A systematic review and meta-analysis on the comorbidity of premenstrual dysphoric disorder or premenstrual syndrome with mood disorders: prevalence, clinical and neurobiological correlates
Bibliographic record
Abstract
BACKGROUND: Mood disorders are among the leading causes of disease burden worldwide, with 20-70% of affected individuals experiencing comorbid premenstrual disorders. This systematic review and meta-analysis investigated the comorbidity of premenstrual dysphoric disorder (PMDD) or premenstrual syndrome (PMS) with non-reproductive mood disorders. AIMS: We aimed to determine the pooled prevalence of PMDD/PMS with adult mood disorders, assess the impact of comorbidity on clinical course and summarise the associated neurobiological findings. METHOD: Eligible studies were identified through Embase, MEDLINE and APA PsycINFO from inception to 22 January 2024 (PROSPERO, no. CRD42021246796). Studies on women ('females') with diagnoses of PMDD/PMS and mood disorders were included. Risk of bias was assessed using National Institutes of Health quality assessment tools. A random-effects, pooled-prevalence meta-analysis was conducted using the Comprehensive Meta-Analysis software, categorising diagnostic sampling strategies as follows: mood disorders diagnosed first, PMDD/PMS diagnosed first or concurrent diagnoses. A narrative synthesis explored secondary outcomes, including illness course and biomarkers. RESULTS: = 3646) contributing to the meta-analysis. Seven studies focused on bipolar disorders, 18 on unipolar depressive disorders and 14 on mixed samples of bipolar and unipolar disorders. Random-effects pooled-prevalence meta-analyses showed consistently high comorbidity rates between PMDD/PMS and mood disorders, ranging from 42% (95% CI: 30%, 55%) to 49% (95% CI: 38%, 60%) across sampling strategies. Risk of bias varied, with methodological heterogeneity noted. CONCLUSIONS: This review underscores high comorbidity rates between PMDD/PMS and mood disorders, regardless of sampling strategy, and highlights the need for research into clinical and neurobiological characteristics specific to this comorbidity. Limitations include study heterogeneity, reliance on cross-sectional designs and provisional PMDD/PMS diagnoses. Future research should address these gaps to inform diagnostic and therapeutic advancements tailored to this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".