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The NMR chemical shifts of a substance in a complex mixture strongly depend on the composition of the mixture itself, as many weak interactions occur that are hardly predictable. Chemical shift variability is the major obstacle to automatically assigning, and subsequently quantitating, metabolite signals in body fluids, particularly urine. Here we demonstrate that the chemical shifts of signals in urine are actually predictable. This is achieved by constructing ca. 4000 artificial mixtures where the concentrations of 52 most abundant urine metabolites—including 11 inorganic ions—are varied, to sparsely but efficiently populate an N-dimensional concentration matrix. A strong relationship is established between the concentration matrix and the chemical shift matrix, so that chemical shifts of > 90 metabolite signals can be accurately predicted in real urine samples. The concentrations of the invisible inorganic ions are also accurately predicted, along with those of albumin and of several other abundant urine components. The NMR chemical shifts of a substance in urine strongly depend on the composition of the mixture itself, and this makes automatic assignment for quantification very difficult. Here the authors show the chemical shifts of signals and the concentration of NMR-invisible inorganic ions in urine, are predictable.

Background Phenylketonuria (PKU; OMIM#261600) is a rare metabolic disorder caused by mutations in the phenylalanine hydroxylase (PAH) gene resulting in high phenylalanine (Phe) in blood and brain. If not treated early this results in intellectual disability, behavioral and psychiatric problems, microcephaly, motor deficits, eczematous rash, autism, seizures, and developmental problems. There is a controversial discussion of whether patients with PKU have an additional risk for atherosclerosis due to interference of Phe with cholesterol synthesis and LDL-cholesterol regulation. Since cholesterol also plays a role in membrane structure and myelination, better insight into the clinical significance of the impact of Phe on lipoprotein metabolism is desirable. In 22 treated PKU patients (mean age 38.7 years) and 14 healthy controls (mean age 35.2 years), we investigated plasma with NMR spectroscopy and quantified 105 lipoprotein parameters (including lipoprotein subclasses) and 24 low molecular weight parameters. Analysis was performed on a 600 MHz Bruker AVANCE IVDr spectrometer as previously described. Results Concurrent plasma Phe in PKU patients showed a wide range with a mean of 899 μmol/L (50–1318 μmol/L). Total cholesterol and LDL-cholesterol were significantly lower in PKU patients versus controls: 179.4 versus 200.9 mg/dL ( p  < 0.02) and 79.5 versus 104.1 mg/dL ( p  < 0.0038), respectively. PKU patients also had lower levels of 22 LDL subclasses with the greatest differences in LDL2 Apo-B, LDL2 Particle Number, LDL2-phospholipids, and LDL2-cholesterol ( p  < 0.0001). There was a slight negative correlation of total cholesterol and LDL-cholesterol with concurrent Phe level. VLDL5-free cholesterol, VLDL5-cholesterol, VLDL5-phospholipids, and VLDL4-free cholesterol showed a significant ( p  < 0.05) negative correlation with concurrent Phe level. There was no difference in HDL and their subclasses between PKU patients and controls. Tyrosine, glutamine, and creatinine were significantly lower in PKU patients compared to controls, while citric and glutamic acids were significantly higher. Conclusions Using NMR spectroscopy, a unique lipoprotein profile in PKU patients can be demonstrated which mimics a non-atherogenic profile as seen in patients treated by statins.

Phenylketonuria (PKU) is a rare metabolic disorder caused by mutations in the phenylalanine hydroxylase gene. Depending on the severity of the genetic mutation, medical treatment, and patient dietary management, elevated phenylalanine (Phe) may occur in blood and brain tissues. Research has recently shown that high Phe not only impacts the central nervous system, but also other organ systems (e.g., heart and microbiome). This study used ex vivo proton nuclear magnetic resonance (1H-NMR) analysis of urine samples from PKU patients (mean 14.9 ± 9.2 years, n = 51) to identify the impact of elevated blood Phe and PKU treatment on metabolic profiles. Our results found that 24 out of 98 urinary metabolites showed a significant difference (p < 0.05) for PKU patients compared to age-matched healthy controls (n = 51) based on an analysis of urinary metabolome. These altered urinary metabolites were related to Phe metabolism, dysbiosis, creatine synthesis or intake, the tricarboxylic acid (TCA) cycle, end products of nicotinamide-adenine dinucleotide degradation, and metabolites associated with a low Phe diet. There was an excellent correlation between the metabolome and genotype of PKU patients and healthy controls of 96.7% in a confusion matrix model. Metabolomic investigations may contribute to a better understanding of PKU pathophysiology.

