Ethnicity, Dietary Factors, Patterns and Gene-Diet Interactions and their Association with Intraocular Pressure and Glaucoma: The Canadian Longitudinal Study on Aging
Bibliographic record
Abstract
Objectives: Our goal was to examine the associations of alcohol consumption, and dietary factors, patterns and supplements with intraocular pressure (IOP) and glaucoma and to assess whether any associations are modified by a glaucoma polygenic risk score (PRS). We also sought to identify whether race/ethnicity is associated with IOP and glaucoma and explore potential social, behavioral, genetic and health-related reasons. Methods: Cross-sectional analysis of data from the Canadian Longitudinal Study on Aging Comprehensive (CLSA) Cohort, consisting of 30,097 adults ages 45 to 85 years, was done. Alcohol consumption frequency and type were measured by interviewer-administered questionnaire. Total alcohol intake (grams/week) was estimated. Nutrition was assessed using a validated 36-item Short Diet Questionnaire. Participants were asked to report if they took calcium or iron supplements in the last month. We scored participants according to their adherence to the Mediterranean-Style Dietary Pattern Score and to an antioxidant-rich dietary pattern score derived from CLSA data using weighted partial least squares. Race/ethnicity was obtained using an interviewer-administered questionnaire. IOP was measured in mmHg using the Reichert Ocular Response Analyzer. Participants reported a diagnosis of glaucoma from a doctor. A glaucoma PRS developed by Craig et al. was constructed using CLSA genomic data. Logistic and linear regression models were used to adjust for demographic, behavioral, and health variables. Results: Daily drinkers had higher IOP compared to those who never drank (beta coefficient (β) =0.45, 95% confidence interval (CI): 0.05, 0.86). An increase in total weekly alcohol intake (per 5 drinks) was also associated with higher IOP (β=0.20, 95% CI: 0.15, 0.26). The association between total alcohol intake and IOP was stronger in those with a higher genetic risk of glaucoma (P for interaction term= 0.041). Consuming calcium supplements was associated with lower IOP (β=-0.16, 95% confidence interval (CI): -0.31, 0.00) and increased odds of glaucoma (OR (odds ratio)= 1.30, 95% CI: 1.08, 1.56). Supplementation with iron and adherence to a Mediterranean or antioxidant-rich diet were not associated with IOP and glaucoma. Black individuals had higher mean IOP levels (β= 1.46, 95% CI, 0.63, 2.30) while Chinese, Japanese and Korean (β = -1.00, 95% CI, -1.62, -0.38) and Southeast Asian and Filipino individuals (β = -1.56, 95% CI, - 2.68, -0.43) had lower mean IOP levels as compared to White individuals after adjustment for sociodemographic, behavioral, genetic, and health-related variables. Black people were more likely to report glaucoma as compared to White people after adjustment (OR = 2.43, 95% CI, 1.27, 4.64). Latin American people (OR = 2.64, 95% CI, 1.02, 6.82) were also more likely to report glaucoma but this association was no longer statistically significant after adjusting for the PRS (OR=2.39, 95% CI 0.93, 6.13). Conclusions: Alcohol frequency and total alcohol intake were associated with elevated IOP but not with glaucoma. The PRS modified the association between total alcohol intake and IOP. Supplemental calcium is associated with reduced IOP but increased odds of glaucoma. Racial and ethnic differences in IOP and glaucoma were also identified. Adjusting for sociodemographic, behavioral, genetic, and health-related variables did not fully explain these differences. Longitudinal research is needed to further explore the reasons for these differences, to understand their relevance to disease pathogenesis and progression and to further elucidate the interactions of dietary and genetic factors on their risk of disease.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | low |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".