Higher n3-fatty acid status is associated with lower risk of iron depletion among food insecure Canadian Inuit women
Bibliographic record
Abstract
BACKGROUND: High rates of iron deficiency and anemia are common among Inuit and Arctic women despite a traditional diet based on animal source foods. However, representative data on iron status and relevant determinants for this population are lacking. The objectives were to determine the prevalence of anemia and depletion of iron stores, then to identify correlates of iron status in non-pregnant Canadian Inuit women. METHODS: In a cross-sectional survey of 1550 women in the International Polar Year Inuit Health Survey, 2007-2008, hemoglobin, serum ferritin, soluble transferrin receptor (on a subset), C-reactive protein (CRP), RBC fatty acid composition, and H pylori serology were analyzed on fasting venous blood. Sociodemographic, food security status, anthropometric, dietary, and health data were collected. Correlates of iron status were assessed with multivariate linear and logistic models. RESULTS: Anemia was observed in 21.7% and iron deficient erythropoiesis in 3.3% of women. For women with CRP ≤ 10 mg/L (n = 1260) 29.4% had depleted iron stores. Inadequate iron intakes were observed in 16% of premenopausal and <1% of postmenopausal women. Among food insecure women, higher long-chain (n-3) polyunsaturated fatty acid (LC-PUFA) status, which reflects a more traditional food pattern, was associated with reduced risk of iron depletion. CONCLUSIONS: Iron depletion and anemia are a concern for Inuit women despite adequate total dietary iron intake primarily from heme sources. The high prevalence of H. pylori exposure, together with dietary iron adequacy, suggests an inflammation-driven iron deficiency and mild anemia. The anti-inflammatory properties of LC-PUFA may be important for iron status in this population.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".