Profiling Endogenous Serum Estrogen and Estrogen-Glucuronides by Liquid Chromatography−Tandem Mass Spectrometry
Bibliographic record
Abstract
Estrogens, namely, 17beta-estradiol (E(2)), are conjugated to glucuronides (G), and this metabolic conversion is part of their tissular-concentration control-mechanism. This inactivation process has been observed, in addition to the liver, in several estrogen-dependent tissues and the resulting polar metabolites are detected in circulation. We developed and validated a highly sensitive and specific mass spectrometry-based method to directly measure estrogen-G serum levels. The method uses deuterated standards but does not involve enzymatic hydrolysis, a major improvement over previous techniques. Estrone (E(1)), E(1)-sulfate, E(2), the 3-G of E(1), E(2), 2-methoxy-E(1) (2-MeOE(1)) and 2-methoxy-E(2) (2-MeOE(2)), and the 17-G of E(2) were measured in serum of 19 premenopausal and 10 postmenopausal healthy women. Two extractions, solid-phase and liquid-liquid, were performed to isolate the estrogens. Estrogens were then quantified by mass spectrometry in the negative MRM ion mode using an API3200 spectrometer with a turbo ionspray source. The method selectively measured estrogen glucuronides with sensitivity > or = 5 pg/mL, accuracy 90-111%, and reproducibility (CV = 1.4-13.3%). The method is applicable between 5 and 1000 pg/mL. For the ovarian follicular phase, the major metabolite found was E(1)-3G, with E(2)-3G and 2-MeOE(1)-3G found in lesser amounts (54, 10.4, and 7.8 pg/mL, respectively) These concentrations are 2.6- to 3-fold greater than found for luteal-phase estrogens. The concentrations of E(2)-17G and 2-MeOE(2)-3G were usually less than the limit of quantification. In serum of postmenopausal women, E(1)-3G was the most abundant estrogen found (30.9 pg/mL). Our method profiles estrogens and estrogen-glucuronides and may represent a new tool to identify biomarkers in hormone-dependent diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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 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".