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

Metabolomic profiling of COVID-19 using serum and urine samples in intensive care and medical ward cohorts

The COVID-19 pandemic remains a significant global health threat, with uncertainties persisting regarding the factors determining whether individuals experience mild symptoms, severe conditions, or succumb to the disease. This study presents an NMR metabolomics-based approach, analysing 80 serum and urine samples from COVID-19 patients (34 intensive care patients and 46 hospitalized patients) and 32 from healthy controls. Our research identifies discriminant metabolites and clinical variables relevant to COVID-19 diagnosis and severity. These discriminant metabolites play a role in specific pathways, mainly "Phenylalanine, tyrosine and tryptophan biosynthesis", "Phenylalanine metabolism", "Glycerolipid metabolism" and "Arginine and proline metabolism". We propose a three-metabolite diagnostic panel-comprising isoleucine, TMAO, and glucose-that effectively discriminates COVID-19 patients from healthy individuals, achieving high efficiency. Furthermore, we found an optimal biomarker panel capable of efficiently classify disease severity considering both clinical characteristics (obesity/overweight, dyslipidemia, and lymphocyte count) together with metabolites content (ethanol, TMAO, tyrosine and betaine).

Recent development on nanomaterial-based biosensors for identifying thyroid tumor biomarkers

The incidence of thyroid tumors has been increasing yearly over the past decade, making it the fourth highest tumor in women. This places various biological burdens on those affected. Currently, thyroid tumors are primarily diagnosed using percutaneous fine needle aspiration and ultrasound. However, these methods are complex, expensive, and less accurate, and they may fail to detect some thyroid nodules. As an alternative, researchers are focusing on blood-based biomarkers in addition to the traditional diagnostic methods, assisted predominantly by nanomaterials. Early identification of thyroid cancer is crucial as it is highly treatable. Various sensing systems have been developed using nanomaterial-mediated approaches to enhance the detection system. Nanomaterials are effectively applied in biosensors for surface functionalization and are conjugated with biomolecules to improve the interaction with the target analyte. This review discusses nanomaterial-assisted thyroid tumor detection, with a special focus on nanomaterial-based biosensors.

Accurate Prediction of 1H NMR Chemical Shifts of Small Molecules Using Machine Learning

NMR is widely considered the gold standard for organic compound structure determination. As such, NMR is routinely used in organic compound identification, drug metabolite characterization, natural product discovery, and the deconvolution of metabolite mixtures in biofluids (metabolomics and exposomics). In many cases, compound identification by NMR is achieved by matching measured NMR spectra to experimentally collected NMR spectral reference libraries. Unfortunately, the number of available experimental NMR reference spectra, especially for metabolomics, medical diagnostics, or drug-related studies, is quite small. This experimental gap could be filled by predicting NMR chemical shifts for known compounds using computational methods such as machine learning (ML). Here, we describe how a deep learning algorithm that is trained on a high-quality, "solvent-aware" experimental dataset can be used to predict 1H chemical shifts more accurately than any other known method. The new program, called PROSPRE (PROton Shift PREdictor) can accurately (mean absolute error of <0.10 ppm) predict 1H chemical shifts in water (at neutral pH), chloroform, dimethyl sulfoxide, and methanol from a user-submitted chemical structure. PROSPRE (pronounced "prosper") has also been used to predict 1H chemical shifts for >600,000 molecules in many popular metabolomic, drug, and natural product databases.

Plasma metabolites analysis of patients with papillary thyroid cancer: A preliminary untargeted 1H NMR-based metabolomics

Metabolomics plays a crucial role in identifying molecular biomarkers that can differentiate pathological conditions. In the case of thyroid cancer, it is essential to accurately diagnose malignancy from benignity to avoid unnecessary surgeries. The objective of this research was to apply untargeted NMR-based metabolomics in order to identify metabolic biomarkers that can distinguish between plasma samples of patients with papillary thyroid cancer (PTC) and multinodular goiter (MNG), as well as PTC and healthy individuals. The study included a cohort of 55 patients who were divided into three groups: PTC (n=20), MNG (n=16), and healthy (n=19). Plasma samples were collected from all participants and subjected to 1H NMR spectroscopy. Differential metabolites were identified using chemometric pattern recognition algorithms. The obtained metabolic profile had the potential to differentiate PTC from healthy plasma, but not from MNG. In patients diagnosed with PTC, a total of 18 compounds were discovered, revealing elevated levels of leucine, lysine, and 4-acetamidobutyric acid, while acetate, proline, acetoacetate, 3-hydroxybutyrate, glutamate, pyruvate, cystine, glutathione, asparagine, ethanolamine, histidine, tyrosine, myo-inositol, and glycerol along with a lipid compound were found to be lower in comparison to those of healthy individuals. According to the area under the curve (AUC) of the receiver operating characteristic curve, this particular profile exhibited an impressive capability of 85% to discern PTC from healthy subjects (AUC=0.853, sensitivity=78.95, specificity=84.21). The utilization of the 1H NMR-based metabolomics approach revealed considerable promise in the identification of PTC from healthy plasma specimens. The modifications noticed in the plasma metabolites have the potential to act as practical biomarkers that are non-invasive and could suggest transformations in the metabolic profile of thyroid tumors.

Metabolomic Profiling of Second-Trimester Amniotic Fluid for Predicting Preterm Delivery: Insights from NMR Analysis

Preterm delivery (PTD) is a notable pregnancy complication, affecting one out of every ten births. This study set out to investigate whether analyzing the metabolic composition of amniotic fluid (AF) collected from pregnant women during the second trimester of pregnancy could offer valuable insights into prematurity. The research employed 1H-NMR metabolomics to examine AF samples obtained from 17 women who gave birth prematurely (between 29+0 and 36+5 weeks of gestation) and 43 women who delivered at full term. The application of multivariate analysis revealed metabolites (dimethylglycine, glucose, myo-inositol, and succinate) that can serve as possible biomarkers for the prognosis and early diagnosis of preterm delivery. Additionally, pathway analysis unveiled the most critical metabolic pathways relevant to our research hypothesis. In summary, these findings suggest that the metabolic composition of AF in the second trimester can be a potential indicator for identifying biomarkers associated with the risk of PTD.

