Differential Isotope Labeling of 38 Dietary Polyphenols and Their Quantification in Urine by Liquid Chromatography Electrospray Ionization Tandem Mass Spectrometry
Bibliographic record
Abstract
A large number of polyphenols are consumed with the diet and may contribute to the prevention of chronic diseases such as cardiovascular diseases, diabetes, cancers, and neurodegenerative diseases. More comprehensive methods are needed to measure exposure to this complex family of bioactive plant compounds in epidemiological studies. We report here a novel method enabling the simultaneous measurement in urine of 38 polyphenols representative of the main classes and subclasses found in the diet. This method is based on differential (12)C-/(13)C-isotope labeling of polyphenols through derivatization with isotopic dansyl chloride reagents and on the analysis of the labeled polyphenols by tandem mass spectrometry. This derivatization approach overcomes the need for costly labeled standards. Different conditions for enzyme hydrolysis of polyphenol glucuronides and sulfate esters, extraction, and dansylation of unconjugated aglycones were tested and optimized. Limits of quantification varied from 0.01 to 1.1 μM depending on polyphenols. Intrabatch coefficients of variation varied between 3.9% and 9.6%. Interbatch variations were lower than 15% for 31 compounds and lower than 29% for 6 additional polyphenols out of the 38 tested. Thirty seven polyphenols were validated and then analyzed in 475, 24 h urine samples from the European Prospective Investigation on Cancer and Nutrition (EPIC) study. Thirty four polyphenols could be detected and successfully estimated and showed large interindividual variations of concentrations (2-3 orders of magnitude depending on the compound), with median concentrations spanning from 0.01 to over 1000 μM for all 34 compounds.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".