Association of Co-Exposure to Cadmium and Lead with CKD and Cardiovascular Disease (CVD) Comorbidity in US Adults: NHANES 2003-2018
Bibliographic record
Abstract
Background: Environmental exposure to cadmium (Cd) and lead (Pb) have been implicated in both CKD and CVD. The two metals can be detected together at or above the respective population medians in 45% of the US population 6 years and older. Although both metals exhibit similar renal pathology and toxic mechanisms, the relationship between the metal mixture and the CKD and CVD comorbidity is not clear. The objective of this cross-sectional study was to examine the associations of Cd and Pb exposure with CKD and CVD outcomes and to see if the two metals are independently associated with the outcomes. Methods: We included data from the National Health and Examination Survey (NHANES) 2003-2018 participants 20 years and older with urine Cd, blood Pb, serum creatinine, and urine albumin/creatinine measurements (n=12,851). Pregnant women and participants with CKD stage 5 were excluded. We used generalized logistic regression to calculate adjusted odds ratios for CVD, CKD, or both CKD and CVD relative to none of these co-morbidities. We tested for interactions, both on multiplicative and additive scales, between the two metals with and without other covariates (gender, race/ethnicity, attained education, smoking, alcohol use, body mass index (BMI), and quadratic age). Results: Both multiplicative and additive interaction effects were not statistically significant at the p-value of 0.05 level. It is possible that our sample size was too small to detect the effect. The final model included urine Cd, blood Pb, gender, race/ethnicity, education, smoking, alcohol use, BMI, and quadratic age. The Cd effect was statistically significantly associated with CKD+CVD (OR=1.19, 95%CI=1.03-1.38, p=0.02) after adjusting for the effect of Pb and other covariates, whereas Pb was significantly associated with CKD (OR=1.22, 95%CI=1.07-1.39, p=0.003) and CVD+CKD (OR=1.39, 95%CI=1.16-1.65, p=0.0004) after adjusting for the effect of Cd and other covariates. Conclusions: These results suggest that the effects of Cd and Pb are independent each other, and more pronounced in participants who had both CKD and CVD, supporting their role as risk factors even at exposures below the levels typically associated with occupational hazards. However, reverse causation because of underlying kidney damage cannot be ruled out. Funding: Other U.S. Government Support
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".