{"paper_id":"d4ee54b0-584e-4ab9-a94e-bebe5a7b0c42","body_text":"Abstract\n!\nObjective: The etiology of endometriosis is still a\nresearch field in which few consistent data are\navailable. Large case –control studies or even co-\nhort studies are rare, and most of the published\ndata are conflicting. The aim of the present study\nwas therefore to examine common epidemiologi-\ncal and endometriosis-specific risk factors in a\nGerman case–control study.\nDesign: From 2001 to 2010, a pool of 595 laparos-\ncopically confirmed cases and 475 controls were\nrecruited in a hospital-based setting. After match-\ning for age, 298 cases and 300 controls remained\nin the pool. Age at menarche, menstrual cycle\nlength, duration of menstrual bleeding, number\nof pregnancies, live births, miscarriages, use of\ncontraceptive pills, body mass index (BMI), and\nsmoking status were analyzed with logistic re-\ngression models predicting endometriosis case –\ncontrol status.\nResults: Menstrual cycle length, duration of men-\nstrual bleeding, number of pregnancies, number\nof miscarriages, and smoking status, as relevant\npredictors for endometriosis case –control status,\nwere identified as risk factors for endometriosis.\nOther factors such as age at menarche, number of\nlive births, ever having used contraceptive pills,\nand BMI were not predictive.\nConclusions: This hospital-based case –control\nstudy reproduced most of the familiar risk factors.\nComparison of this study with others reveals a\nwide variety of effect sizes and directions of asso-\nciation with risk factors and may increase the in-\nformation available about the characteristics of\nthe patient population being treated in the rele-\nvant hospital setting.\nZusammenfassung\n!\nEinleitung: Die Ätiologie der Endometriose bleibt\nein Forschungsgebiet mit spärlichen Erkenntnis-\nsen. Große Fall-Kontroll-Studien oder Kohorten-\nstudien sind selten, und die publizierten Daten\nwidersprechen sich auf den ersten Blick zum\ngrößten Teil. Ziel unserer Studie war es deswegen,\nin einer deutschen Fall-Kontroll-Studie Risikofak-\ntoren zu identifizieren und zu beschreiben.\nMethoden: Von 2001 bis 2010 wurde ein Pool von\n595 laparoskopisch diagnostizierten Endometrio-\nsepatientinnen und 475 in einem krankenhausba-\nsierten Design rekrutiert. Von diesem Pool wurden\n298 Fälle und 300 Kontrollen altersgematched.\nErgebnisse: Alter bei der Menarche, Zyklusdauer,\nDauer der Periodenblutung, Anzahl der Schwan-\ngerschaften und Lebendgeburten, Fehlgeburten,\nPilleneinnahme, Body-Mass-Index (BMI) und\nRaucherstatus wurden in logistischen Regres-\nsionsmodellen auf ihre Prädiktion für den Endo-\nmetriose-Fall-Kontroll-Status untersucht. Dauer\nder Periodenblutung, Dauer des Zyklus, Anzahl\nder Schwangerschaften, Anzahl der Fehlgeburten\nund Raucherstatus waren relevante und signifi-\nkante Risikofaktoren für die Endometriose. Die an-\nderen Faktoren wie Alter bei Menarche, Anzahl der\nLebendgeburten, Pillennutzung und BMI waren im\nmultivariaten Modell nicht prädiktiv in Bezug auf\nden Fall-Kontroll-Status. In dieser krankenhaus-\nbasierten Fall-Kontroll-Studie konnten die meis-\nten, bekannten Risikofaktoren für eine Endome-\ntrioseerkrankung reproduziert werden.\nSchlussfolgerung: Vergleicht man die Studie mit\nanderen, so fällt auf, dass die Variabilität der Ef-\nfektstärke und der Richtung der Prädiktion für die\nRisikofaktoren in den meisten Studien unter-\nschiedlich ist. Die Analyse der Risikofaktoren im\neigenen Kollektiv könnte helfen, die Population\nder Endometriosepatientinnen besser zu verste-\nhen, die im entsprechenden Krankenhaus behan-\ndelt werden.