Alterations in the amino acid profile in patients with papillary thyroid carcinoma with and without Hashimoto's thyroiditis

Purpose

Amino acids (AAs) play important physiological roles in living cells. Some amino acid changes in blood are specific for autoimmune disorders, and some are specific for thyroid cancer. The aims of this study were to profile AA metabolites in the serum of patients with papillary thyroid carcinoma (PTC0) without Hashimoto's thyroiditis (HT) and patients with PTC with HT (PTC1) and predict whether AA metabolites are associated with thyroid disease, thyroid hormone and thyroid autoantibodies.

Methods

A total of 95 serum samples were collected, including 28 healthy controls (HCs), 28 PTC0 patients and 39 PTC1 patients. Serum samples were analyzed by high-performance liquid chromatography-triple stage quadrupole-mass spectrometry (HPLC-TSQ-MS), and twenty-one amino acids (AAs) were detected.

Results

The serum concentration of glutamic acid was significantly elevated in PTC1 patients compared with PTC0 patients. Lysine was the second amino acid that differentiated these two groups of PTC patients. In addition, the serum concentrations of glycine, alanine and tyrosine were significantly reduced in both PTC patient groups compared to the HC group. These AAs were also correlated with thyroid hormones and antibodies. Five amino acid markers, namely, glycine, tyrosine, glutamic acid, glutamine and arginine, separated/distinguished PTC0 patients from healthy subjects, and eight AA markers, the same AAs as above without arginine but with alanine, leucine, valine and histidine, separated/distinguished PTC1 patients from healthy subjects based on ROC analysis.

Conclusion

Compared with the HCs, changes in AAs in PTC0 and PTC1 patients showed similar patterns, suggesting the possibility of a common pathophysiological basis, which confirms preliminary research that PTC is significantly associated with pathologically confirmed HT. We found two AAs, lysine and alanine, that can perform diagnostic functions in distinguishing PTC1 from PTC0.

Compound-Specific 1D 1H NMR Pulse Sequence Selection for Metabolomics Analyses

NMR-based metabolomics approaches have been used in a wide range of applications, for example, with medical, plant, and marine samples. One-dimensional (1D) ¹H NMR is routinely used to find out biomarkers in biofluids such as urine, blood plasma, and serum. To mimic biological conditions, most NMR studies have been carried out in an aqueous solution where the high intensity of the water peak is a major problem in obtaining a meaningful spectrum. Different methods have been used to suppress the water signal, including 1D Carr-Purcell-Meiboom-Gill (CPMG) presat, consisting of a T₂ filter to suppress macromolecule signals and reduce the humped curve in the spectrum. 1D nuclear Overhauser enhancement spectroscopy (NOESY) is another method for water suppression that is used routinely in plant samples with fewer macromolecules than in biofluid samples. Other common 1D ¹H NMR methods such as 1D ¹H presat and 1D ¹H ES have simple pulse sequences; their acquisition parameters can be set easily. The proton with presat has just one pulse and the presat block causes water suppression, while other 1D ¹H NMR methods including those mentioned above have more pulses. However, it is not well known in metabolomics studies because it is used only occasionally and in a few types of samples by metabolomics experts. Another effective method is excitation sculpting to suppress water. Herein, we evaluate the effect of method selection on signal intensities of commonly detected metabolites. Different classes of samples including biofluid, plant, and marine samples were investigated, and recommendations on the advantages and limitations of each method are presented.

Cancer insights from magnetic resonance spectroscopy of cells and excised tumors

Multinuclear ex vivo magnetic resonance spectroscopy (MRS) of cancer cells, xenografts, human cancer tissue, and biofluids is a rapidly expanding field that is providing unique insights into cancer. Starting from the 1970s, the field has continued to evolve as a stand-alone technology or as a complement to in vivo MRS to characterize the metabolome of cancer cells, cancer-associated stromal cells, immune cells, tumors, biofluids and, more recently, changes in the metabolome of organs induced by cancers. Here, we review some of the insights into cancer obtained with ex vivo MRS and provide a perspective of future directions. Ex vivo MRS of cells and tumors provides opportunities to understand the role of metabolism in cancer immune surveillance and immunotherapy. With advances in computational capabilities, the integration of artificial intelligence to identify differences in multinuclear spectral patterns, especially in easily accessible biofluids, is providing exciting advances in detection and monitoring response to treatment. Metabolotheranostics to target cancers and to normalize metabolic changes in organs induced by cancers to prevent cancer-induced morbidity are other areas of future development.

Serum nuclear magnetic resonance metabolomics analysis of human metastatic colorectal cancer: Biomarkers and pathway analysis

We describe the use of nuclear magnetic resonance metabolomics to analyze blood serum samples from healthy individuals (n = 26) and those with metastatic colorectal cancer (CRC; n = 57). The assessment, employing both linear and nonlinear multivariate data analysis techniques, revealed specific metabolite changes associated with metastatic CRC, including increased levels of lactate, glutamate, and pyruvate, and decreased levels of certain amino acids and total fatty acids. Biomarker ratios such as glutamate-to-glutamine and pyruvate-to-alanine were also found to be related to CRC. The study also found that glutamate was linked to progression-free survival and that both glutamate and 3-hydroxybutyrate were risk factors for metastatic CRC. Additionally, gas chromatography coupled to flame-ionization detection was utilized to analyze the fatty acid profile and pathway analysis was performed on the profiled metabolites to understand the metabolic processes involved in CRC. A correlation was also found between the presence of certain metabolites in the blood of CRC patients and certain clinical features.

Metabolomics-A Tool to Find Metabolism of Endocrine Cancer

Clinical endocrinology entails an understanding of the mechanisms involved in the regulation of tumors that occur in the endocrine system. The exact cause of endocrine cancers remains an enigma, especially when discriminating malignant lesions from benign ones and early diagnosis. In the past few years, the concepts of personalized medicine and metabolomics have gained great popularity in cancer research. In this systematic review, we discussed the clinical metabolomics studies in the diagnosis of endocrine cancers within the last 12 years. Cancer metabolomic studies were largely conducted using nuclear magnetic resonance (NMR) and mass spectrometry (MS) combined with separation techniques such as gas chromatography (GC) and liquid chromatography (LC). Our findings revealed that the majority of the metabolomics studies were conducted on tissue, serum/plasma, and urine samples. Studies most frequently emphasized thyroid cancer, adrenal cancer, and pituitary cancer. Altogether, analytical hyphenated techniques and chemometrics are promising tools in unveiling biomarkers in endocrine cancer and its metabolism disorders.