The study of metabolic responses to drugs, environmental changes, and diseases is a new promising area of metabonomic research. Metabolic fingerprints can be obtained by analytical techniques such as nuclear magnetic resonance (NMR). In principle, alterations of these fingerprints due to appearance/disappearance or concentration changes of metabolites can provide early evidences of, for example, onset of diseases. A major drawback in this approach is the strong day-to-day variability of the individual metabolic fingerprint, which should be rather called a metabolic \"snapshot.\" We show here that a thorough statistical analysis performed on NMR spectra of human urine samples reveals an invariant part characteristic of each person, which can be extracted from the analysis of multiple samples of each single subject. This finding (i) provides evidence that individual metabolic phenotypes may exist and (ii) opens new perspectives to metabonomic studies, based on the possibility of eliminating the daily \"noise\" by multiple sample collection.

Background Metabolic syndrome (MetS) is a multimorbid long-term condition without consensual medical definition and a diagnostic based on compatible symptomatology. Here we have investigated the molecular signature of MetS in urine. Methods We used NMR-based metabolomics to investigate a European cohort including urine samples from 11,754 individuals (18–75 years old, 41% females), designed to populate all the intermediate conditions in MetS, from subjects without any risk factor up to individuals with developed MetS (4–5%, depending on the definition). A set of quantified metabolites were integrated from the urine spectra to obtain metabolic models (one for each definition), to discriminate between individuals with MetS. Results MetS progression produces a continuous and monotonic variation of the urine metabolome, characterized by up- or down-regulation of the pertinent metabolites (17 in total, including glucose, lipids, aromatic amino acids, salicyluric acid, maltitol, trimethylamine N -oxide, and p -cresol sulfate) with some of the metabolites associated to MetS for the first time. This metabolic signature, based solely on information extracted from the urine spectrum, adds a molecular dimension to MetS definition and it was used to generate models that can identify subjects with MetS (AUROC values between 0.83 and 0.87). This signature is particularly suitable to add meaning to the conditions that are in the interface between healthy subjects and MetS patients. Aging and non-alcoholic fatty liver disease are also risk factors that may enhance MetS probability, but they do not directly interfere with the metabolic discrimination of the syndrome. Conclusions Urine metabolomics, studied by NMR spectroscopy, unravelled a set of metabolites that concomitantly evolve with MetS progression, that were used to derive and validate a molecular definition of MetS and to discriminate the conditions that are in the interface between healthy individuals and the metabolic syndrome.

BackgroundA major challenge from the moment a child is delivered is the adaptation to the extrauterine life, where rapid metabolic changes take place. The study of these changes during the first days of human life may assist in the understanding of the metabolic processes that occur at this critical period, which is likely to provide significant clinical insights. To date, metabolomics has become a powerful field, ideal for the monitoring of such dynamic variations, since it offers the possibility to identify alterations in metabolic profiles, even on daily basis.MethodsThe study included 253 healthy newborns (GA 35 to 40 weeks) from the region of Western Greece. Urine samples were collected immediately after birth and at the third day of life. NMR-based metabolomics was used to compare the metabolic urinary profiles of newborns from the first and third day of their life, assessing the impact of six perinatal factors; delivery mode, prematurity, maternal smoking, gender, nutrition and neonatal jaundice.ResultsAnalysis of urine metabolic fingerprint from the first and third day of life, coupled with multivariate statistics, provides insights into the details of early life metabolic profile differentiation. Αt the third day of life metabolic adaptations are evident, as many differences were noted in urine of healthy neonates within the first 72 h postpartum. Trends in differentiation of metabolites levels between the two groups, late preterm and term newborns, have been also observed.ConclusionsNewborn’s urine metabolic profiles confirmed the rapid changes in their metabolism after birth. Further, ongoing research will enable us to develop one reference model of urinary metabolomics in healthy newborns during the period of adaptation to the extra-uterine life.

Background Metabolic syndrome (MetS) is a cluster of medical conditions and risk factors correlating with insulin resistance that increase the risk of developing cardiometabolic health problems. The specific criteria for diagnosing MetS vary among different medical organizations but are typically based on the evaluation of abdominal obesity, high blood pressure, hyperglycemia, and dyslipidemia. A unique, quantitative and independent estimation of the risk of MetS based only on quantitative biomarkers is highly desirable for the comparison between patients and to study the individual progression of the disease in a quantitative manner. Methods We used NMR-based metabolomics on a large cohort of donors (n = 21,323; 37.5% female) to investigate the diagnostic value of serum or serum combined with urine to estimate the MetS risk. Specifically, we have determined 41 circulating metabolites and 112 lipoprotein classes and subclasses in serum samples and this information has been integrated with metabolic profiles extracted from urine samples. Results We have developed MetSCORE, a metabolic model of MetS that combines serum lipoprotein and metabolite information. MetSCORE discriminate patients with MetS (independently identified using the WHO criterium) from general population, with an AUROC of 0.94 (95% CI 0.920–0.952, p < 0.001). MetSCORE is also able to discriminate the intermediate phenotypes, identifying the early risk of MetS in a quantitative way and ranking individuals according to their risk of undergoing MetS (for general population) or according to the severity of the syndrome (for MetS patients). Conclusions We believe that MetSCORE may be an insightful tool for early intervention and lifestyle modifications, potentially preventing the aggravation of metabolic syndrome.

