Dataset Information

1H Nuclear Magnetic Resonance (NMR)-Based Cerebrospinal Fluid and Plasma Metabolomic Analysis in Type 2 Diabetic Patients and Risk Prediction for Diabetic Microangiopathy.

ABSTRACT: Insulin resistance and metabolic derangement are present in patients with type 2 diabetes mellitus (T2DM). However, the metabolomic signature of T2DM in cerebrospinal fluid (CSF) has not been investigated thus far. In this prospective metabolomic study, fasting CSF and plasma samples from 40 T2DM patients to 36 control subjects undergoing elective surgery with spinal anesthesia were analyzed by 1H nuclear magnetic resonance (NMR) spectroscopy. NMR spectra of CSF and plasma metabolites were analyzed and correlated with the presence of T2DM and diabetic microangiopathy (retinopathy, nephropathy, and neuropathy) using an area under the curve (AUC) estimation. CSF metabolomic profiles in T2DM patients vs. controls revealed significantly increased levels of alanine, leucine, valine, tyrosine, lactate, pyruvate, and decreased levels of histidine. In addition, a combination of alanine, histidine, leucine, pyruvate, tyrosine, and valine in CSF showed a superior correlation with the presence of T2DM (AUC:0.951), diabetic retinopathy (AUC:0.858), nephropathy (AUC:0.811), and neuropathy (AUC:0.691). Similar correlations also appeared in plasma profiling. These metabolic alterations in CSF suggest decreasing aerobic metabolism and increasing anaerobic glycolysis in cerebral circulation of patients with T2DM. In conclusion, our results provide clues for the metabolic derangements in diabetic central neuropathy among T2DM patients; however, their clinical significance requires further exploration.

SUBMITTER: Lin HT

PROVIDER: S-EPMC6616639 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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<sup>1</sup>H Nuclear Magnetic Resonance (NMR)-Based Cerebrospinal Fluid and Plasma Metabolomic Analysis in Type 2 Diabetic Patients and Risk Prediction for Diabetic Microangiopathy.

Lin Huan-Tang HT Cheng Mei-Ling ML Lo Chi-Jen CJ Lin Gigin G Lin Shu-Fu SF Yeh Jiun-Ting JT Ho Hung-Yao HY Lin Jr-Rung JR Liu Fu-Chao FC

Journal of clinical medicine 20190619 6

Insulin resistance and metabolic derangement are present in patients with type 2 diabetes mellitus (T2DM). However, the metabolomic signature of T2DM in cerebrospinal fluid (CSF) has not been investigated thus far. In this prospective metabolomic study, fasting CSF and plasma samples from 40 T2DM patients to 36 control subjects undergoing elective surgery with spinal anesthesia were analyzed by <sup>1</sup>H nuclear magnetic resonance (NMR) spectroscopy. NMR spectra of CSF and plasma metabolites ...[more]

PMID: 31248127

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Project description:Periodontitis is characterized by the loss of the supporting tissues of the teeth in an inflammatory-infectious context. The diagnosis relies on clinical and X-ray examination. Unfortunately, clinical signs of tissue destruction occur late in the disease progression. Therefore, it is mandatory to identify reliable biomarkers to facilitate a better and earlier management of this disease. To this end, saliva represents a promising fluid for identification of biomarkers as metabolomic fingerprints. The present study used high-resolution 1H-nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate statistical analysis to identify the metabolic signature of active periodontitis. The metabolome of stimulated saliva of 26 patients with generalized periodontitis (18 chronic and 8 aggressive) was compared to that of 25 healthy controls. Principal Components Analysis (PCA), performed with clinical variables, indicated that the patient population was homogeneous, demonstrating a strong correlation between the clinical and the radiological variables used to assess the loss of periodontal tissues and criteria of active disease. Orthogonal Projection to Latent Structure (OPLS) analysis showed that patients with periodontitis can be discriminated from controls on the basis of metabolite concentrations in saliva with satisfactory explained variance (R2X = 0.81 and R2Y = 0.61) and predictability (Q2Y = 0.49, CV-AUROC = 0.94). Interestingly, this discrimination was irrespective of the type of generalized periodontitis, i.e. chronic or aggressive. Among the main discriminating metabolites were short chain fatty acids as butyrate, observed in higher concentrations, and lactate, γ-amino-butyrate, methanol, and threonine observed in lower concentrations in periodontitis. The association of lactate, GABA, and butyrate to generate an aggregated variable reached the best positive predictive value for diagnosis of periodontitis. In conclusion, this pilot study showed that 1H-NMR spectroscopy analysis of saliva could differentiate patients with periodontitis from controls. Therefore, this simple, robust, non-invasive method, may offer a significant help for early diagnosis and follow-up of periodontitis.

Dataset Information

1H Nuclear Magnetic Resonance (NMR)-Based Cerebrospinal Fluid and Plasma Metabolomic Analysis in Type 2 Diabetic Patients and Risk Prediction for Diabetic Microangiopathy.

Publications

<sup>1</sup>H Nuclear Magnetic Resonance (NMR)-Based Cerebrospinal Fluid and Plasma Metabolomic Analysis in Type 2 Diabetic Patients and Risk Prediction for Diabetic Microangiopathy.

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