Skip to main content
The Egyptian Journal of Neurology, Psychiatry and Neurosurgery
Download PDF

Relation between metabolic syndrome and psychiatric symptoms severity in women with premenstrual syndrome

The Egyptian Journal of Neurology, Psychiatry and Neurosurgery volume 60, Article number: 18 (2024) Cite this article

Abstract

Background

Premenstrual syndrome (PMS) is a clinical condition characterized by recurrent moderate-to-severe affective, physical, and behavioral symptoms during the menstrual cycle. The present study aimed to assess the relation between metabolic syndrome (MetS) and symptoms severity in patients with premenstrual syndrome (PMS). This multicentric propensity score matched analysis included 300 women with PMS. They comprised 150 women with MetS and 150 women without MetS. Diagnosis of PMS was based on the validated Arabic version of Premenstrual Symptoms Screening Tool (PSST). For the diagnosis of MetS, we adopted the Harmonized Joint Scientific Statement (HJSS) on metabolic syndrome recommendations.

Results

The present study included 300 patients with PMS. They comprised 150 patients with MetS and 150 patients without MetS. Comparison between the studied groups regarding the demographic and clinical data showed that patients with MetS had significantly higher BMI (30.5 ± 3.0 versus 25.2 ± 3.5 kg/m2, p < 0.001) and longer symptoms duration (4.4 ± 0.9 versus 3.3 ± 1.1 days, p < 0.001). Patients with MetS included higher frequency of moderate-to-severe PMS (35.3% versus 20.7%, p = 0.005). In patients with MetS, it was found that patients with moderate-to-severe PMS have significantly higher BMI, younger age at menarche and longer symptoms duration. Multivariate logistic regression analysis identified age at menarche [OR (95% CI): 0.7 (0.55–0.9), p = 0.005], PMS symptoms duration [OR (95% CI): 4.45 (3.0–6.6), p < 0.001] and MetS [OR (95% CI): 1.67 (1.34–2.53), p = 0.017] as significant predictors of moderate-to-severe PMS.

Conclusions

MetS is related to symptoms severity in PMS patients.

Introduction

Premenstrual syndrome (PMS) is a clinical condition characterized by a cluster of recurrent affective, physical, and behavioral symptoms of variable severity occurring during the luteal phase of menstrual cycle and usually fading within few days of menstruation [1, 2]. While most women experience premenstrual symptoms in the reproductive age, the condition presents with severe and debilitating symptoms in only 5.0% of women [3]. The premenstrual dysphoric disorder (PMDD) constitutes the most severe form of psychiatric premenstrual symptoms [4]. Interestingly, PMS was linked to postpartum depression (PPD) in affected women [5]. One third of women were found to have PPD as shown by one study [6] with endocrinal imbalance reported as a contributing factor [7].

The reported risk factors for PMS include young age, alcohol consumption, family history of PMS, genetic predisposition [8], vitamin D deficiency [9], dietary and lifestyles behaviors [10] and air pollution [11]. Suggested pathogenic mechanisms are dysfunctional reward responsiveness [12], altered leptin metabolism [13], altered cortisol awakening response [14] and augmented oxidative stress [15].

Severe somatic forms of PMS may respond to nutritional supplements, herbal remedies, lifestyle modifications, exercise or hormonal treatment. However, efficacy of these interventions remains debatable. So, identification of factors related to severe forms of PMS may be crucial for appropriate management of the condition [16, 17].

Metabolic syndrome (MetS) is a cluster of clinical and biochemical risk factors including obesity, elevated blood pressure, hyperglycemia and dyslipidemia. The estimated prevalence of MetS among premenopausal women ranges from 5.4% and 55.5% [18]. The condition is related to a wide range of morbidities affecting almost all body systems [19].

While the medial literature is replete with studies discussing the relation between MetS and many clinical conditions, no study assessed the clinical significance of MetS in PMS patients. So, the present study sought to assess the prevalence of MetS in a group of Egyptian women with PMS and to discover the relation between various parameters of MetS and clinical severity of PMS.

