Interaction of mercury exposure and DNA methylation with sustained attention in children in a novel analysis of epigenetic susceptibility
Bibliographic record
Abstract
Abstract The etiology of attention-deficit/hyperactivity disorder (ADHD) remains poorly understood, despite it being one of the most common neurodevelopmental disorders worldwide. Past research suggests methylmercury exposure and DNA methylation (DNAm) levels are each associated with ADHD in children, yet whether they interact to affect ADHD is unknown. Leveraging data from a longitudinal cohort of children in Mexico, this novel epigenetic–environment interaction study identified significant interactions between childhood mercury exposure (measured at 6–12 years of age) and adolescent blood leukocyte DNAm in their association with sustained attention [quantified via the Conners continuous performance test, 3rd edition (CPT3)] measured on average 5.6 ± 0.99 years later. Using adjusted linear regression, we assessed associations between hair and urine mercury concentrations and CPT3 scores reflecting inattention, impulsivity, vigilance, and sustained attention (N = 399). We then tested the interaction between mercury and DNAm at loci previously associated with the CPT3 outcomes (N = 374). Significant associations between mercury and CPT3 differed in magnitude and direction depending on the mercury biomarker and CPT3 variable. These associations often differed by gender. For example, urine mercury was positively associated with vigilance scores in males [β = 1.31(SE = 0.65), P = .045] but not in females [β = −0.20 (SE = 0.81), P = .80). In all children, three significant mercury–DNAm interactions were identified for either inattention or vigilance outcomes. Among females, 155 significant interaction terms were identified for the inattention models. In males, three significant interactions were identified for the impulsivity model. Overall, results suggest in some cases DNAm can influence the association between mercury exposure and ADHD-like symptoms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".