Inflammation, depression and cardiovascular disease in women: the role of the immune system across critical reproductive events
Bibliographic record
Abstract
Women are at increased risk for developing depression and cardiovascular disease (CVD) across the lifespan and their comorbidity is associated with adverse outcomes that contribute significantly to rates of morbidity and mortality in women worldwide. Immune-system activity has been implicated in the etiology of both depression and CVD, but it is unclear how inflammation contributes to sex differences in this comorbidity. This narrative review provides an updated synthesis of research examining the association of inflammation with depression and CVD, and their comorbidity in women. Recent research provides evidence of pro-inflammatory states and sex differences associated with alterations in the hypothalamic-pituitary-adrenal axis, the renin-angiotensin-aldosterone system and the serotonin/kynurenine pathway, that likely contribute to the development of depression and CVD. Changes to inflammatory cytokines in relation to reproductive periods of hormonal fluctuation (i.e. the menstrual cycle, perinatal period and menopause) are highlighted and provide a greater understanding of the unique vulnerability women experience in developing both depressed mood and adverse cardiovascular events. Inflammatory biomarkers hold substantial promise when combined with a patient's reproductive and mental health history to aid in the prediction, identification and treatment of the women most at risk for CVD and depression. However, more research is needed to improve our understanding of the mechanisms underlying inflammation in relation to their comorbidity, and how these findings can be translated to improve women's health.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".