{"paper_id":"1af26df8-53f1-4993-9c2a-7e249a02d383","body_text":"Dysmenorrhoea (period pain) is the most common gynaecological condition in women of reproductive age ( Iacovides  et al.  2015 ). It is characterised by cyclical menstrual pain, which may be accompanied by headaches, nausea, vomiting, diarrhoea, bloating or fatigue ( Iacovides  et al.  2015 ). Prevalence rates for dysmenorrhoea range from 16 to 91% ( Ju  et al.  2014 ) or from 2 to 36% for severe pain ( Söderman  et al.  2019 ). It has a substantial impact on school or university attendance, social activities and quality of life ( Söderman  et al.  2019 ) as well as significant economic consequences ( Ju  et al.  2014 ). However, despite its high prevalence and personal and societal impacts, dysmenorrhoea is generally understudied, and the aetiology is not yet fully understood.\nRisk factors for dysmenorrhoea include smoking, longer bleeding duration, heavy menstrual flow and high stress levels ( Tavallaee  et al.  2011 ,  Muluneh  et al.  2018 ). Family history (FHx) has also been recognised as an important risk factor ( Ozerdogan  et al.  2009 ,  Iacovides  et al.  2015 ,  Muluneh  et al.  2018 ). This is usually defined as having a first-degree relative (mother, sister) with dysmenorrhoea and can increase the risk of dysmenorrhoea 3.5-fold compared to individuals with no FHx ( Ozerdogan  et al.  2009 ). Women with FHx of dysmenorrhoea also tend to experience more severe pain ( Tavallaee  et al.  2011 ). This could be due to shared environments and learned behaviours regarding pain ( Hu  et al.  2020 ), but a genetic component is also likely, considering twin studies have previously estimated heritability to be 38% for menstrual pain ( Silberg  et al.  1987 ).\nIt is important to acknowledge that primary dysmenorrhoea (PDM) (no underlying pelvic pathology; comprising 90% of cases) ( Gutman  et al.  2022 ) is likely to have distinct risk factors and pathogenesis to secondary dysmenorrhoea (SDM) (pain associated with an underlying disease, for example, endometriosis, adenomyosis, fibroids or pelvic inflammatory disease) ( Durand  et al.  2021 ). While a laparoscopy is the only definitive method for confirming the diagnosis of PDM (by excluding SDM causes not visible on imaging), many studies report a PDM diagnosis on the basis of clinical judgement, e.g. using one or a combination of history taking, clinical examination or ultrasonography, potentially skewing the research focused on PDM.\nCurrently, pharmacological treatments for PDM are limited to hormonal therapies (usually aiming to suppress menstruation or prevent thickening of the uterine lining) or non-steroidal anti-inflammatory drugs (NSAIDs). NSAIDs have been shown to be 4.4 times more effective than placebo in treating PDM ( Marjoribanks  et al.  2015 ), supporting the theory that overproduction of prostaglandins, particularly PGF2a and PGE2, contributes to the pathogenesis of dysmenorrhoea ( Iacovides  et al.  2015 ). However, current treatment strategies do not provide adequate relief for all who experience dysmenorrhoea, and there is an increasing move away from hormone therapies, especially among adolescents and young adults ( Hellström  et al.  2019 ). Thus, novel therapies are urgently needed. A better understanding of the genetic determinants of dysmenorrhoea is key to informing the development of new therapies. Advancing knowledge in this area is likely to have a far-reaching impact given the influence of dysmenorrhoea on all domains of life and the increasing evidence that menstrual pain is associated with adverse long-term health outcomes, such as mental health diagnoses ( Zhao  et al.  2021 ) and chronic pain ( Reid-Mccann  et al.  2025 ).\nThis systematic review aims to summarise the current evidence for determining the associations between gene polymorphisms and PDM to further inform the field.\n\nThis review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement ( Page  et al.  2021 ), and the protocol was registered on PROSPERO (registration ID: CRD42023455337) on 17 August 2023.