (PD) patients and healthy controls [105]. PD could be detected with high accuracy through a neural network algorithm comprising several variables (NMR data included). Within the metabolites accounting for group discrimination, pyruvate was highlighted as a key compound and the genetic basis of its variation was investigated by gene expression analysis. In a later study combining NMR analysis of blood serum, along with cerebrospinal fluid (CSF), the metabolic profiles of idiopathic intracranial hypertension (IIH), multiple sclerosis (MS) and cerebrovascular disease (CVD) were investigated [106]. Each disease was characterised by a distinct metabolic pattern and was shown to produce a different impact on both serum and CSF profiles. For IIH, both biofluids provided classification models with reasonable sensitivity and specificity (>70%), whereas MS could only be predicted based on the CSF metabolic profile, CVD being more easily recognised through seric variations. Multiple sclerosis (MS) has also been the subject of a recent work, where random forest (RF) classification and regression analysis has been applied to the NMR spectra of serum from MS patients and healthy controls [107]. The proposed RF model comprising two metabolites (glucose and valine) showed 93% accuracy in predicting MS within an independent validation sample set. Moreover, NMR data was successfully used to predict serum selenium levels, known to relate to the development of MS. 2.7. Mental and behavioural The serum metabolic profiles of bipolar disorder patients treated with lithium and with other non-lithium medications have been compared to that of control subjects in a serum NMR metabolomics study [108]. The distinction between the three groups was related to differences in the levels of lipids, lipid metabolism-related molecules and a few amino acids, with some changes having been linked to lithium treatment. Major depressive disorder (MDD) has been investigated in two recent NMR metabolomics studies, one regarding the profiling of plasma [109] and the other addressing urinalysis [110]. Both studies considered separate training and test sets, each including first-episode drug-naïve depressed patients and healthy controls. In plasma, lipoproteins, lipid metabolism-related molecules, some amino acids, creatine and creatinine were the key metabolites accounting for group discrimination [109], whereas in urine a panel of five metabolites (N-methylnicotinamide, m-hydroxyphenylacetate, malonate, alanine and formate), selected through regression analysis, provided the highest predictive power for MDD [110]. The metabolomics approach has also been applied to the study of schizophrenia in studies involving multiple analytical techniques (NMR included). Cai et al. reported the global NMR and ultra high performance liquid chromatography mass spectrometry (UPLC–MS) profiling of plasma and urine from first-episode neuroleptic-naïve schizophrenia (FENNS) patients and healthy controls, together with the targeted tandem MS analysis of neurotransmitter metabolites [111]. Significant changes were noted for a large number of metabolites, leading the authors to suggest disturbances in several biochemical pathways. Moreover, three metabolites were proposed as potential biomarkers of risperidone treatment efficacy, namely pregnanediol, citrate and ␣-ketoglutarate. In a later study, NMR of urine was combined with gas chromatography with time of flight mass spectrometry (GC–TOFMS) of both urine and serum collected from schizophrenic patients and healthy subjects [112].