Biobanking potential for biomedical research in endocrinology

Biobanking is an actively developing scientific area that provides tools for conducting biomedical research, increasing the reliability and reproducibility of their results. In endocrinology, more and more attention is paid to the study of molecular and genetic markers of diseases for the selection of new points of influence in treatment, the development of targeted therapy and a strategy for personalized prevention. This approach is designed to solve the problems of endocrine disorders, their complications, causing significant damage to the individual and he population health, and reduce the financial burden of chronic endocrine disorders. To increase the reliability and reproducibility of research results, requirements for working with biological material should be strictly complied. The use of biobanking will increase the validity of data obtained in clinical trials in endocrinology. There are successful examples of Russian and foreign studies using the capabilities of biobanks aimed at studying diabetes, polycystic ovary syndrome, adenomas and other endocrine disorders. The article discusses the prospects for partnership with biobanks in the framework of endocrinology research. The purpose of this review is to analyze the literature to systematize knowledge for application of biobanking in biomedical research in the field of endocrinology.

Characterization of Lactate Metabolism Score in Breast and Thyroid Cancers to Assist Immunotherapy via Large-Scale Transcriptomic Data Analysis

Breast cancer (BC) and thyroid cancer (TC) have the highest rate of incidence, especially in women. Previous studies have revealed that lactate provides energetic and anabolic support to cancer cells, thus serving as an important oncometabolite with both extracellular and intracellular signaling functions. However, the correlation of lactate metabolism scores with thyroid and breast cancer immune characteristics remains to be systematically analyzed. To investigate the role of lactate at the transcriptome level and its correlation with the clinical outcome of BC and TC, transcriptome data of 1,217 patients with breast cancer (BC) and 568 patients with thyroid cancer (TC) were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets with their corresponding clinical and somatic mutation data. The lactate metabolism score was calculated based on a single-sample gene set enrichment analysis (ssGSEA). The results showed that lactate metabolism-related genes and lactate metabolism scores was significantly associated with the survival of patients with BRCA and THCA. Notably, the lactate metabolism scores were strongly correlated with human leukocyte antigen (HLA) expression, tumor-infiltrating lymphocyte (TIL) infiltration, and interferon (IFN) response in BC and TC. Furthermore, the lactate metabolism score was an independent prognostic factor and could serve as a reliable predictor of overall survival, clinical characteristics, and immune cell infiltration, with the potential to be applied in immunotherapy or precise chemotherapy of BC and TC.

1H-NMR Based Metabolomics Technology Identifies Potential Serum Biomarkers of Colorectal Cancer Lung Metastasis in a Mouse Model

Background

Lung metastasis is a common metastasis site of colorectal cancer which largely reduces the quality of life and survival rates of patients. The discovery of potential novel diagnostic biomarkers is very meaningful for the early diagnosis of colorectal cancer with lung metastasis.

Methods

In the present study, the metabonomic profiling of serum samples of lung metastasis mice was analyzed by ¹H-nuclear magnetic resonance (¹H-NMR). Principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to elucidate the distinguishing metabolites between different groups, and all achieved excellent separations, which indicated that metastatic mice could be differentiated from control mice based on the metabolic profiles at serum levels. Furthermore, during lung metastasis of colorectal cancer, metabolic phenotypes changed significantly, and some of metabolites were identified.

Results

Among these metabolites, approximately 15 were closely associated with the lung metastasis process. Pathway enrichment analysis results showed deregulation of metabolic pathways participating in the process of lung metastasis, such as synthesis and degradation of ketone bodies pathway, amino acid metabolism pathway and pyruvate metabolism pathway.

Conclusion

The present study demonstrated the metabolic disturbances of serum samples of mice during the lung metastasis process of colorectal cancer and provides potential diagnostic biomarkers for the disease.

Molecular Probes, Chemosensors, and Nanosensors for Optical Detection of Biorelevant Molecules and Ions in Aqueous Media and Biofluids

Synthetic molecular probes, chemosensors, and nanosensors used in combination with innovative assay protocols hold great potential for the development of robust, low-cost, and fast-responding sensors that are applicable in biofluids (urine, blood, and saliva). Particularly, the development of sensors for metabolites, neurotransmitters, drugs, and inorganic ions is highly desirable due to a lack of suitable biosensors. In addition, the monitoring and analysis of metabolic and signaling networks in cells and organisms by optical probes and chemosensors is becoming increasingly important in molecular biology and medicine. Thus, new perspectives for personalized diagnostics, theranostics, and biochemical/medical research will be unlocked when standing limitations of artificial binders and receptors are overcome. In this review, we survey synthetic sensing systems that have promising (future) application potential for the detection of small molecules, cations, and anions in aqueous media and biofluids. Special attention was given to sensing systems that provide a readily measurable optical signal through dynamic covalent chemistry, supramolecular host-guest interactions, or nanoparticles featuring plasmonic effects. This review shall also enable the reader to evaluate the current performance of molecular probes, chemosensors, and nanosensors in terms of sensitivity and selectivity with respect to practical requirement, and thereby inspiring new ideas for the development of further advanced systems.

Diagnostic approach to thyroid cancer based on amino acid metabolomics in saliva by ultra-performance liquid chromatography with high resolution mass spectrometry

Thyroid cancer is a malignant disease with dramatically low advanced-stage 10-year survival. Meanwhile, the metabolites in saliva are becoming a wealthy source of disease biomarkers. However, there is a lack of non-invasive analytical methods for the identification of biomarkers in saliva for the preoperative diagnosis of thyroid cancer. Therefore, we developed an ultra-high performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS) method to simultaneously determine the metabolic levels of 10 amino acids in saliva, aiming to study the amino acid metabolism profile to promote early diagnosis of thyroid cancer. We tested unstimulated whole saliva from patients with papillary thyroid carcinoma (PTC; n = 61) and healthy controls (HC; n = 61), and used receiver operating characteristic (ROC) curves to establish the diagnostic value of potential markers. The method validation results showed good precision, linearity (R² > 0.99), recovery (92.2 %-110.3 %), intra- and inter-day precision (RSD < 7 % and RSD < 9 %, respectively). The concentration of 10 amino acids was significantly different between PTC and HC in human salivary analysis (P < 0.05), the area under the curve (AUC) values of a single marker for the diagnosis of PTC were ranging from 0.678 to 0.833. A panel of alanine, valine, proline, phenylalanine was selected in combination yielded the AUC of 0.936, which will improve the accuracy of early diagnosis of thyroid cancer (sensitivity: 91.2 %; specificity: 85.2 %). This study proved the possibility of salivary amino acid biomarkers for PTC early diagnosis, providing a simple auxiliary way for the non-invasive diagnosis of thyroid cancer.