\nRisk Factors for Endometriosis\nin a German Case –Control Study\nRisikofaktoren für Endometriose\nin einer deutschen Fall-Kontroll-Studie\nAuthors S. Burghaus 1, P. Klingsiek 1, P. A. Fasching 1, A. Engel 1, L. Häberle 1, P. L. Strissel 1, M. Schmidt 1, K. Jonas 1, J. D. Strehl 2,\nA. Hartmann 2, J. Lermann 1, A. Boosz 1, F. C. Thiel 1,A .M ü l l e r1, M. W. Beckmann 1, S. P. Renner 1\nAffiliations 1 Department of Gynecology and Obstetrics, Erlangen University Hospital, Friedrich-Alexander University\nErlangen-Nuremberg, Erlangen\n2 Institute of Pathology, Erlangen University Hospital, Friedrich-Alexander University Erlangen-Nuremberg,\nErlangen\nSchlüsselwörter\nl\" Endometriose\nl\" Risiko\nl\" Fall‑Kontroll‑Studie\nl\" Epidemiologie\nl\" Genetik\nKey words\nl\" endometriosis\nl\" risk factor\nl\" case‑control study\nl\" epidemiology\nl\" genetics\nreceived 28. 10. 2011\nrevised 7. 11. 2011\naccepted 7. 11. 2011\nBibliography\nDOI http://dx.doi.org/\n10.1055/s-0031-1280436\nGeburtsh Frauenheilk 2011; 71:\n1073–1079 © Georg Thieme\nVerlag KG Stuttgart · New York ·\nISSN 0016‑5751\nCorrespondence\nPeter A. Fasching\nDepartment of Gynecology\nand Obstetrics\nErlangen University Hospital\nFriedrich-Alexander University\nof Erlangen-Nuremberg\nUniversitätsstraße 21–23\n91054 Erlangen\npeter.fasching@uk-erlangen.de\n1073\nBurghaus S et al. Risk Factors for … Geburtsh Frauenheilk 2011; 71: 1073 –1079\nOriginal Article\n\n\nIntroduction\n!\nEndometriosis is a chronic disease that affects 4 –30 % of all wom-\nen of reproductive age [1 –3]. It is also one of the most frequent\ngynecological diseases. The prevalence of endometriosis in wom-\nen who present with infertility problems is even higher (up to\n50 %) [3–6]. However it can reasonably be assumed that the prev-\nalence is about 10 % [3]. Pelvic pain during menstruation is the\nmain symptom in patients with endometriosis. The disease is\ncharacterized by the presence of endometrial cells outside the\nuterus. They are located mainly in the rectouterine pouch, but\ncan also be found in the vesicouterine pouch, abdominal wall,\novaries, and uterus [7 –9], and may also be more widespread at\nmore unusual locations [10].\nThe increased rate of the disease among first-degree relatives of\npatients with endometriosis may also suggest a genetic predis-\nposition [11]. Due to the recurrent nature of the condition, which\nhinders women in their occupational and private lives, endome-\ntriosis creates substantial health-care costs, which have been es-\ntimated as exceeding $ 22 billion in the United States in 2002 [12]\nand totaling approximately € 2 billion (2 000 000 000) in Ger-\nmany [13].\nTreatment options mainly consist of medication and surgical\ntherapy. Surgical removal of the lesion is often the first-line ther-\napy. Surgical treatment using minimally invasive approaches is\nusually preferred, but more extensive surgery may become nec -\nessary in cases of deeply infiltrating endometriosis [14, 15].\nAbout 19 –45 % of all endometriosis patients suffer one or more\nrecurrences within 5 years, leading to repeated operations in\nmany patients [16–20]. Medication includes treatment with non-\nsteroidal anti-inflammatory drugs (NSAIDs), antihormonal treat-\nment with gonadotropin-releasing hormone (GnRH) analogs, or\naromatase inhibitors [21 –23]. Some forms of treatment, such\nthe selective estrogen receptor modulator raloxifene, even short-\nen the time to recurrence in endometriosis patients [24].\nThe pathogenesis of endometriosis is considered to be complex.\nHistorically a metaplastic transformation of peritoneal cells or\nthe still favourably discussed retrograde menstruation of endo-\nmetrial cells through the tubes into the peritoneal cavity [26].\nOn a molecular level different pathways such as the estrogen\nand progesterone pathway, vasculogenesis, sphingolipids, pros-\ntaglandins, and cytokines appear to be involved [27 –35]. Some\ninformation has been obtained about the etiology, but there is a\nlack of large case –control studies on the disease and especially of\nstudies using a population-based design.