\nA comprehensive search strategy was designed by EH, MS and KV with advice from a medical librarian, using key terms relating to dysmenorrhoea and genetic factors, for example, ‘dysmenorrhea’, ‘dysmenorrhoea’, ‘menstrual pain’, ‘polymorphisms’ and ‘SNPs’. The search strategy was subsequently trialled and iteratively optimised via multiple pilot searches, which were cross-checked against a list of target articles collated from a preliminary exploration of the literature. Final searches were conducted in October 2023 using three databases (MEDLINE (Ovid), Embase (Ovid) and Web of Science Core Collection). The search strategy used in MEDLINE can be seen in Supplementary Fig. 1 (see section on  Supplementary materials  given at the end of the article).\nAuto-alerts were set up following the October 2023 search so that, as new articles were added to the databases, the searches were automatically re-run to identify any additional relevant articles. Any such articles were evaluated and screened until November 2025.\nSearch results were retrieved and duplicates were removed, both automatically in Endnote 21 and manually by EH, when necessary. References were then imported into Rayyan ( Ouzzani  et al.  2016 ) for blinded screening. Two authors (EH and MS) independently screened the titles and abstracts (primary screening) and then read the full text of the remaining articles in the second screening stage. Each stage was followed by a conflict resolution meeting to resolve any differences in inclusion/exclusion of articles and establish a consensus. Additional articles identified up to November 2025 were screened by EH and then discussed with MS to validate their inclusion/exclusion.\nAll English language studies investigating specific genes/polymorphisms associated with PDM in females were included. No restriction was placed on year of publication. Non-human studies and studies not published in peer-reviewed journals were excluded. Full inclusion and exclusion criteria are listed in  Table 1 . Given that PDM is often poorly defined, with a lack of as yet unidentified pathology assumed to mean the lack of pathology present, we also included studies that did not differentiate between PDM and SDM. However, any articles relating only to SDM were excluded.\nInclusion and exclusion criteria.\nHumans – female only\nAnimal studies\nPDM as defined by the study\nSDM – e.g. confirmed diagnosis of endometriosis, adenomyosis or any other identifiable pelvic pathologies\n○ If the study does not differentiate between PDM and SDM, it will still be included\nAge range: menarche to end of life\nDysmenorrhoea due to intrauterine contraceptive device\nStudy design of any type\nCancer diagnosis\nStudies presenting original data regarding specific genes/polymorphisms involved in PDM in females\nStudies not published in peer-reviewed journals – grey literature, conference abstracts, PhD theses, pre-prints, case reports, letters, editorials, reviews, opinion pieces\nArticles not in English language\nPDM, primary dysmenorrhoea; SDM, secondary dysmenorrhoea.\nThe relevant data from eligible studies were extracted following the second screening stage. The following data were extracted and tabulated: paper title; first author; year published; ethnic origin of participants; country of study; study design/type of study; how PDM is defined; inclusion criteria for PDM subjects; how SDM cases were excluded; how healthy controls were defined; number of participants; number of controls; mean age of participants; mean age of controls; method of genotyping; statistical analysis software used; genetic associations found (results of the study); other significant variables associated with primary dysmenorrhoea; and author conclusions.\nThe quality of the included studies was also evaluated at this stage to assess the risk of bias. The STREGA guidelines ( Little  et al.  2009 ) were used for every study, and the Newcastle–Ottawa Scale (NOS) ( Wells  et al.  2000 ), in addition, for case–control studies. The NOS involves three sections, in which stars are awarded for fulfilling certain criteria – selection of cases and controls (max. four stars); comparability of cases and controls (max. two stars); and ascertainment of exposure (max. three stars). Studies were ranked as having a high (0–3 stars), medium (4–6 stars) or low (7–9 stars) risk of bias. All studies were included in the review regardless of the methodological quality assessed by these tools.\nData extraction and quality assessment were carried out by one investigator (EH) and, upon completion, checked independently by MS.