Brain-dead and coma patients exhibit different serum metabolic profiles: preliminary investigation of a novel diagnostic approach in neurocritical care

There is a clear difference between severe brain damage and brain death. However, in clinical practice, the differentiation of these states can be challenging. Currently, there are no laboratory tools that facilitate brain death diagnosis. The aim of our study was to evaluate the utility of serum metabolomic analysis in differentiating coma patients (CP) from individuals with brain death (BD). Serum samples were collected from 23 adult individuals with established diagnosis of brain death and 24 patients in coma with Glasgow Coma Scale 3 or 4, with no other clinical symptoms of brain death for at least 7 days after sample collection. Serum metabolomic profiles were investigated using proton nuclear magnetic resonance (NMR) spectroscopy. The results obtained were examined by univariate and multivariate data analysis (PCA, PLS-DA, and OPLS-DA). Metabolic profiling allowed us to quantify 43 resonance signals, of which 34 were identified. Multivariate statistical modeling revealed a highly significant separation between coma patients and brain-dead individuals, as well as strong predictive potential. The findings not only highlight the potential of the metabolomic approach for distinguishing patients in coma from those in the state of brain death but also may provide an understanding of the pathogenic mechanisms underlying these conditions.

A glance at the actual role of glutamine metabolism in thyroid tumorigenesis

Thyroid cancers (TCs) are the most prevalent malignancy of the endocrine system and the seventh most common cancer in women. According to estimates from the Global Cancer Observatory (GCO) in 2020, the incidence of thyroid cancer globally was 586,000 cases. As thyroid cancer incidences have dramatically increased, identifying the most important metabolic pathways and biochemical markers involved in thyroid tumorigenesis can be critical strategies for controlling the prevalence and ultimately treatment of this disease. Cancer cells undergo cellular metabolism and energy alteration in order to promote cell proliferation and invasion. Glutamine is one of the most abundant free amino acids in the human body that contributes to cancer metabolic remodeling as a carbon and nitrogen source to sustain cell growth and proliferation. In the present review, glutamine metabolism and its regulation in cancer cells are highlighted. Thereafter, emphasis is given to the perturbation of glutamine metabolism in thyroid cancer, focusing on metabolomics studies.

NMR-Based Metabolomics in Cancer Research

Nuclear magnetic resonance (NMR) spectroscopy offers reproducible quantitative analysis and structural identification of metabolites in various complex biological samples, such as biofluids (plasma, serum, and urine), cells, tissue extracts, and even intact organs. Therefore, NMR-based metabolomics, a mainstream metabolomic platform, has been extensively applied in many research fields, including pharmacology, toxicology, pathophysiology, nutritional intervention, disease diagnosis/prognosis, and microbiology. In particular, NMR-based metabolomics has been successfully used for cancer research to investigate cancer metabolism and identify biomarker and therapeutic targets. This chapter highlights the innovations and challenges of NMR-based metabolomics platform and its applications in cancer research.

Metabolomics of Oral/Head and Neck Cancer

Oral/head and neck cancer is the sixth most common human malignancies in the world. Despite the treatment advances in surgery, chemotherapy, and radiotherapy, the patient survival has not been significantly improved in the past several decades. As a new methodological approach, metabolomics may help reveal the metabolic reprogramming mechanisms underlying head and neck cancer cell proliferation, invasion, and metastasis and may be used to identify metabolite biomarkers for clinical applications of the disease. In this chapter, we briefly review recent metabolomic applications in head and neck cancer.

Malignant and benign thyroid nodule differentiation through the analysis of blood plasma with terahertz spectroscopy

The liquid and lyophilized blood plasma of patients with benign or malignant thyroid nodules and healthy individuals were studied by terahertz (THz) time-domain spectroscopy and machine learning. The blood plasma samples from malignant nodule patients were shown to have higher absorption. The glucose concentration and miRNA-146b level were correlated with the sample's absorption at 1 THz. A two-stage ensemble algorithm was proposed for the THz spectra analysis. The first stage was based on the Support Vector Machine with a linear kernel to separate healthy and thyroid nodule participants. The second stage included additional data preprocessing by Ornstein-Uhlenbeck kernel Principal Component Analysis to separate benign and malignant thyroid nodule participants. Thus, the distinction of malignant and benign thyroid nodule patients through their lyophilized blood plasma analysis by terahertz time-domain spectroscopy and machine learning was demonstrated.

PSYCHE-A Valuable Experiment in Plant NMR-Metabolomics

¹H-NMR is a very reproducible spectroscopic method and, therefore, a powerful tool for the metabolomic analysis of biological samples. However, due to the high complexity of natural samples, such as plant extracts, the evaluation of spectra is difficult because of signal overlap. The new NMR “Pure Shift” methods improve spectral resolution by suppressing homonuclear coupling and turning multiplets into singlets. The PSYCHE (Pure Shift yielded by Chirp excitation) and the Zangger–Sterk pulse sequence were tested. The parameters of the more suitable PSYCHE experiment were optimized, and the extracts of 21 Hypericum species were measured. Different evaluation criteria were used to compare the suitability of the PSYCHE experiment with conventional ¹H-NMR. The relationship between the integral of a signal and the related bin value established by linear regression demonstrates an equal representation of the integrals in binned PSYCHE spectra compared to conventional ¹H-NMR. Using multivariate data analysis based on both techniques reveals comparable results. The obtained data demonstrate that Pure Shift spectra can support the evaluation of conventional ¹H-NMR experiments.

