Abstract 4357481: Modification of the Association of Functional Limitation and cardiovascular diseases by Toxic Metals exposure among US Adults
Bibliographic record
Abstract
Background: Functional limitations are common among U.S. adults and are established predictors of cardiovascular disease (CVD). However, it is unclear whether exposure to toxic metals modifies the association between functional limitation and CVD risk. Methods: We analyzed data on 6,337 U.S. adults (≥18 years) from the National Health and Nutrition Examination Survey 2021–2023. Functional status was assessed in three domains: cognition, mobility, and seeing. Toxic metal exposures (blood lead, cadmium, mercury, manganese, selenium) were measured as "high" (≥90th percentile) vs "low." The outcome was CVD, defined as any history of heart failure, coronary heart disease, angina, myocardial infarction, or stroke. Survey-weighted descriptive statistics, cross-tabulations, and survey-weighted, multiple-imputed logistic regression models (adjusted for age, sex, race, education, income, BMI, diet, physical activity, sedentary time, alcohol, and smoking) were used to estimate main and interaction effects. Results: Functional limitations were prevalent: cognitive—47.8%, mobility—32.8%, and seeing—40.3%. CVD prevalence was 12.8%. Each metal was "high" in ~10% of participants in the study population. Across all domains and metals, CVD rates were lowest among those with no functional limitation and low metal exposure (cognition × lead: 8.6%) and highest among those with both limitation and high metal (cognition × high lead: 23.8%; mobility × high lead: 32.8%; seeing × high lead: 26.3%). In multivariable regression, any difficulty in cognition was 1.52 times (aOR for CVD: 1.52, 95% CI: 1.28–1.80), mobility 2.22 times (aOR 2.22, 1.09–4.09), and seeing 1.59 times (aOR 1.59, 1.07–2.44) were independently associated with higher CVD risk, regardless of toxic metal exposure. No significant interaction was observed between high metal exposure and functional limitation. Conclusions: Functional limitation in cognition, mobility, and seeing is a strong, independent predictor of CVD in U.S. adults. Although CVD prevalence was highest among those with high toxic metals exposure and functional limitation, toxic metals exposure did not significantly modify the strength of this association in multivariable analysis in the study population.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".