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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.
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Biomarcadores , Espectroscopia de Ressonância Magnética , Síndrome Metabólica , Metabolômica , Valor Preditivo dos Testes , Humanos , Síndrome Metabólica/diagnóstico , Síndrome Metabólica/sangue , Síndrome Metabólica/epidemiologia , Síndrome Metabólica/urina , Feminino , Masculino , Biomarcadores/sangue , Biomarcadores/urina , Pessoa de Meia-Idade , Medição de Risco , Adulto , Idoso , Lipoproteínas/sangue , Prognóstico , Fatores de Risco , Fatores de Risco Cardiometabólico , Adulto JovemRESUMO
Despite numerous studies in human patients and animal models for phenylketonuria (PKU; OMIM#261600), the pathophysiology of PKU and the underlying causes of brain dysfunction and cognitive problems in PKU patients are not well understood. In this study, lumbar cerebral spinal fluid (CSF) was obtained immediately after blood sampling from early-treated adult PKU patients who had fasted overnight. Metabolite and amino acid concentrations in the CSF of PKU patients were compared with those of non-PKU controls. The CSF concentrations and CSF/plasma ratios for glucose and lactate were found to be below normal, similar to what has been reported for glucose transporter1 (GLUT1) deficiency patients who exhibit many of the same clinical symptoms as untreated PKU patients. CSF glucose and lactate levels were negatively correlated with CSF phenylalanine (Phe), while CSF glutamine and glutamate levels were positively correlated with CSF Phe levels. Plasma glucose levels were negatively correlated with plasma Phe concentrations in PKU subjects, which partly explains the reduced CSF glucose concentrations. Although brain glucose concentrations are unlikely to be low enough to impair brain glucose utilization, it is possible that the metabolism of Phe in the brain to produce phenyllactate, which can be transported across the blood-brain barrier to the blood, may consume glucose and/or lactate to generate the carbon backbone for glutamate. This glutamate is then converted to glutamine and carries the Phe-derived ammonia from the brain to the blood. While this mechanism remains to be tested, it may explain the correlations of CSF glutamine, glucose, and lactate concentrations with CSF Phe.
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Encéfalo , Glucose , Fenilalanina , Fenilcetonúrias , Humanos , Fenilcetonúrias/metabolismo , Fenilcetonúrias/líquido cefalorraquidiano , Glucose/metabolismo , Adulto , Masculino , Fenilalanina/líquido cefalorraquidiano , Fenilalanina/sangue , Fenilalanina/metabolismo , Feminino , Encéfalo/metabolismo , Ácido Láctico/líquido cefalorraquidiano , Ácido Láctico/metabolismo , Ácido Láctico/sangue , Adulto Jovem , Glutamina/metabolismo , Glutamina/líquido cefalorraquidiano , Glutamina/sangue , Glicemia/metabolismoRESUMO
Pancreatic ductal adenocarcinoma (PDAC) is difficult to diagnose in the early stages and lacks reliable biomarkers. The scope of this project was to establish quantitative nuclear magnetic resonance (NMR) spectroscopy to comprehensively study blood serum alterations in PDAC patients. Serum samples from 34 PDAC patients obtained before and after pancreatectomy as well as 83 age- and sex-matched control samples from healthy donors were analyzed with in vitro diagnostics research (IVDr) proton NMR spectroscopy at 600 MHz. Uni- and multivariate statistics were applied to identify significant biofluid alterations. We identified 29 significantly changed metabolites and 98 lipoproteins when comparing serum from healthy controls with those of PDAC patients. The most prominent features were assigned to (i) markers of pancreatic function (e.g., glucose and blood triglycerides), (ii) markers related to surgery (e.g., ketone bodies and blood cholesterols), (iii) PDAC-associated markers (e.g., amino acids and creatine), and (iv) markers for systemic disturbances in PDAC (e.g., gut metabolites DMG, TMAO, DMSO2, and liver lipoproteins). Quantitative serum NMR spectroscopy is suited as a diagnostic tool to investigate PDAC. Remarkably, 2-hydroxybutyrate (2-HB) as a previously suggested marker for insulin resistance was found in extraordinarily high levels only after pancreatectomy, suggesting this metabolite is the strongest marker for pancreatic loss of function.