Diagnosis of papillary thyroid carcinoma by 1H NMR spectroscopy-based metabolomic analysis of whole blood

The incidence rate of thyroid carcinoma, especially papillary thyroid carcinoma (PTC), has increased significantly over time. As a primary pathway for metastasis, the lymphatic system is an important prognostic factor for PTC patients. Although the metabolic changes in PTC patients have been investigated in extensive studies, few studies focused on the whole blood metabolic profiling of PTC patients. In this study, we investigated the ¹H NMR-based metabolic profiles of whole blood samples that were obtained from healthy individuals and PTC patients, with or without lymph node metastasis. The estimation of the predictive potential of metabolites was evaluated using multivariate statistical analyses, which revealed that the whole blood carries information that is sufficient for distinguishing between PTC patients and healthy individuals. However, PTC patients were not well classified as positive or negative according to the lymph nodes. We did not find a metabolite that could discriminate the presence of lymph node metastasis. Further studies with larger sample sizes are needed to elucidate significant metabolites to indicate the presence of lymph node metastasis in patients with PTC.

The Potential of Metabolomics in the Diagnosis of Thyroid Cancer

Thyroid cancer is the most common endocrine system malignancy. However, there is still a lack of reliable and specific markers for the detection and staging of this disease. Fine needle aspiration biopsy is the current gold standard for diagnosis of thyroid cancer, but drawbacks to this technique include indeterminate results or an inability to discriminate different carcinomas, thereby requiring additional surgical procedures to obtain a final diagnosis. It is, therefore, necessary to seek more reliable markers to complement and improve current methods. "Omics" approaches have gained much attention in the last decade in the field of biomarker discovery for diagnostic and prognostic characterisation of various pathophysiological conditions. Metabolomics, in particular, has the potential to identify molecular markers of thyroid cancer and identify novel metabolic profiles of the disease, which can, in turn, help in the classification of pathological conditions and lead to a more personalised therapy, assisting in the diagnosis and in the prediction of cancer behaviour. This review considers the current results in thyroid cancer biomarker research with a focus on metabolomics.

The Blood Biomarkers of Thyroid Cancer

Introduction

With the gradual increase in the incidence of thyroid cancer, people’s attention to thyroid cancer has also gradually increased. Although the prognosis of thyroid cancer is rather mild compared to other cancers, it will still bring a heavy psychological burden on people who have been diagnosed. At present, the diagnosis of thyroid cancer mainly depends on ultrasound and percutaneous fine needle aspiration (pFNA). Due to the unsatisfactory accuracy of the diagnosis methods we use now, there are still some thyroid nodules that cannot be clearly diagnosed before surgery.

Methods

In this article, we have searched for relevant research on blood markers of thyroid cancer in the past five years and categoried them into four groups.

Discussion

Though we have not found a biomarker which can diagnose thyroid cancer both sensitively and specifically, we do found many substances that are related to it, and have the potential to recognize it and help the diagnosis. And perhaps combined models can do it better.

Plasma Metabolic Profiling of Human Thyroid Nodules by Gas Chromatography-Mass Spectrometry (GC-MS)-Based Untargeted Metabolomics

One of the challenges in the area of diagnostics of human thyroid cancer is a preoperative diagnosis of thyroid nodules with indeterminate cytology. Herein, we report an untargeted metabolomics analysis to identify circulating thyroid nodule metabolic signatures, to find new novel metabolic biomarkers. Untargeted gas chromatography-quadrupole-mass spectrometry was used to ascertain the specific plasma metabolic changes of thyroid nodule patients, which consisted of papillary thyroid carcinoma (PTC; n = 19), and multinodular goiter (MNG; n = 16), as compared to healthy subjects (n = 20). Diagnostic models were constructed using multivariate analyses such as principal component analysis, orthogonal partial least squares-discriminant analysis, and univariate analysis including One-way ANOVA and volcano plot by MetaboAnalyst and SIMCA software. Because of the multiple-testing issue, false discovery rate p-values were also computed for these functions. A total of 60 structurally annotated metabolites were subjected to statistical analysis. A combination of univariate and multivariate statistical analyses revealed a panel of metabolites responsible for the discrimination between thyroid nodules and healthy subjects, with variable importance in the projection (VIP) value greater than 0.8 and p-value less than 0.05. Significantly altered metabolites between thyroid nodules versus healthy persons are those associated with amino acids metabolism, the tricarboxylic acid cycle, fatty acids, and purine and pyrimidine metabolism, including cysteine, cystine, glutamic acid, α-ketoglutarate, 3-hydroxybutyric acid, adenosine-5-monophosphate, and uracil, respectively. Further, sucrose metabolism differed profoundly between thyroid nodule patients and healthy subjects. Moreover, according to the receiver operating characteristic (ROC) curve analysis, sucrose could discriminate PTC from MNG (area under ROC curve value = 0.92). This study enhanced our understanding of the distinct metabolic pathways associated with thyroid nodules, which enabled us to distinguish between patients and healthy subjects. In addition, our study showed extensive sucrose metabolism in the plasma of thyroid nodule patients, which provides a new metabolic signature of the thyroid nodule’s tumorigenesis. Accordingly, it suggests that sucrose can be considered as a circulating biomarker for differential diagnosis between malignancy and benignity in indeterminate thyroid nodules.

Psychopharmacological effects of riparin III from Aniba riparia (Nees) Mez. (Lauraceae) supported by metabolic approach and multivariate data analysis

Background

Currently there is a high prevalence of humor disorders such as anxiety and depression throughout the world, especially concerning advanced age patients. Aniba riparia (Nees) Mez. (Lauraceae), popular known as “louro”, can be found from the Amazon through Guianas until the Andes. Previous studies have already reported the isolation of alkamide-type alkaloids such as riparin III (O-methyl-N-2,6-dyhydroxy-benzoyl tyramine) which has demonstrated anxiolytic and antidepressant-like effects in high doses by intraperitoneal administration.

Methods

Experimental protocol was conducted in order to analyze the anxiolytic-like effect of riparin III at lower doses by intravenous administration to Wistar rats (Rattus norvegicus) (n = 5). The experimental approach was designed to last 15 days, divided in 3 distinct periods of five days: control, anxiogenic and treatment periods. The anxiolytic-like effect was evaluated by experimental behavior tests such as open field and elevated plus-maze test, combined with urine metabolic footprint analysis. The urine was collected daily and analyzed by ¹H NMR. Generated data were statistically treated by Principal Component Analysis in order to detect patterns among the distinct periods evaluated as well as biomarkers responsible for its distinction.

