{"paper_id":"8bf9ece6-5f00-477c-83d1-6a23ace9c0c4","body_text":"Mahmoudi et al. \nMiddle East Fertility Society Journal           (2025) 30:43  \nhttps://doi.org/10.1186/s43043-025-00256-3\nRESEARCH Open Access\n© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which \npermits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the \noriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or \nother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line \nto the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory \nregulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this \nlicence, visit http://creativecommons.org/licenses/by/4.0/.\nMiddle East Fertility\nSociety Journal\nGene expression and demographic factors \nassociated with endometriosis incidence: \na landscape genetic approach\nHamta Mahmoudi1, Parnian Pam1, Kimia Javidmehr1, Ashraf Moini2,3,4 and Zahra Noormohammadi1* \nAbstract \nBackground Endometriosis is a chronic inflammatory disease that results in female infertility. It is considered \na complex disorder that plays a role in the impacts of endometriosis on infertility. The present study was performed \nin the Iranian women population to provide data on the genetic basis of endometriosis and the role played by differ-\nent demographic variables like lifestyle factors, locality, ethnicity, etc.\nMethods The individuals were divided into two groups: 50 samples, including 25 women with endometriosis and 25 \ncontrols. Endometrial tissue and whole blood samples were used for gene expression of MFN2, PINK1, PRKN, and their \nnine SNPs genotyping, respectively. The multivariate computational methods used for analyzing data on the above-\nmentioned tasks included factor multiple logistic regression, factor analysis of mixed data (FAMD), and redundancy \nanalysis (RDA). STRING was used for protein–protein interaction and K-means clustering.\nResults The findings revealed a significant difference (P < 0.05) in the magnitude of gene expression in the target \ngenes studied. PPI interaction (P < 0.0001) with FDR < 0.001 showed the interaction between three genes and clus-\ntered together. The FAMD analysis showed that the target genes’ SNP variability is the most contributing vari-\nable in differentiating the cases and controls studied. A significant association between the genes and the SNPs \nstudied, as well as with demographic variables, was observed. The RDA analysis revealed a significant association \nbetween geographical variables, the gene’ expression magnitude, and the SNPs’ genotypes. In addition, sPCA analy-\nses showed a significant positive and negative eigenvalue (global and local structuring, respectively) of the genetic \ncontent of the studied samples by geographical variables.\nConclusion The present study, based on gene expressions and their related SNPs, showed the contribution of these \ndata to geographical and demographical variables.\nKeywords Endometriosis, Demography, Geographical variants, Redundancy analysis\n*Correspondence:\nZahra Noormohammadi\nmarjannm@yahoo.com; z-nouri@srbiau.ac.ir; znouri@iau.ac.ir\nFull list of author information is available at the end of the article\n\nPage 2 of 11Mahmoudi et al. Middle East Fertility Society Journal           (2025) 30:43 \nBackground\nInfertility is a disease of the reproductive system that \naffects the capacity of an individual to reproduce [1].\nSuccessful reproduction results from complex pro -\ncesses required for developing functional gonads and \nother reproductive organs, along with sex determination, \ngametogenesis, and the ability to carry a pregnancy. Any \ndefect or malfunction in these processes results in repro -\nductive disorders and infertility, impacting approximately \n10–15% of couples worldwide [1].\nIn general, about 10% of women experience infertility, \nwith only about 35% are due to female factors that affect \novarian development, oocyte maturation, fertilization \ncompetence, etc. The rest is due to genetic disorders like \nchromosome abnormalities, DNA sequence mutations, \nand non-coding RNAs. In addition, epigenetic modifica -\ntions may also be associated with female infertility [2].\nEndometriosis is a chronic inflammatory disease that \nresults in female infertility in about 30%–50% of infertile \nwomen and causes pelvic adhesions and distorted pelvic \nanatomy. It is considered a complex disorder as different \ncauses, like immunological, endocrine, biochemical, and \ngenetic disorders, poor quality of the oocyte, embryo, \nand endometrial environment, play a role in the impacts \nof endometriosis on infertility [3].