A Cross-Sectional Study of the Associations between Biomarkers of Vitamin D, Iron Status, and Hemoglobin in South African Women of Reproductive Age: the Healthy Life Trajectories Initiative, South Africa
Bibliographic record
Abstract
Vitamin D deficiency and anemia impact the health of women of reproductive age. Evidence suggests an inverse relationship between serum vitamin D (25-hydroxyvitamin D [25(OH)D]) and anemia/iron deficiency, but less is known about these associations in women of reproductive age, in particular in a setting with a combined burden of micronutrient deficiency, food insecurity, and obesity. We aimed to assess the associations between 25(OH)D and biomarkers of iron and anemia in a cohort of women of reproductive age from Soweto, South Africa. The prevalence of vitamin D deficiency was also assessed. In this cross-sectional substudy of the Healthy Life Trajectories Initiative (HeLTI) South Africa pilot trial, 25(OH)D, iron markers (ferritin and soluble transferrin receptor [sTFR]), and altitude-adjusted hemoglobin (Hb) were measured in 493 women aged 18 to 25 years. Associations between iron deficiency/anemia and vitamin D status were evaluated using multivariable logistic regression, adjusting for confounders including fat mass index (FMI). Structural equation modeling (SEM) was performed to evaluate direct and indirect pathways between 25(OH)D, iron and anemia markers, and covariates. Of 493 participants, 136 (27.6%) had vitamin D insufficiency (25(OH)D ≥12–20 ng/mL), whereas 28 (5.6%) had vitamin D deficiency (<12 ng/mL). Anemia and iron deficiency were not significantly associated with vitamin D category (25(OH)D<20 ng/mL compared with ≥20 ng/mL) in multivariable logistic regression analyses. In SEM, log-transformed 25(OH)D was not significantly associated with Hb, ferritin, or sTFR, but it was significantly associated with season of data collection, hormonal contraceptive use, and FMI (total effects: B = 0.17, 95% CI: 0.104, 0.236, P < 0.001; B: 0.10, 95% CI: 0.041, 0.154, P < 0.001; B: -0.01, 95%CI: -0.016, -0.003, P = 0.003, respectively). No significant association between vitamin D (25(OH)D), anemia (Hb), and iron markers was found. The inverse relationship between FMI and vitamin D status emphasizes the overlap between adiposity and micronutrient deficiencies in young South African women, exacerbating their risk of disease development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".