Monosodium Glutamate Consumption Disrupts Vitamin B6 Status in Rat Kidney
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Monosodium glutamate (MSG) is widely used as a food additive and flavor enhancer, but concerns have emerged regarding its potential effects on kidney function and vitamin B6 metabolism. This study investigated the impact of varying levels of dietary MSG intake on vitamin B6 status in a rat model. Male Wistar rats were divided into four groups and administered MSG in their drinking water at concentrations of 0, 0.5, 1.5, and 3.0 g % for 12 weeks. Three isoforms of vitamin B6, pyridoxal (PL), pyridoxal 5′-phosphate (PLP), and pyridoxic acid (PA), were measured in plasma, liver, kidney, and urine using HPLC-MS. Kidney metabolites were analyzed by 1 H nuclear magnetic resonance spectroscopy to assess MSG-related metabolic changes. We found that plasma PL and PLP levels were elevated in all MSG-treated groups compared with controls, regardless of dose. This correlated with increased hepatic PLP levels and a decreased PLP catabolic ratio (PAr). In contrast to the liver, PLP levels in the kidney were significantly lower compared to controls. Metabolomics profiling of kidney tissue revealed elevated levels of various amino acids and metabolic intermediates in the MSG-treated rats. In conclusion, MSG consumption cannot induce vitamin B6 deficiency systematically, such as in the liver and plasma, as long as dietary vitamin B6 is adequate. However, depletion of PLP occurred locally in the kidney. Because MSG consumption may affect renal vitamin B6 status, there is a need for further studies to elucidate the underlying mechanisms and assess the potential health implications of habitual MSG intake.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".