\nBougie et al. (2019) [4] reported that the risk of endome-\ntriosis increased 3–15 times among first-degree relatives. \nAdditionally, racial and ethnic differences could affect \nthe prevalence of diagnosed endometriosis. For instance, \nAsian women had a higher risk, and Black women had a \nlower risk of endometriosis than White women [4].\nThe complex and varying nature of endometriosis is \nalso evident from the results of genome-wide associa -\ntion studies (GWAS) performed in different groups. For \nexample, the study of European and East Asian descent \nidentified 42 genome-wide significant loci comprising \n49 distinct association signals in endometriosis [5]. A \nsimilar study [6] reported single nucleotide polymor -\nphisms (SNPs) that appear over-represented in patients \nwith endometriosis, particularly those with more exten -\nsive disease (stage III/IV) [7], and several groups have \nreported variants that are associated with endometriosis \nin individuals of European and Japanese origin [8]. How -\never, the Angioni et  al. (2020) [9] study concerned with \nthe genotypes and allele frequency of target SNPs in the \nSardinian population and their association with endome -\ntriosis did not show a significant association between the \nstudied variants of the genes and a greater risk of devel -\noping endometriosis. Therefore, it has been suggested \nthat specific risk alleles could act differently in the patho-\ngenesis of the disease in different ethnic populations, and \nit is important to study the genetic basis of endometriosis \nin different populations [9].\nVarious demographic factors are considered to affect \nthe prevalence of endometriosis in different parts of the \nworld. These factors include body mass index (BMI) [10], \nethnicity [4–11], age [12], chronic stress [13], and envi -\nronmental factors [14].\nDifferent studies based on several factors associated \nwith endometriosis from various populations and geo -\ngraphical areas greatly differ, making it very difficult to \ndraw any definite conclusions, and therefore, these vari -\nables should be studied locally and with a particular eth -\nnic group.\nTherefore, we performed the present study on Iranian \nwomen, both normal and diagnosed with endometriosis. \nThis study considered the gene expression of three genes, \nnamely, MFN2 (Mitofusin-2), PINK1 (PTEN-induced \nputative kinase 1), and PRKN (Parkin RBR E3 Ubiquitin \nProtein Ligase), and eleven SNPs related to these genes, \nnamely, rs68121389, rs117341007, and rs1393563943 \n(from the PRKN  gene), rs513414, rs3077908, rs512550, \nrs2078073, and rs1043502 (from the PINK1 gene), \nrs3088064, rs1042842, and rs41278636 (from the MFN2 \ngene). In addition, we used several demographic variables \nlike age, ethnicity, BMI, and lifestyle factors related to \neating, smoking, etc.\nThe genes selected in the present study, including MFN2, \nPINK1, and PRKN, are known to be associated with female \ninfertility or cervical cancer progression [see, for exam -\nple, 15–18]. These genes are involved in the female repro-\nductive gland and mitophagy. Moreover, the proteins of \nthese three genes are interrelated in a network of protein \ninteractions (https:// string- db. org/ cgi/ netwo rk).\nWe used different computation methods to study sig -\nnificant differences between healthy women and those \ndiagnosed with endometriosis, to reveal an association \nbetween environmental factors and gene expression, and \nthe SNPs and the association between demographic vari -\nables and gene expression. To our knowledge, this is the \nfirst report of its kind from Iran.\nMethods\nSampling\nIn total 50 individuals were studied comprised of \nnormal unaffected persons and those diagnosed with \nendometriosis. Inclusion criteria for women with \nendometriosis included patients who were diagnosed \nwith moderate to severe endometriosis based on sono -\ngraphic or laparoscopic findings, characterized by the \npresence of at least one endometrioma exceeding 3 cm \nin diameter. Additional inclusion criteria required par -\nticipants to be between 20 and 40 years old and eligible \nfor oocyte or embryo cryopreservation.