Systemic inflammation and the risk of depression in people with type 2 diabetes
Bibliographic record
Abstract
Abstract Background Depression is a common co-morbidity in diabetes. The mechanisms underlying the association between depression and diabetes are poorly understood. Although risk factors, such as poor lifestyle behaviours, obesity, and stress have been identified, emerging evidence suggests that systemic inflammation may play an important role in the pathogenesis and recurrence of depression in people with diabetes. The aim of the present study was to evaluate if the inflammatory marker C-reactive protein (CRP) is associated with an increased risk of major depression episodes in people with type 2 diabetes. Methods A prospective, community-based study was conducted in Quebec, Canada. Individuals were recruited from the CARTaGENE (CaG) cohort, a population-based survey of Quebec residents aged 40 to 69 years. Our sample included 719 individuals with type 2 diabetes and 1423 individuals without diabetes. Individuals were assessed at baseline and 5 years after baseline. Major depression disorders were assessed using a clinical interview (CIDI). Inflammatory markers were assessed from blood samples. Elevated CRP levels were defined as ≥ 3 mg/L. Results Participants with both diabetes and elevated CRP levels had the highest risk of major depressive episodes (adjusted OR = 1.90, 95% CI 1.45, 2.50), compared to those without diabetes and without elevated CRP levels. The risk of major depressive episodes in individuals with diabetes without elevated CRP episodes was lower (adjusted OR = 1.21, 95% CI 0.85, 1.73) and similar to the risk of those without diabetes and elevated CRP levels (adjusted OR = 1.15, 95% CI 0.94, 1.39). Discussion The study highlights the interaction between diabetes, inflammatory makers, and depression in a community sample. Early identification, monitoring, and management of elevated inflammation levels might be an important depression prevention strategy in people with type 2 diabetes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".