Ethnicity, Dietary Factors, Patterns and Gene-Diet Interactions and their Association with Intraocular Pressure and Glaucoma: The Canadian Longitudinal Study on Aging
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Étiquettes directes de modèles (non validées)
Étiquettes de catégorie et de devis d'étude par modèle, issues des rondes d'étiquetage. C'est une sortie machine, non validée, et le désaccord entre modèles est livré comme donnée. Aucun devis ici n'est encore validé contre MEDLINE.
| Bras | Catégories | Devis d'étude | Confiance |
|---|---|---|---|
| gemma | aucune catégorie Domaine: non disponible · Genre: Empirique Porte sur le système de recherche canadien: non · Porte sur un sujet canadien: oui | Observationnel | low |
| gpt | aucune catégorie Domaine: non disponible · Genre: Empirique Porte sur le système de recherche canadien: non · Porte sur un sujet canadien: oui | Observationnel | low |
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéeÉtiqueté directement par 2 modèles lisant le dossier complet.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».