Depressive symptoms and glycated hemoglobin A1c: a reciprocal relationship in a prospective cohort study
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to evaluate the dynamic association between depressive symptoms and glycated hemoglobin A1c (HbA1c) levels using data from the English Longitudinal Study of Ageing (ELSA). METHOD: The sample was comprised of 2886 participants aged ⩾50 years who participated in three clinical assessments over an 8-year period (21% with prediabetes and 7% with diabetes at baseline). Structural equation models were used to address reciprocal associations between depressive symptoms and HbA1c levels and to evaluate the mediating effects of lifestyle-related behaviors and cardiometabolic factors. RESULTS: We found a reciprocal association between depressive symptoms and HbA1c levels: depressive symptoms at one assessment point predicted HbA1c levels at the next assessment point (standardized β = 0.052) which in turn predicted depressive symptoms at the following assessment point (standardized β = 0.051). Mediation analysis suggested that both lifestyle-related behaviors and cardiometabolic factors might mediate the association between depressive symptoms and HbA1c levels: depressive symptoms at baseline predicted lifestyle-related behaviors and cardiometabolic factors at the next assessment, which in turn predicted HbA1c levels 4 years later. A similar association was observed for the other direction: HbA1c levels at baseline predicted lifestyle-related behaviors and cardiometabolic factors at the next assessment, which in turn predicted depressive symptoms 4 years later. CONCLUSIONS: Our results suggest a dynamic relationship between depressive symptoms and HbA1c which might be mediated by both lifestyle and cardiometabolic factors. This has important implications for investigating the pathways which could link depressive symptoms and increased risk of 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".