Dataset Information

Urinary 1H-NMR metabolomics can distinguish pancreatitis patients from healthy controls.

ABSTRACT: The characterization of the urinary metabolome may yield biomarkers indicative of pancreatitis.We establish a non-invasive technique to compare urinary metabolic profiles in patients with acute and chronic pancreatitis to healthy controls.Urine was obtained from healthy controls (HC, n=5), inpatients with mild acute pancreatitis (AP, n=5), and outpatients with chronic pancreatitis (CP, n=5). Proton nuclear magnetic resonance spectra were obtained for each sample. Metabolites were identified and quantified in each spectrum; resulting concentrations were normalized to account for differences in dilution among samples. Kruskal-Wallis test, post-hoc Mann-Whitney U tests, and principal component analysis were performed to identify metabolites that discriminate healthy controls, acute pancreatitis, and chronic pancreatitis.Sixty metabolites were identified and quantified; five were found to differ significantly (P<0.05) among the three groups. Of these, citrate and adenosine remained significant after validation by random permutation. Principal component analysis demonstrated that healthy control urine samples can be differentiated from patients with chronic pancreatitis or acute pancreatitis; chronic pancreatitis patients could not be distinguished from acute pancreatitis patients.This metabolomic investigation demonstrates that this non-invasive technique offers insight into the metabolic states of pancreatitis. Although the identified metabolites cannot conclusively be defined as biomarkers of disease, future studies will validate our findings in larger patient cohorts.

SUBMITTER: Lusczek ER

PROVIDER: S-EPMC4500934 | biostudies-literature | 2013 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Urinary 1H-NMR metabolomics can distinguish pancreatitis patients from healthy controls.

Lusczek Elizabeth R ER Paulo Joao A JA Saltzman John R JR Kadiyala Vivek V Banks Peter A PA Beilman Greg G Conwell Darwin L DL

JOP : Journal of the pancreas 20130310 2

<h4>Context</h4>The characterization of the urinary metabolome may yield biomarkers indicative of pancreatitis.<h4>Objectives</h4>We establish a non-invasive technique to compare urinary metabolic profiles in patients with acute and chronic pancreatitis to healthy controls.<h4>Methods</h4>Urine was obtained from healthy controls (HC, n=5), inpatients with mild acute pancreatitis (AP, n=5), and outpatients with chronic pancreatitis (CP, n=5). Proton nuclear magnetic resonance spectra were obtaine ...[more]

PMID: 23474563

Similar Datasets

Project description:BackgroundPhenylketonuria (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.ResultsConcurrent 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.ConclusionsUsing 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.

Project description:Both nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS) play important roles in metabolomics. The complementary features of NMR and MS make their combination very attractive; however, currently the vast majority of metabolomics studies use either NMR or MS separately, and variable selection that combines NMR and MS for biomarker identification and statistical modeling is still not well developed. In this study focused on methodology, we developed a backward variable elimination partial least-squares discriminant analysis algorithm embedded with Monte Carlo cross validation (MCCV-BVE-PLSDA), to combine NMR and targeted liquid chromatography (LC)/MS data. Using the metabolomics analysis of serum for the detection of colorectal cancer (CRC) and polyps as an example, we demonstrate that variable selection is vitally important in combining NMR and MS data. The combined approach was better than using NMR or LC/MS data alone in providing significantly improved predictive accuracy in all the pairwise comparisons among CRC, polyps, and healthy controls. Using this approach, we selected a subset of metabolites responsible for the improved separation for each pairwise comparison, and we achieved a comprehensive profile of altered metabolite levels, including those in glycolysis, the TCA cycle, amino acid metabolism, and other pathways that were related to CRC and polyps. MCCV-BVE-PLSDA is straightforward, easy to implement, and highly useful for studying the contribution of each individual variable to multivariate statistical models. On the basis of these results, we recommend using an appropriate variable selection step, such as MCCV-BVE-PLSDA, when analyzing data from multiple analytical platforms to obtain improved statistical performance and a more accurate biological interpretation, especially for biomarker discovery. Importantly, the approach described here is relatively universal and can be easily expanded for combination with other analytical technologies.

Dataset Information

Urinary 1H-NMR metabolomics can distinguish pancreatitis patients from healthy controls.

Publications

Urinary 1H-NMR metabolomics can distinguish pancreatitis patients from healthy controls.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets