Determinants for Urinary and Plasma Isoflavones in Humans After Soy Intake
Bibliographic record
Abstract
Consumption of soy foods leads to a biphasic appearance pattern of isoflavones (IFLs) in blood and urine, with peaks appearing at 1-2 h and 4-8 h after intake, but its causes are not understood. IFLs were measured repeatedly from plasma and/or urine after intake of soy foods, IFL glucosides, or aglycons without or with a mildly or radically reduced gut flora as a result of oral antibiotic (AB) treatment, or this combined with mechanical bowel preparation (AB+MBP). The typical biphasic IFL pattern in blood and/or urine was observed when a soy protein drink without (control) or with AB treatment or when IFL glucosides or aglycons were consumed. Soy intake combined with AB+MBP or consumption of puerarin led to a shift of the second peak to much later times. The first peak was absent after puerarin intake. Total urinary IFL recovery was more than 50% lower in the first 24 h, but overall 61% higher after AB+MBP vs. the control. When the area under the curves for corresponding time intervals were compared, individual or total urinary IFL excretion rates were highly correlated with individual or total plasma IFL levels (r=0.85-0.91; P <0.001). At the same urinary excretion rate three times more genistein than daidzein remained in the circulation. We conclude that urinary IFL excretion rates reflect circulating IFL levels, with daidzein appearing less in blood and more in urine than genistein. The first and second IFL peaks are due to uptake in the small and large intestine, respectively. The latter is the major locus of uptake (90%) at usual dietary IFL doses (0.15-1.5 mmol/kg body weight). A reduced gut flora delayed IFL uptake but led overall to increased urinary recovery because of less bacterial degradation in the intestine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".