Association of socioeconomic status and nine modifiable lifestyle factors on esophageal cancer risk in European and East Asian populations — Evidence from a comprehensive Mendelian randomization study
Bibliographic record
Abstract
Introduction: Background and aims: the intricate relationships between socioeconomic factors, modifiable lifestyle choices, and esophageal cancer risk remain uncertain. We aim to investigate the associations of socioeconomic status, modifiable lifestyle factors, and esophageal cancer risk. Methods: we employed multiple Mendelian randomization (MR) analyses, including three different MR approaches. GWAS databases from European and East Asian populations, encompassing variables such as household income, educational attainment, and the Townsend deprivation index (TDI), were analyzed. The risk of esophageal cancer was assessed using data from three distinct cohorts of European and East Asian descent (Database 1: n = 476,306; Database 2: n = 372,756; Database 3: n = 160,589). Nine modifiable lifestyle factors were incorporated in the multivariable and mediation MR analyses. Meta-analysis was employed to synthesize results across the three datasets. Results: higher household income was connected with a reduced esophageal cancer risk (odds ratio (OR) = 0.698, 95 % confidence interval (95 % CI): 0.556-0.876, p = 0.002). Body mass index (BMI) partially mediated the relationship between household income and the risk of esophageal cancer (OR = 0.914, 95 % CI: 0.841-0.992, p = 0.031, mediation ratio: 27.23 %). However, no significant evidence was found to support a direct association between educational attainment, TDI, and esophageal cancer risk. Conclusions: these findings suggest that higher household income is inversely associated with esophageal cancer risk, with BMI acting as a partial mediator of this relationship. Accordingly, targeted early screening and preventive measures for esophageal cancer should be prioritized among low-income populations, particularly those with obesity.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".