Skip to main content
Download PDF
Download ePub

Association of preconception premenstrual disorders with perinatal depression: an analysis of the perinatal clinical database of a single Japanese institution

BioPsychoSocial Medicine volume 18, Article number: 24 (2024) Cite this article

Abstract

Background

Recent studies have identified premenstrual disorders (PMDs) as a risk factor for postpartum depression. However, routine screening for preconception PMDs is not yet common in Japan. This study investigated the association between preconception PMDs and perinatal depression in a single tertiary care setting.

Methods

We analyzed data from pregnant women who gave birth at Kyoto University Hospital between April 2020 and October 2023. The Premenstrual Symptoms Screening Tool was administered at the first postconception visit to retrospectively assess PMD status before the current pregnancy. The Edinburgh Postnatal Depression Scale (EPDS) was administered during pregnancy and one month postpartum as a prospective measure of perinatal depression. EPDS cutoff values were set at 12/13 during pregnancy and 8/9 at one month postpartum.

Results

Of the 781 women analyzed, 53 had preconception PMD. Univariate and multivariate logistic regression analyses revealed that preconception PMD was associated with an EPDS score of ≥ 13 during pregnancy, with a crude odds ratio (OR) of 5.78 (95% confidence interval [CI]: 2.70–11.75) and an adjusted OR of 3.71 (95% CI: 1.54–8.35). For an EPDS score of ≥ 9 at 1 month postpartum, the crude OR was 3.36 (95% CI: 1.79–6.12) and the adjusted OR was 2.16 (95% CI: 1.04–4.35).

Conclusions

Our findings indicate that preconception PMDs are a significant risk factor for both depression during pregnancy and postpartum depression. These results support the implementation of preconception PMD screening during antenatal checkups as a preventive measure and to identify women in need of early mental health care.

Background

Perinatal depression, as defined by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), refers to a major depressive episode occurring during pregnancy or within the first four weeks postpartum [1]. It is a significant public health concern due to its high prevalence and its detrimental effects on mothers, infants, and families. A 2017 systematic review and meta-analysis reported an overall pooled prevalence of perinatal depression at 11.9% (95% confidence interval [CI]: 11.4–12.5) [2]. Depression during pregnancy is linked to increased risks of preterm delivery and low birth weight [3]. Additionally, perinatal depression is associated with poor maternal bonding, child maltreatment, maternal suicide, and adverse developmental outcomes in children, including deficits in social–emotional, neurocognitive, language, motor, and adaptive behaviors [4, 5]. Symptoms of depression during pregnancy also predict postpartum depression [6], with some cases beginning prior to delivery [7]. Early identification and intervention for at-risk pregnant women are crucial for preventing perinatal depression [8].

Premenstrual syndrome (PMS) encompasses a range of physical and emotional symptoms that occur during the luteal phase before menstruation and generally diminish once menstruation begins. The severity and pattern of these symptoms can vary widely among individuals. A more severe form of PMS, known as premenstrual dysphoric disorder (PMDD) [9], is recognized as a psychiatric disorder in the DSM-5 [1]. Both PMS and PMDD are often referred to collectively as premenstrual disorders (PMDs) [10,11,12]. Despite increasing public awareness of PMDs, many women with severe symptoms do not seek medical help. Recent studies have increasingly explored the link between PMDs and perinatal depression, primarily focusing on postpartum depression, with fewer addressing depression during pregnancy [13,14,15,16]. Although screening for PMDs during preconception could potentially facilitate early intervention for perinatal mental health issues, this practice is not yet widespread in Japan.

Prenatal and postpartum mental health issues are gaining increased recognition in society. However, clinical records often prioritize physical health information, with psychosocial and mental health data frequently being insufficient or fragmented. Since 2020, our institution has been using a comprehensive perinatal clinical database we developed that integrates psychosocial and mental health information to enable a more holistic approach to perinatal care. This database includes data from questionnaires on preconception PMDs as well as prenatal and postpartum depression. This study used the database to investigate the association between preconception PMDs and depression during pregnancy and postpartum.