\nA narrative synthesis was conducted, summarising the significant genetic variants ( P  < 0.05 for candidate gene studies;  P  < 5 × 10 −8  for GWASs) identified as being associated with PDM, as well as their specific genetic location and known or potential biological function. Meta-analysis was planned for genetic variants, including subgroup analysis based on ethnicity, if sufficient data were available.\n\nThe original search (October 2023) yielded 774 articles after duplicates were removed. Publication year ranged from 1950 to 2023. A total of 748 articles (97%) were excluded during the title/abstract screening, meaning that 26 articles progressed to the second screening stage. After a full-text review, a further 11 articles (42%) were excluded, leaving 15 articles to be included in the systematic review. Evaluation of the search auto-alerts until November 2025 identified five potentially relevant articles ( Nacar  et al.  2023 ,  Chen  et al.  2024 ,  Hsu  et al.  2024 ,  Li  et al.  2024 ,  Liu  et al.  2024 ); however, none of these met the inclusion criteria following a full-text review. A detailed search and screening history is shown in  Fig. 1 .\nFlow chart detailing identification, screening and inclusion of studies in this review.\nOf the 15 included studies, seven (46.7%) were case–control studies, four (26.7%) were cross-sectional studies, and four (26.7%) were GWASs.\nThe characteristics of the included case–control and cross-sectional studies are summarised in  Table 2 . Five studies involved Turkish participants ( Ozsoy  et al.  2015 ,  Dogru  et al.  2016 ,  Ozsoy  et al.  2016 ,  Esen  et al.  2020 ,  Nacar  et al.  2022 ), two studies had a Nigerian population ( Olasore  et al.  2022 ,  Olasore  et al.  2023 ), and there was one study with each of the following populations: Taiwanese ( Lee  et al.  2014 ), Chinese ( Wu  et al.  2000 ), Asian ( Donayeva  et al.  2023 ) and South Korean ( Woo  et al.  2010 ). It is important to note that  Olasore  et al . (2022)  make reference to over 250 different ethnic groups within Nigeria, so their population is likely to be diverse. The mean age of participants ranged from 15 to 26 years. Two studies were conducted in adolescents (aged ≤ 17 years) ( Woo  et al.  2010 ,  Donayeva  et al.  2023 ). One study did not report the mean age of participants ( Wu  et al.  2000 ). For case–control studies, sample sizes ranged from 200 ( Lee  et al.  2014 ) to 302 ( Ozsoy  et al.  2016 ) ( Table 3 ). For cross-sectional studies, sample sizes ranged from 102 ( Olasore  et al.  2022 ) to 435 individuals ( Wu  et al.  2000 ) ( Table 3 ).\nCharacteristics of case–control and cross-sectional (candidate gene) studies.\nValues are mean age in years.\nAll participants were aged between 16 and 17 years.\nMean age of both cohorts was 20.6 years.\nMean age of both cohorts was 21.0 years.\nInner Central Black Sea region.\nBlack Sea region.\nMiddle Black Sea region.\nKazakhstan Aktobe region.\nGI, gastrointestinal; NRS, numerical rating scale; NSAIDs, non-steroidal anti-inflammatory drugs; OCP, oral contraceptive pill; PCOS, polycystic ovary syndrome; VAS, visual analogue scale; PDM, primary dysmenorrhoea; SDM, secondary dysmenorrhoea.\nFindings from case–control and cross-sectional (candidate gene) studies.\nP  values in bold indicate polymorphisms significantly associated with PDM ( P  < 0.05).\nSummary statistic for association with recurrent dysmenorrhoea (no association found for occasional dysmenorrhoea – data not presented here).\nrs# was not reported in paper and could not be determined after Google search. PDM, primary dysmenorrhoea.\nIt is important to note that five of the seven case–control studies (71%) were conducted by the same research group and recruited patients from the same hospital in the Central Black Sea region of Turkey ( Ozsoy  et al.  2015 ,  Dogru  et al.  2016 ,  Ozsoy  et al.  2016 ,  Esen  et al.  2020 ,  Nacar  et al.  2022 ). Therefore, it is possible that the same or a very similar population was used for each, especially given the almost identical mean ages of cases and controls ( Table 2 ).