Toenail and serum levels as biomarkers of iron status in pre- and postmenopausal women: correlations and stability over eight-year follow-up
Bibliographic record
Abstract
Iron status is often assessed in epidemiologic studies, and toenails offer a convenient alternative to serum because of ease of collection, transport, and storage, and the potential to reflect a longer exposure window. Very few studies have examined the correlation between serum and toenail levels for trace metals. Our aim was to compare iron measures using serum and toenails on both a cross-sectional and longitudinal basis. Using a subset of the US-wide prospective Sister Study cohort, we compared toenail iron measures to serum concentrations for iron, ferritin and percent transferrin saturation. Among 146 women who donated both blood and toenails at baseline, a subsample (59%, n = 86) provided specimens about 8 years later. Cross-sectional analyses included nonparametric Spearman's rank correlations between toenail and serum biomarker levels. We assessed within-woman maintenance of rank across time for the toenail and serum measures and fit mixed effects models to measure change across time in relation to change in menopause status. Spearman correlations at baseline (follow-up) were 0.08 (0.09) for serum iron, 0.08 (0.07) for transferrin saturation, and - 0.09 (- 0.17) for ferritin. The within-woman Spearman correlation for toenail iron between the two time points was higher (0.47, 95% CI 0.30, 0.64) than for serum iron (0.30, 95% CI 0.09, 0.51) and transferrin saturation (0.34, 95% CI 0.15, 0.54), but lower than that for ferritin (0.58, 95% CI 0.43, 0.73). Serum ferritin increased over time while nail iron decreased over time for women who experienced menopause during the 8-years interval. Based on cross-sectional and repeated assessments, our evidence does not support an association between serum biomarkers and toenail iron levels. Toenail iron concentrations did appear to be moderately stable over time but cannot be taken as a proxy for serum iron biomarkers and they may reflect physiologically distinct fates for iron.
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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 | 0.002 | 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".