Methods

Study design and participants

The perinatal clinical database of Kyoto University Hospital (a tertiary care facility) includes the data of all pregnant women treated at the hospital. A patient is registered in the database during the first visit postconception. The Edinburgh Postnatal Depression Scale (EPDS) was administered mid-pregnancy and one month postpartum. The Premenstrual Symptoms Screening Tool (PSST) was administered during the first visit postconception and used to assess the patient’s PMD status before conception. Information from the PSST and EPDS are included in the database. This study was conducted using the data of pregnant women who underwent live deliveries at the hospital between April 2020 and October 2023 after at least 22 weeks of gestation. Women were excluded if they had missing PSST data, had missing EPDS data during pregnancy and postpartum or were experiencing a second or subsequent pregnancy during the study period. The study protocol was reviewed and approved by the Ethics Committee of Kyoto University Graduate School and Faculty of Medicine (Approval No. R2262). Informed consent was obtained through an opt-out system, with participants receiving detailed information via disclosure documents.

Measurements

Exposure: preconception PMDs

The Japanese version of the PSST was administered during the first visit postconception to assess premenstrual symptoms prior to the current pregnancy. The PSST, originally developed by Steiner et al. based on the PMDD criteria from the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) [17], is a widely used self-report tool for screening PMDs [18,19,20,21,22]. In this study, the PSST was translated into Japanese by the original developers and used with permission from the copyright holder. The PSST includes 14 premenstrual symptoms and five types of life interference caused by these symptoms, each rated on a 4-point Likert scale: “not at all,” “mild,” “moderate,” or “severe.” Based on the PSST results, participants were categorized as follows: those meeting DSM-IV criteria for PMDD were classified as having “PMDD”; those with clinically evident PMS symptoms but not meeting PMDD criteria were classified as having “moderate/severe PMS”; and all others were classified as having “no/mild PMS.” In this study, participants with “PMDD” or “moderate/severe PMS” were grouped together in a PMDs group, whereas the others were classified into a non-PMDs group. To maintain objectivity, the obstetricians and midwives involved in patient care were not informed of the PSST results.

Outcomes: perinatal depression

The Japanese version of the EPDS was used to prospectively assess perinatal depression. The EPDS was administered during pregnancy, generally in the second trimester, and again at one month postpartum [23]. The EPDS is a widely used self-report questionnaire designed to screen for postpartum depression. It consists of 10 items rated on a 4-point scale (0–3), with total scores ranging from 0 to 30 [24]. In this study, a cutoff score of 8/9 was used for assessing postpartum depression. The EPDS has demonstrated validity and reliability in Japanese populations [23]. Although there is no universally established cutoff for the EPDS during pregnancy in Japan, this study used a cutoff value of 12/13 based on a previous study [25].

Statistical analysis

First, to show the characteristics of the PMDs and non-PMDs groups, continuous variables are presented as means with standard deviations and categorical variables are presented as frequencies and percentages. We then analyzed the association between PMDs during preconception and depression during pregnancy and one month postpartum using univariate and multivariate logistic regression. The adjustment factors included age (continuous variable), pre-pregnancy body mass index (categorized as < 18.5, 18.5–25, or ≥ 25), parity (nullipara or multipara), the mother’s educational level (junior high school/high school or higher), history of psychiatric or psychosomatic consultations (yes or no), and issues such as relationship problems with a partner, financial difficulties, violence, or unemployment (yes or no). The number of adjustment factors was based on the one-in-ten rule [26]. All analyses were performed using R version 4.3.1.

Results

Of the 1,172 women treated during the study period, 781 were included in the analysis. We excluded 365 women with missing PSST data, 2 with missing EPDS data during pregnancy and postpartum, and 24 with a second or subsequent pregnancy during the study period. The non-PMDs group comprised 728 women classified as having “no/mild PMS” based on the PSST, whereas the PMDs group included 53 women: 42 with “moderate/severe PMS” and 11 with “PMDD” (Fig. 1). The characteristics of each group are detailed in Table 1. The PMDs group was more likely to have a history of psychiatric or psychosomatic consultations and to experience problems related to partner relationships, finances, violence, or unemployment.