\nEndometrial tissue and whole blood of patients were \ncollected and stored at—80C for further studies. The \n\nPage 3 of 11\nMahmoudi et al. Middle East Fertility Society Journal           (2025) 30:43 \n \nstudy was approved by the ethics committee (No. \nIR.IAU.SRB.REC. 1401.315). All patients signed a writ -\nten informed consent. The questionnaire based on \ndemographic data like age, ethnicity, BMI, smoking, \neducation, job, lifestyle, and geographical variant was \nfilled out by each participant.\nReverse transcription and quantitative PCR (RT‑qPCR)\nWe performed the gene expression analysis of MFN2, \nPINK1, and PRKN  as the target genes, and the 18srRNA \ngene was utilized as a reference gene [19] for the nor -\nmalization of each sample. Total RNA was extracted \nfrom endometrial tissue by Favor prep kit (CAT. NO: \nFABRK001) according to the manufacturer’s protocol. \ncDNA synthesis was performed using the Parstous kit \n(CAT. NO: A101161). Real-time PCR for three genes \nwas conducted in a thermocycler Rotorgene (QIAGEN, \nGermany) in strip tubes including 12.5  µl AMPLI -\nCON SYBR Green master mix (Cat. No: A324402), 2 µl \ncDNA, and 10 pM forward and reverse primers of each \ngene (Table  1). A thermal program for each gene was \nperformed based on the annealing temperature of each \nprimer pair (Table  1). All reactions were performed in \nduplicate. We used the Pffafl formula for normalization \nand fold change calculation [20].\nSNP genotyping\nFor genotypes of nine SNPs related to three genes, \nnamely, rs68121389, and rs117341007 (from the PRKN  \ngene), rs513414, rs512550, rs2078073, and rs1043502 \n(from the PINK1 gene), rs3088064, rs1042842, and \nrs41278636 (from the gene MFN2), genomic DNA was \nextracted from blood samples by the Salting-out protocol \nand stored at – 20 °C. The quantity and quality of DNA \nwere checked by a Nanodrop spectrometer and 0.8% \nagarose gel electrophoresis, respectively. PCR sequenc -\ning was performed for the 3´UTR region of each gene \nas follows: 200 ng/µl DNA, 10 pg/µl of each forward and \nreverse primer (Table  1) along with the master mix PCR \nreaction (ParsTous Co., Iran). Amplification was per -\nformed using a 98-well thermal cycle (Applied Biosys -\ntems, USA) at annealing temperatures between 60 and \n65ºC based on each primer pair’s melting temperature \n(Table 1). The PCR product was visualized by 1.5% aga -\nrose gel electrophoresis. PCR products were sequenced \nbased on the Sanger sequencing protocol by Pishgam \nCo., Iran.\nData analyses\nAll data analyses were performed on 999 times \npermutations.\nGene expression analysis\nThe expression delta-CT data were log-transformed \nfor all the following analyses. To study differences in \nthe magnitude of gene expression between normal and \naffected individuals we performed a t-test (Independent \nand normal distribution data) and Mann–Whitney U test \n(nonparametric statistical test), followed by a box plot \nconstruction as performed in PAST ver.4.\nTable 1 Primer names and their sequences used in gene expression and SNP sequencing\nPrimers for Gene Expression test\nGene Accession number Primer Primer sequence Primer length TM Prod-\nuct \nsize\nPINK1 NM_032409 Forward GAG TAT GGA GCA GTC ACT TACAG 23 58/32 144\nReverse CAG CAC ATC AGG GTA GTC G 19 57/65\nPRKN NM_004562.3 Forward TGG GAG AAG AGC AGT ACA ACCG 22 98/61 207\nReverse CCC CTT CAT GGT ACG CTT CTT TAC 24 45/61\nMFN2 NM_014874 Forward CTA CAC TGG CTC CAA CTG C 19 58/15 132\nReverse TCA ATT TTC TTG TTC ATG GCGG 22 58/09\n18 s NC_002753.1 Forward GTA ACC CGT TGA ACC CCA TT 20 57/93 151\nReverse CCA TCC AAT CGG TAG TAG CG 20 58/09\nPrimers for SNP Sequencing\nGene Primer Primer sequence Primer length TM Prod-\nuct \nsize\nPINK1 Forward TAC TAA AAG AAC ATG GCA TCC TCT GT 26 60/30 936\nReverse TTT AAC TGT GAA ATG ATG GTT CTC CC 26 59/79\nPRKN Forward GTC CCT CTT TTC CTA ACT GGC TAA GA 26 61/99 1351\nReverse GCT TGG AGT TGA TAT GAG AAT GGC TA 26 60/97\nMFN2 Forward GAT TGT TGG AGG ATG ATG TAA GGG TGT 27 62/91 1214\nReverse GGA AAC ATG TCT CTT AAA GGG CAC AAC 27 62/80\n\nPage 4 of 11Mahmoudi et al. Middle East Fertility Society Journal           (2025) 30:43 \nWe used regression and correlation analyses to study \nthe relationship between the genes studied as performed \nin R 4.3. Similarly, the collinearity between genes was \nchecked by factor analysis of mixed data (FAMD) as per -\nformed in packages factoextra and FactoMineR in R 4.3.