A panel of selected metabolites (glycerate, eicosenoic acid, pyruvate and cystine in plasma, and ␤-hydroxybutyrate in both plasma and urine) was suggested to discriminate schizophrenic patients from normal controls with high accuracy. On the other hand, first-episode and recurrent schizophrenic patients could not be distinguished based on the biofluid metabolic profiles. 2.8. Respiratory system Chronic obstructive pulmonary disease (COPD) has recently been addressed by two NMR metabolomics studies of blood serum/plasma [113,114]. One of these studies considered plasma samples from COPD patients and healthy controls and investigated the effects of 8 weeks endurance exercise training on the plasma profiles of both groups. The authors found significant metabolic differences at rest, as well as after the exercise period, mainly reflecting altered muscle bioenergetics in COPD patients [114]. In another study, serum metabolomic profiles of COPD patients were found to correlate with different features of COPD severity, assessed by an array of physiological and biochemical measurements. The authors suggested that NMR metabolomics could play a role in the stratification of COPD patients according to their protein turnover, mitochondrial function and nutritional status [113]. Asthma is another chronic respiratory disease that has been profiled by NMR metabolomics. In a study comprising serum samples from adult asthmatics and healthy controls, serum metabolites and lipids were suggested to correlate with asthma severity and to reflect processes like hypermethylation, response to hypoxia and immune reaction [115]. This study complemented an earlier one on the urine analysis of asthmatic children at stable and exacerbated states, which could be discriminated from healthy controls and from each other with more than 90% accuracy [116]. The main contributors for discrimination were suggested to relate with disturbed TCA cycle, stress on energy metabolism, altered protein and amino acid metabolism and allergic inflammation. Urine NMR metabolomics has also been explored to define a metabolic signature for pneumococcal pneumonia and to evaluate its specificity by comparison with the metabolic profiles characterising other lung infections and diseases (e.g. asthma and COPD), as well as different conditions causing non-infectious metabolic stress (e.g. myocardial infarction, congestive heart failure and trauma) [117]. Multivariate models were able to predict S. pneumonia infection with high accuracy (>90%) even when considering a separate blind validation set. 2.9. Pregnancy, childbirth and the puerperium Prenatal disorders have been investigated by several metabolomic studies of maternal biofluids. NMR profiling of blood plasma/serum has proved promising in predicting both early-onset and late-onset preeclampsia, either by considering selected metabolites alone or their combination with conventional maternal demographical and clinical markers [118,119]. The identification of novel markers for gestational diabetes mellitus (GDM) has been the main purpose of a recent study encompassing the NMR analysis of maternal urine collected at two time points during pregnancy and once after childbirth [120]. Although the excretion of citrate correlated with GDM severity, no reliable biomarkers could be found for GDM through multivariate analysis of NMR data. These results somehow contrasted with those of Diaz et al. reporting increased levels of short chain hydroxyl-acids in the second trimester urine of women subsequently developing GDM [121]. In addition to GDM, the same authors addressed other prenatal disorders by NMR metabolomics of maternal urine and blood plasma [121], together with amniotic fluid [122]. Among the disorders studied, foetal malformations were shown to produce

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the strongest impact on the metabolite composition of all biofluids, the overall changes having been suggested to reflect enhanced anaerobic glycolysis, increased gluconeogenesis, altered choline metabolism and nucleotide metabolism disturbances. Chromosomal disorders and preterm delivery groups showed urinary changes mainly in choline and 2-hydroxyisobutyrate, respectively, whereas no statistically relevant changes were detected for subjects later experiencing premature rupture of membranes [121,122]. A few recent studies have applied NMR metabolomics to newborn biofluid samples. For instance, the composition of umbilical cord arterial plasma was found to differ significantly between very-low-birth-weight (VLBW) and normo-ponderal full term (FT) neonates [123]. The tandem analysis of these differences with those found in the corresponding maternal plasma allowed metabolic changes to be discussed in the context of altered maternal–foetal nutrient exchange and birth-related stress. The metabolic composition of urine from neonates, collected non-invasively, has also proved informative in regard to metabolic maturity at birth [124] and to intrauterine growth retardation (IUGR) [125]. Distinct metabolic urinary patterns were found for full term and preterm newborns, with amino acid biosynthesis and metabolism having been proposed as key processes underlying foetal maturity [124]. In what regards preterm neonates with IUGR, compared to preterm neonates of suitable weight for their gestational age at birth, three metabolic pathways involving a few amino acids and the urea cycle were suggested to be altered [125]. 2.10. Infectious and parasitic Different types of viral hepatitis have recently been investigated by NMR metabolomics. An exploratory study of urine from patients diagnosed with hepatitis C and from healthy controls reported high discrimination accuracy between the two groups, without specifying, however, the metabolites accounting for that discrimination [126]. In another study involving the analysis of both plasma and urine, the metabolic profile of patients with hepatitis E was compared to that of patients with hepatitis B as well as to healthy controls [127]. The main pathways suggested to be altered in hepatitis E-infected subjects were glycolysis, TCA cycle, urea cycle and amino acid metabolism. HIV-1 infection has been addressed through NMR profiling of blood plasma and cerebrospinal fluid (CSF) [128]. Statistical integration of data from the two biofluids provided insights into their metabolic relationships. Regarding parasitic diseases, the urinary NMR profile of malaria patients (infected with Plasmodium vivax) was shown to differ from that of healthy subjects and non-malarial fever patients [129]. In particular, ornithine and pipecolic acid were highlighted as potential urinary markers of P. vivax malaria. Schistosomiasis, the second most impacting parasitic disease next to malaria, has also been investigated through NMR metabolomics of urine collected from children and young adults at different time points before and after treatment [130]. The Schistosoma mansoni infection was reported to cause metabolic alterations presumably related to gut microflora, energy metabolism and liver function. 3. Conclusions This paper reviewed the main applications of 1 H NMR metabolomics of blood and urine in disease research, in the last 5 years. It becomes clear that the most studied diseases have comprised several types of cancer, with recent interests developing on a wide range of other diseases (e.g. digestive, nervous, respiratory systems, mental and pregnancy disorders). The work developed in recent years has involved mainly the analysis of blood serum and