Fig. 1
figure 1

Flowchart of patient inclusion. Abbreviations: EPDS, Edinburgh Postnatal Depression Scale; PMDD, premenstrual dysphoric disorder; PMDs, premenstrual disorders; PMS, premenstrual syndrome; PSST, Premenstrual Symptoms Screening Tool

Full size image
Table 1 Basic characteristics of the PMDs and non-PMDs groups
Full size table

The percentage of women with an EPDS score of ≥ 13 during pregnancy was 22.6% in the PMDs group compared with 4.7% in the non-PMDs group (Table 2). At one month postpartum, 34.0% of the women in the PMDs group had an EPDS score of ≥ 9, as opposed to 12.9% of the women in the non-PMDs group. Logistic regression analysis results are presented in Table 2. In univariate analysis, preconception PMDs were associated with an EPDS score of ≥ 13 during pregnancy, with a crude odds ratio (OR) of 5.78 (95% CI: 2.70–11.75). This association persisted after adjusting for age, history of psychiatric or psychosomatic consultations, and issues related to partner relationships, finances, violence, or unemployment, with an adjusted OR of 3.71 (95% CI: 1.54–8.35). Preconception PMDs were also associated with an EPDS score of ≥ 9 at one month postpartum in univariate analysis (crude OR: 3.36 [95% CI: 1.79–6.12]). This association remained significant after adjusting for age, psychiatric or psychosomatic consultations, problems related to partner relationships, finances, violence, or unemployment as well as maternal educational level and parity, with an adjusted OR of 2.17 (95% CI: 1.04–4.35).

Table 2 Perinatal depression assessed using EPDS in the PMDs and non-PMDs groups
Full size table

Discussion

This prospective analysis of 781 pregnant women at a tertiary care hospital revealed that preconception PMDs are a significant risk factor for both depression during pregnancy and postpartum depression, with adjusted ORs of 3.71 (95% CI: 1.54–8.35) and 2.17 (95% CI: 1.04–4.35), respectively. These findings suggest that screening for preconception PMDs, along with assessing other risk factors for perinatal depression, is crucial for the early identification and management of women at risk. Early detection can facilitate timely mental health interventions and preventive measures. Although the diagnostic criteria for PMDs typically involve prospective symptom documentation, recall-based screening remains a practical approach, given that many women seek medical care only after becoming pregnant.

Increasing evidence suggests that preconception PMD is a significant risk factor for postpartum depression. In a systematic review and meta-analysis, Cao et al. demonstrated that pregnant women with a history of PMS had a higher risk of postpartum depression than those without PMS, with an OR of 2.20 (95% CI: 1.81–2.68) [13]. Additionally, Gastaldon et al. identified PMS as one of the most robust risk factors in an umbrella review of systematic reviews and meta-analyses on postpartum depression and depressive symptoms [27]. Our findings align with those of the aforementioned studies. However, there is limited research on the association between preconception PMDs and depression during pregnancy. Sugawara et al. conducted a prospective cohort study involving 1,329 Japanese pregnant women and found that those with premenstrual irritability before pregnancy had significantly higher scores on Zung’s Self-Rating Depression Scale throughout pregnancy and postpartum (at six time points: early pregnancy, mid-pregnancy, late pregnancy, 5 days postpartum, 1 month postpartum, and 6 months postpartum) compared with those without such irritability [16]. Similarly, Pataky et al. found in a study involving 687 pregnant women from German-speaking countries that PMS/PMDD, assessed using the PSST, was associated with increased odds of an EPDS score ≥ 10 during the perinatal period (late pregnancy, 1–2 weeks postpartum, and 4–6 weeks postpartum) [14]. Although our study used different scales and targeted a different population, these findings collectively support the notion that pre-pregnancy PMDs are also a risk factor for depression during pregnancy.

