Investigating the longitudinal association between diabetes and anxiety: a systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: Previous research has indicated an association between diabetes and anxiety. However, no synthesis has determined the direction of this association. The aim of this study was to determine the longitudinal relationship between anxiety and diabetes. METHODS: We searched seven databases for studies examining the longitudinal relationship between anxiety and diabetes. Two independent reviewers screened studies from a population aged 16 or older that examined either anxiety as a risk factor for incident diabetes or diabetes as a risk factor for incident anxiety. Studies that met eligibility criteria were put forward for data extraction and meta-analysis. RESULTS: In total 14 studies (n = 1 760 800) that examined anxiety as a risk factor for incident diabetes and two (n = 88 109) that examined diabetes as a risk factor for incident anxiety were eligible for inclusion in the review. Only studies examining anxiety as a risk factor for incident diabetes were put forward for the meta-analysis. The least adjusted (unadjusted or adjusted for age only) estimate indicated a significant association between baseline anxiety with incident diabetes (odds ratio 1.47, 1.23-1.75). Furthermore, most-adjusted analyses indicated a significant association between baseline anxiety and incident diabetes. Included studies that examined diabetes to incident anxiety found no association. CONCLUSIONS: There was an association between baseline anxiety and incident diabetes. The results also indicate the need for more research to examine the direction of association from diabetes to incident anxiety. This work adds to the growing body of evidence that poor mental health increases the risk of developing 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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.026 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".