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Carcinoma Ductal Pancreático , Neoplasias Pancreáticas , Humanos , Pancreatectomia , Neoplasias Pancreáticas/patologia , Carcinoma Ductal Pancreático/diagnóstico , Carcinoma Ductal Pancreático/cirurgia , Metabolômica/métodos , Biomarcadores TumoraisRESUMO
OBJECTIVES: The stratification of individuals suffering from acute and post-acute SARS-CoV-2 infection remains a critical challenge. Notably, biomarkers able to specifically monitor viral progression, providing details about patient clinical status, are still not available. Herein, quantitative metabolomics is progressively recognized as a useful tool to describe the consequences of virus-host interactions considering also clinical metadata. METHODS: The present study characterized the urinary metabolic profile of 243 infected individuals by quantitative nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography mass spectrometry (LC-MS). Results were compared with a historical cohort of noninfected subjects. Moreover, we assessed the concentration of recently identified antiviral nucleosides and their association with other metabolites and clinical data. RESULTS: Urinary metabolomics can stratify patients into classes of disease severity, with a discrimination ability comparable to that of clinical biomarkers. Kynurenines showed the highest fold change in clinically-deteriorated patients and higher-risk subjects. Unique metabolite clusters were also generated based on age, sex, and body mass index (BMI). Changes in the concentration of antiviral nucleosides were associated with either other metabolites or clinical variables. Increased kynurenines and reduced trigonelline excretion indicated a disrupted nicotinamide adenine nucleotide (NAD+) and sirtuin 1 (SIRT1) pathway. CONCLUSIONS: Our results confirm the potential of urinary metabolomics for noninvasive diagnostic/prognostic screening and show that the antiviral nucleosides could represent novel biomarkers linking viral load, immune response, and metabolism. Moreover, we established for the first time a casual link between kynurenine accumulation and deranged NAD+/SIRT1, offering a novel mechanism through which SARS-CoV-2 manipulates host physiology.
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COVID-19 , Humanos , COVID-19/diagnóstico , Sirtuína 1 , NAD , SARS-CoV-2 , Metabolômica/métodos , Biomarcadores/urina , Antivirais , Teste para COVID-19RESUMO
BACKGROUND: Diagnostic approaches like the nuclear magnetic resonance spectroscopy (NMR) based quantification of metabolites, lipoproteins, and inflammation markers has helped to identify typical alterations in the blood serum of COVID-19 patients. However, confounders such as sex, and comorbidities, which strongly influence the metabolome, were often not considered. Therefore, the aim of this NMR study was to consider sex, as well as arterial hypertension (AHT), when investigating COVID-19-positive serum samples in a large age-and sex matched cohort. METHODS: NMR serum data from 329 COVID-19 patients were compared with 305 healthy controls. 134 COVID-19 patients were affected by AHT. These were analyzed together with NMR data from 58 hypertensives without COVID-19. In addition to metabolite, lipoprotein, and glycoprotein data from NMR, common laboratory parameters were considered. Sex was considered in detail for all comparisons. RESULTS: Here, we show that several differences emerge from previous NMR COVID-19 studies when AHT is considered. Especially, the previously described triglyceride-rich lipoprotein profile is no longer observed in COVID-19 patients, nor an increase in ketone bodies. Further alterations are a decrease in glutamine, leucine, isoleucine, and lysine, citric acid, HDL-4 particles, and total cholesterol. Additionally, hypertensive COVID-19 patients show higher inflammatory NMR parameters than normotensive patients. CONCLUSIONS: We present a more precise picture of COVID-19 blood serum parameters. Accordingly, considering sex and comorbidities should be included in future metabolomics studies for improved and refined patient stratification. Due to metabolic similarities with other viral infections, these results can be applied to other respiratory diseases in the future.