Our findings highlight the importance of managing PMDs even before conception. Addressing PMDs as an indicator of underlying issues and intervening with modifiable factors could potentially prevent various conditions, including perinatal depression. Both PMDs and perinatal depression involve complex pathophysiological mechanisms, with neuroendocrine, neurotransmitter, immune-inflammatory, and genetic factors contributing to their development. Although these mechanisms are not fully understood, there may be some overlap between the two conditions [28,29,30]. Epidemiologically, several common risk factors for PMDs and perinatal depression include nutritional deficiencies. Deficiencies of vitamin D, calcium, zinc, iron, and polyunsaturated fatty acids have been associated with these symptoms and may improve with supplementation [31,32,33,34,35,36,37,38,39]. Enhancing nutritional status prior to pregnancy may help reduce the risk of developing perinatal depression. Additionally, adversarial childhood experiences are a significant common risk factor for both PMDs and perinatal depression [40,41,42,43,44]. Providing strong social support for women with such experiences may mitigate their risk of developing these conditions.

Limitations

This study has several limitations. First, both the EPDS and PSST are self-report screening tools, and the psychometric properties of the Japanese version of the PSST have not yet been thoroughly examined. Notably, recall bias may have influenced the study results: the participants were asked to recall their experiences with premenstrual symptoms before pregnancy. Further research is needed to determine the most effective method for screening PMDs before pregnancy during antenatal checkups. Additionally, establishing a specialized system for preconception medical examinations and enhancing educational efforts to encourage women with PMDs to seek medical care could improve diagnosis and management. Second, there was a significant amount of missing PSST data. The study was conducted in a manner that did not interfere with routine outpatient operations, which may have led to some data being overlooked or forgotten. Third, as the study was conducted at a tertiary care hospital, the findings may not be generalizable to other settings.

Conclusions

In this study conducted at a tertiary care hospital, preconception PMDs were found to be associated with an increased risk of both depression during pregnancy and at one month postpartum, as measured by self-administered questionnaires. These findings underscore the importance of screening for preconception PMDs during antenatal checkups. Early detection through such screening can facilitate timely mental health interventions and preventive measures for pregnant women.

Data availability

The datasets analyzed during the current study are available from the corresponding author on reasonable request.

Abbreviations

CI:

Confidence interval

DSM-5:

Diagnostic and statistical manual of mental disorders, Fifth Edition

DSM-IV:

Diagnostic and statistical manual of mental disorders, Fourth Edition

EPDS:

Edinburgh postnatal depression scale

OR:

Odds ratio

PMDD:

Premenstrual dysphoric disorder

PMDs:

Premenstrual disorders

PMS:

Premenstrual syndrome

PSST:

Premenstrual symptoms screening tool

References

  1. American Psychiatry Association. Diagnostic and statistical manual of mental disorders. 5th ed. Washington DC: American Psychiatric Publishing; 2013.

    Book  Google Scholar 

  2. Woody CA, Ferrari AJ, Siskind DJ, Whiteford HA, Harris MG. A systematic review and meta-regression of the prevalence and incidence of perinatal depression. J Affect Disord. 2017;219:86–92.

    Article  CAS  PubMed  Google Scholar 

  3. Jarde A, Morais M, Kingston D, Giallo R, MacQueen GM, Giglia L, et al. Neonatal outcomes in women with untreated antenatal depression compared with women without depression: a systematic review and meta-analysis. JAMA Psychiat. 2016;73:826–37.

    Article  Google Scholar 

  4. Ayers S, Bond R, Webb R, Miller P, Bateson K. Perinatal mental health and risk of child maltreatment: a systematic review and meta-analysis. Child Abuse Negl. 2019;98: 104172.

    Article  PubMed  Google Scholar 

  5. Rogers A, Obst S, Teague SJ, Rossen L, Spry EA, Macdonald JA, et al. Association between maternal perinatal depression and anxiety and child and adolescent development: a meta-analysis. JAMA Pediatr. 2020;174:1082–92.

    Article  PubMed  Google Scholar 

  6. Verreault N, Da Costa D, Marchand A, Ireland K, Dritsa M, Khalifé S. Rates and risk factors associated with depressive symptoms during pregnancy and with postpartum onset. J Psychosom Obstet Gynaecol. 2014;35:84–91.