\nTo check protein interactions of three genes studied in \nKEGG servers, we used STRING ver. 11.5. K-means clus -\ntering was conducted, and FDR and co-expression scores \nwere calculated.\nContribution of variables in differentiating cases and controls\nAll data studied were coded as multinomial data and \nused in FAMD (Factor analysis of mixed data) analyses. \nThe codings used are as follows:\nS = Status (case versus control samples), \nG1-G3 = Genes MFN2, PINK1, and PRKN , respec -\ntively.\nR1-R9 = SNPs 1–9, respectively,\nPersonal_data = P1-P6 (Age, education, career, \nheight, weight, and BMI, respectively.\nLocality = L1-L2 (ethnic and city, respectively).\nlifestyle factors = H1-H4 (Air pollution, smoking, fast \nfood, and plastic_container, respectively.\nAssociation between variables studied\nWe performed a chi-square test for ordinal data to study \nthe relationships between the studied variables as per -\nformed in the package ordinal in R 4.3. For this, we used \nthe same coded multinomial data as before, after 999 \ntimes permutations.\nAssociation studies between geographical variables \nand genetic data\nWe performed the RDA (Redundancy Analysis) to study \nthe association between geographical variables and gene \nexpression, as well as SNPs’ genotypes. In addition, we \nused the spatial principal components analysis (sPCA), \nbased on geographical variables, the longitude, lati -\ntude, and altitude of the studied samples to show their \nrole in the genetic structuring of the samples studied. \nThese analyses were performed in PAST ver. 4 and R 4.3, \nrespectively.\nResults\nGene expression analyses\nT-test and Mann–Whitney test revealed a significant \ndifference in the magnitude of gene expression between \nnormal and affected individuals for the three genes stud -\nied (P value < 0.01).\nThe paired-sample regression and correlation analyses \nof the genes studied indicated a significant association \nbetween the studied genes (P value < 0.01, Fig.  1 A-C). In \naddition, the FAMD plot (Fig.  1, D) showed collinearity \nbetween the genes, and based on variance analysis, the \nPINK1 and MFN2 genes contributed the most to differ -\nentiating the normal and affected individuals.\nBased on K-means clustering of protein–protein inter -\naction (Fig.  2), 3 clusters were formed with PPI enrich -\nment P-value = 6.09e-06 and average local clustering \ncoefficient 0.816. Proteins in red color constructed a \ncluster including INK1, MFN2, UBC, UBA52, PRKN, \nPARK7, FBXO7, HTRA2. They are involved in mitophagy \nwith a strength of 2.29 and FDR = 6.37E-13. Therefore, \nour experimental results of gene expression indicate \nthat these genes are correlated in the same pathway and \naccord with their protein–protein interaction network.\nGenotyping\nContribution of variables in differentiating cases and controls\nThe FAMD plot showing the contribution of variables \nin differentiating the case and normal individuals is pre -\nsented in Fig. 3.\nIt shows that the studied SNPs (R1–R9) are the most \ncontributing variables in differentiating the cases and \ncontrols studied. However, these SNPs differ in the role \nthey play as they are placed at different angles of the \nFAMD plot. For example, the SNPs coded R1 and R4 are \nplaced differently from the rest of the studied SNPs.\nThe expression level of the studied genes MFN2 and \nPRKN (coded G1 and G3 in Fig. 3), personal data, namely \neducation (coded P2), lifestyle data, air pollution (coded \nH1), and the city where the sample lives (L2), grouped as \nthe second most contributing variable that differentiates \nthe case versus the control individuals. The rest of the \nvariables studied seem to play a comparatively lesser role \nin differentiating the studied samples.\nAssociation between case/control samples and the variables \nstudied\nThe chi-square test for ordinal data showed a significant \nassociation between the status (Normal versus affected \nindividuals) and the genes studied, as well as the SNPs 1 \nand 3–8 (P value < 0.01).\nA significant association was obtained for Status and \nfast food, age, and weight (P value < 0.01). Similarly, the \nanalysis showed a significant association between the \nstatus and education and the locality (city) of the studied \nsamples. These results indicate that the variables that are \nsignificantly associated with case versus control samples \nplay a potential role in causing endometriosis.