Methods

This cross-sectional study was conducted at outpatient clinics of gynecology and obstetrics departments. The study protocol was approved by the Institutional Review Board, and all patients gave informed consent before enrollment. The study findings were reported according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) initiative recommendations [20].

The study included 300 women with PMS. All patients were submitted to thorough clinical and gynecological examination. Diagnosis of PMS was based on the validated Arabic version of Premenstrual Symptoms Screening Tool (PSST). PSST includes 19 items assessing patients’ psychiatric symptoms including anger, anxiety, depression and others in line with DSM-IV. The tool showed high internal consistency (Cronbach’s α = 0.92) and good construct validity [21]. Every item is rated as “not at all,” “mild,” “moderate,” or “severe.” Patients were classified to have moderate-to-severe PMS if at least one of the first 4 items was severe AND at least 4 of the first 14 items were moderate to severe AND at least one of the last five items was severe according to the instructions of Steiner and colleagues [22].

Exclusion criteria were receiving treatment for PMS or PMDD, associated psychosomatic disorders, associated gynecological conditions or other psychiatric conditions.

Patients included 150 patients with MetS and 150 patients without MetS. The studied groups were matched using a propensity score matching analysis calculated by logistic regression. Factors included in the score were age, age at menarche and cycle duration.

For the diagnosis of MetS, we adopted the Harmonized Joint Scientific Statement (HJSS) on metabolic syndrome recommendations. MetS criteria included elevated waist circumference (≥ 80 cm), elevated triglycerides levels (≥ 150 mg/dL), reduced high-density lipoprotein cholesterol (HDL-C) (< 50 mg/dL), elevated blood pressure (systolic ≥ 130 and/or diastolic ≥ 85 mm Hg) and elevated fasting glucose (≥ 100 mg/dL). MetS was diagnosed in the presence of any three criteria [23]. Data obtained from the present study was statistically analyzed using statistical package of social sciences (SPSS) 20 (IBM, USA). Numerical data were expressed as mean ± SD while categorical data were presented as number and percent. Numerical data were compared using t test and categorical data were compared using Chi-square test. Logistic regression analysis was used to identify predictors of PMS severity. p value less than 0.05 was considered statistically significant.

Results

The present study included 300 patients with PMS. They comprised 150 patients with MetS and 150 patients without MetS. Comparison between the studied groups regarding the demographic and clinical data showed that patients with MetS had significantly higher BMI (30.5 ± 3.0 versus 25.2 ± 3.5 kg/m2, p < 0.001) and longer symptoms duration (4.4 ± 0.9 versus 3.3 ± 1.1 days, p < 0.001). Patients with MetS included higher frequency of moderate-to-severe PMS (35.3% versus 20.7%, p = 0.005) (Table 1, Fig. 1).

Table 1 The reported demographic and clinical data in the studied patients (n = 300)
Full size table
Fig. 1
figure 1

PMS severity in women with MetS and women without

Full size image

In patients with MetS, it was found that patients with moderate-to-severe PMS have significantly higher BMI, younger age at menarche and longer symptoms duration. In patients without MetS, it was found that patients with moderate-to-severe PMS have significantly younger age at menarche and longer symptoms duration (Table 2).

Table 2 Relation between PMS severity and clinical data in the studied groups (n = 300)
Full size table

In patients with MetS, it was found that patients with moderate-to-severe PMS have longer waist circumference, higher triglycerides levels, higher diastolic blood pressure and higher number of MetS criteria (Table 3).

Table 3 Relation between PMS severity and MetS components in MetS patients
Full size table

Multivariate logistic regression analysis identified age at menarche [OR (95% CI): 0.7 (0.55–0.9), p = 0.005], PMS symptoms duration [OR (95% CI): 4.45 (3.0–6.6), p < 0.001] and MetS [OR (95% CI): 1.67 (1.34–2.53), p = 0.017] as significant predictors of moderate-to-severe PMS (Table 4).

