A methodology for examining the association between plasma volume and micronutrient biomarker mass and concentration in healthy eumenorrheic women
Bibliographic record
Abstract
Background Accurate estimation and interpretation of nutritional biomarker concentrations are important in nutritional research, clinical care, and public health surveillance. Plasma volume (PV) may affect the interpretation of plasma biomarkers but is rarely measured. We aimed to examine the association between plasma volume (PV) and micronutrient biomarker concentrations and mass as part of pilot work to develop methods. Methods Nine healthy women with regular menstrual cycles provided fasting blood samples to measure micronutrient biomarkers. Indocyanine green was injected, and five timed blood draws were taken from 2 to 5 min to measure PV. Visits were scheduled around menstrual cycle day 2. Retinol, 25-hydroxyvitamin D, riboflavin, alpha-tocopherol, zinc, copper, magnesium, manganese, cobalt, iron, and ferritin concentrations were measured in serum. Total circulating micronutrient biomarker mass was calculated from PV and concentration. Results The mean PV was 2067 ± 470 mL. PV correlated positively with concentration of iron ( r = 0.87, P = 0.005); other correlations were weaker with p > 0.05. PV and total mass of retinol ( r = 0.90), 25(OH)D ( r = 0.75), zinc ( r = 0.88), copper ( r = 0.83), magnesium ( r = 0.93), manganese ( r = 0.72), and iron ( r = 0.92) were strongly correlated (all p < 0.05). PV was positively correlated with circulating micronutrient mass for most biomarkers, implying that concentrations are maintained at different volumes of plasma. Larger studies are needed to further examine these relationships. Conclusion Though there appear to be some association between micronutrient biomarker mass and plasma volume, we are unable to draw a firm conclusion about any relationship from these results because of the small sample size. We consider these findings as a preliminary analysis to establish methods for future studies.
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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 | 0.001 | 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".