\n\nPage 5 of 11\nMahmoudi et al. Middle East Fertility Society Journal           (2025) 30:43 \n \nAssociation between gene expression and SNPs with other \nvariables\nA significant association was obtained between the \nstudied genes’ expression magnitude and air pollution, \nfast food, and plastic containers (P  value < 0.01). The \nsame holds for gene 1 and gene 2 expression magnitude \nwith age, and gene 2 with all other personal data (P1–\nP6) studied.\nThe chi-square test for ordinal data showed a signifi -\ncant association between the studied SNPs and ethnic -\nity and locality (P -value < 0.01), and the gene expression \ndata ( P -value < 0.001). Additionally, a significant asso -\nciation was obtained between SNPs and age, education, \nweight, and BMI (P  -value < 0.01), as well as with air \npollution, smoking, fast food, and plastic containers \n(p-value < 0.01).\nA significant association was also obtained \nbetween personal data (P1–P6) and lifestyle factors \n(H1–H4, P value < 0.01).\nIt was interesting to see that the ethnicity and the local \narea of the individuals may play a role in endometriosis \nand the genes’ expression level and their related studied \nSNPs’ genotype differences. Therefore, we performed \nthe RDA (Redundancy analysis) to study the association \nbetween geographical variables and gene expression as \nwell as SNPs’ genotypes. In addition, we used the spatial \nFig. 1 Regression and FAMD plots of the gene studied show their significant association and collinearity. A PINK1 and MNF2, B PRKN and MNF2, C \nPINK1 and PRKN regressions, D FAMD plot based on gene expressions\n\nPage 6 of 11Mahmoudi et al. Middle East Fertility Society Journal           (2025) 30:43 \nFig. 2 K-means clustering based on PPI with FDR < 0.001. Three clusters were constructed. Proteins studied grouped in one cluster (red color)\nFig. 3 Representative plots of sPCA analysis based on the combination of longitude and latitude in the studied individuals. A The connection \nnetwork. B, C Genetic clines formed due to spatial variables. D The positive and negative Eigenvalues show the significant role of the global \nand local structuring of the genetic data\n\nPage 7 of 11\nMahmoudi et al. Middle East Fertility Society Journal           (2025) 30:43 \n \nprincipal components analysis (sPCA), based on geo -\ngraphical variables, the longitude, latitude, and altitude \nof the studied samples to show their role in the genetic \nstructuring of the samples studied.\nRDA and sPCA results\nThe RDA analysis after 999 times permutation revealed \na significant association between geographical variables \nthe genes’ expression magnitude, and the SNPs’ geno -\ntypes (R-squared = 0.40, P = 0.05).\nThe results of sPCA are presented in Figs. 2, 3, and 4. In \nthe first sPCA analysis, we used the combination of lon -\ngitude + latitude, while in the second sPCA, we used the \ncombination of latitude + altitude.\nThe connection network, eigenvalues, and genetic \nclines are provided in Figs.  3 and 4. These analyses \nshowed significant positive eigenvalue (global structur -\ning), and negative eigenvalue (local structuring) of the \ngenetic content of the studied samples by geographical \nvariables.\nThese results indicate the role played by geographical \nvariables and their association with the genes’ expression \nmagnitude and the allele as well as genotype frequencies \nobserved.\nThe contribution of the studied genetic variables to the \nspatial structuring of the sample is presented in Fig.  5. In \nsPCA with the combination of longitude + latitude, the \ngene MFN2, and PINK1, as well as the SNPs 1, 3, and 7, \nare the most contributing variables.\nIn the sPCA based on the combination of longi -\ntude + altitude, the most contributing variables are the \nstatus and the SNPs 2, 4, 5, 7, and 8. Similarly, in sPCA \nbased on the combination of latitude + altitude, the most \ncontributing variables are the gene PRKN, and the SNPs \n1, 4, 5, 7, and 8. Therefore, the spatial variables operat -\ning in the locality in which the studied individuals live \nplay a significant role in structuring and affecting the \ngene expression as well as the genotypes of the samples \nstudied.