\nThere have been several reports linking characteristics of the\nmenstrual cycle with endometriosis, such as age at menarche,\nduration of menstruation, and length of the cycle [2, 36 –42]. It is\nthought that an increased frequency of and duration of menstru-\nations is associated with endometriosis. This applies to risk fac -\ntors such as early menarche, long duration of menstruation, short\nmenstrual cycles, and lower parity. However, the data are not\nconsistent with regard to risk estimates or the direction of the\nrisk.\nSimilarly inconsistent data have been reported for other risk fac -\ntors such as body mass index (BMI), physical activity, and smok-\ning. There are some data suggesting an inverse relationship be-\ntween BMI and endometriosis, but many studies have not found\nthis association [43]. The Nurses ʼ Health Study observed women\nprospectively and found no clear association between physical\nactivity and endometriosis [44]. There are also inconsistent data\nwith regard to smoking. It has been reported that smoking re-\nduces the estrogen level and thus the risk of endometriosis [40],\nbut other studies did not identify any influence on the risk. There\nare also no differences between active and former smokers [2, 45].\nAlthough the pathogenesis and etiology are poorly understood,\nthere have been several studies that strengthen the hypothesis\nthat endometriosis has complex genetic traits [46 –48]. Familial\nclustering has been described in several studies [49, 50]. A large\nlinkage analysis including more than 1100 families with at least\ntwo cases of endometriosis in the family identified two loci, one\non chromosome 10q26 and another on chromosome 20p13 [51].\nHowever, follow-up candidate gene studies in these regions did\nnot identify a specific gene or genetic variation [52].\nA genome-wide association study (GWAS) in Japan identified a\nsingle nucleotide polymorphism (SNP) in CDKN2B‑AS and an\nSNP close to WNT4 at the genome-wide significance level [53]. A\nGWAS conducted in Australia, the United States, and the United\nKingdom also identified an SNP in proximity to NFE2L3 and\nHOXA10.\nAs the genetic causes of endometriosis are being investigated in\nmore detail, the aim of the present investigation was to conduct\na case–control study with epidemiological data to allow analysis\nnot only of the genetic causes of the disease, but also of gene-en-\nvironment interactions. This study reports on the clinical data\nfrom this case –control study.\nPatients and Methods\n!\nThis analysis is based on 298 endometriosis cases and 300 con-\ntrols, who were recruited between 2001 and 2010 in a pool of\n595 endometriosis patients and 475 healthy controls as part of a\nhospital-based case –control study, the Bavarian Endometriosis\nStudy (BENS). Women were eligible if they were aged at least 18\nand were willing to complete an epidemiological questionnaire\nand provide a blood sample for genetic analysis. They were re-\ncruited as endometriosis patients if the disease was confirmed\nby surgery, either macroscopically (n = 18) or with histological\nexamination (n = 281). Controls were eligible if they reported no\nprevious abdominal surgery and no pelvic pain syndrome. All of\nthe women provided written informed consent, and the ethics\ncommittee of the institution ʼs medical faculty approved the\nstudy.\nAll of the women completed a structured and assisted question-\nnaire designed to provide comprehensive epidemiological risk\nfactor data on their previous reproductive history, menstrual\ncycle characteristics, previous medical history, family history,\nand lifestyle factors. The questionnaire has previously been used\nin other case –control studies on breast cancer [54 –56]. In addi-\ntion to these parameters, specific questions were asked about en-\ndometriosis, such as previous operations and pelvic pain charac -\nteristics (lower bowel pain, dyschezia, dysuria, and dyspareunia).