We report an extensive 600 MHz NMR trial of quantitative lipoprotein and small-molecule measurements in human blood serum and plasma. Five centers with eleven 600 MHz NMR spectrometers were used to analyze 98 samples including 20 quality controls (QCs), 37 commercially sourced, paired serum and plasma samples, and two National Institute of Science and Technology (NIST) reference material 1951c replicates. Samples were analyzed using rigorous protocols for sample preparation and experimental acquisition. A commercial lipoprotein subclass analysis was used to quantify 105 lipoprotein subclasses and 24 low molecular weight metabolites from the NMR spectra. For all spectrometers, the instrument specific variance in measuring internal QCs was lower than the percentage described by the National Cholesterol Education Program (NCEP) criteria for lipid testing [triglycerides <2.7%; cholesterol <2.8%; low-density lipoprotein (LDL) cholesterol <2.8%; high-density lipoprotein (HDL) cholesterol <2.3%], showing exceptional reproducibility for direct quantitation of lipoproteins in both matrixes. The average relative standard deviations (RSDs) for the 105 lipoprotein parameters in the 11 instruments were 4.6% and 3.9% for the two NIST samples, whereas they were 38% and 40% for the 37 commercially sourced plasmas and sera, respectively, showing negligible analytical compared to biological variation. The coefficient of variance (CV) obtained for the quantification of the small molecules across the 11 spectrometers was below 15% for 20 out of the 24 metabolites analyzed. This study provides further evidence of the suitability of NMR for high-throughput lipoprotein subcomponent analysis and small-molecule quantitation with the exceptional required reproducibility for clinical and other regulatory settings.

Lipoprotein profiling of human blood by 1H nuclear magnetic resonance (NMR) spectroscopy is a rapid and promising approach to monitor health and disease states in medicine and nutrition. However, lack of standardization of measurement protocols has prevented the use of NMR-based lipoprotein profiling in metastudies. In this study, a standardized NMR measurement protocol was applied in a ring test performed across three different laboratories in Europe on plasma and serum samples from 28 individuals. Data was evaluated in terms of (i) spectral differences, (ii) differences in LPD predictions obtained using an existing prediction model, and (iii) agreement of predictions with cholesterol concentrations in high- and low-density lipoproteins (HDL and LDL) particles measured by standardized clinical assays. ANOVA-simultaneous component analysis (ASCA) of the ring test spectral ensemble that contains methylene and methyl peaks (1.4–0.6 ppm) showed that 97.99% of the variance in the data is related to subject, 1.62% to sample type (serum or plasma), and 0.39% to laboratory. This interlaboratory variation is in fact smaller than the maximum acceptable intralaboratory variation on quality control samples. It is also shown that the reproducibility between laboratories is good enough for the LPD predictions to be exchangeable when the standardized NMR measurement protocol is followed. With the successful implementation of this protocol, which results in reproducible prediction of lipoprotein distributions across laboratories, a step is taken toward bringing NMR more into scope of prognostic and diagnostic biomarkers, reducing the need for less efficient methods such as ultracentrifugation or high-performance liquid chromatography (HPLC).

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Inborn errors of metabolism (IEMs) are rare diseases produced by the accumulation of abnormal amounts of metabolites, toxic to the newborn. When not detected on time, they can lead to irreversible physiological and psychological sequels or even demise. Metabolomics has emerged as an efficient and powerful tool for IEM detection in newborns, children, and adults with late onset. In here, we screened urine samples from a large set of neonates (470 individuals) from a homogeneous population (Basque Country), for the identification of congenital metabolic diseases using NMR spectroscopy. Absolute quantification allowed to derive a probability function for up to 66 metabolites that adequately describes their normal concentration ranges in newborns from the Basque Country. The absence of another 84 metabolites, considered abnormal, was routinely verified in the healthy newborn population and confirmed for all but 2 samples, of which one showed toxic concentrations of metabolites associated to ketosis and the other one a high trimethylamine concentration that strongly suggested an episode of trimethylaminuria. Thus, a non-invasive and readily accessible urine sample contains enough information to assess the potential existence of a substantial number (>70) of IEMs in newborns, using a single, automated and standardized 1H- NMR-based analysis.

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

Prostate cancer is the second most common tumor and the fifth cause of cancer-related death among men worldwide. PC cells exhibit profound signaling and metabolic reprogramming that account for the acquisition of aggressive features. Although the metabolic understanding of this disease has increased in recent years, the analysis of such alterations through noninvasive methodologies in biofluids remains limited. Here, we used NMR-based metabolomics on a large cohort of urine samples (more than 650) from PC and benign prostate hyperplasia (BPH) patients to investigate the molecular basis of this disease. Multivariate analysis failed to distinguish between the two classes, highlighting the modest impact of prostate alterations on urine composition and the multifactorial nature of PC. However, univariate analysis of urine metabolites unveiled significant changes, discriminating PC from BPH. Metabolites with altered abundance in urine from PC patients revealed changes in pathways related to cancer biology, including glycolysis and the urea cycle. We found out that metabolites from such pathways were diminished in the urine from PC individuals, strongly supporting the notion that PC reduces nitrogen and carbon waste in order to maximize their usage in anabolic processes that support cancer cell growth.

BackgroundTriiodothyronine (T3) and thyroxine (T4) as the main secretion products of the thyroid affect nearly every human tissue and are involved in a broad range of processes ranging from energy expenditure and lipid metabolism to glucose homeostasis. Metabolomics studies outside the focus of clinical manifest thyroid diseases are rare. The aim of the present investigation was to analyze the cross-sectional and longitudinal associations of urinary metabolites with serum free T4 (FT4) and thyroid-stimulating hormone (TSH).MethodsUrine Metabolites of participants of the population-based studies Inter99 (n = 5620) and Health2006/Health2008 (n = 3788) were analyzed by 1H-NMR spectroscopy. Linear or mixed linear models were used to detect associations between urine metabolites and thyroid function.ResultsCross-sectional analyses revealed positive relations of alanine, trigonelline and lactic acid with FT4 and negative relations of dimethylamine, glucose, glycine and lactic acid with log(TSH). In longitudinal analyses, lower levels of alanine, dimethylamine, glycine, lactic acid and N,N-dimethylglycine were linked to a higher decline in FT4 levels over time, whereas higher trigonelline levels were related to a higher FT4 decline. Moreover, the risk of hypothyroidism was higher in subjects with high baseline trigonelline or low lactic acid, alanine or glycine values.ConclusionThe detected associations mainly emphasize the important role of thyroid hormones in glucose homeostasis. In addition, the predictive character of these metabolites might argue for a potential feedback of the metabolic state on thyroid function. Besides known metabolic consequences of TH, the link to the urine excretion of trigonelline, a marker of coffee consumption, represents a novel finding of this study and given the ubiquitous consumption of coffee requires further research.

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