Associations between obesity and ocular health in Spanish adults
Bibliographic record
Abstract
Abstract Introduction Obesity has been associated with poor vascular health, but not in a Spanish population. Therefore, the study aimed to investigate associations between obesity and cataract, wearing glasses or contact lenses, and trouble seeing in a large representative sample of the Spanish adult population. Methods Cross‐sectional data from the Spanish National Health Survey 2017 were analyzed. Body mass index (BMI) was calculated and obesity was defined as BMI ≥ 30 kg/m 2 . Ocular health included three dichotomous variables (presence vs absence): self‐reported cataract, wearing glasses or contact lenses, and trouble seeing. Multivariable logistic regressions were used to assess associations between obesity (independent variable) and ocular health outcomes (dependent variables). Covariates included in the analysis were sex, age, marital status, education, smoking, alcohol, and diabetes. Results A total of 23 089 participants were included (54.1% female; mean [SD] age = 53.4 [18.9] years). After adjusting for sex, age, marital status, education, smoking, alcohol, diabetes, and wearing glasses or contact lenses (for the trouble seeing analysis only), obesity was found to be a risk factor for cataract (odds ratio [OR] = 1.22; 95% confidence interval [CI], 1.09‐1.37) and trouble seeing (OR = 1.20; 95% CI, 1.09‐1.32) but not for wearing glasses or contact lenses (OR = 0.99; 95% CI, 0.91‐1.08). These findings were corroborated in participants ≥64 years. Conclusions In this large representative sample of Spanish adults, we found that obesity was a risk factor for cataract and trouble seeing. Lifestyle interventions aiming at the reduction of obesity in this population may indirectly improve ocular health. Such lifestyle interventions are important to implement considering the rising trend of obesity in Spain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".