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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".