The functionality of our human body is driven by a large number of small molecules, called metabolites. These metabolites can be associated with health but also disease conditions. In this study, we used a technology called nuclear magnetic resonance spectroscopy (NMR) to determine metabolite and protein concentrations in the blood of acutely-infected COVID-19 patients and compared these results with disease severity and clinical laboratory data. We particularly focus on patients with the very common cardiovascular condition, arterial hypertension (AHT), and important factors such as sex, age and medication. Our findings provide a more detailed insight into COVID-19 and which individuals are at higher risk for more severe disease.
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Low-field (LF) benchtop NMR is a new family of instruments available on the market, promising for fast metabolic fingerprinting and targeted quantification of specific metabolites despite a lack of sensitivity and resolution with respect to high-field (HF) instruments. In the present study, we evaluated the possibility to use the urinary metabolic fingerprint generated using a benchtop LF NMR instrument for an early detection of sepsis in preterm newborns, considering a cohort of neonates previously investigated by untargeted metabolomics based on Mass Spectrometry (MS). The classifier obtained behaved similarly to that based on MS, even if different classes of metabolites were taken into account. Indeed, investigating the regions of interest mainly related to the development of sepsis by a HF NMR instrument, we discovered a set of relevant metabolites associated to sepsis. The set included metabolites that were not detected by MS, but that were reported as relevant in other published studies. Moreover, a strong correlation between LF and HF NMR spectra was observed. The high reproducibility of the NMR spectra, the interpretability of the fingerprint in terms of metabolites and the ease of use make LF benchtop NMR instruments promising in discovering early-onset sepsis.
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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.
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Fenilcetonúrias , Humanos , Espectroscopia de Prótons por Ressonância Magnética , Fenilcetonúrias/genética , Fenótipo , Genótipo , Espectroscopia de Ressonância Magnética , Fenilalanina/genéticaRESUMO
Background: Traditional diagnosis is based on histology or clinical-stage classification which provides no information on tumor metabolism and inflammation, which, however, are both hallmarks of cancer and are directly associated with prognosis and severity. This project was an exploratory approach to profile metabolites, lipoproteins, and inflammation parameters (glycoprotein A and glycoprotein B) of borderline ovarian tumor (BOT) and high-grade serous ovarian cancer (HGSOC) for identifying additional useful serum markers and stratifying ovarian cancer patients in the future. Methods: This project included 201 serum samples of which 50 were received from BOT and 151 from high-grade serous ovarian cancer (HGSOC), respectively. All the serum samples were validated and phenotyped by 1H-NMR-based metabolomics with in vitro diagnostics research (IVDr) standard operating procedures generating quantitative data on 38 metabolites, 112 lipoprotein parameters, and 5 inflammation markers. Uni- and multivariate statistics were applied to identify NMR-based alterations. Moreover, biomarker analysis was carried out with all NMR parameters and CA-125. Results: Ketone bodies, glutamate, 2-hydroxybutyrate, glucose, glycerol, and phenylalanine levels were significantly higher in HGSOC, while the same tumors showed significantly lower levels of alanine and histidine. Furthermore, alanine and histidine and formic acid decreased and increased, respectively, over the clinical stages. Inflammatory markers glycoproteins A and B (GlycA and GlycB) increased significantly over the clinical stages and were higher in HGSOC, alongside significant changes in lipoproteins. Lipoprotein subfractions of VLDLs, IDLs, and LDLs increased significantly in HGSOC and over the clinical stages, while total plasma apolipoprotein A1 and A2 and a subfraction of HDLs decreased significantly over the clinical stages. Additionally, LDL triglycerides significantly increased in advanced ovarian cancer. In biomarker analysis, glycoprotein inflammation biomarkers behaved in the same way as the established clinical biomarker CA-125. Moreover, CA-125/GlycA, CA-125/GlycB, and CA-125/Glycs are potential biomarkers for diagnosis, prognosis, and treatment response of epithelial ovarian cancer (EOC). Last, the quantitative inflammatory parameters clearly displayed unique patterns of metabolites, lipoproteins, and CA-125 in BOT and HGSOC with clinical stages I-IV. Conclusion: 1H-NMR-based metabolomics with commercial IVDr assays could detect and identify altered metabolites and lipoproteins relevant to EOC development and progression and show that inflammation (based on glycoproteins) increased along with malignancy. As inflammation is a hallmark of cancer, glycoproteins, thereof, are promising future serum biomarkers for the diagnosis, prognosis, and treatment response of EOC. This was supported by the definition and stratification of three different inflammatory serum classes which characterize specific alternations in metabolites, lipoproteins, and CA-125, implicating that future diagnosis could be refined not only by diagnosed histology and/or clinical stages but also by glycoprotein classes.
