Dairy Intake and Iodine Status in Pregnant and Lactating Women: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background/Objectives: Despite salt iodization, iodine deficiency during pregnancy and lactation is re-emerging in many industrialized countries, necessitating an evaluation of the role of dairy in supporting iodine status during these critical periods. Methods: We conducted a systematic review and meta-analysis in accordance with PRISMA guidelines. Ten databases were searched through March 2025 using ProQuest Dialog™ (Version 75.0). Study quality was assessed using either the Quality Evaluation for Observational Studies tool or the Risk of Bias 2 tool, depending on study design. Random-effects models were applied, with certainty of evidence rated using the GRADE framework. Publication bias, sensitivity analyses, and subgroup analyses were also performed. Results: Fifty-one publications met the eligibility criteria, including 50 publications of observational studies and 1 publication of a randomized controlled trial (RCT), with most studies conducted on pregnant women. Higher dairy intake was associated with significantly greater urinary iodine concentration (UIC), (23 studies; standardized mean difference: 0.326; 95% confidence interval [CI]: 0.228, 0.424; p < 0.001; I2 = 57.31%; low-certainty), and significantly lower odds of iodine deficiency (11 studies; odds ratio: 0.58; 95% CI: 0.48, 0.70; p < 0.001; I2 = 0%; moderate-certainty). Associations were stronger in studies conducted during later pregnancy, in higher-quality studies, and when the dairy food was specifically milk. Dairy contributed to ~27% of iodine intake from foods. Breast milk iodine concentration (BMIC) findings were inconsistent, though in one RCT, iodine-fortified milk improved BMIC and UIC. Conclusions: Dairy intake supports adequate iodine status during pregnancy and lactation. RCTs would be valuable in further investigating the role of dairy in supporting iodine status, particularly in lactating women. (PROSPERO CRD420251054576)
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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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.038 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".