Results

It was observed on treatment group that cortisol, biomarker related to physiological stress was reduced, indicating anxiolytic-like effect of riparin III, probably through activation of 5-HT_2A receptors, which was corroborated by behavioral tests.

Conclusion

¹H NMR urine metabolic footprint combined with multivariate data analysis have demonstrated to be an important diagnostic tool to prove the anxiolytic-like effect of riparin III in a more efficient and pragmatic way.

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

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

Fast LC-ESI-MS/MS analysis and influence of sampling conditions for gut metabolites in plasma and serum

In the past few years, the gut microbiome has been shown to play an important role in various disorders including in particular cardiovascular diseases. Especially the metabolite trimethylamine-N-oxide (TMAO), which is produced by gut microbial metabolism, has repeatedly been associated with an increased risk for cardiovascular events. Here we report a fast liquid chromatography tandem mass spectrometry (LC-MS/MS) method that can analyze the five most important gut metabolites with regards to TMAO in three minutes. Fast liquid chromatography is unconventionally used in this method as an on-line cleanup step to remove the most important ion suppressors leaving the gut metabolites in a cleaned flow through fraction, also known as negative chromatography. We compared different blood matrix types to recommend best sampling practices and found citrated plasma samples demonstrated lower concentrations for all analytes and choline concentrations were significantly higher in serum samples. We demonstrated the applicability of our method by investigating the effect of a standardized liquid meal (SLM) after overnight fasting of 25 healthy individuals on the gut metabolite levels. The SLM did not significantly change the levels of gut metabolites in serum.

Advances in metabolomics of thyroid cancer diagnosis and metabolic regulation

Thyroid cancers (TCs) are the most frequent endocrine malignancy with an unpredictable fast-growing incidence, especially in females all over the world. Fine-needle aspiration biopsy (FNAB) analysis is an accurate diagnostic method for detecting thyroid nodules and classification of TC. Though simplicity, safety, and accuracy of FNAB, 15-30% of cases are indeterminate, and it is not possible to determine the exact cytology of the specimen. This demands the need for innovative methods capable to find crucial biomarkers with adequate sensitivity for diagnosis and prediction in TC researches. Cancer-based metabolomics is a vast emerging field focused on the detection of a large set of metabolites extracted from biofluids or tissues. Using analytical chemistry procedures allows for the potential recognition of cancer-based metabolites for the purposes of advancing the era of personalized medicine. Nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) coupled with separation techniques e.g., gas chromatography (GC) and liquid chromatography (LC) are the main approaches for metabolic studies in cancers. The immense metabolite profiling has provided a chance to discover novel biomarkers for early detection of thyroid cancer and reduce unnecessary aggressive surgery. In this review, we recapitulate the recent advances and developed methods of diverse metabolomics tools and metabolic phenotypes of thyroid cancer, following a brief discussion of recent challenges in the thyroid cancer diagnosis.

Metabolomic studies as a tool for determining the post-mortem interval (PMI) in stillborn calves

Background

Perinatal mortality may vary between herds, but the cost of deaths are always higher than value of the calf. When diagnosing the cause of a calf’s death it is important to determine when it occurred, before or after calving. Metabolomics is widely used to identify many human diseases, but quite rarely applied in veterinary science. The aim of this study was to compare the metabolic profiles of calves with different times of death and those of calves born alive. Into the study, twenty one healthy controls (singleton, normal assisted calving, born alive) and 75 stillborn (SB) calves (with a gestation length of ≥260 days, SB, or dead within 6 h of birth) were enrolled. Plasma and urine from SB and control calves were investigated by proton nuclear magnetic resonance based metabolomic methods. SB calves were divided into four PMI groups. One PMI group included calves that died after calving and the other groups - three comprised in utero deaths, based on pathophysiological changes (lung inflation, autolysis in internal organs, hemoglobin imbibition in the pleura and aortic arch). Partial Least Squares - Discriminant Analysis models based on plasma metabolites were calculated, reflecting assumed data clustering.

Results

Twenty six metabolites in plasma and 29 in urine changed significantly with PMI according to one way analysis of variance. Half the metabolites in plasma and the majority in urine increased with PMI. Six metabolites increased simultaneously in plasma and urine: acetate, sn-glycero-3-phosphocholine (GPC), leucine, valine, creatine, and alanine.

Conclusions

Post-mortem changes in calves were associated with molecular variations in blood plasma and urine, showing the greatest differences for the group in which the post-mortem pathological changes were the most advanced. The results of the study show that evaluation of calf plasma or urine may be used as a diagnostic method for the determination of the PMI. Moreover, the metabolites, which unambiguously increased or decreased, can be used as potential biomarkers of PMI.

Electronic supplementary material

The online version of this article (10.1186/s12917-019-1935-4) contains supplementary material, which is available to authorized users.

Ultra-Clean Pure Shift 1H-NMR applied to metabolomics profiling

Even though Pure Shift NMR methods have conveniently been used in the assessment of crowded spectra, they are not commonly applied to the analysis of metabolomics data. This paper exploits the recently published SAPPHIRE-PSYCHE methodology in the context of plant metabolome. We compare single pulse, PSYCHE, and SAPPHIRE-PSYCHE spectra obtained from aqueous extracts of Physalis peruviana fruits. STOCSY analysis with simplified SAPPHIRE-PSYCHE spectra of six types of Cape gooseberry was carried out and the results attained compared with classical STOCSY data. PLS coefficients analysis combined with 1D-STOCSY was performed in an effort to simplify biomarker identification. Several of the most compromised proton NMR signals associated with critical constituents of the plant mixture, such as amino acids, organic acids, and sugars, were more cleanly depicted and their inter and intra correlation better reveled by the Pure Shift methods. The simplified data allowed the identification of glutamic acid, a metabolite not observed in previous studies of Cape gooseberry due to heavy overlap of its NMR signals. Overall, the results attained indicated that Ultra-Clean Pure Shift spectra increase the performance of metabolomics data analysis such as STOCSY and multivariate coefficients analysis, and therefore represent a feasible and convenient additional tool available to metabolomics.