\nWe assessed whether and how these studies differentiated between PDM and SDM in their populations. Among these 11 studies, nine differentiated between PDM and SDM ( Wu  et al.  2000 ,  Lee  et al.  2014 ,  Ozsoy  et al.  2015 ,  2016 ,  Dogru  et al.  2016 ,  Esen  et al.  2020 ,  Nacar  et al.  2022 ,  Donayeva  et al.  2023 ,  Olasore  et al.  2023 ). Three used ultrasonography ( Lee  et al.  2014 ,  Dogru  et al.  2016 ,  Donayeva  et al.  2023 ), while three others reported using a clinical examination only ( Ozsoy  et al.  2015 ,  Esen  et al.  2020 ,  Nacar  et al.  2022 ). The remaining three studies used history alone to exclude women with an existing diagnosis of conditions related to SDM, such as endometriosis, PCOS, myoma or ovarian cyst ( Wu  et al.  2000 ,  Ozsoy  et al.  2016 ,  Olasore  et al.  2023 ). No studies reported using laparoscopy to exclude SDM. Two studies made no attempt to differentiate between PDM and SDM ( Woo  et al.  2010 ,  Olasore  et al.  2022 ); therefore, it is likely that some SDM cases are present in these study cohorts.\nThe results of the 11 case–control and cross-sectional studies are detailed in  Table 3 .\nTwenty different polymorphisms were investigated across the 11 candidate gene studies, selected specifically by studies due to their known or suspected roles in oestrogen metabolism, cytokine, prostaglandin or other inflammatory signalling, circadian regulation of hormone and pain pathways, and pain modulation ( Table 3 ). Three polymorphisms ( ESR1  PvuII,  IL4  VNTR and  GSTM1 ) were each investigated in two separate studies, but otherwise each study investigated a different polymorphism.\nTwo studies found that the  ESR1  -397T>C (PvuII) polymorphism was significantly ( P  < 0.05) associated with dysmenorrhoea. One study found that the C allele (OR: 1.46, 95% CI: 1.06–2.01,  P  = 0.021) and CC genotype (OR: 2.60, 95% CI: 1.40–4.95,  P  = 0.002) of the  ESR1  -397T>C (PvuII) polymorphisms were associated with an increased risk of PDM ( Ozsoy  et al.  2016 ).  Woo  et al . (2010)  also reported that this polymorphism was associated with PDM under the dominant model (CC + CT:TT) (OR: 3.38, 95% CI: 1.39–8.21,  P  = 0.007), suggesting that the C allele in the  ESR1  polymorphism is associated with dysmenorrhoea.\nTwo studies found that the  IL4  intron 3 VNTR polymorphism was not associated with PDM. This was consistent in  Ozsoy  et al.  (2015)  using the P2P2 + P1P2:P1P1 genotype (OR: 1.1, 95% CI: 0.2–6.2,  P  = 0.886), P2P2: P1P2 + P1P1 genotype (OR: 0.8, 95% CI: 0.4–1.3,  P  = 0.357) or allele frequencies (OR: 0.8, 95% CI: 0.5–1.3,  P  = 0.440) ( Ozsoy  et al.  2015 ,  Esen  et al.  2020 , also using the P1P1 + P1P2:P2P2 genotypes (OR: 0.67, 95% CI: 0.38–1.16,  P  > 0.05), P1P1:P1P2 + P2P2 genotypes (OR: 0.48, 95% CI: 0.01–6.38,  P  > 0.05) or allele frequencies (OR: 0.71, 95% CI: 0.43–1.15,  P  > 0.05) ( Esen  et al.  2020 ). The replication of this finding increases confidence in a true lack of effect; although the estimates are imprecise, sample sizes are small ( n  = 236–294), and generalisability is limited by both studies being conducted in a Turkish population.\nWith regard to the  GSTM1  polymorphism (either the presence or absence of  GSTM1 ), two studies produced contrasting results.  GSTM1  absence was associated with recurrent PDM in one study ( Wu  et al.  2000 ) (adjusted OR: 1.8, CI: 1.0–3.4) ( n  = 435), and in the same study,  GSTM1  absence also increased the risk of recurrent PDM when combined with the  CYP2D6  Aa/aa (adjusted OR: 3.1, 95% CI: 1.2–8.0) or  CYP2D6  AA genotype (adjusted OR: 2.3, 95% CI: 1.0–5.1) ( Wu  et al.  2000 ). However,  Woo  et al.  (2010)  ( n  = 202) found that the  GSTM1 -absent polymorphism was not associated with dysmenorrhoea (unadjusted OR: 0.6, 95% CI: 0.3–1.4,  P  = 0.2) ( Woo  et al.  2010 ).\nFor the 17 polymorphisms investigated by a single study, six were found to be significantly ( P  < 0.05) associated with PDM:  BDNF  Val66Met ( Lee  et al.  2014 ),  TNF  -308G>A ( Dogru  et al.  2016 ),  PER3  VNTR ( Nacar  et al.  2022 ),  COX2  -1195G>A ( Olasore  et al.  2022 ),  VDR  TaqI ( Donayeva  et al.  2023 ) and  eNOS  Glu298Asp ( Olasore  et al.  2023 ). Two combined genotypes were also associated with PDM:  CYP2D6/GSTM1  variant genotypes ( Wu  et al.  2000 ) and  ESR1  AG-TC (heterozygous at XbaI and PvuII) ( Ozsoy  et al.  2016 ). In addition, eight of the investigated polymorphisms and one combined genotype were not found to be associated with dysmenorrhoea:  CYP2D6  ( Wu  et al.  2000 );  GSTT1  ( Woo  et al.  2010 );  GSTP1  ( Woo  et al.  2010 );  MTHFR  C677T ( Ozsoy  et al.  2015 );  MIF  -173G>C ( Dogru  et al.  2016 );  ESR1  -351A>G (XbaI),  IL6  -572G>C and  IL6  -597G>A ( Ozsoy  et al.  2016 ); and the combined  IL6  -572G>C/-597G>A genotype ( Ozsoy  et al.  2016 ).