Table 4 Predictors of moderate-to-severe PMS in the studied patients
Full size table

Discussion

PMS particularly its severe form is a challenging clinical condition. Available treatment options frequently fail to achieve satisfactory clinical response. Integrated approach for management of the condition requires identification of possible risk factors. Considering the high prevalence of MetS and its relation to many morbidities, the present study aimed to assess the clinical significance of MetS in PMS patients and its relation to its severity.

Many previous studies identified some MetS components as risk factors for PMS. In the study of Masho and colleagues [24], the authors concluded that obesity is strongly associated with PMS. Likewise, the study of Bertone-Johnson and colleagues [25] noted a significant linear relation between increased BMI and incident PMS. In contrast, the study of Sharifan and colleagues [26] found no relation between BMI and severity of PMS. In another study, increased cholesterol level was reported as a risk factor for PMS [27].

Interestingly, the current study noted patients with MetS experienced significantly severe form of PMS. Previous studies identified as association between MetS and some elements of severe PMS. For example, the association between MetS and sleep troubles was reported in younger [28] and older [29] women and in the general population [30]. Moreover, it was found that MetS patients significantly higher frequency of severe joint aches. While none of the studied patients had clinical diagnosis of joint pathologies, the association between MetS and inflammatory joint diseases is well-documented [31].

Our study recognized patients age at menarche, symptoms duration and MetS as significant independent predictors of PMS severity. The relation between older age and severity of PMS was previously reported [32]. The association between MetS and severity of PMS may be explained by the inflammatory background of both conditions. PMS is characterized by elevated levels of proinflammatory cytokines [33]. The low-grade inflammation in MetS can produce augmented release of many proinflammatory cytokines like tumor necrosis factor α and interleukin 6 [34]. Of note, the recent study of Ter Horst and colleagues [35] suggested that women with MetS may also have lower expression of the anti-inflammatory adipokine adiponectin.

Conclusions

MetS is associated more severe form of PMS. It’s recommended to conduct interventional study to assess if control of MetS components can affect PMS symptoms. Conclusions of the present study may be limited by its cross-sectional design. Probably, longitudinal studies assessing the relation between changes in MetS components and PMS symptoms may provide better approach to judge the relation between MetS and severity of PMS symptoms.

Data availability

The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.

Abbreviations

BMI:

Body mass index

DBP:

Diastolic blood pressure

FBG:

Fasting blood glucose

HDL:

High-density lipoprotein

HJSS:

Harmonized Joint Scientific Statement

MetS:

Metabolic syndrome

PMS:

Premenstrual syndrome

PSST:

Premenstrual Symptoms Screening Tool

STROBE:

Strengthening the Reporting of Observational Studies in Epidemiology

References

  1. Sanchez-Santos MT, Judge A, Gulati M, Spector TD, Hart DJ, Newton JL, et al. Association of metabolic syndrome with knee and hand osteoarthritis: a community-based study of women. Semin Arthritis Rheum. 2019;48(5):791–8. https://doi.org/10.1016/j.semarthrit.2018.07.007.

    Article  CAS  PubMed  Google Scholar 

  2. Imai A, Ichigo S, Matsunami K, Takagi H. Premenstrual syndrome: management and pathophysiology. Clin Exp Obstet Gynecol. 2015;42(2):123–8.

    Article  CAS  PubMed  Google Scholar 

  3. Kwan I, Onwude JL. Premenstrual syndrome. BMJ Clin Evid 2015;2015

  4. Lanza di Scalea T, Pearlstein T. Premenstrual dysphoric disorder. Med Clin North Am. 2019;103(4):613–28. https://doi.org/10.1016/j.mcna.2019.02.007.

    Article  PubMed  Google Scholar 

  5. Amiel Castro RT, Pataky EA, Ehlert U. Associations between premenstrual syndrome and postpartum depression: a systematic literature review. Biol Psychol. 2019;147: 107612. https://doi.org/10.1016/j.biopsycho.2018.10.014.

    Article  CAS  PubMed  Google Scholar 

  6. Ahmed GK, Elbeh K, Shams RM, Malek MAA, Ibrahim AK. Prevalence and predictors of postpartum depression in Upper Egypt: a multicenter primary health care study. J Affect Disord. 2021;1(290):211–8. https://doi.org/10.1016/j.jad.2021.04.046.

