Longitudinal associations between depression and diabetes complications: a systematic review and meta‐analysis
Bibliographic record
Abstract
To conduct a systematic review and meta-analysis of longitudinal studies assessing the bi-directional association between depression and diabetes macrovascular and microvascular complications. Embase, Medline and PsycINFO databases were searched from inception through 27 November 2017. A total of 4592 abstracts were screened for eligibility. Meta-analyses used multilevel random/mixed-effects models. Quality was assessed using the Newcastle-Ottawa scale. Twenty-two studies were included in the systematic review. Sixteen studies examined the relationship between baseline depression and incident diabetes complications, of which nine studies involving over one million participants were suitable for meta-analysis. Depression was associated with an increased risk of incident macrovascular (HR = 1.38; 95% CI: 1.30-1.47) and microvascular disease (HR = 1.33; 95% CI: 1.25-1.41). Six studies examined the association between baseline diabetes complications and subsequent depression, of which two studies involving over 230 000 participants were suitable for meta-analysis. The results showed that diabetes complications increased the risk of incident depressive disorder (HR = 1.14; 95% CI: 1.07-1.21). The quality analysis showed increased risk of bias notably in the representativeness of selected cohorts and ascertainment of exposure and outcome. Depression in people with diabetes is associated with an increased risk of incident macrovascular and microvascular complications. The relationship between depression and diabetes complications appears bi-directional. However, the risk of developing diabetes complications in depressed people is higher than the risk of developing depression in people with diabetes complications. The underlying mechanisms warrant further research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.026 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.034 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".