\nThe characteristics of GWASs are summarised in  Table 4 . Participants in these studies were of European ( Jones  et al.  2016 ), Mainland Eastern Chinese ( Li  et al.  2017 ), Japanese ( Hirata  et al.  2018 ) and Taiwanese Han Chinese ( Lee  et al.  2022 ) ancestries. Total cohort sizes ranged from 6,770 ( Li  et al.  2017 ) to 15,206 ( Lee  et al.  2022 ), while the mean ages of cases and controls ranged from 19.9 and 19.4 years, respectively ( Li  et al.  2017 ), to 38.4 and 40.9 years, respectively ( Lee  et al.  2022 ). Two studies did not report mean age at group level for cases and controls ( Jones  et al.  2016 ,  Hirata  et al.  2018 ); however,  Jones  et al.  (2016)  reported that participants were between 18 and 45 years, while  Hirata  et al.  (2018)  had mean ages (SD) ranging from 31 (6.4) to 35 (7.0) when presented by pain severity level.\nCharacteristics of genome-wide association studies (GWAS).\nStudy measured dysmenorrhea severity with an ordinal variable: 1,785 women reported no pain; 10,106 women reported at least some pain.\nNot reported in study.\nStudy reported that participants were between 18 and 45 years old.\nStudy measured dysmenorrhea severity using an ordinal scale: 477 women reported no pain; 10,871 women reported at least some pain.\nAge was presented for five dysmenorrhea severity levels: mean (SD) for no pain was 34.7(7.0) years; for the highest pain level, mean (SD) was 31.3 (6.4) years.\nPCOS, polycystic ovary syndrome; VAS, visual analogue scale; PDM, primary dysmenorrhea; SDM, secondary dysmenorrhea.\nIn terms of differentiation between PDM and SDM cases, one study used clinical examination only ( Li  et al.  2017 ), while another excluded women with existing diagnoses of conditions related to SDM from their medical history ( Lee  et al.  2022 ). Two studies did not differentiate between PDM and SDM ( Jones  et al.  2016 ,  Hirata  et al.  2018 ).\nSeven index SNPs reached genome-wide significance (GWS) ( P  < 5 × 10 −8 ) in their respective studies ( Table 5 ). All seven SNPs co-localised with three genes ( NGF ,  IL1  and  ZMIZ1 ), with associations at  NGF  and  IL1  loci being replicated in at least one other study. All four GWASs found an association between SNPs located near the  NGF  locus and PDM – this represents the most widely validated association based on currently available data in this field. Notably, the associations with  NGF  were replicated across different ethnic groups ( n  = 3 East Asian and  n  = 1 European), while the findings relating to  IL1  were replicated in two East Asian populations ( Hirata  et al.  2018 ,  Lee  et al.  2022 ).  Hirata  et al.  (2018)  found that the two  NGF  SNPs that  Jones  et al.  (2016)  and  Li  et al.  (2017)  had identified in their studies (rs7523086 and rs752381, respectively) were in high linkage disequilibrium (LD,  r 2  > 0.8) with their GWS SNP (rs12030576). Similarly,  Lee  et al.  (2022)  reported that all three previously identified  NGF  SNPs were in high LD ( r 2  > 0.8) with the GWS SNP they found at this locus (rs2982742).\nSignificant loci for primary dysmenorrhea from genome-wide association studies (GWAS).\nNot reported in study.\nValue is effect size (95% CI) (per unit increase in dysmenorrhea severity on a 4-point ordinal scale).\nAssociations at  IL1  were reported by both  Hirata  et al.  (2018)  and  Lee  et al.  (2022)  – SNPs rs80111889 and rs11676014, respectively – with LD analysis by  Lee  et al.  (2022)  replicating  Hirata  et al. ’s (2018)  SNP finding. An association between  IL1A  gene (SNP rs3783550) and PDM was acknowledged by  Li  et al.  (2017) , but it did not reach GWS, after neither principal component analysis nor gene-based and pathway-based analyses ( P  = 2.6 × 10 −5 ).  Hirata  et al.  (2018) , nevertheless, found this SNP (rs3783550) to be in LD with their  IL1  variant, suggesting some involvement in dysmenorrhoea.\nThe finding at  ZMIZ1  was not replicated, being identified only by  Li  et al.  (2017) . However, it is worth mentioning that all SNPs reported by  Li  et al.  (2017)  only reached significance after replication analysis in an independent cohort.\nEach GWAS also conducted a functional analysis of its GWS findings to identify the putative biological roles of GWS.\nAll GWASs identified index SNPs that were co-localised with the  NGF  gene, each of which were found to be non-coding (three intergenic ( Jones  et al.  2016 ,  Li  et al.  2017 ,  Hirata  et al.  2018 ) and one intronic to  NGF  ( Lee  et al.  2022 )). Each study suggested putative regulatory functions of the identified genetic loci.  