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plasma, followed by an increasing emphasis on urine due to the recognised importance of exploring and optimising less- or noninvasive strategies which could circumvent and/or complement many established invasive clinical procedures. Furthermore, careful and detailed analysis of the existing reports reveals an increasing awareness of the scientific community in regard to the challenges and needs in disease metabolomics studies, namely in relation to study design, sample collection and quality, data quality assurance, reliable means of data analysis and model validation and, finally, confirmation of metabolite biomarkers. This latter aspect poses, perhaps, one of the greatest challenges, triggering many recent developments in metabolic pathway analysis and modelling. In fact, several papers have addressed all the above aspects critically, having set the tone for important future developments envisaging several possible clinical applications of disease metabolomics. Acknowledgments The authors acknowledge funding from the European Regional Development Fund-FEDER through the Competitive Factors Thematic Operational Programme-COMPETE and the Foundation for Science and Technology-FCT, Portugal (PEst-C/CTM/LA0011/2013). S.O.D. thanks FCT for the SFRH/BD/73343/2010 grant. The Portuguese National NMR Network (RNRMN), supported with FCT funds, is also acknowledged. References [1] J.K. Nicholson, J.C. Lindon, E. Holmes, ‘Metabonomics’: understanding the metabolic responses of living systems to pathophysiological stimuli via multivariate statistical analysis of biological NMR spectroscopic data, Xenobiotica 29 (1999) 1181–1189. [2] O. Fiehn, Metabolomics – the link between genotypes and phenotypes, Plant Mol. Biol. 48 (2002) 155–171. [3] T.M.D. Ebbels, R. Cavill, Bioinformatic methods in NMR-based metabolic profiling, Prog. Nucl. Magn. Reson. Spectrosc. 55 (2009) 361–374. [4] M. Eliasson, S. Rannar, J. Trygg, From data processing to multivariate validation – essential steps in extracting interpretable information from metabolomics data, Curr. Pharm. Biotechnol. 12 (2011) 996–1004. [5] R. Bujak, E. Daghir, J. Rybka, P. Koslinski, M.J. Markuszewski, Metabolomics in urogenital cancer, Bioanalysis 3 (2011) 913–923. [6] E.M. DeFeo, C.-L. Wu, W.S. McDougal, L.L. Cheng, A decade in prostate cancer: from NMR to metabolomics, Nat. Rev. Urol. 8 (2011) 301–311. [7] D.J.Y. Ng, K.K. Pasikanti, E.C.Y. Chan, Trend analysis of metabonomics and systematic review of metabonomics-derived cancer marker metabolites, Metabolomics 7 (2011) 155–178. [8] B.J. Trock, Application of metabolomics to prostate cancer, Urol. Oncol. 29 (2011) 572–581. [9] Q.N. Van, T.D. Veenstra, H.J. Issaq, Metabolic profiling for the detection of bladder cancer, Curr. Urol. Rep. 12 (2011) 34–40. [10] I.F. Duarte, A.M. Gil, Metabolic signatures of cancer unveiled by NMR spectroscopy of human biofluids, Prog. Nucl. Magn. Reson. Spectrosc. 62 (2012) 51–74. [11] J.L. Griffin, H. Atherton, J. Shockcor, L. Atzori, Metabolomics as a tool for cardiac research, Nat. Rev. Cardiol. 8 (2011) 630–643. [12] T. Senn, S.L. Hazen, W.H.W. Tang, Translating metabolomics to cardiovascular biomarkers, Prog. Cardiovasc. Dis. 55 (2012) 70–76. [13] E.P. Rhee, R.E. Gerszten, Metabolomics and cardiovascular biomarker discovery, Clin. Chem. 58 (2012) 139–147. [14] L.C. Heather, X. Wang, J.A. West, J.L. Griffin, A practical guide to metabolomic profiling as a discovery tool for human heart disease, J. Mol. Cell. Cardiol. 55 (2013) 2–11. [15] N. Friedrich, Metabolomics in diabetes research, J. Endocrinol. 215 (2012) 29–42. [16] B. Xie, M.J. Waters, H.J. Schirra, Investigating potential mechanisms of obesity by metabolomics, J. Biomed. Biotechnol. 2012 (2012), ID 805683. [17] T. Pacchiarotta, A.M. Deelder, O.A. Mayboroda, Metabolomic investigations of human infections, Bioanalysis 4 (2012) 919–925. [18] L.J. Sitole, A.A. Williams, D. Meyer, Metabonomic analysis of HIV-infected biofluids, Mol. Biosyst. 9 (2013) 18–28. [19] A. Gupta, N. Bansal, B. Houston, Metabolomics of urinary tract infection: a new uroscope in town, Expert Rev. Mol. Diagn. 12 (2012) 361–369. [20] R.H. Weiss, K. Kim, Metabolomics in the study of kidney diseases, Nat. Rev. Nephrol. 8 (2012) 22–33. [21] A. Noto, F. Cibecchini, V. Fanos, M. Mussap, NGAL and metabolomics: the single biomarker to reveal the metabolome alterations in kidney injury, Biomed. Res. Int. 2013 (2013), ID 612032.

