Association between heavy metal exposure and poor working memory and possible mediation effect of antioxidant defenses during aging
Bibliographic record
Abstract
BACKGROUND: Inverse associations have been observed between memory performance and blood concentrations of cadmium (Cd) and lead (Pb). Low antioxidant cell activity has also been linked to decline in memory due to aging. However, it has not yet been established whether the heavy metal-memory relationship is mediated by differences in antioxidant activity. METHODS: We examined Cd and Pb levels, as well as oxidative stress parameters, in blood samples from 125 older adults (age range 50-82years). The Counting Span Test (CST) was used to evaluate working memory capacity (WMC). The Monte Carlo Method for Assessing Mediation (MCMAM) was used to analyze the mediation role of antioxidant activity in the heavy metals-memory association. RESULTS: High blood Cd (BCd) concentration alone, and in combination with elevated blood Pb (BPb) concentration, was associated with poor WMC (p≤0.001) and low enzymatic antioxidant defenses (p≥0.006). The variance in WMC accounted for by BCd or by BCd combine with BPb was 20.6% and 18.6%, respectively. The MCMAM revealed that the influence of BCd and BPb concentrations on WMC was mediated by low antioxidant capacity (confidence interval - CI: 0.072 to -0.064 for BCd; CI: -0.062 to -0.045 for BPb). CONCLUSION: These findings showed Pb and Cd blood concentration in older adults, even at levels below the current recommended threshold, was negatively associated with WMC and that this relationship may be partly mediated by low antioxidant defenses. Knowledge on the environmental factors that negatively influence brain and cognition during aging can help inform public policy strategies to prevent and control the adverse effects of environmental contaminant exposure during aging.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".