Li  et al.  (2017)  predicted that most SNPs (including the index SNP and those in LD) resided within promoter and/or enhancer elements, while  Jones  et al.  (2016)  also reported that half of SNPs in the cluster overlapped with promoter/enhancer histone marks in gynaecological tissues, suggesting that these variants lie within a regulatory domain that may control the gene expression.  Lee  et al.  (2022)  also predicted 21 of 24 SNPs within the  NGF -situated cluster to alter regulatory binding motifs.\nJones  et al.  (2016) ,  Hirata  et al.  (2018) , and  Lee  et al.  (2022)  also reported the relevance of their GWAS hits to  RP4-663N10.1  – a conserved antisense long non-coding RNA (lncRNA) that spans the length of the  NGF  gene. In tissue expression analysis,  Jones  et al.  (2016)  reported highest expression of  RP4-663N10.1  in adipose tissue and gynaecological tissues, including the cervix, the fallopian tubes and the uterus. Similarly,  Hirata  et al.  (2018)  found moderate-to-strong support for the co-localisation of the index and LD SNPs with  RP4-663N10.1  eQTLs (expression quantitative trait loci) in adipose, ovarian and uterus tissues. eQTLs are genetic loci that regulate expression levels of mRNAs or proteins and thus explain some variation within a gene expression phenotype ( Nica & Dermitzakis 2013 ). However, these studies found conflicting directions of effect, with  Hirata  et al.  (2018)  finding an increased expression of  RP4-663N10.1  in gynaecological tissues associated with increased dysmenorrhoea, while  Jones  et al.  (2016)  finding an increased dysmenorrhoea pain severity associated with reduced  RP4-663N10.1  expression. Fifteen SNPs in  Lee  et al.  (2022)  were eQTLs for  RP4-663N10.1  but in aortic tissue only.\nHirata  et al.  (2018)  found that  IL1  SNPs (rs80111889 and 42 high LD variants) were intronic to  IL1A  or intergenic regions between  IL1A  and  IL1B  or  IL1A  and  CKAP2L . Strong support was found for co-localisation with  IL1A  eQTLs. Fourteen of the 42 high LD SNPs overlapped promoter/enhancer regions for ≥20 tissues.  Lee  et al.  (2022)  found that SNPs at the  IL1  locus were mostly located in intergenic regions and, similarly to  Hirata  et al.  (2018) , were predicted to have a regulatory function. A novel finding was that two SNPs at the  IL1B  locus were found to be eQTLs for  NT5DC4 , a gene within the NT5DC family that encodes a 5′-nucleotidase that catalyses hydrolysis of intracellular 5′ nucleotides ( Singgih  et al.  2021 ). Any relevance to dysmenorrhoea has not yet been explored ( Cros-Perrial & Jordheim 2025 ).\nTissue-specific regulatory analysis for the  ZMIZ1  index SNP (rs76518691) and its LD SNPs highlighted activity marks in adipose-derived mesenchymal stem cells ( Li  et al.  2017 ).\nAll 15 studies were evaluated using the STREGA guidelines. Several checklist items were absent from many, if not all, studies, demonstrating that improvement in the reporting of genetics studies is needed. Surprisingly, the majority of studies did not report participant recruitment periods, with some also not specifying where the population was recruited from. Two further key areas lacking in reporting quality were the laboratory genotyping methods, where many failed to report where genotyping was done and how the DNA was stored prior to analysis, and any discussion of confounders or sources of potential bias by the authors. Some studies also failed to report genetic variants with widely used nomenclature, often lacking the rs#. Supplementary Table 1 provides a comprehensive overview of our STREGA evaluation of all 15 studies.\nOf the seven case–control studies assessed using the NOS, five had high risk of bias and two had medium risk of bias. No studies adequately matched cases and controls or reported the non-response rate. Other common issues were studies not reporting where controls were recruited from and how PDM cases and controls were defined. The full NOS risk-of-bias assessment can be seen in Supplementary Table 2.\nThere were insufficient data to undertake a meta-analysis as had been planned.