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Influence of Freezing and Storage Procedure on Human Urine Samples in NMR-Based Metabolomics.

NMR Metabolomics Analysis of Parkinson's Disease.

Prediction of Gestational Diabetes through NMR Metabolomics of Maternal Blood.

Serum and urine 1H NMR-based metabolomics in the diagnosis of selected thyroid diseases.

NMR Metabolomics in Ionizing Radiation.

Phenotypic Characterization Analysis of Human Hepatocarcinoma by Urine Metabolomics Approach.

NMR-Based Milk Metabolomics.

MS Translator for the Enhanced Simultaneous Analysis of Metabolomics Mixtures by NMR Spectroscopy and Mass Spectrometry: Application to Human Urine.

Databases and Software for NMR-Based Metabolomics.

Urine NMR metabolomics analysis of breastfeeding biomarkers during and after pregnancy in a large prospective cohort study.

Prognosis Biomarkers of Severe Sepsis and Septic Shock by 1H NMR Urine Metabolomics in the Intensive Care Unit.

NMR spectroscopy for metabolomics and metabolic profiling.

NMR-based metabolomics: from sample preparation to applications in nutrition research.

NMR-based metabolomics analysis of leptomeningeal carcinomatosis.

Evaluation of fast 2D NMR for metabolomics.

Understanding Metabolomics in Biomedical Research.

Translational metabolomics in cancer research.

Serum metabolomics in animal models and human disease.

Making Sense of Blood-Based Proteomics and Metabolomics in Psychiatric Research.

¹³C NMR metabolomics: applications at natural abundance.

(13)C NMR Metabolomics: INADEQUATE Network Analysis.

Multidimensional approaches to NMR-based metabolomics.

Mass spectrometric based approaches in urine metabolomics and biomarker discovery.

Low-sulfated chondroitin sulfate in human blood and urine.