\nDiscussion\nIn the present study, the FAMD plot showed collinear -\nity between the genes. Also, PINK1 and MFN2 genes \ncontributed the most to differentiating the normal and \naffected individuals. k-means clustering based on PPI also \nshowed high interaction with the lowest FDR. The co-\nexpression score of these proteins is 0.100. These proteins \nare ubiquitin protein ligase binding in the mitophagy \npathway. PINK1 encoded mitochondrially targeted Ser-\nThr kinase with cellular functions like autophagic deg -\nradation of dysfunctional mitochondria. PRKN, as a \nSer-Thr kinase dysfunction, depolarized mitochondria \nthrough the phosphorylation of MFN2 with mitochon -\ndrial outer membrane GTPase [21, 22].\nFig. 4 Representative plots of sPCA analysis based on the combination of latitude and altitude in the studied individuals. A The connection \nnetwork. B The positive and negative Eigenvalues show the significant role of the global and local structuring of the genetic data. C, D Genetic \nclines formed due to spatial variables\n\nPage 8 of 11Mahmoudi et al. Middle East Fertility Society Journal           (2025) 30:43 \nGene ontology of genes studied indicated that these \ngenes are involved in the female reproductive gland and \nmitophagy. Also, several studies reported that genes \nselected in the present study are known to be associ -\nated with female infertility or cervical cancer progression \n[15–18].\nGenotyping and endometriosis\nIn FAMD analysis of variables in the present work indi -\ncated that the studied SNPs (R1-R9) are the most contrib-\nuting variables in differentiating the cases and controls \nstudied. However, these SNPs differ in the role they play \nas they are placed at different angles of the FAMD plot. \nIn addition, the chi-square test for ordinal data showed \na significant association between the status (Normal ver -\nsus affected individuals), and the gene studies, as well \nas the SNPs 1, 3–8. These SNPs were also significantly \nassociated with the ethnicity and locality of the studied \nindividuals.\nThese results were supported by the RDA and spatial \nprincipal components analysis after 999 times permuta -\ntion revealed a significant association between geograph -\nical variables and the genes’ expression magnitude and \nthe SNPs’ genotypes and that some of the genetic vari -\nants are associated with different combinations of geo -\ngraphical variables.\nThere have been controversial reports on the associa -\ntion of different SNPs and endometriosis. For example, \n[23], by using GWAS and functional network analyses \nreported an association between the risk genes and their \nvariant SNPs with endometriosis in the Taiwanese-Han \npopulation, while Angioni et  al. (2020) [9], reported no \nassociation between the investigated variants of the genes \nand a greater risk of developing endometriosis in the Sar-\ndinian population. It has been suggested that the racial/\nethnic disparities in the process of hormone regulation \nand nutrition metabolism may be the reason for contro -\nversial results on the genetics of endometriosis across \ndifferent populations [23].\nDemographic variables and endometriosis\nThe present study reports an association between some \nof the demographic variables and endometriosis. A sig -\nnificant association was obtained for the status and fast \nfood, age, weight, and education of the studied samples. \nThese variables play a potential role in causing endo -\nmetriosis. Additionally, a significant association was \nobtained between SNP variation and age, education, \nweight, and BMI, as well as with air pollution, smoking, \nfast food, and plastic containers.\nSmoking\nVarious studies report the controversial role of demo -\ngraphic variables on the incidence of endometriosis, and \nthere is no reasonable consistency in the role of environ -\nmental factors in endometriosis etiopathogenesis. For \nexample, Polak et al. [14] report that about 1.7% of preg -\nnant women worldwide smoke. Smoking may reduce the \nFig. 5 The contributing variables in spatial structuring of the studied individuals based on the first two eigenvalues of sPCA. LAT: latitude, Long: \nlongitude, and Alt: altitude\n\nPage 9 of 11\nMahmoudi et al. Middle East Fertility Society Journal           (2025) 30:43 \n \nrisk of endometriosis in later life among female fetuses. \nThough its mechanism is unknown, it has been suggested \nthat “nicotine, along with its metabolite cotinine, may \nsuppress the aromatase-dependent conversion of andro -\ngens to estrogen, stimulate apoptosis, and inhibit angio -\ngenesis and therefore, may inhibit the development of \nendometriosis” [14].