The association of retinal age gap with schizophrenia: a cross-sectional analysis
Bibliographic record
Abstract
Schizophrenia, a chronic neuropsychiatric disorder increasingly recognized as a multisystemic disease, is associated with accelerated brain ageing. Using deep learning, we investigated the retina, as a window into the central nervous system, as a surrogate of biological ageing in individuals with schizophrenia. This cross-sectional study was nested within AlzEye, a retrospective cohort of individuals aged ≥40 years attending Moorfields Eye Hospital (2008-2018). Retinal age was predicted from color retinal photographs using a convolutional neural network. The difference between predicted retinal age and chronological age, termed the retinal age gap, was estimated in all individuals. Associations between schizophrenia and retinal age gap were assessed using adjusted linear mixed-effects models. From a cohort of 98,629, 214 individuals had schizophrenia. They were slightly younger than unaffected (61.6 +/- 12.1 vs 64.5 +/- 13.5 years) and more likely to have hypertension (82% vs 47%) and diabetes mellitus (72% vs 27%). Individuals with schizophrenia had a significantly greater retinal age gap (0.76 years, 95% CI: 0.03, 1.49, p=0.04) when adjusting for age, sex, ethnicity, and socioeconomic status. When adjusting for hypertension and diabetes mellitus, there was no significant difference in retinal age gap between groups (0.20, 95% CI: -0.53, 0.93). In this ethnically and socioeconomically diverse urban population, individuals with schizophrenia had an increased retinal age gap. This was attributable to the increased prevalence of hypertension and diabetes mellitus implicating medical comorbidity, which is modifiable, as the driver of accelerated central nervous system ageing.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| 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".