\n\nThis review qualitatively summarises the current state of our understanding of the genetic associations with PDM. We identified evidence for loci at genes involved in inflammatory ( IL1 ,  TNF ), pain ( NGF ,  BDNF ) and oestrogen metabolism ( ESR1 ) pathways. Our review also highlights how poorly PDM is defined within studies and the limited data covering populations of mixed ethnicity, with only one transethnic finding ( NGF ). Since several of the gene findings have also been identified as endometriosis loci, this highlights how important proper differentiation between PDM and SDM is in order to understand their disease profiles and inform future treatments but also raises the important question of how distinct PDM and SDM really are.\nGiven that dysmenorrhoea is, by definition, a pain condition, it is perhaps unsurprising that the best replicated finding across studies was an association between  NGF  SNPs and PDM. NGF is a neurotrophin that stimulates nerve growth and has a well-established role in pain pathophysiology ( Barker  et al.  2020 ). Both pre-clinical and human studies have demonstrated a role for NGF in nociceptor sensitisation in both the short term and longer term. Importantly, in the context of dysmenorrhoea (which is related to contractions of the uterine muscle), NGF administration has been shown to be associated with the development of muscle pain ( Lewin  et al.  1993 ,  Petty  et al.  1994 ). In addition, NGF can trigger the release of inflammatory mediators and BDNF ( Barker  et al.  2020 ).\nA recent large-scale GWAS for endometriosis ( Rahmioglu  et al.  2023 ) also identified a locus at the  NGF  gene. There is also evidence that NGF-containing peritoneal fluid from endometriosis patients increases neurite outgrowth ( Barcena De Arellano  et al.  2011 ). However, other studies are inconsistent in their findings relating NGF levels to endometriosis pain symptoms ( Barcena De Arellano  et al.  2013 ,  Kajitani  et al.  2013 ). BDNF has also been shown to relate to symptom severity and is suggested to have an important role in endometriosis-associated pain ( Ding  et al.  2018 ).\nAn established role of NGF in PDM could open the door to novel therapeutic opportunities, such as anti-NGF therapies. These have already been explored in other chronic pain conditions, including in women with interstitial cystitis/bladder pain syndrome ( Nickel  et al.  2016 ), a visceral pain phenotype with many features in common with dysmenorrhoea. However, significant adverse events, including abnormal peripheral sensation in trials of these therapies in osteoarthritis ( Zhao  et al.  2022 ), have led to the recommendation that more research and long-term follow-up are needed to address the current inconsistent, inconclusive safety outcomes.\nThe  ESR1  PvuII polymorphism was also identified as a risk factor in two studies ( Woo  et al.  2010 ,  Ozsoy  et al.  2016 );  ESR1  is known to be involved in oestrogen signalling. While there is a current lack of research on their role in PDM,  ESR1  in particular has been investigated in relation to endometriosis. Studies have demonstrated higher oestrogen receptor expression in endometriosis lesions of women with moderate-to-severe dysmenorrhoea, compared to those with absent or mild pain ( Pluchino  et al.  2020 ), potentially demonstrating a pain-specific role of oestrogen in dysmenorrhoea. Given that hormonal anti-oestrogen contraceptives are also used as treatment for PDM, the role of  ESR1  should be explored further. Interestingly, however, none of the PDM-focused GWASs identified  ESR1  as a significant variant, while endometriosis-focused GWASs have ( Rahmioglu  et al.  2023 ). This could be explained by the fact that studies included in this review likely failed to exclude all secondary dysmenorrhoea cases.\nInflammatory processes, particularly prostaglandins, have long been considered to be the key pathways involved in dysmenorrhoea, particularly PDM ( Iacovides  et al.  2015 ). Studies examining the inflammatory profiles of the menstrual effluent have demonstrated higher levels of both PGE2 (though not a consistent finding) and PGF2α in those with dysmenorrhoea compared to those without, in addition to other inflammatory mediators, including leukotrienes and other epoxy eicosatetraenoic acids ( Powell  et al.  1985 ,  Bieglmayer  et al.  1995 ). Interestingly, adolescents with dysmenorrhoea using NSAIDs had higher levels of PGF2α than those who did not use this medication, and this finding needs a more detailed investigation to better inform treatment paradigms ( Kyathanahalli  et al.  2025 ). Both IL1 and TNF cytokines stimulate prostaglandin production ( Hertelendy  et al.  2001 ,  Barcikowska  et al.  2020 ), as well as other inflammatory mediators and nitric oxide ( Bradley 2008 ,  Gabay  et al.  2010 ). Therefore, a genetic association is biologically plausible. However, inflammation is also a key feature of SDM-related conditions, such as endometriosis and adenomyosis.