    Article  PubMed  Google Scholar 

  7. Underwood L, Waldie K, D’Souza S, Peterson ER, Morton S. A review of longitudinal studies on antenatal and postnatal depression. Arch Womens Ment Health. 2016;19:711–20.

    Article  PubMed  Google Scholar 

  8. Dennis CL, Dowswell T. Psychosocial and psychological interventions for preventing postpartum depression. Cochrane Database Syst Rev. 2013;(2):CD001134.

  9. Yonkers KA, O’Brien PM, Eriksson E. Premenstrual syndrome. Lancet. 2008;371:1200–10.

    Article  PubMed  PubMed Central  Google Scholar 

  10. O’Brien PM, Bäckström T, Brown C, Dennerstein L, Endicott J, Epperson CN, et al. Towards a consensus on diagnostic criteria, measurement and trial design of the premenstrual disorders: the ISPMD Montreal consensus. Arch Womens Ment Health. 2011;14:13–21.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Takeda T. Premenstrual disorders: premenstrual syndrome and premenstrual dysphoric disorder. J Obstet Gynaecol Res. 2023;49:510–8.

    Article  PubMed  Google Scholar 

  12. American College of Obstetricians and Gynecologists. Management of premenstrual disorders: ACOG clinical practice guideline No. 7. Obstet Gynecol. 2023;142:1516–33.

  13. Cao S, Jones M, Tooth L, Mishra GD. History of premenstrual syndrome and development of postpartum depression: A systematic review and meta-analysis. J Psychiatr Res. 2020;121:82–90.

    Article  PubMed  Google Scholar 

  14. Pataky EA, Ehlert U. Longitudinal assessment of symptoms of postpartum mood disorder in women with and without a history of depression. Arch Womens Ment Health. 2020;23:391–9.

    Article  PubMed  Google Scholar 

  15. Abe Y, Sirichokchatchawan W, Sangkomkamhang U, Satthapisit S, Maes M. Antenatal depressive symptoms are strongly predicted by the severity of pre-menstrual syndrome: results of partial least squares analysis. Int J Clin Health Psychol. 2023;23: 100356.

    Article  PubMed  Google Scholar 

  16. Sugawara M, Toda MA, Shima S, Mukai T, Sakakura K, Kitamura T. Premenstrual mood changes and maternal mental health in pregnancy and the postpartum period. J Clin Psychol. 1997;53:225–32.

    Article  CAS  PubMed  Google Scholar 

  17. Steiner M, Macdougall M, Brown E. The premenstrual symptoms screening tool (PSST) for clinicians. Arch Womens Ment Health. 2003;6:203–9.

    Article  CAS  PubMed  Google Scholar 

  18. Hariri FZ, Moghaddam-Banaem L, Siah Bazi S, Saki Malehi A, Montazeri A. The Iranian version of the premenstrual symptoms screening tool (PSST): a validation study. Arch Womens Ment Health. 2013;16:531–7.

    Article  PubMed  Google Scholar 

  19. Mahfoud Z, Emam R, Anchassi D, Omran S, Alhaj N, Al-Abdulla S, et al. Premenstrual dysphoric disorder in Arab women: validation and cultural adaptation of the Arabic version of the premenstrual screening tool. Women Health. 2019;59:631–45.

    Article  PubMed  Google Scholar 

  20. Câmara RA, Köhler CA, Frey BN, Hyphantis TN, Carvalho AF. Validation of the Brazilian Portuguese version of the premenstrual symptoms screening tool (PSST) and association of PSST scores with health-related quality of life. Braz J Psychiatry. 2017;39:140–6.

    Article  PubMed  Google Scholar 

  21. Pacitti F, Iorio C, Riccobono G, Iannitelli A, Pompili A. Assessment of premenstrual symptoms: validation of the Italian version of the premenstrual symptoms screening tool. Riv Psichiatry. 2021;56:246–53.

    Google Scholar 

  22. Bentz D, Steiner M, Meinlschmidt G. SIPS–screening instrument for premenstrual symptoms. The German version of Premenstrual Symptoms Screening Tool to assess clinically relevant disturbances. Nervenarzt. 2012;83:33–9.