\nDiet\nSeveral studies show an association between diet and the \ndevelopment of endometriosis [14]. Dietary factors may \naffect sex hormones and strive for pro- or antioxidant \nand pro-inflammatory effects which have considerable \nroles in endometriosis. Additionally, the contaminants \naccompanying food production may also affect the inci -\ndence of this disorder.\nA diet rich in fruits that supply large amounts of provi -\ntamin A can decrease the incidence of endometriosis, and \nvitamin A suppresses the formation of the pro-inflamma-\ntory interleukin-6, a cytokine whose high levels are found \nin the amniotic fluid in women with endometriosis. Simi-\nlarly, citrus fruits with high amount of vitamin C, which \ninhibits inflammation and exerts antioxidant effects. \nHowever, there are reports on the opposite and negative \neffects of fruits which can increase the risk of disorder \nincidence in American women which may be associated \nwith the large quantity of pesticides used during cultiva -\ntion in the US. A diet rich in red meat is also known to \nincrease the risk of endometriosis [14].\nEducation\nThe studies performed on the association between the \nsocioeconomic status of populations and the incidence \nrisk of endometriosis have shown that a high socioeco -\nnomic status (SES) or education level has been associated \nwith a higher frequency of endometriosis. It may reflect \nbetter detection and patient care of women with high \nSES [24, 25].\nBody mass index (BMI)\nVarious studies differ in their reports on the role of BMI \nin the risk of endometriosis. In general, a lower body \nmass index (BMI) is thought to be associated with endo -\nmetriosis [26, 27], but women with normal BMI also \ndevelop endometriosis [10].\nYunhui et  al. [10] studied the association between \nBMI and surgically diagnosed endometriosis in Chinese \nwomen. They concluded that there is no association \nbetween BMI and the incidence of endometriosis, but \nthere was a significant increase in the incidence in obese \nwomen, compared with women of normal weight.\nIn a similar study in Australia, women with a nor -\nmal BMI were more likely to have endometriosis, in \ncomparison with women with underweight [28]. The dif -\nferences in the inverse association of BMI and endome -\ntriosis between Australia’s study and other studies could \nbe explained by differences in the study populations.\nSpatial patterns of endometriosis incidence and the role \nof geographical variables\nThe present study reported that the role played by geo -\ngraphical variables, longitude, latitude, and altitude of the \nlocalities in which diagnosed individuals live can shape \nthe genetic variants of the target risk genes and may \naffect the magnitude of gene expression leading to the \nincidence of endometriosis in the Iranian population.\nLe Moal et al. [29] studied the risk factors for the inci -\ndence of endometriosis in different geographical regions \nof France and reported geographical heterogeneity in its \nincidence and concluded that geography may influence \nthis risk and state that this finding is the first step in the \nquest of clarifying environmental or other factors that \nmay be contributing to the development of the disease.\nA similar study was performed by Cataby et al. [30] on \nthe spatial pattern of endometriosis incidence by apply -\ning Bayesian approaches to Disease Mapping. They used \ndata on the incident cases of endometriosis in women \naged 15–50  years in the Friuli Venezia Giulia region in \nthe calendar period 2004–2017. They reported a very \nstrong north–south spatial gradient related to endome -\ntriosis incidence and identified a group of five neighbor -\ning municipalities at higher risk in the industrialized and \npolluted southeast part of the region.\nFeng et al. [31] investigated the burden implication of \nendometriosis in 204 countries and territories from 1990 \nto 2019. They used estimated annual percentage changes \n(EAPCs) and disability-adjusted life-years (DALYs) of \nendometriosis with the classified data by region, country, \nage, and socio-demographic index (SDI). They reported \n“an increase in global incidence and DALYs of endome -\ntriosis, but a decrease in the age-standardized incidence \nrate (ASIR) and age-standardized DALY rate of endome -\ntriosis. The largest decreases in the ASIR and age-stand -\nardized DALY rate of endometriosis were observed in \nQatar and Oman, respectively” .