A Systematic Review of the Bidirectional Relationship Between Post-traumatic Stress Disorder (PTSD) and the Development of Type 2 Diabetes
Bibliographic record
Abstract
Emerging evidence suggests a bidirectional relationship between post-traumatic stress disorder (PTSD) and type 2 diabetes (T2D), though the underlying mechanisms and population-specific risks remain unclear. This systematic review synthesizes current research on the PTSD-T2D link, focusing on biological pathways, behavioral mediators, and demographic disparities. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a comprehensive search across PubMed/Medical Literature Analysis and Retrieval System Online (MEDLINE), Excerpta Medica database (Embase), PsycINFO, and Web of Science. Eligible studies examined PTSD as a risk factor for T2D or vice versa in adults (≥18 years). Two reviewers independently screened articles, extracted data, and assessed bias using the Newcastle-Ottawa Scale (NOS) and Cochrane tools. Thirteen studies met the inclusion criteria. PTSD increased T2D risk and was associated with worse glycemic control (e.g., elevated glycated hemoglobin A1c (HbA1c)). T2D populations exhibited higher PTSD prevalence (30%-50% in high-trauma groups), particularly among women, refugees, and veterans. Biological mechanisms (hypothalamic-pituitary-adrenal (HPA) axis dysregulation, chronic inflammation) and behavioral factors (sedentary lifestyle, poor adherence) drove the bidirectional relationship. Conflict-affected populations showed heightened vulnerability, with war-displaced individuals facing compounded metabolic and mental health burdens. PTSD and T2D share a complex, bidirectional relationship influenced by neuroendocrine, inflammatory, and behavioral pathways. High-risk groups (e.g., refugees, veterans, and women) may benefit from integrated screening and trauma-informed diabetes care. Future research should prioritize longitudinal designs to clarify causality and evaluate targeted interventions.
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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 | 0.009 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".