Factors Associated With Retinal Vessel Traits in the Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Purpose: To determine the factors cross-sectionally and longitudinally associated with retinal vessel diameter, total area, and tortuosity in the Canadian Longitudinal Study on Aging (CLSA). Methods: Of the 30,097 adults between ages 45 and 85 years old in the CLSA Comprehensive Cohort, 26,076 had at least one retinal image gradable by QUARTZ, a deep-learning algorithm that automatically assessed image quality, distinguished between arterioles and venules, and estimated retinal vessel traits over the entire retina. Questions were asked about demographic, lifestyle, and medical factors. Blood pressure, cholesterol, and C-reactive protein were measured. Participants returned for follow-up 3 years later. Multiple linear regression was used to provide adjusted estimates. Results: Current smoking was strongly associated with wider arteriolar and venular diameters and their widening over 3 years (P < 0.05). Current smoking was also associated with a larger arteriolar and venular area and a 3-year increase in venular area (P < 0.05). Obesity was positively associated with venular diameter, total venular area, 3-year change in total venular area, and venular tortuosity (P < 0.05). Diastolic blood pressure was negatively associated with both arteriolar and venular diameter, area, and tortuosity, both cross-sectionally and longitudinally (P < 0.05). Diabetes was associated with wider arteriolar diameters cross-sectionally, and type 1 diabetes was associated with 3-year widening of arteriolar diameters (P < 0.05). Conclusions: This work provides comprehensive information on the factors associated with retinal vessel traits and their change. Factors such as smoking, obesity, blood pressure, and diabetes were longitudinally related to retinal vessel traits, which play a role in the development of eye disease.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".