The role of ultra-processed food consumption and depression on type 2 diabetes incidence: a prospective community study in Quebec, Canada
Bibliographic record
Abstract
OBJECTIVES: The goal of the present study was to evaluate the association between depression and ultra-processed food (UPF) consumption as risk factors for developing type 2 diabetes (T2D). DESIGN: A prospective community study. SETTING: Baseline data (2009-2010) from CARTaGENE community health study from Quebec, Canada, were used. Food and drink consumption was assessed using the Canadian-Diet History Questionnaire II and grouped according to their degree of processing by the NOVA classification, and participants were categorised into tertiles of UPF (g/d). Depression was defined using either a validated cut-off score on the Patient Health Questionnaire-9 or antidepressant use. The outcome was the incidence of T2D, examined in 3880 participants by linking survey data with administrative health insurance data. Cox regression models estimated the associations between UPF, depression and incident T2D. PARTICIPANTS: 40-69-year-old individuals at baseline. RESULTS: In total, 263 (6·8 %) individuals developed T2D. Participants with high depressive symptoms and high UPF consumption showed the highest risk for T2D (adjusted hazard ratios (aHR) = 1·58, 95 % CI (0·98, 2·68)), compared to those with low depressive symptoms and low UPF consumption. The risk for T2D was similar when high depressive symptoms and antidepressant use were combined with high UPF (aHR 1·62, 95 % CI (1·02, 2·57)). CONCLUSIONS: This study shows that co-occurring depression and high UPF consumption were associated with a higher risk for T2D. Early management and monitoring of both risk factors might be essential for diabetes prevention.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".