  23. Okano T, Murata M, Masuji F, Tamaki R, Nomura J, Miyaoka H. Validation and reliability of Japanese version of EPDS (Edinburgh Postnatal Depression Scale) [Japanese article]. Arch Psychiatr Diagn Clin Eval. 1996;7:525–33.

    Google Scholar 

  24. Cox JL, Holden JM, Sagovsky R. Detection of postnatal depression. Development of the 10-item Edinburgh postnatal depression scale. Br J Psychiatry. 1987;150:782–6.

    Article  CAS  PubMed  Google Scholar 

  25. Usuda K, Nishi D, Okazaki E, Makino M, Sano Y. Optimal cut-off score of the Edinburgh Postnatal Depression Scale for major depressive episode during pregnancy in Japan. Psychiatry Clin Neurosci. 2017;71:836–42.

    Article  PubMed  Google Scholar 

  26. Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR. A simulation study of the number of events per variable in logistic regression analysis. J Clin Epidemiol. 1996;49:1373–9.

    Article  CAS  PubMed  Google Scholar 

  27. Gastaldon C, Solmi M, Correll CU, Barbui C, Schoretsanitis G. Risk factors of postpartum depression and depressive symptoms: umbrella review of current evidence from systematic reviews and meta-analyses of observational studies. Br J Psychiatry. 2022;221:591–602.

    Article  PubMed  Google Scholar 

  28. Payne JL, Maguire J. Pathophysiological mechanisms implicated in postpartum depression. Front Neuroendocrinol. 2019;52:165–80.

    Article  CAS  PubMed  Google Scholar 

  29. Yonkers KA, Simoni MK. Premenstrual disorders. Am J Obstet Gynecol. 2018;218:68–74.

    Article  PubMed  Google Scholar 

  30. Marrocco J, Einhorn NR, Petty GH, Li H, Dubey N, Hoffman J, et al. Epigenetic intersection of BDNF Val66Met genotype with premenstrual dysphoric disorder transcriptome in a cross-species model of estradiol add-back. Mol Psychiatry. 2020;25:572–83.

    Article  CAS  PubMed  Google Scholar 

  31. Arab A, Golpour-Hamedani S, Rafie N. The association between vitamin D and premenstrual syndrome: A systematic review and meta-analysis of current literature. J Am Coll Nutr. 2019;38:648–56.

    Article  CAS  PubMed  Google Scholar 

  32. Heidari H, Amani R, Feizi A, Askari G, Kohan S, Tavasoli P. Vitamin D supplementation for premenstrual syndrome-related inflammation and antioxidant markers in students with vitamin D deficient: a randomized clinical trial. Sci Rep. 2019;9:14939.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Zhao XH, Zhang ZH. Risk factors for postpartum depression: an evidence-based systematic review of systematic reviews and meta-analyses. Asian J Psychiatry. 2020;53: 102353.

    Article  Google Scholar 

  34. Arab A, Rafie N, Askari G, Taghiabadi M. Beneficial role of calcium in premenstrual syndrome: A systematic review of current literature. Int J Prev Med. 2020;11:156.

    Article  PubMed  PubMed Central  Google Scholar 

  35. Jafari F, Tarrahi MJ, Farhang A, Amani R. Effect of zinc supplementation on quality of life and sleep quality in young women with premenstrual syndrome: a randomized, double-blind, placebo-controlled trial. Arch Gynecol Obstet. 2020;302:657–64.

    Article  CAS  PubMed  Google Scholar 

  36. Jafari F, Amani R, Tarrahi MJ. Effect of zinc supplementation on physical and psychological symptoms, biomarkers of inflammation, oxidative stress, and brain-derived neurotrophic factor in young women with premenstrual syndrome: a randomized, double-blind, placebo-controlled trial. Biol Trace Elem Res. 2020;194:89–95.

    Article  CAS  PubMed  Google Scholar 

  37. Moya E, Phiri N, Choko AT, Mwangi MN, Phiri KS. Effect of postpartum anaemia on maternal health-related quality of life: a systematic review and meta-analysis. BMC Public Health. 2022;22:364.