    Article  Google Scholar 

  7. Khedr EM, Ramadan ES, Osman MN, Ahmed GK. Risk factors-related first episode postpartum psychosis among Egyptian women: the role of psychosocial and the biological factors. Egypt J Neurol Psychiatry Neurosurgery. 2023;59(1):51.

    Article  Google Scholar 

  8. Pakharenko L, Vorobii V, Kurtash N, Basiuha I. Association of ace gene polymorphism with the development of premenstrual syndrome. Georgian Med News. 2019;294:37–41.

    Google Scholar 

  9. Fatemi M, Allahdadian M, Bahadorani M. Comparison of serum level of some trace elements and vitamin D between patients with premenstrual syndrome and normal controls: a cross-sectional study. Int J Reprod Biomed. 2019;17(9):647–52. https://doi.org/10.18502/ijrm.v17i9.5100.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Hashim MS, Obaideen AA, Jahrami HA, Radwan H, Hamad HJ, Owais AA, et al. Premenstrual syndrome is associated with dietary and lifestyle behaviors among university students: a cross-sectional study from Sharjah, UAE. Nutrients. 2019. https://doi.org/10.3390/nu11081939.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Lin SY, Yang YC, Chang CY, Hsu WH, Lin CC, Jiang CC, et al. Association of fine-particulate and acidic-gas air pollution with premenstrual syndrome risk. QJM. 2020;113(9):643–50. https://doi.org/10.1093/qjmed/hcaa096.

    Article  CAS  PubMed  Google Scholar 

  12. Hou L, Chang L, Chen L, Zhou R. Reduced reward responsiveness in women with moderate—to—severe premenstrual syndrome: evidence from a probabilistic reward task. Front Psychiatry. 2020;11:28. https://doi.org/10.3389/fpsyt.2020.00028.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Nicolau ZFM, Bezerra AG, Polesel DN, Andersen ML, Tufik S, Hachul H. Plasma leptin and premenstrual syndrome: a review. Obstet Gynecol Surv. 2017;72(11):659–62. https://doi.org/10.1097/OGX.0000000000000502.

    Article  PubMed  Google Scholar 

  14. Hou L, Huang Y, Zhou R. Premenstrual syndrome is associated with altered cortisol awakening response. Stress. 2019;22(6):640–6. https://doi.org/10.1080/10253890.2019.1608943.

    Article  CAS  PubMed  Google Scholar 

  15. Incebiyik A, Camuzcuoglu A, Hilali NG, Ulas T, Vural M, Camuzcuoglu H, et al. Serum oxidative stress, visfatin and apelin in healthy women and those with premenstrual syndrome. J Obstet Gynaecol. 2015;35(2):188–92. https://doi.org/10.3109/01443615.2014.948399.

    Article  CAS  PubMed  Google Scholar 

  16. Appleton SM. Premenstrual syndrome: evidence-based evaluation and treatment. Clin Obstet Gynecol. 2018;61(1):52–61. https://doi.org/10.1097/GRF.0000000000000339.

    Article  PubMed  Google Scholar 

  17. Yesildere Saglam H, Orsal O. Effect of exercise on premenstrual symptoms: a systematic review. Complement Ther Med. 2020;48: 102272. https://doi.org/10.1016/j.ctim.2019.102272.

    Article  PubMed  Google Scholar 

  18. Hallajzadeh J, Khoramdad M, Izadi N, Karamzad N, Almasi-Hashiani A, Ayubi E, et al. Metabolic syndrome and its components in premenopausal and postmenopausal women: a comprehensive systematic review and meta-analysis on observational studies. Menopause. 2018;25(10):1155–64. https://doi.org/10.1097/GME.0000000000001136.

    Article  PubMed  Google Scholar 

  19. Kumari R, Kumar S, Kant R. An update on metabolic syndrome: metabolic risk markers and adipokines in the development of metabolic syndrome. Diabetes Metab Syndr. 2019;13(4):2409–17. https://doi.org/10.1016/j.dsx.2019.06.005.