\nConclusion\nThe present study reports a detailed analysis of an asso -\nciation study concerned with endometriosis disorder in \nthe Iranian population and its genetic basis, including \ngene expression magnitude, and SNP variability, as well \nas several demographic and geographical variables. Dif -\nferent multivariate statistical and bioinformatic analyses \nrevealed a significant difference in gene expression mag -\nnitude of the target genes, namely, MFN2, PINK1, and \nPRKN. A significant association was observed between \n\nPage 10 of 11Mahmoudi et al. Middle East Fertility Society Journal           (2025) 30:43 \nthe SNP variability of the target genes and gene expres -\nsion magnitude and the incidence of the disorder. A sig -\nnificant association also occurred between the status \n(Normal versus affected individuals) and demographic \nvariables like diet, age, weight, education, and the local -\nity (city) of the studied samples. Similarly, an association \nwas observed between the SNP variability and ethnic -\nity, locality, and demographic data. The spatial principal \ncomponents and redundancy analyses revealed a sig -\nnificant association between geographical variables, the \ngenes’ expression magnitude, and the SNP variability. \nIn addition, sPCA analyses showed a significant posi -\ntive eigenvalue (global structuring) and negative eigen -\nvalue (local structuring) of the genetic content of the \nstudied samples by geographical variables. The present \nstudy, based on gene expressions and their related SNPs, \nshowed the contribution of these data to geographical \nand demographic variables.\nThere was some limitation in the present study such \nas sampling and collecting accurate samples with all \nincluded criteria. Furthermore, there was reliance on \ncross-sectional and bioinformatic data for selecting suit -\nable pathways for studies.\nAbbreviations\nFAMD  Factor analysis of mixed data\nRDA  Redundancy analysis\nsPCA  Spatial principal components analysis\nPPI  Protein-protein interaction\nFDR  False discovery rate\nSupplementary Information\nThe online version contains supplementary material available at https:// doi. \norg/ 10. 1186/ s43043- 025- 00256-3.\nSupplementary Material 1: Table S1. The SNPs’ genotypes in the case and \nnormal individuals studied\nAcknowledgements\nWe thank the patients for their help. We also acknowledge the Science and \nResearch Branch, Islamic Azad University for providing a laboratory.\nAuthors’ contributions\nZ.N. and A.M. had conventionalization of the project, Z.N., H.M., K.J., and P .P . \nwrote the main manuscript, Z.N. did data analyses, H.M., K.J., and P .P . collected \nsamples and performed laboratory work.\nFunding\nThere is no funding to declare.\nData availability\nThe current study is not publicly available due to personal document confi-\ndentiality. Data are available from the corresponding author on request.\nDeclarations\nEthics approval and consent to participate\nThe project proposal was reviewed by the Ethics Committee of the Islamic \nAzad University Science and Research Branch, and was approved with the ID \nnumber, No. IR.IAU.SRB.REC.1401.315. Informed consent was obtained from \nindividuals.\nConsent for publication\nNot applicable.\nCompeting interests\nThe authors declare no competing interests.\nAuthor details\n1 Department of Biology, SR.C, Islamic Azad University, Tehran, Iran. 2 Depart-\nment of Endocrinology and Female Infertility, Reproductive Biomedicine \nResearch Center, Royan Institute for Reproductive Biomedicine, ACECR, Tehran, \nIran. 3 Breast Disease Research Center (BDRC), Tehran University of Medical Sci-\nence, Tehran, Iran. 4 Department of Obstetrics and Gynecology, Arash Women’s \nHospital, Tehran University of Medical Sciences, Tehran, Iran. \nReceived: 17 May 2025   Accepted: 16 September 2025\nReferences\n 1. Yatsenko SA, Rajkovic A (2019) Genetics of human female infertility. Biol \nReprod 101:549–566. https:// doi. org/ 10. 1093/ biolre/ ioz084\n 2. Bazrgar M, Gourabi H (2023) Editorial: genetics of female infertility. 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Clin Exp Obstet \nGynecol 49:235. https:// doi. org/ 10. 31083/j. ceog4 910235\nPublisher’s Note\nSpringer Nature remains neutral with regard to jurisdictional claims in pub-\nlished maps and institutional affiliations.","source_license":"CC0","license_restricted":false}