Identification of β-hydroxybutyrate as a potential biomarker for female papillary thyroid cancer

Aim: β-Hydroxybutyrate (BHB) was proved to be a differential metabolite of papillary thyroid cancer (PTC) by semiquantitative analysis, while whether BHB could be used as a potential biomarker for female PTC still needed to be validated. Materials & methods: An LC-MS/MS method with surrogate matrix was established to validate serum BHB in specified PTC patients. Conclusion: Serum BHB levels in PTCs were significantly higher than those in HCs. For both serum BHB with a cut-off value of 312.5 ng/ml and serum BHB/creatinine of 33.5 μmol/mmol, a promising prediction rate to distinguish low-grade PTCs from HCs with satisfactory sensitivity and specificity was achieved, indicating that serum BHB might be proved as a useful biomarker for low-grade female PTC.

Oncometabolites as biomarkers in thyroid cancer: a systematic review

INTRODUCTION

Thyroid cancer (TC) is an important common endocrine malignancy, and its incidence has increased in the past decades. The current TC diagnosis and classification tools are fine-needle aspiration (FNA) and histological examination following thyroidectomy. The metabolite profile alterations of thyroid cells (oncometabolites) can be considered for current TC diagnosis and management protocols.

METHODS

This systematic review focuses on metabolite alterations within the plasma, FNA specimens, and tissue of malignant TC contrary to benign, goiter, or healthy TC samples. A systematic search of MEDLINE (PubMed), Scopus, Embase, and Web of Science databases was conducted, and the final 31 studies investigating metabolite biomarkers of TC were included.

RESULTS

A total of 15 targeted studies and 16 untargeted studies revealed several potential metabolite signatures of TC such as glucose, fructose, galactose, mannose, 2-keto-d-gluconic acid and rhamnose, malonic acid and inosine, cholesterol and arachidonic acid, glycosylation (immunoglobulin G [IgG] Fc-glycosylation), outer mitochondrial membrane 20 (TOMM20), monocarboxylate transporter 4 (MCT4), choline, choline derivatives, myo-/scyllo-inositol, lactate, fatty acids, several amino acids, cell membrane phospholipids, estrogen metabolites such as 16 alpha-OH E1/2-OH E1 and catechol estrogens (2-OH E1), and purine and pyrimidine metabolites, which were suggested as the TC oncometabolite.

CONCLUSION

Citrate was suggested as the first most significant biomarker and lactate as the second one. Further research is needed to confirm these biomarkers as the TC diagnostic oncometabolite.

Metabolite assignment of ultrafiltered synovial fluid extracted from knee joints of reactive arthritis patients using high resolution NMR spectroscopy

Currently, there are no reliable biomarkers available that can aid early differential diagnosis of reactive arthritis (ReA) from other inflammatory joint diseases. Metabolic profiling of synovial fluid (SF)-obtained from joints affected in ReA-holds great promise in this regard and will further aid monitoring treatment and improving our understanding about disease mechanism. As a first step in this direction, we report here the metabolite specific assignment of ¹ H and ¹³ C resonances detected in the NMR spectra of SF samples extracted from human patients with established ReA. The metabolite characterization has been carried out on both normal and ultrafiltered (deproteinized) SF samples of eight ReA patients (n = 8) using high-resolution (800 MHz) ¹ H and ¹ H─¹³ C NMR spectroscopy methods such as one-dimensional ¹ H CPMG and two-dimensional J-resolved¹ H NMR and homonuclear ¹ H─¹ H TOCSY and heteronuclear¹ H─¹³ C HSQC correlation spectra. Compared with normal SF samples, several distinctive ¹ H NMR signals were identified and assigned to metabolites in the ¹ H NMR spectra of ultrafiltered SF samples. Overall, we assigned 53 metabolites in normal filtered SF and 64 metabolites in filtered pooled SF sample compared with nonfiltered SF samples for which only 48 metabolites (including lipid/membrane metabolites as well) have been identified. The established NMR characterization of SF metabolites will serve to guide future metabolomics studies aiming to identify/evaluate the SF-based metabolic biomarkers of diagnostic/prognostic potential or seeking biochemical insights into disease mechanisms in a clinical perspective.

Serum-plasma matched metabolomics for comprehensive characterization of benign thyroid nodule and papillary thyroid carcinoma

Metabolomics offers a noninvasive methodology to identify metabolic markers for pathogenesis and diagnosis of diseases. This work aimed to characterize circulating metabolic signatures of benign thyroid nodule (BTN) and papillary thyroid carcinoma (PTC) via serum-plasma matched metabolomics. A cohort of 1,540 serum-plasma matched samples and 114 tissues were obtained from healthy volunteers, BTN and PTC patients enrolled from 6 independent centers. Untargeted metabolomics was determined by liquid chromatography-quadrupole time-of-flight mass spectrometric and multivariate statistical analyses. The use of serum-plasma matched samples afforded a broad-scope detection of 1,570 metabolic features. Metabolic phenotypes revealed significant pattern differences for healthy versus BTN and healthy versus PTC. Perturbed metabolic pathways related mainly to amino acid and lipid metabolism. It is worth noting that, BTN and PTC showed no significant differences but rather overlap in circulating metabolic signatures, and this observation was replicated in all study centers. For differential diagnosis of healthy versus thyroid nodules (BTN + PTC), a panel of 6 metabolic markers, namely myo-inositol, α-N-phenylacetyl-L-glutamine, proline betaine, L-glutamic acid, LysoPC(18:0) and LysoPC(18:1) provided area under the curve of 97.68% in the discovery phase and predictive accuracies of 84.78-98.18% in the 4 validation centers. Taken together, serum-plasma matched metabolomics showed significant differences in circulating metabolites for healthy versus nodules but not for BTN versus PTC. Our results highlight the true metabolic nature of thyroid nodules, and potentially decrease overtreatment that exposes patients to unnecessary risks.

Diagnosis of post-surgical fine-needle aspiration biopsies of thyroid lesions with indeterminate cytology using HRMAS NMR-based metabolomics

INTRODUCTION

Ultrasound examination coupled with fine-needle aspiration (FNA) cytology is the gold standard for the diagnosis of thyroid cancer. However, about 10-40% of these analyses cannot be conclusive on the malignancy of the lesions and lead to surgery. The cytological indeterminate FNA biopsies are mainly constituted of follicular-patterned lesions, which are benign in 80% of the cases.

