Dataset Information

Urinary metabolic profile and its predictive indexes after MSG consumption in rat.

ABSTRACT: Monosodium glutamate (MSG) is a widely used food additive with conflicting evidence regarding its potential effects on human health, with proposed relevance for obesity and metabolic syndrome (MetS) or chronic kidney disease. As being able to accurately quantify the MSG dietary intake would help clarify the open issues, we constructed a predictive formula to estimate the daily intake of MSG in a rat model based on the urinary metabolic profile. Adult male Wistar rats were divided into groups receiving different daily amounts of MSG in drinking water (0.5, 1.5, and 3.0 g%), no MSG, and MSG withdrawal after 3.0% MSG treatment for 4 weeks. We then analyzed 24-hour urine samples for chemistries and metabolites using 1H NMR spectrometry and observed a strong correlation between urine pH, sodium, bicarbonate, alpha-ketoglutarate, citrate, fumarate, glutamate, methylamine, N-methyl-4-pyridone-3-carboxamide, succinate, and taurine and the daily MSG intake. Following the multiple linear regression analysis a simple formula model based on urinary Na+, citrate, and glutamate was most accurate and could be validated for estimating daily MSG intake. In conclusion, we propose that the daily MSG intake correlates with urinary metabolites in a rat model and that this new tool for monitoring the impact of MSG on health measures.

SUBMITTER: Sukmak M

PROVIDER: S-EPMC11371250 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Urinary metabolic profile and its predictive indexes after MSG consumption in rat.

Sukmak Manatsaphon M Kyaw Thin Su TS Nahok Kanokwan K Sharma Amod A Silsirivanit Atit A Lert-Itthiporn Worachart W Japrung Deanpen D Pinlaor Somchai S Anutrakulchai Sirirat S Selmi Carlo C Slupsky Carolyn M CM Hammock Bruce D BD Cha'on Ubon U

PloS one 20240903 9

Monosodium glutamate (MSG) is a widely used food additive with conflicting evidence regarding its potential effects on human health, with proposed relevance for obesity and metabolic syndrome (MetS) or chronic kidney disease. As being able to accurately quantify the MSG dietary intake would help clarify the open issues, we constructed a predictive formula to estimate the daily intake of MSG in a rat model based on the urinary metabolic profile. Adult male Wistar rats were divided into groups rec ...[more]

PMID: 39226266

Similar Datasets

Project description:BACKGROUND: Androgen-dependent proteins (lipocalins) circulate in blood of male rats and mice and, being small (~ 18 kDa), pass freely into glomerular filtrate. Some are salvaged in proximal nephrons but some escape in urine. Several organic molecules can bind to these proteins causing, where salvage occurs, nephropathy including malignancy in renal cortex. In urine, both free lipocalins and ligands contribute to an increasingly-recognised vital biological role in social communication between adults, especially in the dark where reliance is on smell and taste. Crystal structure of the first-characterised lipocalin of male rats, ?2u-globulin, has been determined and peptide sequences for others are available, but no study of occurrence during early puberty has been made. We have followed temporal occurrence in urine of juveniles (n?=?3) for non-invasive pilot study by high resolution gradient mini-gel electrophoresis, tryptic digest of excised protein bands, and LC-MS/MS of digest to identify peptide fragments and assign to specific lipocalins. Study objective refers directly to external availability for social communication but also indirectly to indicate kinetics of circulating lipocalins to which some xenobiotics may bind and constitute determinants of renal disease. RESULTS: Mini-gels revealed greater lipocalin complexity than hitherto recognised, possibly reflecting post-translational modifications. Earliest patterns comprised rat urinary protein 1, already evident in Sprague-Dawley and Wistar strains at 36 and 52 days, respectively. By 44 and 57 days major rat protein (?2u-globulin) occurred as the progressively more dominant protein, though as two forms with different electrophoretic mobility, characterised by seven peptide sequences. No significant change in urinary testosterone had occurred in Wistars when major rat protein became evident, but testosterone surged by 107 days concomitant with the marked abundance of excreted lipocalins. CONCLUSIONS: Qualitative temporal changes in the composition of excreted lipocalins early in puberty, and apparent increase in major urinary protein as two resolvable forms, should catalyse systematic non-invasive study of urinary lipocalin and testosterone dynamics from early age, to illuminate this aspect of laboratory rodent social physiology. It could also define the potential temporal onset of nephrotoxic ligand risk, applicable to young animals used as toxicological models.

