Association of central obesity with retinal neurodegeneration: Cross‐sectional and longitudinal evidence from two countries
Bibliographic record
Abstract
OBJECTIVE: This study aimed to evaluate the association of central obesity with retinal neurodegeneration. METHODS: Databases from the UK Biobank study and the Chinese Ocular Imaging Project (COIP) were included for cross-sectional and longitudinal analyses, respectively. Retinal ganglion cell-inner plexiform layer thickness (GCIPLT) measured by optical coherence tomography (OCT) was used as a retinal indicator of neurodegeneration. All subjects were divided into six obesity phenotypes according to BMI (normal, overweight, obesity) and waist to hip ratio (WHR; normal, high). Multivariable linear regression models were fitted to investigate the association of obesity phenotypes with GCIPLT. RESULTS: A total of 22,827 and 2082 individuals from UK Biobank (mean age: 55.06 [SD 8.27] years, women: 53.2%) and COIP (mean age: 63.02 [SD 8.35 years], women: 61.9%) were included, respectively. Cross-sectional analysis showed GCIPLT was significantly thinner in normal BMI/high WHR individuals compared with normal BMI/normal WHR individuals (β = -0.33 μm, 95% CI = -0.61, -0.04, p = 0.045). But thinner GCIPLT was not observed in individuals with obesity/normal WHR. After 2-year follow-up in COIP, normal BMI/high WHR was associated with accelerated GCIPLT thinning (β = -0.28 μm/y, 95% CI = -0.45, -0.10, p = 0.02), whereas obesity/normal WHR was not. CONCLUSIONS: Even with normal weight, central obesity was associated with accelerated GCIPLT thinning cross-sectionally and longitudinally.
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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 | 0.000 | 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".