The role of metal element exposure and oxidative stress in mild cognitive impairment: Evidence from a Shanghai elderly cohort
Bibliographic record
Abstract
This study, based on a cross-sectional study of elderly individuals in Shanghai, explored the associations between mixed metal element exposure and mild cognitive impairment (MCI), as well as the mediating role of oxidative stress. We assessed MCI using the Montreal Cognitive Assessment (MoCA) scale and measured plasma levels of amyloid β 40 (Aβ 40 ), Aβ 42 , Aβ 42/40 , total tau (T-tau) and phosphorylated tau (P-tau) proteins. Spearman’s correlation analysis, linear regression, weighted quantile sum (WQS) regression, and quantile g-computation (qgcomp) modeling were used to assess the associations between metal elements and MCI, and to analyze the mediating role of oxidative stress using mediation models. The results showed significant differences in blood cadmium (Cd), lead (Pb), copper (Cu), zinc (Zn), and selenium (Se) levels between the MCI group and the normal cognition group. MoCA scores were negatively correlated with Cd, Pb, and Cu levels, and positively correlated with Zn and Se levels. Aβ 42 and Aβ 42/40 were negatively correlated with Cu and positively correlated with Zn and Se. P-tau was positively correlated with mercury (Hg). The WQS index was significantly negatively correlated with the MoCA score, with Cu contributing the most (84.0 %). Meanwhile, Aβ 42 and Aβ 42/40 were significantly positively correlated with the WQS index, with Se (34.0 %) and Zn (58.3 %) being the main contributors. Qgcomp modeling also found that mixed exposure to metal elements was significantly negatively correlated with the MoCA score, with Cu given the highest negative weight (-0.601). SOD partially mediated the correlation of Cu, Zn, Se, and WQS index with Aβ 42 (mediation ratio of 14.3–27.8 %). This study provides population-based evidence for identifying factors and potential mechanisms affecting MCI.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".