Project description:Autism Spectrum Disorder (ASD) encompasses a clinical spectrum of neurodevelopmental conditions that display significant heterogeneity in etiology, symptomatology, and severity. We previously compared 30 young children with idiopathic ASD and 30 unrelated typically-developing controls, detecting an imbalance in several compounds belonging mainly to the metabolism of purines, tryptophan and other amino acids, as well as compounds derived from the intestinal flora, and reduced levels of vitamins B6, B12 and folic acid. The present study describes significant urinary metabolomic differences within 14 pairs, including one child with idiopathic ASD and his/her typically-developing sibling, tightly matched by sex and age to minimize confounding factors, allowing a more reliable identification of the metabolic fingerprint related to ASD. By using a highly sensitive, accurate and unbiased approach, suitable for ensuring broad metabolite detection coverage on human urine, and by applying multivariate statistical analysis, we largely replicate our previous results, demonstrating a significant perturbation of the purine and tryptophan pathways, and further highlight abnormalities in the "phenylalanine, tyrosine and tryptophan" pathway, essentially involving increased phenylalanine and decreased tyrosine levels, as well as enhanced concentrations of bacterial degradation products, including phenylpyruvic acid, phenylacetic acid and 4-ethylphenyl-sulfate. The outcome of these within-family contrasts consolidates and extends our previous results obtained from unrelated individuals, adding further evidence that these metabolic imbalances may be linked to ASD rather than to environmental differences between cases and controls. It further underscores the excess of some gut microbiota-derived compounds in ASD, which could have diagnostic value in a network model differentiating the metabolome of autistic and unaffected siblings. Finally, it points toward the existence of a "metabolic autism spectrum" distributed as an endophenotype, with unaffected siblings possibly displaying a metabolic profile intermediate between their autistic siblings and unrelated typically-developing controls.

Project description:ObjectiveCurrently, distinct use of clinical data, routine laboratory indicators or the detection of diabetic autoantibodies in the diagnosis and management of diabetes mellitus is limited. Hence, this study was aimed to screen the indicators, and to establish and validate a multifactorial logistic regression model nomogram for the non-invasive differential prediction of type 1 diabetes mellitus.MethodsClinical data, routine laboratory indicators, and diabetes autoantibody profiles of diabetic patients admitted between September 2018 and December 2022 were retrospectively analyzed. Logistic regression was used to select the independent influencing factors, and a prediction nomogram based on the multiple logistic regression model was constructed using these independent factors. Moreover, the predictive accuracy and clinical application value of the nomogram were evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC).ResultsA total of 522 diabetic patients were included in this study. These patients were randomized into training and validation sets in a 7:3 ratio. The predictors screened included age, prealbumin (PA), high-density lipoprotein cholesterol (HDL-C), islet cells autoantibodies (ICA), islets antigen 2 autoantibodies (IA-2A), glutamic acid decarboxylase antibody (GADA), and C-peptide levels. Based on these factors, a multivariate model nomogram was constructed, which had an Area Under Curve (AUC) of 0.966 and 0.961 for the training set and validation set, respectively. Subsequently, the calibration curves demonstrated a strong accuracy of the graph; the DCA and CIC results indicated that the graph could be used as a non-invasive valid predictive tool for the differential diagnosis of type 1 diabetes mellitus, clinically.ConclusionThe established prediction model combining patient's age, PA, HDL-C, ICA, IA-2A, GADA, and C-peptide can assist in differential diagnosis of type 1 diabetes mellitus and type 2 diabetes mellitus and provides a basis for the clinical as well as therapeutic management of the disease.

Dataset Information

Urinary metabolic profile and its predictive indexes after MSG consumption in rat.

Publications

Urinary metabolic profile and its predictive indexes after MSG consumption in rat.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets