Ultra-Processed Foods Consumption, Depression, and the Risk of Diabetes and its Complications in a Population-Based Sample
Bibliographic record
Abstract
Worldwide, type 2 diabetes mellitus (T2D) is an increasingly prevalent chronic disease and a public health concern. Unhealthy diets, such as those with high ultra-processed foods (UPF) consumption, have been identified as one of the behavioral risk factors related to T2D and its consequences. Depression is a serious mood disorder commonly comorbid with diabetes and a potentially modifiable risk factor for T2D. Previous studies have focused on depression and unhealthy diets consumption as independent risk factors for T2D and its complications, but unhealthy diets and depression might also be comorbid. Therefore, in this thesis, I will examine the interaction between depression and UPF and its association with T2D and its complications.In this study, the research question was “What is the relationship between T2D, UPF consumption, and depressive symptoms?” Data from the CARTaGENE a population-based prospective cohort study in the Province of Quebec (Canada), were used to address this question by completing three independent but related studies shaped by research objective and presented in standalone chapters (3, 4, and 5). CARTaGENE and an administrative health database (Quebec’s health care plan), were linked to generate the study sample. The first objective was to explore the potential additive interaction between UPF consumption and depression on the incidence of T2D. Results of Cox regression modelling showed that respondents with high depressive symptoms and high UPF consumption at the baseline showed the highest risk for T2D (Hazard Ratio (HR) = 1.75 (95 % CI 1.04 - 2·95)) in a model adjusted for age and sex compared to respondents with low depressive symptoms and low UPF consumption. Further, the risk for T2D when high depressive symptoms and antidepressant use were combined with high UPF was HR =1.62 (95 % CI 1.02 -2·57) in a fully adjusted model.The second objective was to investigate a potential additive interaction between UPF consumption and depression on the incidence of diabetes-related complications. Data from the same cohort as in the first study were used. However, for this objective, we examined the T2D complications among respondents with T2D at baseline by linking CARTaGENE survey data with administrative health care plan data. Results by a Cox regression model indicate that individuals with depressive symptoms and higher UPF consumption at baseline had a higher risk (HR = 2.43 (95 % CI 1.18 - 4.99)) of developing T2D related micro-and macro complications in a model adjusted for sex and age compared to those with neither condition. Further, higher risks for diabetes complications were observed when high depressive symptoms and antidepressant use were combined with high UPF consumption (HR = 2.59 (95 % CI 1.32 - 5.06)) in a fully adjusted model. These results suggest an interaction between depression and UPF consumption in relation to an increased risk of diabetes-related complications.Finally, although depression has been linked with T2D incidence, the underlying mechanism remains unclear. The third objective was thus to explore if the relationship between depression and T2D incidence might be mediated by UPF consumption. Using logistic regression and mediation analysis, we found that a retrospectively reported depression diagnosis was associated with a higher risk of T2D (Odd ratios = 1.58 (95 % CI 1.05 - 2.36). Further, UPF consumption and Body Mass Index (BMI) at baseline might be an indirect mechanism linking depression and T2D risk.Overall, the results showed that individuals with co-occurring depression and high UPF consumption might represent a subgroup particularly vulnerable to T2D incidence and its complications. They would benefit from improved identification, greater monitoring, and preventive, integrated care that draws on strategies embracing the newly generated evidence on the interaction between T2D, depression, and UPF consumption
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".