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Lung cancer is the leading cause of cancer-related mortality worldwide. In order to improve its overall survival, early diagnosis is required. Since current screening methods still face some pitfalls, such as high false positive rates for low-dose computed tomography, researchers are still looking for early biomarkers to complement existing screening techniques in order to provide a safe, faster, and more accurate diagnosis. Biomarkers are biological molecules found in body fluids, such as plasma, that can be used to diagnose a condition or disease. Metabolomics has already been shown to be a powerful tool in the search for cancer biomarkers since cancer cells are characterized by impaired metabolism, resulting in an adapted plasma metabolite profile. The metabolite profile can be determined using nuclear magnetic resonance, or NMR. Although metabolomics and NMR metabolite profiling of blood plasma are still under investigation, there is already evidence for its potential for early-stage lung cancer diagnosis, therapy response, and follow-up monitoring. This review highlights some key breakthroughs in this research field, where the most significant biomarkers will be discussed in relation to their metabolic pathways and in light of the altered cancer metabolism.

Accurate identification and quantification of human plasma metabolites can be challenging in crowded regions of the NMR spectrum with severe signal overlap. Therefore, this study describes metabolite spiking experiments on the basis of which the NMR spectrum can be rationally segmented into well-defined integration regions, and this for spectrometers having magnetic field strengths corresponding to 1 H resonance frequencies of 400 MHz and 900 MHz. Subsequently, the integration data of a case-control dataset of 69 lung cancer patients and 74 controls were used to train a multivariate statistical classification model for both field strengths. In this way, the advantages/disadvantages of high versus medium magnetic field strength were evaluated. The discriminative power obtained from the data collected at the two magnetic field strengths is rather similar, i.e. a sensitivity and specificity of respectively 90 and 97% for the 400 MHz data versus 88 and 96% for the 900 MHz data. This shows that a medium-field NMR spectrometer (400-600 MHz) is already sufficient to perform clinical metabolomics. However, the improved spectral resolution (reduced signal overlap) and signal-to-noise ratio of 900 MHz spectra yield more integration regions that represent a single metabolite. This will simplify the unraveling and understanding of the related, disease disturbed, biochemical pathways. Copyright © 2017 John Wiley & Sons, Ltd., (Copyright © 2017 John Wiley & Sons, Ltd.)

Background: Type 1 diabetes mellitus (T1DM) is one of the most common pediatric diseases and its incidence is rising in many countries. Recently, it has been shown that metabolites other than glucose play an important role in insulin deficiency and the development of diabetes. The aim of our study was to look for discriminating variation in the concentrations of small-molecule metabolites in the plasma of T1DM children as compared to non-diabetic matched controls using proton nuclear magnetic resonance (1H-NMR)-based metabolomics. Methods: A cross-sectional study was set-up to examine the metabolic profile in fasting plasma samples from seven children with poorly controlled T1DM and seven non-diabetic controls aged 8-18 years, and matched for gender, age and BMI-SDS. The obtained plasma 1H-NMR spectra were rationally divided into 110 integration regions, representing the metabolic phenotype. These integration regions reflect the relative metabolite concentrations and were used as statistical variables to construct (train) a classification model in discriminating between T1DM patients and controls. Results: The total amount of variation explained by the model between the groups is 81.0% [ R2Y(cum)] and within the groups is 75.8% [ R2X(cum)]. The predictive ability of the model [ Q2(cum)] obtained by cross-validation is 50.7%, indicating that the discrimination between the groups on the basis of the metabolic phenotype is valid. Besides the expected higher concentration of glucose, the relative concentrations of lipids (triglycerides, phospholipids and cholinated phospholipids) are clearly lower in the plasma of T1DM patients as compared to controls. Also the concentrations of the amino acids serine, tryptophan and cysteine are slightly decreased. Conclusions: The present study demonstrates that metabolic profiling of plasma by 1H-NMR spectroscopy allows to discriminate between T1DM patients and controls. The metabolites that significantly differ between both groups might point to disturbances in biochemical pathways including (1) choline deficiency, (2) increased gluconeogenesis, and (3) glomerular hyperfiltration. Although the sample size of this study is still somewhat limited and a validation should be performed, the proof of principle looks promising and justifies a deeper investigation of the diagnostic possibilities of 1H-NMR metabolomics in follow-up studies. [ABSTRACT FROM AUTHOR]

Variations in sample collection, processing and storage within the field of clinical metabolomics might hamper its effective implementation. In this study, the impact of relevant preanalytical conditions on the plasma H NMR metabolic profile was examined. The biobanking community recently developed a method for coding preanalytical conditions called the Standard PREanalytical Code (SPREC). It is envisaged that SPREC will ultimately identify which samples are fit for a particular analysis, based on prior validation by a panel of experts in the respective field. In an effort to validate SPREC for H NMR plasma metabolomics, we have coded the conditions used here, when possible, according to SPREC and evaluated its power to identify preanalytical conditions that affect the plasma H NMR metabolic profile. From all preanalytical conditions studied, only prolonged processing delays (3 and 8 h) have a significant impact on the plasma H NMR metabolic profile as compared to the reference condition (30 min). Principal component analysis shows a clear systematic shift as a function of increasing processing delay. Nevertheless, the inter-individual variation is clearly much larger than this preanalytical variation, indicating that the impact on multivariate group classification will be minimal. Nonetheless, we recommend to keep the time gap between blood collection and centrifugation similar for all samples within a study. The implementation of SPREC within clinical metabolomics allows for an appropriate sample encoding and exclusion of samples that were subjected to unwanted, interfering preanalytical conditions. Without doubt, it will contribute to the validation of H NMR metabolomics in clinical, biobank and multicenter research settings. [ABSTRACT FROM AUTHOR]

This study reports an accurate assignment of the resonance signals present in H-NMR spectra of human blood plasma. Hereto, blood plasma was spiked with 32 different metabolites in relevant concentrations since reported chemical shift values show quite some variability depending on the biofluid under study and the applied experimental measuring conditions. The resulting information was used to rationally divide the H-NMR spectrum in 110 well-defined integration regions for application in metabolomics. A case-control dataset of 53 breast cancer patients and 52 controls was investigated in order to demonstrate the proof of principle. After removal of noisy variables, i.e. variables exceeding a premised threshold for the coefficient of variation, the groups could be discriminated by OPLS-DA multivariate statistics with a sensitivity and specificity of 83 and 94 %, respectively. In addition, the classification was validated in a small but independent cohort. The proposed methodology might pave the way towards a better understanding of disturbances in disease-related biochemical pathways and so, to the clinical relevance of study findings. [ABSTRACT FROM AUTHOR]