OBJECTIVES

The development of a FNAB classification approach based on the metabolic phenotype of the lesions, complementary to cytology and other molecular tests in order to limit the number of patients undergoing unnecessary thyroidectomy.

METHODS

We explored the potential of a NMR-based metabolomics approach to improve the quality of the diagnosis from FNABs, using thyroid tissues collected post-surgically.

RESULTS

The NMR-detected metabolites were used to produce a robust OPLSDA model to discriminate between benign and malignant tumours. Malignancy was correlated with amino acids such as tyrosine, serine, alanine, leucine and phenylalanine and anti-correlated with myo-inositol, scyllo-inositol and citrate. Diagnosis accuracy was of 84.8% when only indeterminate lesions were considered.

CONCLUSION

These results on model FNAB indicate that there is a clear interest in exploring the possibility to export NMR metabolomics to pre-surgical diagnostics.

Application of metabolomics in prediction of lymph node metastasis in papillary thyroid carcinoma

PURPOSE

The aim of this study was to find useful metabolites to predict lymph node (LN) metastasis in patients with papillary thyroid cancer (PTC) through a metabolomics approach and investigate the potential role of metabolites as a novel prognostic marker.

MATERIALS AND METHODS

Fifty-two consecutive patients (median age: 41.5 years, range 15-74 years) were enrolled who underwent total thyroidectomy and central LN dissection with or without lateral LN dissection in Severance Hospital between October 2013 and July 2015. The study specimens were provided by the Severance Hospital Gene Bank, and consisted of PTC from each patient. The specimens were prepared for proton nuclear magnetic resonance (1H-NMR) spectroscopy. Spectral data by 1H-NMR spectroscopy were acquired, processed, and analyzed. Patients were grouped in three ways, according to the presence of LN metastasis, central LN metastasis and lateral LN metastasis. Chi-square test and the student t-test were used to analyze categorical variables and continuous variables, respectively. The Mann-Whitney U test was used for univariate analysis of metabolites. Orthogonal projections to latent structure discriminant analysis (OPLS-DA) was used for multivariate analysis to discriminate metabolic differences between the two groups.

RESULTS

Among 52 patients, 32 had central LN metastasis and 19 had lateral LN metastasis. No clinical or histopathological characteristic was significantly different for all comparisons. On univariate analysis, no metabolite showed significant difference for all comparisons. On multivariate analysis, OPLS-DA did not discriminate the presence and absence of LN metastasis. Lactate was found to be the most promising metabolite.

CONCLUSIONS

No metabolite could discriminate the presence of LN metastasis. However, lactate was found to be the most promising metabolite for discrimination. Further studies with larger sample sizes are needed to elucidate significant metabolites which can indicate the presence of LN metastasis in patients with PTC.

Metabolomics-A Promising Approach to Pituitary Adenomas

Background: Metabolomics-the novel science that evaluates the multitude of low-molecular-weight metabolites in a biological system, provides new data on pathogenic mechanisms of diseases, including endocrine tumors. Although development of metabolomic profiling in pituitary disorders is at an early stage, it seems to be a promising approach in the near future in identifying specific disease biomarkers and understanding cellular signaling networks. Objectives: To review the metabolomic profile and the contributions of metabolomics in pituitary adenomas (PA). Methods: A systematic review was conducted via PubMed, Web of Science Core Collection and Scopus databases, summarizing studies that have described metabolomic aspects of PA. Results: Liquid chromatography tandem mass spectrometry (LC-MS/MS) and nuclear magnetic resonance (NMR) spectrometry, which are traditional techniques employed in metabolomics, suggest amino acids metabolism appears to be primarily altered in PA. N-acetyl aspartate, choline-containing compounds and creatine appear as highly effective in differentiating PA from healthy tissue. Deoxycholic and 4-pyridoxic acids, 3-methyladipate, short chain fatty acids and glucose-6-phosphate unveil metabolite biomarkers in patients with Cushing's disease. Phosphoethanolamine, N-acetyl aspartate and myo-inositol are down regulated in prolactinoma, whereas aspartate, glutamate and glutamine are up regulated. Phosphoethanolamine, taurine, alanine, choline-containing compounds, homocysteine, and methionine were up regulated in unclassified PA across studies. Intraoperative use of ultra high mass resolution matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), which allows localization and delineation between functional PA and healthy pituitary tissue, may contribute to achievement of complete tumor resection in addition to preservation of pituitary cell lines and vasopressin secretory cells, thus avoiding postoperative diabetes insipidus. Conclusion: Implementation of ultra high performance metabolomics analysis techniques in the study of PA will significantly improve diagnosis and, potentially, the therapeutic approach, by identifying highly specific disease biomarkers in addition to novel molecular pathogenic mechanisms. Ultra high mass resolution MALDI-MSI emerges as a helpful clinical tool in the neurosurgical treatment of pituitary tumors. Therefore, metabolomics appears to be a science with a promising prospect in the sphere of PA, and a starting point in pituitary care.

Metabolomics for Prediction of Relapse in Graves' Disease: Observational Pilot Study

Background: There is a lack of biochemical markers for early prediction of relapse in patients with Graves' disease [GD], which may help to direct treatment decisions. We assessed the prognostic ability of a high-throughput proton NMR metabolomic profile to predict relapse in a well characterized cohort of GD patients. Methods: Observational study investigating patients presenting with GD at a Swiss hospital endocrine referral center and an associated endocrine outpatient clinic. We measured 227 metabolic markers in the blood of patients before treatment initiation. Main outcome was relapse of hyperthyroidism within 18 months of stopping anti-thyroid drugs. We used ROC analysis with AUC to assess discrimination. Results: Of 69 included patients 18 (26%) patients had a relapse of disease. The clinical GREAT score had an AUC of 0.68 (95% CI 0.63-0.70) to predict relapse. When looking at the metabolomic markers, univariate analysis revealed pyruvate and triglycerides in medium VLDL as predictors with AUCs of 0.73 (95% CI 0.58-0.84) and 0.67 (95% CI 0.53-0.80), respectively. All other metabolomic markers had lower AUCs. Conclusion: Overall, metabolomic markers in our pilot study had low to moderate prognostic potential for prediction of relapse of GD, with pyruvate and triglycerides being candidates with acceptable discriminatory abilities. Our data need validation in future larger trials.