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Background: Deep metabolomic, proteomic and immunologic phenotyping of patients suffering from an infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have matched a wide diversity of clinical symptoms with potential biomarkers for coronavirus disease 2019 (COVID-19). Several studies have described the role of small as well as complex molecules such as metabolites, cytokines, chemokines and lipoproteins during infection and in recovered patients. In fact, after an acute SARS-CoV-2 viral infection almost 10-20% of patients experience persistent symptoms post 12 weeks of recovery defined as long-term COVID-19 syndrome (LTCS) or long post-acute COVID-19 syndrome (PACS). Emerging evidence revealed that a dysregulated immune system and persisting inflammation could be one of the key drivers of LTCS. However, how these biomolecules altogether govern pathophysiology is largely underexplored. Thus, a clear understanding of how these parameters within an integrated fashion could predict the disease course would help to stratify LTCS patients from acute COVID-19 or recovered patients. This could even allow to elucidation of a potential mechanistic role of these biomolecules during the disease course. Methods: This study comprised subjects with acute COVID-19 (n=7; longitudinal), LTCS (n=33), Recov (n=12), and no history of positive testing (n=73). 1H-NMR-based metabolomics with IVDr standard operating procedures verified and phenotyped all blood samples by quantifying 38 metabolites and 112 lipoprotein properties. Univariate and multivariate statistics identified NMR-based and cytokine changes. Results: Here, we report on an integrated analysis of serum/plasma by NMR spectroscopy and flow cytometry-based cytokines/chemokines quantification in LTCS patients. We identified that in LTCS patients lactate and pyruvate were significantly different from either healthy controls (HC) or acute COVID-19 patients. Subsequently, correlation analysis in LTCS group only among cytokines and amino acids revealed that histidine and glutamine were uniquely attributed mainly with pro-inflammatory cytokines. Of note, triglycerides and several lipoproteins (apolipoproteins Apo-A1 and A2) in LTCS patients demonstrate COVID-19-like alterations compared with HC. Interestingly, LTCS and acute COVID-19 samples were distinguished mostly by their phenylalanine, 3-hydroxybutyrate (3-HB) and glucose concentrations, illustrating an imbalanced energy metabolism. Most of the cytokines and chemokines were present at low levels in LTCS patients compared with HC except for IL-18 chemokine, which tended to be higher in LTCS patients. Conclusion: The identification of these persisting plasma metabolites, lipoprotein and inflammation alterations will help to better stratify LTCS patients from other diseases and could help to predict ongoing severity of LTCS patients.
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COVID-19 , Humanos , Citocinas , SARS-CoV-2 , Triglicerídeos , Proteômica , Inflamação , Quimiocinas , Síndrome , Apolipoproteínas , LipoproteínasRESUMO
There are about 1500 genetic metabolic diseases. A small number of treatable diseases are diagnosed by newborn screening programs, which are continually being developed. However, most diseases can only be diagnosed based on clinical symptoms or metabolic findings. The main biological fluids used are urine, plasma and, in special situations, cerebrospinal fluid. In contrast to commonly used methods such as gas chromatography and high performance liquid chromatography mass spectrometry, ex vivo proton spectroscopy (1 H-NMR) is not yet used in routine clinical practice, although it has been recommended for more than 30 years. Automatic analysis and improved NMR technology have also expanded the applications used for the diagnosis of inborn errors of metabolism. We provide a mini-overview of typical applications, especially in urine but also in plasma, used to diagnose common but also rare genetic metabolic diseases with 1 H-NMR. The use of computer-assisted diagnostic suggestions can facilitate interpretation of the profiles. In a proof of principle, to date, 182 reports of 59 different diseases and 500 reports of healthy children are stored. The percentage of correct automatic diagnoses was 74%. Using the same 1 H-NMR profile-targeted analysis, it is possible to apply an untargeted approach that distinguishes profile differences from healthy individuals. Thus, additional conditions such as lysosomal storage diseases or drug interferences are detectable. Furthermore, because 1 H-NMR is highly reproducible and can detect a variety of different substance categories, the metabolomic approach is suitable for monitoring patient treatment and revealing additional factors such as nutrition and microbiome metabolism. Besides the progress in analytical techniques, a multiomics approach is most effective to combine metabolomics with, for example, whole exome sequencing, to also diagnose patients with nondetectable metabolic abnormalities in biological fluids. In this mini review we also provide our own data to demonstrate the role of NMR in a multiomics platform in the field of inborn errors of metabolism.