\nEndometriosis had to be confirmed either histologically or mac -\nroscopically. The information was obtained either from the origi-\nnal surgery reports or the pathological reports from the patient\ncharts.\nStatistical considerations\nThe cases and controls were matched at a ratio of 1 : 1 by age at\nthe time of diagnosis and interview, respectively, within deciles.\nCharacteristics of cases and controls are presented as means and\nstandard deviations, or counts and percentages. For each charac -\nteristic, a simple logistic regression model was used to calculate\n1074\nBurghaus S et al. Risk Factors for … Geburtsh Frauenheilk 2011; 71: 1073 –1079\nOriginal Article\n\n\nunadjusted odds ratios (OR) and the corresponding 95 % confi-\ndence intervals.\nMultifactorial logistic regression analyses were carried out with\nthe predictors age at menarche, menstrual cycle length, duration\nof menstrual bleeding, number of pregnancies, number of live\nbirths, number of miscarriages, use of contraceptive pills at any\ntime, BMI, and smoking status, categorized as shown in l\n\" Table 1\nto identify a set of predictors that were together associated with\nendometriosis case –control status. Five hundred bootstrap sam-\nples of the same size as the data set were selected with replace-\nment. For each bootstrap sample, a stepwise backward logistic\nmodel selection procedure starting with all predictors was car-\nried out to obtain the best model according to the Akaike infor-\nmation criterion. The retained predictors from each bootstrap\nsample were recorded, and a final variable selection was made\nby applying a procedure proposed by Sauerbrei and Schumacher\n[57] to our setting. In this procedure, the most frequent (> 70 %)\npredictors were selected, and, because of correlation, the predic -\ntor with the larger frequency out of each highly frequent predic -\ntor pair (> 90 %) was chosen. A multifactorial logistic regression\nmodel using these finally selected predictors was fitted to calcu-\nlate adjusted ORs with its 95 % confidence intervals. Repetitive\nvariable selections were carried out to stabilize the stepwise re-\ngression results [58].\nThe predictive ability of the final model was measured by the\narea under the curve (AUC) of the receiver operating characteris-\ntics. The AUC ranges from 0.5 (random prediction) to 1 (perfect\nprediction). In relation to overfitting, the AUC was evaluated with\n10-fold cross-validation with 20 repetitions and with the 0.632+\nbootstrapping method with 500 bootstrap samples [59, 60].\nAll of the tests were two-sided, and a p value < 0.05 was regarded\nas statistically significant. Calculations were carried out using the\nR system for statistical computing (version 2.13.1; R Develop-\nment Core Team, Vienna, Austria, 2011).\nResults\n!\nFrom a pool of 595 endometriosis patients and 475 controls, 298\nendometriosis patients and 300 controls remained for the final\nanalysis after matching for age. The average age of cases was\n37.2 (± 8.7), while that of controls was 37.3 (± 9.4). Common\npatient characteristics for cases and controls are presented in\nl\n\" Table 1.\nIn the univariate analysis, age at menarche, menstrual cycle\nlength, parity, number of miscarriages, and smoking status were\nfound to differ significantly between cases and controls. Women\nwith a higher age at menarche seemed to have a lower likelihood\nof being in the group of cases. The OR for women aged 15 or older\nat the time of menarche was 0.44 (95 % CI, 0.23 to 0.87) in com-\nparison with women who had the menarche at age 11 or earlier.