Maternal hypothyroidism and subsequent metabolic outcomes in children: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: As the fetus relies on maternal thyroid hormones in early pregnancy, maternal hypothyroidism plays an important role in fetal development. However, the association between maternal hypothyroidism and metabolic disease in offspring is unclear. OBJECTIVE: To examine the association between maternal hypothyroidism in pregnancy and metabolic outcomes (obesity, hypertension, type 2 diabetes mellitus, and dyslipidemia) in children < 18 years. METHODS: We systematically searched 5 databases from inception to May 2023. Eligible studies included cohort, case-control, and randomized controlled trials involving children born to mothers with or without hypothyroidism in pregnancy. Data were pooled across studies using random-effects models for outcomes reported in at least three studies. Quality assessment was performed using the ROBINS-E tool for observational studies and the Cochrane Risk of Bias tool for trials. RESULTS: The search identified 3221 articles, of which 7 studies were included (1 trial, 6 observational). All studies were conducted outside of North America and ranged in size from 250 to > 1 million children. The follow-up time ranged from 6 to 20 years. Included studies support an increased risk of hypertension and glucose dysregulation in offspring exposed to maternal hypothyroidism (hypertension: OR 1.08, 95% CI 0.75, 1.57 and HR 1.81, 95% CI 1.21, 2.69; diabetes: RR 2.7, 95% CI 0.7, 10). In the pooled analysis, maternal hypothyroidism was not associated with obesity in offspring (OR 1.04, 95% CI 0.64, 1.70). CONCLUSION: This study found inconsistent evidence on the association between maternal hypothyroidism in pregnancy and metabolic outcomes in offspring, though associations with hypertension and glucose dysregulation are possible.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.025 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".