Age‐Related Cataract Is Associated with Type 2 Diabetes and Statin Use
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.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".