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Erros Inatos do Metabolismo , Criança , Recém-Nascido , Humanos , Erros Inatos do Metabolismo/diagnóstico , Erros Inatos do Metabolismo/genética , Erros Inatos do Metabolismo/metabolismo , Prótons , Cromatografia Gasosa-Espectrometria de Massas , Espectroscopia de Ressonância Magnética , ComputadoresRESUMO
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.
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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.
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BACKGROUND: The aim of this study was to gain an increased understanding of the aetiology of breast cancer, by investigating possible associations between serum lipoprotein subfractions and metabolites and the long-term risk of developing the disease. METHODS: From a cohort of 65,200 participants within the Trøndelag Health Study (HUNT study), we identified all women who developed breast cancer within a 22-year follow-up period. Using nuclear magnetic resonance (NMR) spectroscopy, 28 metabolites and 89 lipoprotein subfractions were quantified from prediagnostic serum samples of future breast cancer patients and matching controls (n = 1199 case-control pairs). RESULTS: Among premenopausal women (554 cases) 14 lipoprotein subfractions were associated with long-term breast cancer risk. In specific, different subfractions of VLDL particles (in particular VLDL-2, VLDL-3 and VLDL-4) were inversely associated with breast cancer. In addition, inverse associations were detected for total serum triglyceride levels and HDL-4 triglycerides. No significant association was found in postmenopausal women. CONCLUSIONS: We identified several associations between lipoprotein subfractions and long-term risk of breast cancer in premenopausal women. Inverse associations between several VLDL subfractions and breast cancer risk were found, revealing an altered metabolism in the endogenous lipid pathway many years prior to a breast cancer diagnosis.
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Neoplasias da Mama , Neoplasias da Mama/epidemiologia , Estudos de Coortes , Feminino , Humanos , Lipoproteínas , Pré-Menopausa , TriglicerídeosRESUMO
Metachromatic leukodystrophy (MLD) is a lysosomal storage disease caused by a deficiency of the arylsulfatase A (ARSA). ARSA deficiency leads to an accumulation of sulfatides primarily in the nervous system ultimately causing demyelination. With evolving therapeutic options, there is an increasing need for indicators to evaluate disease progression. Here, we report targeted metabolic urine profiling of 56 MLD patients including longitudinal sampling, using 1H (proton) nuclear magnetic resonance (NMR) spectroscopy. 1H-NMR urine spectra of 119 MLD samples and 323 healthy controls were analyzed by an in vitro diagnostics research (IVDr) tool, covering up to 50 endogenous and 100 disease-related metabolites on a 600-MHz IVDr NMR spectrometer. Quantitative data reports were analyzed regarding age of onset, clinical course, and therapeutic intervention. The NMR data reveal metabolome changes consistent with a multiorgan affection in MLD patients in comparison to controls. In the MLD cohort, N-acetylaspartate (NAA) excretion in urine is elevated. Early onset MLD forms show a different metabolic profile suggesting a metabolic shift toward ketogenesis in comparison to late onset MLD and controls. In samples of juvenile MLD patients who stabilize clinically after hematopoietic stem cell transplantation (HSCT), the macrophage activation marker neopterin is elevated. We were able to identify different metabolic patterns reflecting variable organ disturbances in MLD, including brain and energy metabolism and inflammatory processes. We suggest NAA in urine as a quantitative biomarker for neurodegeneration. Intriguingly, elevated neopterin after HSCT supports the hypothesis that competent donor macrophages are crucial for favorable outcome.
