Diabetes, psychiatric conditions and alcohol consumption: Cross-sectional and longitudinal associations in community samples
Notice bibliographique
Résumé
Background: Alcohol consumption is common in individuals with diabetes. Heavy alcohol consumption in individuals with diabetes is associated with an increased risk of developing diabetes-related complications, including neuropathy, retinopathy, nephropathy, and coronary artery disease (CAD). Although heavy alcohol consumption is associated with complications, little is known about patterns of alcohol use among individuals with diabetes. Furthermore, heavy drinking is more common among individuals with certain psychiatric conditions, including major depressive disorder (MDD), bipolar disorder (BD), or generalized anxiety disorder (GAD), compared to the general population, and these disorders are often comorbid with diabetes. Therefore, individuals with diabetes may be at an increased risk of heavy drinking if they have comorbid psychiatric conditions. Additionally, depression is related to an increased risk of diabetes-related complications. Thus, individuals with diabetes and depression who drink heavily may be at a particularly high risk of developing complications. Objectives: The first manuscript aims to investigate how alcohol consumption patterns (frequency; quantity) may differ in those with or without MDD, BD, and GAD, in adults with diabetes compared to those without diabetes. The second manuscript aims to prospectively examine the association of frequency of alcohol use and depressive symptoms on the development of diabetes-related complications in adults with type 2 diabetes (T2D). Methods: Data for the first manuscript were from the cross-sectional 2012 Canadian Community Health Survey-Mental Health, including 14,302 adult participants aged 40-79 (1698 with diabetes). Data were analyzed using hierarchical linear regression models. The second manuscript used data from the five waves of the Evaluation of Diabetes Treatment study, an annual telephone survey of 1413 insulin-naive adults aged 40-76 with T2D at baseline. Longitudinal logistic regression analyses with generalized estimating equations were used to investigate the development of each complication over time. Both analyses were adjusted for various demographic, lifestyle, and health-related covariates. Results: MDD and BD, but not GAD, significantly moderated the association between diabetes status and alcohol quantity, such that the presence of diabetes was strongly and negatively associated with alcohol use when individuals had MDD or BD, and weakly and negatively associated when individuals did not have MDD or BD. This interaction held after adjusting for covariates. There was no interaction with any of the psychiatric conditions and alcohol frequency. The second analysis showed that, even after adjusting for covariates, interactions between alcohol frequency and depressive symptoms were positively significantly related to increased odds of incident neuropathy and CAD, such that those with high depressive symptoms who drank the most frequently had the highest risk for neuropathy and CAD. However, this interaction was not significantly related to odds of developing retinopathy or nephropathy. Conclusions: Among individuals with diabetes, those with comorbid MDD or BD may drink less than those without MDD or BD. This is different from research in the general population, in which individuals with MDD or BD tend to drink more. In addition, individuals with a combination of high depressive symptoms and a high frequency of drinking have a high risk of neuropathy and CAD. Future research is needed to further examine the possible differences among other diabetes-related complications, as well as the possible mechanisms associating diabetes, alcohol use, psychiatric conditions, and complications. This knowledge could help inform future prevention and intervention efforts on heavy alcohol use in individuals with diabetes and the development of diabetes-related complications.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».