Cumulative Lead Exposure and Age-Related Cataract in Middle-Aged and Elderly Women
Bibliographic record
Abstract
Background: Age-related cataract is the leading cause of blindness and visual impairment worldwide, accounting for more than 40% of global blindness. Lead has been proposed as a potentially important risk factor. Possible mechanisms include lead-induced changes in the lens through redox status, glutathione metabolism, lipid peroxidation, post-translational protein modification, and calcium homeostasis. However, only one epidemiologic study has examined the association between cumulative lead exposure and age-related cataract and it was limited to men. Aim: We examined the association of cumulative lead exposure with the risk of age-related cataract in middle-aged and elderly women. Methods: Participants were 502 Boston area participants in the Nurses' Health Study (mean age=63 yrs, range 55-74 yrs) who had lead exposure assessments done in two substudies (bone lead measured in 1993-1995 and 2001-2004), had cataract extraction from 5 years before the bone lead measurement to 2008, and whose cataracts were not congenital or secondary to chronic steroid use, previous intraocular surgery or ocular trauma. We identified cases of cataract extraction by a biennial questionnaire and confirmed all cases with review of medical records. Bone lead levels were measured by K-x-ray fluorescence. We used logistic regression to compute the odds ratio (OR) and 95% confidence interval (CI) for an interquartile range (IQR) increase in bone lead. Results: We identified 48 cases (9.6%) of cataract extraction. After controlling for substudy, age, smoking, and body mass index, neither bone lead measure was associated with cataract (OR=0.80 (95% CI, 0.51, 1.24) for tibia lead (IQR=11 µg/g); OR=0.98 (95% CI, 0.62, 1.53) for patella lead (IQR=13 µg/g). Conclusions: The present study does not support the hypothesis that cumulative lead exposure increases the risk of age-related cataract in women, but further research in larger populations is needed.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".