The complex manifestations of COVID-19 are still not fully decoded on the molecular level. We combined quantitative the nuclear magnetic resonance (NMR) spectroscopy serum analysis of metabolites, lipoproteins and inflammation markers with clinical parameters and a targeted cytokine panel to characterize COVID-19 in a large (534 patient samples, 305 controls) outpatient cohort of recently tested PCR-positive patients. The COVID-19 cohort consisted of patients who were predominantly in the initial phase of the disease and mostly exhibited a milder disease course. Concerning the metabolic profiles of SARS-CoV-2-infected patients, we identified markers of oxidative stress and a severe dysregulation of energy metabolism. NMR markers, such as phenylalanine, inflammatory glycoproteins (Glyc) and their ratio with the previously reported supramolecular phospholipid composite (Glyc/SPC), showed a predictive power comparable to laboratory parameters such as C-reactive protein (CRP) or ferritin. We demonstrated interfaces between the metabolism and the immune system, e.g., we could trace an interleukin (IL-6)-induced transformation of a high-density lipoprotein (HDL) to a pro-inflammatory actor. Finally, we showed that metadata such as age, sex and constitution (e.g., body mass index, BMI) need to be considered when exploring new biomarkers and that adding NMR parameters to existing diagnoses expands the diagnostic toolbox for patient stratification and personalized medicine.

Background 5q spinal muscular atrophy (SMA) is a disabling and life-limiting neuromuscular disease. In recent years, novel therapies have shown to improve clinical outcomes. Yet, the absence of reliable biomarkers renders clinical assessment and prognosis of possibly already affected newborns with a positive newborn screening result for SMA imprecise and difficult. Therapeutic decisions and stratification of individualized therapies remain challenging, especially in symptomatic children. The aim of this proof-of-concept and feasibility study was to explore the value of 1 H-nuclear magnetic resonance (NMR)-based metabolic profiling in identifying non-invasive diagnostic and prognostic urinary fingerprints in children and adolescents with SMA. Results Urine samples were collected from 29 treatment-naïve SMA patients (5 pre-symptomatic, 9 SMA 1, 8 SMA 2, 7 SMA 3), 18 patients with Duchenne muscular dystrophy (DMD) and 444 healthy controls. Using machine-learning algorithms, we propose a set of prediction models built on urinary fingerprints that showed potential diagnostic value in discriminating SMA patients from controls and DMD, as well as predictive properties in separating between SMA types, allowing predictions about phenotypic severity. Interestingly, preliminary results of the prediction models suggest additional value in determining biochemical onset of disease in pre-symptomatic infants with SMA identified by genetic newborn screening and furthermore as potential therapeutic monitoring tool. Conclusions This study provides preliminary evidence for the use of 1 H-NMR-based urinary metabolic profiling as diagnostic and prognostic biomarker in spinal muscular atrophy.

After SARS-CoV-2 infection, the molecular phenoreversion of the immunological response and its associated metabolic dysregulation are required for a full recovery of the patient. This process is patient-dependent due to the manifold possibilities induced by virus severity, its phylogenic evolution and the vaccination status of the population. We have here investigated the natural history of COVID-19 disease at the molecular level, characterizing the metabolic and immunological phenoreversion over time in large cohorts of hospitalized severe patients (n = 886) and non-hospitalized recovered patients that self-reported having passed the disease (n = 513). Non-hospitalized recovered patients do not show any metabolic fingerprint associated with the disease or immune alterations. Acute patients are characterized by the metabolic and lipidomic dysregulation that accompanies the exacerbated immunological response, resulting in a slow recovery time with a maximum probability of around 62 days. As a manifestation of the heterogeneity in the metabolic phenoreversion, age and severity become factors that modulate their normalization time which, in turn, correlates with changes in the atherogenesis-associated chemokine MCP-1. Our results are consistent with a model where the slow metabolic normalization in acute patients results in enhanced atherosclerotic risk, in line with the recent observation of an elevated number of cardiovascular episodes found in post-COVID-19 cohorts.