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

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.

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Journal
PLoS ONE
Published
2024-09-03
DOI
https://doi.org/10.1371/journal.pone.0309728
Citations
2
Primary Topic
Biochemical Analysis and Sensing Techniques
Type
article
Field-Weighted Citation Impact
0.62

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article

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

Carlo Selmi, Manatsaphon Sukmak, Amod Sharma, Ubon Cha’on et al.
2 citations
PLoS ONE
Biochemical Analysis and Sensing Techniques
0.62
article

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

Carlo Selmi, Manatsaphon Sukmak, Amod Sharma, Ubon Cha’on, Atit Silsirivanit, Deanpen Japrung, Bruce D. Hammock, Somchai Pinlaor, Worachart Lert-itthiporn, Carolyn M. Slupsky, Kanokwan Nahok, Thin Su Kyaw, Sirirat Anutrakulchai
article en
2 citations

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.

PLoS ONEVol. 19(9)
National Science and Technology Development Agency (TH), Humanitas University (IT), Khon Kaen University (TH), National Nanotechnology Center (TH), Cancer Research Center (US), IRCCS Humanitas Research Hospital (IT), UC Davis Comprehensive Cancer Center, University of Arkansas for Medical Sciences (US), University of California, Davis (US)
Vidyasirimedhi Institute of Science and Technology, Khon Kaen University, University of California, Davis, National Institute of Environmental Health Sciences
Openalex Percentile: Top 33%
Biochemical Analysis and Sensing Techniques
0.62
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