    Article  PubMed  Google Scholar 

  20. von Elm E, Altman DG, Egger M, Pocock SJ, Gøtzsche PC, Vandenbroucke JP, et al. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453–7. https://doi.org/10.1016/S0140-6736(07)61602-X.

    Article  Google Scholar 

  21. 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(6):631–45. https://doi.org/10.1080/03630242.2018.1539433.

    Article  PubMed  Google Scholar 

  22. Steiner M, Macdougall M, Brown E. The premenstrual symptoms screening tool (PSST) for clinicians. Arch Womens Ment Health. 2003;6(3):203–9. https://doi.org/10.1007/s00737-003-0018-4.

    Article  CAS  PubMed  Google Scholar 

  23. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation. 2009;120(16):1640–5. https://doi.org/10.1161/CIRCULATIONAHA.109.192644.

    Article  CAS  PubMed  Google Scholar 

  24. Masho SW, Adera T, South-Paul J. Obesity as a risk factor for premenstrual syndrome. J Psychosom Obstet Gynaecol. 2005;26(1):33–9. https://doi.org/10.1080/01443610400023049.

    Article  PubMed  Google Scholar 

  25. Bertone-Johnson ER, Hankinson SE, Willett WC, Johnson SR, Manson JE. Adiposity and the development of premenstrual syndrome. J Womens Health (Larchmt). 2010;19(11):1955–62. https://doi.org/10.1089/jwh.2010.2128.

    Article  PubMed  Google Scholar 

  26. Sharifan P, Jafarzadeh Esfehani A, Zamiri A, Ekhteraee Toosi MS, Najar Sedgh Doust F, Taghizadeh N, et al. Factors associated with the severity of premenstrual symptoms in women with central obesity: a cross-sectional study. J Health Popul Nutr. 2023;42(1):9. https://doi.org/10.1186/s41043-022-00343-5.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Cheng SH, Shih CC, Yang YK, Chen KT, Chang YH, Yang YC. Factors associated with premenstrual syndrome—a survey of new female university students. Kaohsiung J Med Sci. 2013;29(2):100–5. https://doi.org/10.1016/j.kjms.2012.08.017.

    Article  PubMed  Google Scholar 

  28. Simon S, Rahat H, Carreau AM, Garcia-Reyes Y, Halbower A, Pyle L, et al. Poor sleep is related to metabolic syndrome severity in adolescents with PCOS and obesity. J Clin Endocrinol Metab. 2020. https://doi.org/10.1210/clinem/dgz285.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Costemale-Lacoste JF, Asmar KE, Rigal A, Martin S, Kader Ait Tayeb AE, Colle R, et al. Severe insomnia is associated with metabolic syndrome in women over 50 years with major depression treated in psychiatry settings: a METADAP report. J Affect Disord. 2020;264:513–8. https://doi.org/10.1016/j.jad.2019.11.084.

    Article  PubMed  Google Scholar 

  30. Fan L, Hao Z, Gao L, Qi M, Feng S, Zhou G. Non-linear relationship between sleep duration and metabolic syndrome: a population-based study. Medicine (Baltimore). 2020;99(2): e18753. https://doi.org/10.1097/MD.0000000000018753.

    Article  PubMed  Google Scholar 

  31. Kononoff A, Vuolteenaho K, Hämäläinen M, Kautiainen H, Elfving P, Savolainen E, et al. Metabolic syndrome, disease activity, and adipokines in patients with newly diagnosed inflammatory joint diseases. J Clin Rheumatol. 2021;27(8):e349–56. https://doi.org/10.1097/RHU.0000000000001412.

