THE ROLE OF PROSTAGLANDINS IN PRIMARY DYSMENORRHEA AND THE EFFECTIVENESS OF ANTI-INFLAMMATORY THERAPY
Keywords:
primary dysmenorrhea, prostaglandins, PGF2α, PGE2, cyclooxygenaseAbstract
Primary dysmenorrhea is a recurrent menstrual pain syndrome occurring in the absence of
identifiable pelvic pathology. Its most accepted pathophysiological model centers on increased
endometrial production of prostaglandin F2α and prostaglandin E2 during menstruation. These
mediators intensify uterine contractions, increase intrauterine pressure, reduce uterine blood flow,
induce ischemia, and sensitize pain pathways. This article reviews the biological role of
prostaglandins, the clinical rationale for NSAID therapy, timing and safety considerations, realworld
effectiveness, pharmacovigilance, artificial intelligence, and personalized treatment strategies.
The evidence synthesis shows that anti-inflammatory therapy is pathogenetically justified when
primary dysmenorrhea is prostaglandin-dominant. However, treatment success depends on early
initiation, correct dosing, patient education, safety screening, and reassessment when response is
inadequate. Real-world data and AI-based analytics may help identify patients with poor response,
detect adverse drug reaction signals, and improve clinical decision support. The article also
discusses how Uzbekistan’s developing e-prescription and digital health systems may support safer
NSAID use and national evidence generation
complete clinical strategy that includes diagnosis, timing, adherence, contraindication screening,
follow-up, and referral criteria. When this strategy is implemented correctly, prostaglandin
inhibition remains a rational first-line approach. When it fails, clinicians should consider secondary
dysmenorrhea, central sensitization, inadequate exposure, or non-prostaglandin mechanisms rather
than repeating empirical analgesic escalation
References
1. World Health Organization. Menstrual health. Geneva: WHO; 2026. Available from:
https://www.who.int/news-room/fact-sheets/detail/menstrual-health
2. Itani R, Soubra L, Karout S, Rahme D, Karout L, Khojah HMJ. Primary dysmenorrhea:
pathophysiology, diagnosis, and treatment updates. Korean J Fam Med. 2022;43(2):101-108.
doi:10.4082/kjfm.21.0103
3. Marjoribanks J, Ayeleke RO, Farquhar C, Proctor M. Nonsteroidal anti-inflammatory drugs for
dysmenorrhoea. Cochrane Database Syst Rev. 2015;(7):CD001751.
doi:10.1002/14651858.CD001751.pub3
4. American College of Obstetricians and Gynecologists. Committee Opinion No. 760:
Dysmenorrhea and endometriosis in the adolescent. Obstet Gynecol. 2018;132(6):e249-e258.
doi:10.1097/AOG.0000000000002978
5. Becker CM, Bokor A, Heikinheimo O, Horne A, Jansen F, Kiesel L, et al. ESHRE guideline:
endometriosis. Hum Reprod Open. 2022;2022(2):hoac009. doi:10.1093/hropen/hoac009
6. Gutman G, Nunez AT, Fisher M. Dysmenorrhea in adolescents. Curr Probl Pediatr Adolesc
Health Care. 2022;52(5):101186. doi:10.1016/j.cppeds.2022.101186
7. Osayande AS, Mehulic S. Diagnosis and initial management of dysmenorrhea. Am Fam
Physician. 2014;89(5):341-346
8. Smith RP. Dysmenorrhea and menorrhagia. In: Berek JS, editor. Berek & Novak’s Gynecology.
16th ed. Philadelphia: Wolters Kluwer; 2020.
9. Dawood MY. Primary dysmenorrhea: advances in pathogenesis and management. Obstet
Gynecol. 2006;108(2):428-441. doi:10.1097/01.AOG.0000230214.26638.0c
10. Akerlund M. Pathophysiology of dysmenorrhea. Acta Obstet Gynecol Scand Suppl.
1979;87:27-32.
11. Pulkkinen MO. Prostaglandins and the non-pregnant uterus: the pathophysiology of primary
dysmenorrhea. Acta Obstet Gynecol Scand Suppl. 1983;113:63-67.
doi:10.3109/00016348309155200
12. Zheng Q, Huang G, Cao W, Zhao Y. Comparative effectiveness of exercise interventions for
primary dysmenorrhea: a systematic review and network meta-analysis. BMC Womens Health.