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BACKGROUND AND AIMS: Assessment of comprehensive lipoprotein subclass profiles in adolescents and their relation to vascular disease may enhance our understanding of the development of dyslipidemia in early life and inform early vascular prevention. METHODS: Nuclear magnetic resonance was used to measure lipoprotein profiles, including lipids (cholesterol, free cholesterol, triglycerides, phospholipids) and apolipoproteins (apoB-100, apoA1, apoA2) of 17 lipoprotein subclasses (from least dense to densest: VLDL-1 to -6, IDL, LDL-1 to -6, HDL-1 to -4) in n = 1776 14- to 19-year olds (56.6% female) and n = 3027 25- to 85-year olds (51.5% female), all community-dwelling. Lipoprotein profiles were related to carotid intima-media thickness (cIMT) as ascertained by sonography. RESULTS: Adolescents compared to adults had lower triglycerides, total, LDL, and non-HDL cholesterol, and apoB, and higher HDL cholesterol. They showed 26.6-59.8% lower triglyceride content of all lipoprotein subclasses and 21.9-51.4% lower VLDL lipid content. Concentrations of dense LDL-4 to LDL-6 were 36.7-40.2% lower, with also markedly lower levels of LDL-1 to LDL-3, but 24.2% higher HDL-1 ApoA1. In adolescents, only LDL-3 to LDL-5 subclasses were associated with cIMT (range of differences in cIMT for a 1-SD higher concentration, 4.8-5.9 µm). The same associations emerged in adults, with on average 97 ± 42% (mean ± SD) larger effect sizes, in addition to LDL-1 and LDL-6 (range, 6.9-11.3 µm) and HDL-2 to HDL-4, ApoA1, and ApoA2 (range, -7.0 to -17.7 µm). CONCLUSIONS: Adolescents showed a markedly different and more favorable lipoprotein profile compared to adults. Dense LDL subclasses were the only subclasses associated with cIMT in adolescents, implicating them as the potential preferred therapeutic target for primary prevention of cardiovascular disease at this age. In adults, associations with cIMT were approximately twice as large as in adolescents, and HDL-related measures were additionally associated with cIMT.
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Espessura Intima-Media Carotídea , Lipoproteínas , Adolescente , Adulto , HDL-Colesterol , Estudos de Coortes , Feminino , Humanos , Masculino , Estudos Prospectivos , TriglicerídeosRESUMO
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 1H-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 1H-NMR-based urinary metabolic profiling as diagnostic and prognostic biomarker in spinal muscular atrophy.
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Atrofia Muscular Espinal , Distrofia Muscular de Duchenne , Adolescente , Criança , Humanos , Recém-Nascido , Imageamento por Ressonância Magnética , Espectroscopia de Ressonância Magnética , Atrofia Muscular Espinal/diagnóstico , Atrofia Muscular Espinal/genética , Espectroscopia de Prótons por Ressonância MagnéticaRESUMO
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.
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Síndrome Metabólica/urina , Metaboloma , Metabolômica , Espectroscopia de Prótons por Ressonância Magnética , Adolescente , Adulto , Idoso , Biomarcadores/urina , Estudos de Casos e Controles , Progressão da Doença , Europa (Continente) , Feminino , Humanos , Masculino , Síndrome Metabólica/diagnóstico , Pessoa de Meia-Idade , Valor Preditivo dos Testes , Urinálise , Adulto JovemRESUMO
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.
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Nitrogênio , Neoplasias da Próstata , Carbono , Humanos , Masculino , Metabolômica , Neoplasias da Próstata/diagnóstico , Espectroscopia de Prótons por Ressonância MagnéticaRESUMO
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.
Assuntos
Fenilcetonúrias , Adulto , Colesterol , LDL-Colesterol , Humanos , Lipoproteínas , Espectroscopia de Ressonância Magnética , MetabolômicaRESUMO
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.