\nIt is important to note that GWASs identify common genetic variants that do not have high penetrance in the population, meaning that the identified SNPs tend to have small effect sizes. For example,  Jones  et al.  (2016)  reported that the likelihood of PDM increased by 0.1 for each level of pain severity on a four-point scale for their  NGF  SNP. PDM is likely to be polygenic, and much more research is required before targeted interventions can be developed.\nWhile this review was comprehensively conducted, several limitations remain. No grey literature and only English language studies were included, which may mean that some literature was missed. Sample sizes were relatively small in candidate gene studies (ranging from 102 to 435 participants), which may have limited the power needed to detect a significant association. Furthermore, five Turkish studies may have been conducted in the same clinical cohort; therefore, arguably, findings should have been subjected to a Bonferroni correction. Few GWASs have been conducted, and it is possible that these also required greater statistical power to detect associations. Studies were generally of low to medium quality. In addition, few ethnic groups were represented.\nImportantly, there was evident inconsistency in the definition of PDM cases. Without clear exclusion of secondary causes, it is impossible to firmly assert that findings are associated with PDM rather than the presence of underlying pathology. As non-invasive diagnostic procedures for endometriosis improve, this challenge could be reduced for new cohorts, in which previous findings can then be replicated.\n\nThis systematic review has evaluated the current literature on genetics and PDM, showing that both inflammatory and pain pathways appear to be involved. However, the literature is sparse and hampered by poor definitions of PDM and a lack of broad geographic and ethnic coverage, meaning that much remains unknown. Given the limited replicated results in this review, it is clear that further research in this field is urgently required. A better understanding of the pathogenesis of PDM will aid in the development of novel therapies and in identifying at-risk individuals, thus potentially allowing implementation of preventative strategies with the ultimate aim of reducing the burden of this common yet long neglected condition.\n\nEH, IWL, MS, NFST and RRM have no competing interests to declare. KV received research grants from NIHR, NIH and EU IMI-2; received consultancy fees from Gedeon Richter, Bayer Healthcare, Reckitts and Gesynta; and was president of the IASP Special Interest Group on Abdominal and Pelvic Pain (until August 2024). KZ received research grants from NIHR, NIH, Gates Foundation, DoD US, EU Horizon, Aspira Labs Inc., Bayer AG, Chemo Research S.L., Proteomics International Pty Ltd and Roche Diagnostics GmbH and received a personal payment from Bayer AG until June 2022 as royalties associated with scientific collaboration between University of Oxford and Bayer. KZ was also an unpaid board member of the World Endometriosis Society until 2023 and is currently an unpaid board member of the World Endometriosis Research Foundation.\n\nThis project did not receive external funding. RRM is currently funded by a grant from the Medical Research Foundation as part of the UKRI Strategic Priorities Fund (SPF) Advanced Pain Discovery Platform (APDP), a co-funded initiative by UKRI (MRC, BBSRC, ESRC), Versus Arthritis, the Medical Research Foundation and Eli Lilly and Company Ltd (grant ref: MR/W02697/X1). IWL received a postgraduate studentship from MRC-iCASE and Eli Lilly.\n\nKV and MS were involved in conceptualisation and methodology. EH and MS were involved in screening, data extraction and quality assessment. IWL, MS and RRM carried out supervision. EH wrote the original draft of the manuscript. All authors reviewed and edited the manuscript.","source_license":"CC-BY-4.0","license_restricted":false}