Association Between Socioeconomic Status and Major Congenital Anomalies: A Two‐Sample Mendelian Randomization Study
Bibliographic record
Abstract
BACKGROUND: Traditional observational studies suggest that socioeconomic status (SES) may influence the risk of congenital anomalies; however, an association remains unclear due to residual confounding. This study used Mendelian randomization (MR) to explore the potential causal relationship between SES indicators and specific congenital anomalies. METHODS: We performed two-sample MR analyses to explore whether three indicators of SES-educational attainment, household income, and the Townsend Deprivation Index-have a relationship with the risk of major congenital anomalies. Genetic variants associated with these SES indicators were obtained from the MRC Integrative Epidemiology Unit (IEU) OpenGWAS database, based on UK Biobank data. Genetic associations with nine categories of congenital anomalies were sourced from the FinnGen study. The primary MR method was inverse-variance weighted (IVW), with sensitivity analyses and Bonferroni correction applied to account for multiple testing. RESULTS: Prior to correction for multiple testing, higher educational attainment was associated with reduced risk of congenital heart defects (CHDs) (OR = 0.60, 95% CI: 0.41-0.88; p = 0.001), congenital respiratory system malformations (OR = 0.20, 95% CI: 0.06-0.62; p = 0.005), and musculoskeletal malformations (OR = 0.47, 95% CI: 0.29-0.76; p = 0.002). A lower Townsend Deprivation Index was unexpectedly associated with a higher risk of congenital digestive tract anomalies (OR = 4.53, 95% CI: 1.10-18.63; p = 0.036). However, after Bonferroni correction, only the association between educational attainment and CHDs remained significant (adjusted p = 0.02). CONCLUSIONS: We found limited evidence on the association between SES and congenital anomalies. Only higher educational attainment was significantly associated with reduced risk of CHDs after multiple testing correction. Further research with refined methods is needed to clarify these associations.
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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 | 0.037 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".