Age‐Related Cataract Is Associated with Type 2 Diabetes and Statin Use
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: Diabetes has been shown to be a risk factor for age-related (AR) cataract. As statins (HMG-CoA reductase inhibitors) are now commonly prescribed for patients with type 2 diabetes, their impact on AR cataract prevalence should be considered. This study determines associations between AR cataract, type 2 diabetes, and reported statin use in a large optometric clinic population. METHODS: In all, 6397 patient files (ages <1-93 years) were reviewed. Overall prevalence of statin use was calculated for patients with type 2 diabetes (n = 452) and without diabetes (n = 5884). Multivariable logistic regression analysis for AR cataract was performed controlling for patient sex, smoking, high blood pressure, type 2 diabetes, and statin use. RESULTS: The prevalence of statin use (in patients aged >38 years) was 56% for those with type 2 diabetes and 16% for those without diabetes. Type 2 diabetes was significantly associated with nuclear sclerosis (OR = 1.62, 1.14-2.29) and cortical cataract (OR = 1.37, 1.02-1.83). Statin use was associated with nuclear sclerosis (OR = 1.48, 1.09-2.00) and posterior subcapsular cataract (OR = 1.48, 1.07-2.04). The 50% probability of cataract in statin users occurred at age 51.7 and 54.9 years in patients with type 2 diabetes and without diabetes, respectively. In non-statin users, it was significantly later at age 55.1 and 57.3 years for patients with type 2 diabetes and without diabetes, respectively (p < 0.001). CONCLUSIONS: In this population, statin use was substantially higher in patients with type 2 diabetes and was associated with AR cataracts. Further long-term study is warranted to recommend monitoring of crystalline lenses in patients with type 2 diabetes benefiting from statins.
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.
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 it