    Article  PubMed  PubMed Central  Google Scholar 

  38. Chocano-Bedoya PO, Manson JE, Hankinson SE, Johnson SR, Chasan-Taber L, Ronnenberg AG, et al. Intake of selected minerals and risk of premenstrual syndrome. Am J Epidemiol. 2013;177:1118–27.

    Article  PubMed  PubMed Central  Google Scholar 

  39. Mohammadi MM, Dehghan Nayeri N, Mashhadi M, Varaei S. Effect of omega-3 fatty acids on premenstrual syndrome: A systematic review and meta-analysis. J Obstet Gynaecol Res. 2022;48:1293–305.

    Article  CAS  PubMed  Google Scholar 

  40. Racine N, Devereaux C, Cooke JE, Eirich R, Zhu J, Madigan S. Adverse childhood experiences and maternal anxiety and depression: a meta-analysis. BMC Psychiatry. 2021;21:28.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Yang Q, Þórðardóttir EB, Hauksdóttir A, Aspelund T, Jakobsdóttir J, Halldorsdottir T, et al. Association between adverse childhood experiences and premenstrual disorders: a cross-sectional analysis of 11,973 women. BMC Med. 2022;20:60.

    Article  PubMed  PubMed Central  Google Scholar 

  42. Ito K, Doi S, Isumi A, Fujiwara T. Association between childhood maltreatment history and premenstrual syndrome. Int J Environ Res Public Health. 2021;18.

  43. Kulkarni J, Leyden O, Gavrilidis E, Thew C, Thomas EH. The prevalence of early life trauma in premenstrual dysphoric disorder (PMDD). Psychiatry Res. 2022;308:114381.

    Article  PubMed  Google Scholar 

  44. Bertone-Johnson ER, Whitcomb BW, Missmer SA, Manson JE, Hankinson SE, Rich-Edwards JW. Early life emotional, physical, and sexual abuse and the development of premenstrual syndrome: a longitudinal study. J Womens Health (Larchmt). 2014;23:729–39.

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank Ms. Kiwa Kojima for her generous assistance in the laboratory.

Funding

Not applicable.

Author information

Authors and Affiliations

  1. Department of Gynecology and Obstetrics, Kyoto University Graduate School of Medicine, 54 Shogoin-Kawahara-Cho, Sakyo-Ku, Kyoto, 606-8507, Japan

    Takuma Ohsuga, Miho Egawa, Kaori Tsuyuki, Akihiko Ueda, Maya Komatsu, Yoshitsugu Chigusa, Haruta Mogami & Masaki Mandai

Authors
  1. Takuma Ohsuga
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Miho Egawa
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Kaori Tsuyuki
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Akihiko Ueda
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Maya Komatsu
    View author publications

    You can also search for this author inPubMed Google Scholar

  6. Yoshitsugu Chigusa
    View author publications

    You can also search for this author inPubMed Google Scholar

  7. Haruta Mogami
    View author publications

    You can also search for this author inPubMed Google Scholar

  8. Masaki Mandai
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

TO, ME, and KT conceived and designed the study. AU developed the database. TO performed the data analysis and drafted the initial manuscript. All authors reviewed the draft, contributed critically to the interpretation of the data, and approved the final version of the manuscript.

Corresponding author

Correspondence to Miho Egawa.

Ethics declarations

Ethics approval and consent to participate

The study protocol was reviewed and approved by the Ethics Committee of Kyoto University Graduate School and Faculty of Medicine (Approval No. R2262). Informed consent was obtained through an opt-out system, with details provided in the information disclosure documents.

Consent for publication

Not applicable.

Competing interests

Not applicable.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ohsuga, T., Egawa, M., Tsuyuki, K. et al. Association of preconception premenstrual disorders with perinatal depression: an analysis of the perinatal clinical database of a single Japanese institution. BioPsychoSocial Med 18, 24 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13030-024-00323-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s13030-024-00323-7

Keywords

BioPsychoSocial Medicine

ISSN: 1751-0759

Contact us