Methylglyoxal Content in Drinking Coffee as a Cytotoxic Factor
Bibliographic record
Abstract
A causal relationship between metabolic syndrome and methylglyoxal (MG) has been suggested. Consumption of coffee and other types of beverages has been known to produce MG, thus resulting in both nutritional and health concerns. The purpose of this study was to determine the ideal combination of coffee, cream, and sugar in order to minimize MG consumption. Four types of black coffee were tested: espresso, bold, mild, and a decaffeinated mild roast. Sugar and/or cream were added to the coffee samples to test whether MG levels were altered. Using high-performance liquid chromatography, the concentration of MG in various coffee samples was determined. The espresso coffee sample was found to contain the highest level of MG at 230.9 microM. The bold coffee roast had the 2nd highest amount of MG, followed by the mild and decaffeinated varieties. Adding cream to bold coffee significantly reduced its MG level in comparison to the coffee sample without cream. On the other hand, the addition of sugar to the bold coffee did not further increase the MG level in the samples. The cellular damaging effect of MG was shown as there were decreased numbers of cultured HEK-293 cells after 24 h of MG treatment (100 and 300 microM), which is consistent with an increased cell apoptosis induced by MG treatment (100 and 300 microM). Due to the overconsumption of exogenous MG, drinking an excess of any type of coffee poses health risks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".