Metal Mixtures Mediate the Socioeconomic Gradient in Blood Pressure: A Four-Way Decomposition in a Prospective Rural Bangladeshi Cohort
Bibliographic record
Abstract
Background: The causal mechanisms by which socioeconomic status (SES) affects blood pressure (BP) in low- and middle-income countries (LMICs) remain poorly understood. We examined the effects of SES on BP, and the extent to which disparities in metal mixture exposures mediate these effects among rural Bangladeshi adults. Methods: This study included 5923 participants from the Bangladesh Vitamin E and Selenium Trial (BEST), a prospective cohort followed for six years with repeated BP assessments at baseline and three biennial follow-ups. Baseline exposures included SES indicators: education and agricultural land ownership (socioeconomic position, SEP), and metal mixtures: blood arsenic, lead, selenium, and urinary arsenic. We applied the parametric and mediational g-formula, along with generalized weighted quantile sum regression, to estimate total, direct, and indirect effects of SES on BP outcomes and conduct causal mediation analysis with four-way decomposition. Results: Higher education increased BP, whereas SEP decreased the elevation of BP. Both higher education and SEP lowered metal exposures. Metal mixtures mediated the effects of SES on BP. For example, higher education increased systolic blood pressure (SBP) by 3.53 mmHg (95% CI: 2.23, 4.82), while the pure natural indirect effect showed a protective pathway of -0.44 mmHg (95% CI: -0.62, -0.27) through reduced metals. For SEP, nearly 42% of its protective effect on SBP was mediated by lower metal exposures. Conclusions: Socioeconomic differentials in BP outcomes in rural Bangladesh are partly explained by inequalities in metal mixture exposures. Reducing metal exposures may mitigate SES-related disparities in BP measures in LMICS.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".