2024;24:610. doi:10.1186/s12905-024-03453-w
13. Tsai IC, Hsu CW, et al. Comparative effectiveness of different exercises for reducing pain
intensity in primary dysmenorrhea: a systematic review and network meta-analysis of
randomized controlled trials. Sports Med Open. 2024;10:63. doi:10.1186/s40798-024-00718-4
14. Sharma S, Ali K, Narula H. Exercise therapy and electrotherapy as an intervention for primary
dysmenorrhea: a systematic review and meta-analysis. J Lifestyle Med. 2023;13(1):16-26.
doi:10.15280/jlm.2023.13.1.16
15. Heat therapy for primary dysmenorrhea: a systematic review and meta-analysis. Available from:
https://pmc.ncbi.nlm.nih.gov/articles/PMC12876241/
16. Damm T, Lamvu G, Carrillo J, Ouyang C, Feranec J. Continuous vs. cyclic combined hormonal
contraceptives for treatment of dysmenorrhea: a systematic review. Contracept X.
2019;1:100002. doi:10.1016/j.conx.2019.100002
17. Shi J, Leng J. Effect and safety of drospirenone and ethinylestradiol tablets (II) for
dysmenorrhea: a systematic review and meta-analysis. Front Med (Lausanne). 2022;9:938606.
doi:10.3389/fmed.2022.938606
18. Naz MSG, Kiani Z, Rashidi Fakari F, Ozgoli G. The effect of micronutrients on pain
management of primary dysmenorrhea: a systematic review and meta-analysis. J Caring Sci.
2020;9(1):47-56. doi:10.34172/jcs.2020.008
19. Harel Z. Dysmenorrhea in adolescents and young adults: etiology and management. J Pediatr
Adolesc Gynecol. 2006;19(6):363-371. doi:10.1016/j.jpag.2006.09.001
20. French L. Dysmenorrhea. Am Fam Physician. 2005;71(2):285-291.
21. Food and Drug Administration. Real-world evidence. Silver Spring: FDA. Available from:
https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence
22. Food and Drug Administration. Use of real-world evidence to support regulatory decisionmaking
for medical devices: guidance for industry and FDA staff. Silver Spring: FDA; 2025.
23. European Medicines Agency. Real-world evidence. Amsterdam: EMA; 2026. Available from:
24. European Medicines Agency. Data Analysis and Real World Interrogation Network (DARWIN
EU). Amsterdam: EMA; 2026.
25. European Medicines Agency. Guide on real-world evidence provided by EMA: support for
regulatory decision-making. Amsterdam: EMA; 2024.
26. World Health Organization. Ethics and governance of artificial intelligence for health. Geneva:
WHO; 2021.
27. World Health Organization. Ethics and governance of artificial intelligence for health: guidance
on large multi-modal models. Geneva: WHO; 2024
28. Food and Drug Administration. Artificial Intelligence/Machine Learning (AI/ML)-Based
Software as a Medical Device Action Plan. Silver Spring: FDA; 2021.
29. Food and Drug Administration, Health Canada, Medicines and Healthcare products Regulatory
Agency. Good machine learning practice for medical device development: guiding principles.
2021.
30. International Council for Harmonisation. ICH E9(R1): Addendum on estimands and sensitivity
analysis in clinical trials. Geneva: ICH; 2019.
31. International Council for Harmonisation. Reflection paper on real-world evidence terminology
and convergence of general principles regarding planning and reporting of studies using realworld
data. Geneva: ICH; 2023.
32. Hernán MA, Robins JM. Using big data to emulate a target trial when a randomized trial is not
available. Am J Epidemiol. 2016;183(8):758-764. doi:10.1093/aje/kwv254
33. Austin PC. An introduction to propensity score methods for reducing the effects of confounding
in observational studies. Multivariate Behav Res. 2011;46(3):399-424.
doi:10.1080/00273171.2011.568786
34. Kaplan EL, Meier P. Nonparametric estimation from incomplete observations. J Am Stat Assoc.
1958;53(282):457-481.
35. Cox DR. Regression models and life-tables. J R Stat Soc Series B. 1972;34(2):187-220.
36. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The
PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ.
2021;372:n71. doi:10.1136/bmj.n71
37. von Elm E, Altman DG, Egger M, Pocock SJ, Gotzsche PC, Vandenbroucke JP. The STROBE
statement: guidelines for reporting observational studies. Lancet. 2007;370(9596):1453-1457.
doi:10.1016/S0140-6736(07)61602-X
38. Sterne JAC, Savovic J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised
tool for assessing risk of bias in randomized trials. BMJ. 2019;366:l4898.
doi:10.1136/bmj.l4898
39. O‘zbekiston Respublikasi Prezidentining PQ-140-son qarori. Sog‘liqni saqlash tizimini
raqamlashtirishga doir qo‘shimcha chora-tadbirlar to‘g‘risida. Toshkent; 2023.
40. O‘zbekiston Respublikasi Sog‘liqni saqlash vazirligi. Elektron retsept tizimini joriy etish
bo‘yicha axborot. Toshkent; 2025.
41. UzDaily. Uzbekistan launches Electronic Prescription system from 10 December. Tashkent;
2025.
42. Spot. Sog‘liqni saqlash tizimi qanday raqamlashtiriladi? Toshkent; 2023.