Genetic and Environmental Factors in Age-Related Nuclear Cataracts in Monozygotic and Dizygotic Twins
Bibliographic record
Abstract
BACKGROUND: Age-related cataracts are a major public health problem. The relative importance of genes and environment in the causation of nuclear cataracts, the most common form of age-related cataracts, is not known. METHODS: We studied 506 pairs of female twins (226 monozygotic and 280 dizygotic) who were 50 to 79 years old (mean, 62). The amount of nuclear cataract in the right and left eyes was determined objectively by analysis of Scheimpflug lens photographs (yielding three measures) and subjectively with use of the Oxford Clinical Cataract Classification and Grading System (yielding one measure). All eight measures (four in each eye) were subsequently combined in one summary measure of nuclear cataract for each woman. A univariate maximum-likelihood model was used to estimate the variance of the genetic and environmental contributions to each of the measures. RESULTS: The different measures of cataract formation were highly correlated (correlation coefficients, 0.71 to 0.94). The mean scores were similar for the right and left eyes and for monozygotic and dizygotic twins. Quantitative genetic modeling of each of the nuclear-cataract scores invariably resulted in a best-fitting model that involved additive genetic effects, unique environmental effects, and age. The common environmental and dominant genetic effects could be removed from the models without significant loss of fit. The overall heritability in the combined nuclear-cataract score (the proportion of the variance explained by genetic factors) was 48 percent (95 percent confidence interval, 42 to 54 percent); age accounted for 38 percent of the variance (95 percent confidence interval, 31 to 44 percent) and unique environmental effects for 14 percent (95 percent confidence interval, 12 to 18 percent). CONCLUSIONS: Genetic effects are important even in such a clearly age-related disease as nuclear cataract, explaining almost 50 percent of the variation in the severity of this disease.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".