    Article  PubMed  Google Scholar 

  32. Kamat SV, Nimbalkar A, Phatak AG, Nimbalkar SM. Premenstrual syndrome in Anand District, Gujarat: a cross-sectional survey. J Family Med Prim Care. 2019;8(2):640–7. https://doi.org/10.4103/jfmpc.jfmpc_302_18.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Bertone-Johnson ER, Ronnenberg AG, Houghton SC, Nobles C, Zagarins SE, Takashima-Uebelhoer BB, et al. Association of inflammation markers with menstrual symptom severity and premenstrual syndrome in young women. Hum Reprod. 2014;29(9):1987–94. https://doi.org/10.1093/humrep/deu170.

    Article  CAS  PubMed  Google Scholar 

  34. Monserrat-Mesquida M, Quetglas-Llabrés M, Capó X, Bouzas C, Mateos D, Pons A, et al. Metabolic syndrome is associated with oxidative stress and proinflammatory state. Antioxidants (Basel). 2020. https://doi.org/10.3390/antiox9030236.

    Article  PubMed  Google Scholar 

  35. Ter Horst R, van den Munckhof ICL, Schraa K, Aguirre-Gamboa R, Jaeger M, Smeekens SP, et al. Sex-specific regulation of inflammation and metabolic syndrome in obesity. Arterioscler Thromb Vasc Biol. 2020;40(7):1787–800. https://doi.org/10.1161/ATVBAHA.120.314508.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We heartfully thank all patients and their family members.

Funding

None.

Author information

Authors and Affiliations

  1. Clinical Pathology Department, Al-Azhar University, Cairo, Egypt

    Mohamed Basiouny Yahia & Medhat Ali Salah

  2. Department of Internal Medicine, Al-Azhar University, Cairo, Egypt

    Seham Sabry, Mahmoud Osama Ahmed Abd El Fattah & Nashwa El-Khouly

  3. Obstetrics and Gynecology Department, Al-Azhar University, Cairo, Egypt

    Samir Khamis Galal, Mohamed Elsayed Hammour, Wael Abd Elatief Khafagy, Mohamed Alkhouly, Mustafa A. A. Osman & Eman Gomaa Rezk

  4. Parasitology Basic Science Department, Al Rayan National College of Medicine, Al-Madinah, Saudi Arabia

    Hend Mohamed Hussein

  5. Department of Mental Health and Psychological Counseling, Ain Shams University, Cairo, Egypt

    Sarah Hossam Eldin Mostafa & Abdelaziz Mahmoud Abdelaziz

  6. Department of Psychiatry, Ain Shams University, Cairo, Egypt

    Sara Sallam

Authors
  1. Mohamed Basiouny Yahia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Medhat Ali Salah
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Seham Sabry
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mahmoud Osama Ahmed Abd El Fattah
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Nashwa El-Khouly
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Samir Khamis Galal
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Mohamed Elsayed Hammour
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Wael Abd Elatief Khafagy
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Mohamed Alkhouly
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Mustafa A. A. Osman
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Eman Gomaa Rezk
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Hend Mohamed Hussein
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Sarah Hossam Eldin Mostafa
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Abdelaziz Mahmoud Abdelaziz
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Sara Sallam
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceived and designed the experiments: MBY, MAS, SS, MOAA, NE, enrolled the patients: SKG, MEH, WAK, Performed the experiments: MA, MAAS, EGR, data management and analysis: HMH, SHM, AMA and Contributed reagents/materials/analysis tools: SaSa, MBY, MAS, SS, MOAA, NE, prepared the manuscript: SKG, MEH, WAK, read and approve the manuscripts: MA, MAAS, EGR, HMH, SHM, AMA. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Mohamed Basiouny Yahia.

Ethics declarations

Ethics approval and consent to participate

The study protocol was approved by the ethical committee of Al-Azhar University Faculty of Medicine on June, 1, 2022. Informed written consent was obtained from all the patients enrolled in this study.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Informed consent

Written informed consent was obtained from all patients or their legal guardians before enrollment in the study.

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/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yahia, M.B., Salah, M.A., Sabry, S. et al. Relation between metabolic syndrome and psychiatric symptoms severity in women with premenstrual syndrome. Egypt J Neurol Psychiatry Neurosurg 60, 18 (2024). https://doi.org/10.1186/s41983-024-00798-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s41983-024-00798-9

Keywords