Diabetes, psychiatric conditions and alcohol consumption: Cross-sectional and longitudinal associations in community samples
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".