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 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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.036 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".