δ-Catenin Is Genetically and Biologically Associated with Cortical Cataract and Future Alzheimer-Related Structural and Functional Brain Changes
Bibliographic record
Abstract
Multiple lines of evidence suggest that specific subtypes of age-related cataract (ARC) and Alzheimer disease (AD) are related etiologically. To identify shared genetic factors for ARC and AD, we estimated co-heritability of quantitative measures of cataract subtypes with AD-related brain MRI traits among 1,249 members of the Framingham Eye Study who had a brain MRI scan approximately ten years after the eye exam. Cortical cataract (CC) was found to be co-heritable with future development of AD and with several MRI traits, especially temporal horn volume (THV, ρ = 0.24, P<10(-4)). A genome-wide association study using 187,657 single nucleotide polymorphisms (SNPs) for the bivariate outcome of CC and THV identified genome-wide significant association with CTNND2 SNPs rs17183619, rs13155993 and rs13170756 (P<2.6 × 10(-7)). These SNPs were also significantly associated with bivariate outcomes of CC and scores on several highly heritable neuropsychological tests (5.7 × 10(-9) ≤ P<3.7 × 10(-6)). Statistical interaction was demonstrated between rs17183619 and APP SNP rs2096488 on CC (P = 0.0015) and CC-THV (P = 0.038). A rare CTNND2 missense mutation (G810R) 249 base pairs from rs17183619 altered δ-catenin localization and increased secreted amyloid-β(1-42) in neuronal cell culture. Immunohistopathological analysis of lens tissue obtained from two autopsy-confirmed AD subjects and two non-AD controls revealed elevated expression of δ-catenin in epithelial and cortical regions of lenses from AD subjects compared to controls. Our findings suggest that genetic variation in delta catenin may underlie both cortical lens opacities in mid-life and subsequent MRI and cognitive changes that presage the development of AD.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".