The Interplay Between Vitamin D Deficiency, Iron Status, and Anemia Risk in Moroccan Women of Reproductive Age: A Cross-Sectional Analysis
Bibliographic record
Abstract
Background: Vitamin D and iron deficiencies are prevalent among Moroccan women of reproductive age (WRA). Research suggests that Vitamin D deficiency (VDD) may impair iron bioavailability, potentially leading to iron deficiency (ID) and anemia. Objectives: This study investigates associations between vitamin D status, iron levels, and anemia risk in WRA, aged 18–49, from Meknes, Morocco. Methods: A cross-sectional study was conducted among 463 participants, measuring serum 25(OH)D, blood count parameters, iron, ferritin, C-reactive protein, and creatinine. Lifestyle factors, including dietary intake, sun exposure, and physical activity, were assessed through validated questionnaires, and anthropometric data were collected. Linear and logistic regression models analyzed associations, while ROC analysis evaluated VDD’s predictive accuracy for ID and anemia. Results: VDD (25(OH)D < 20 ng/mL) was significantly associated with reduced hemoglobin, hematocrit, red blood cells, and ferritin (all p < 0.01), indicating vitamin D’s role in erythropoiesis and iron storage. Multivariate logistic regression showed that VDD increased the risk of anemia (OR: 7.17, 95% CI: 3.19–19.28, p < 0.001), ID (OR: 2.20, 95% CI: 1.32–3.77, p = 0.007), and IDA (OR: 4.10, 95% CI: 1.73–12.08, p = 0.004). Dietary iron intake was inadequate, showing minimal protective effects against anemia and ID (β(SE): −0.08(0.03), p = 0.030 and β(SE): −0.05(0.02), p = 0.037). Conclusions: VDD is a significant risk factor for impaired iron status and anemia in Moroccan WRA, highlighting the need for targeted nutritional interventions and further research.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".