Gender differences in the relationship between depressive symptoms and diabetes associated with cognitive-affective symptoms
Bibliographic record
Abstract
Background Despite the frequent co-occurrence of depression and diabetes, gender differences in their relationship remain unclear. Aims This exploratory study examined if gender modifies the association between depressive symptoms, prediabetes and diabetes with cognitive-affective and somatic depressive symptom clusters. Method Cross-sectional analyses were conducted on 29 619 participants from the 2007–2018 National Health and Nutrition Examination Survey. Depressive symptoms were measured by the nine-item Patient Health Questionnaire. Multiple logistic regression was used to analyse the relationship between depressive symptoms and diabetes. Multiple linear regression was used to analyse the relationship between depressive symptom clusters and diabetes. Results The odds of having depressive symptoms were greater in those with diabetes compared to those without. Similarly, total symptom cluster scores were higher in participants with diabetes. Statistically significant diabetes–gender interactions were found in the cognitive-affective symptom cluster model. Mean cognitive-affective symptom scores were higher for females with diabetes (coefficient = 0.23, CI: 0.10, 0.36, P = 0.001) than males with diabetes (coefficient = −0.05, CI: −0.16, 0.07, P = 0.434) when compared to the non-diabetic groups. Conclusions Diabetes was associated with higher cognitive-affective symptom scores in females than in males. Future studies should examine gender differences in causal pathways and how diabetic states interact with gender and influence symptom profiles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".