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Detection of Early Disease Risk Factors Associated with Metabolic Syndrome: A New Era with the NMR Metabolomics Assessment.

ABSTRACT: The metabolic syndrome is a multifactorial disease developed due to accumulation and chronification of several risk factors associated with disrupted metabolism. The early detection of the biomarkers by NMR spectroscopy could be helpful to prevent multifactorial diseases. The exposure of each risk factor can be detected by traditional molecular markers but the current biomarkers have not been enough precise to detect the primary stages of disease. Thus, there is a need to obtain novel molecular markers of pre-disease stages. A promising source of new molecular markers are metabolomics standing out the research of biomarkers in NMR approaches. An increasing number of nutritionists integrate metabolomics into their study design, making nutrimetabolomics one of the most promising avenues for improving personalized nutrition. This review highlight the major five risk factors associated with metabolic syndrome and related diseases including carbohydrate dysfunction, dyslipidemia, oxidative stress, inflammation, and gut microbiota dysbiosis. Together, it is proposed a profile of metabolites of each risk factor obtained from NMR approaches to target them using personalized nutrition, which will improve the quality of life for these patients.

SUBMITTER: Hernandez-Baixauli J

PROVIDER: S-EPMC7146483 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Detection of Early Disease Risk Factors Associated with Metabolic Syndrome: A New Era with the NMR Metabolomics Assessment.

Hernandez-Baixauli Julia J Quesada-Vázquez Sergio S Mariné-Casadó Roger R Gil Cardoso Katherine K Caimari Antoni A Del Bas Josep M JM Escoté Xavier X Baselga-Escudero Laura L

Nutrients 20200318 3

The metabolic syndrome is a multifactorial disease developed due to accumulation and chronification of several risk factors associated with disrupted metabolism. The early detection of the biomarkers by NMR spectroscopy could be helpful to prevent multifactorial diseases. The exposure of each risk factor can be detected by traditional molecular markers but the current biomarkers have not been enough precise to detect the primary stages of disease. Thus, there is a need to obtain novel molecular ...[more]

PMID: 32197513

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Project description:Steatosis is the accumulation of triglycerides in hepatic cells wherein fats exceed 5% of the entire liver weight. Although steatotic liver damage is reversible due to the liver's regenerative capability, protracted damage often and typically leads to irreversible conditions such as cirrhosis and hepatocellular carcinoma (HCC). Therefore, early steatotic detection is critical for preventing progression to advanced liver diseases. This also becomes particularly important given the higher prevalence of drug usage, as drugs are a frequent cause of liver damage. Currently, the recommendation to diagnose steatosis is using liver enzymes and performing a liver biopsy. Liver biopsy remains the gold standard method of detection, but the procedure is invasive and an unreliable diagnostic tool. Non-invasive, specific and sensitive diagnostic solutions such as biomarkers are therefore needed for the early detection of steatosis. Our aim is to identify changes in urinary metabolites in tetracycline-induced hepatic steatotic rats at different stages of the diseases using metabolomic-based techniques. Sprague Dawley male rats are treated by intraperitoneal injection (I.P.) with either 62.5 mg kg-1 or 125 mg kg-1 tetracycline, an antibiotic previously known to induce steatosis. We analyse the metabolic profile of the urinary tetracycline induced hepatic steatotic rats using 1H nuclear magnetic resonance (NMR), 2D 1H-1H TOCSY (total correlation spectroscopy) and electrospray liquid chromatography-mass spectrometry (ESI-LC-MS/MS) based metabolomics. The combined analysis of haematoxylin & eosin (H&E), oil red O (ORO) and direct measurement of triglyceride content in the liver tissues of the control samples against 125 mg kg-1 and 62.5 mg kg-1 treated samples, reveals that 125 mg kg-1 tetracycline exposure potentially induces steatosis. The combination of 1H NMR, 2D 1H-1H TOCSY and ESI-LC-MS/MS alongside multivariate statistical analysis, detected a total of 6 urinary metabolites changes, across 6 metabolic pathways. Furthermore, lysine concentration correlates with liver damage as tetracycline dose concentration increases, whilst both H&E and ORO fail to detect hepatocellular damage at the lowest dose concentration. We conclude that the combination of 1H NMR and ESI-LC-MS/MS suggests that these are suitable platforms for studying the pathogenesis of steatosis development, prior to morphological alterations observed in staining techniques and offer a more detailed description of the severity of the steatotic disease.

Project description:BackgroundPolycystic ovary syndrome (PCOS) is a complex reproductive endocrine disorder. And metabolic syndrome (MS) is an important bridge for PCOS patients to develop other diseases, such as diabetes and coronary heart disease. Our aim was to study the potential metabolic characteristics of PCOS-MS and identify sensitive biomarkers so as to provide targets for clinical screening, diagnosis, and treatment.MethodsIn this study, 44 PCOS patients with MS, 34 PCOS patients without MS, and 32 healthy controls were studied. Plasma samples of subjects were tested by ultraperformance liquid chromatography (UPLC) system combined with LTQ-orbi-trap mass spectrometry. The changes of metabolic characteristics from PCOS to PCOS-MS were systematically analyzed. Correlations between differential metabolites and clinical characteristics of PCOS-MS were assessed. Differential metabolites with high correlation were further evaluated by the receiver operating characteristic (ROC) curve to identify their sensitivity as screening indicators.ResultsThere were significant differences in general characteristics, reproductive hormone, and metabolic parameters in the PCOS-MS group when compared with the PCOS group and healthy controls. We found 40 differential metabolites which were involved in 23 pathways when compared with the PCOS group. The metabolic network further reflected the metabolic environment, including the interaction between metabolic pathways, modules, enzymes, reactions, and metabolites. In the correlation analysis, there were 11 differential metabolites whose correlation coefficient with clinical parameters was greater than 0.4, which were expected to be taken as biomarkers for clinical diagnosis. Besides, these 11 differential metabolites were assessed by ROC, and the areas under curve (AUCs) were all greater than 0.7, with a good sensitivity. Furthermore, combinational metabolic biomarkers, such as glutamic acid + leucine + phenylalanine and carnitine C 4: 0 + carnitine C18:1 + carnitine C5:0 were expected to be sensitive combinational biomarkers in clinical practice.ConclusionOur study provides a new insight to understand the pathogenesis mechanism, and the discriminating metabolites may help screen high-risk of MS in patients with PCOS and provide sensitive biomarkers for clinical diagnosis.

Dataset Information

Detection of Early Disease Risk Factors Associated with Metabolic Syndrome: A New Era with the NMR Metabolomics Assessment.

Publications

Detection of Early Disease Risk Factors Associated with Metabolic Syndrome: A New Era with the NMR Metabolomics Assessment.

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