\nSimilarly, the groups of women who had the menarche at ages 12\nand 14 had a lower likelihood of being in the case group ( l\n\" Table\n2). Women with a longer menstrual cycle ( ≥ 29 vs. ≤ 27 days)\nwere more likely to be endometriosis patients (OR 1.73; 95 % CI,\n1.09–2.75). Women who had a longer duration of menstrual\nbleeding had an OR of 1.48 (95 % CI, 0.96 –2.29), but this was not\nsignificant in the univariate analysis ( l\n\" Table 2 ). Numbers of\npregnancies and numbers of live births were distributed in a\nclearly ordinal way. The more pregnancies or live births were re-\nported, the less likely the woman was to be in the group of endo-\nmetriosis patients ( l\n\" Table 2). Women with three or more preg-\nnancies had an OR of 0.30 (95 % CI, 0.18 –0.49). Use of oral contra-\nceptives was also seen less often among cases (OR 0.48; 95 % CI,\n0.29–0.78) and smoking was more prevalent in cases (OR 2.09;\n95 % CI, 1.48–2.95). BMI and number of miscarriages were not as-\nsociated with case–control status (l\n\" Table 2). However, the num-\nber of miscarriages is not independent of the number of pregnan-\ncies.\nWith regard to the multifactorial analysis, the variable selection\nprocess described above in the statistical methods section identi-\nfied menstrual cycle length, duration of menstrual bleeding,\nnumber of pregnancies, number of miscarriages, and smoking\nstatus as relevant predictors for endometriosis case –control sta-\ntus ( l\n\" Table 2 ). The other predictors considered – age at men-\narche, number of live births, use of contraceptive pills at any\ntime, and BMI – were not selected for the final logistic regression\nmodel. This means that their predictive values appeared to be\nirrelevant, or that they can already be explained by the selected\npredictors. Menstrual cycle length and duration of menstrual\nbleeding were positively associated with endometriosis and had\nadjusted ORs of 2.33 (95 % CI, 1.34 –4.04) and 1.86 (95 % CI, 1.07 –\n3.24), respectively. The inverse association with the number of\npregnancies was even stronger, with an adjusted OR of 0.17\n(95 % CI, 0.08–0.40) among women with three or more pregnan-\ncies, in comparison with the univariate analysis. Having one or\nmore miscarriages in the medical history was identified as a sta-\ntistically significant risk factor (adjusted OR 2.76; 95 % CI, 1.28 –\nTable 1 Patient characteristics of endometriosis cases and healthy controls.\nPatient characteristics Cases Controls\nAge (years) Mean\n(SD)\n37.2 (± 8.7) 37.3 (± 9.4)\nAge at menarche (years) ≤ 11 48 (16.5 %) 26 (8.9 %)\n12 79 (27.1 %) 85 (29.1 %)\n13 84 (28.9 %) 72 (24.7 %)\n14 49 (16.8 %) 71 (24.3 %)\n≥ 15 31 (10.7 %) 38 (13.0 %)\nMenstrual cycle length (days) ≤ 27 71 (31.8 %) 96 (35.0 %)\n28 79 (35.4 %) 121 (44.2 %)\n≥ 29 73 (32.7 %) 57 (20.8 %)\nDuration of menstrual\nbleeding (days)\n≤ 4 78 (31.2 %) 123 (43.9 %)\n5 71 (28.4 %) 80 (28.6 %)\n≥ 6 101 (40.4 %) 77 (27.5 %)\nNumber of pregnancies 0 157 (53.2 %) 107 (35.8 %)\n1 61 (20.7 %) 55 (18.4 %)\n2 47 (15.9 %) 69 (23.1 %)\n≥ 3 30 (10.2 %) 68 (22.7 %)\nNumber of live births 0 181 (61.4 %) 130 (43.6 %)\n1 57 (19.3 %) 67 (22.5 %)\n2 44 (14.9 %) 70 (23.5 %)\n≥ 3 13 (4.4 %) 31 (10.4 %)\nNumber of miscarriages 0 253 (86.3 %) 252 (84.6 %)\n≥ 1 40 (13.7 %) 46 (15.4 %)\nUse of contraceptive pill, ever 0 52 (18.4 %) 29 (9.7 %)\n1 231 (81.6 %) 269 (90.3 %)\nBody mass index (kg/m\n2) ≤ 20 31 (12.3 %) 36 (12.3 %)\n20–25 163 (64.4 %) 188 (64.2 %)\n25–30 45 (17.8 %) 51 (17.4 %)\n> 30 14 (5.5 %) 18 (6.1 %)\nSmoking no 102 (41.6 %) 176 (59.9 %)\nyes 143 (58.4 %) 118 (40.1 %)\n1075\nBurghaus S et al. Risk Factors for … Geburtsh Frauenheilk 2011; 71: 1073 –1079\nOriginal Article\n\n\n5.93) independent of the number of pregnancies, for instance.\nSmoking status continued to show a significant association, with\nan adjusted OR of 2.16 (95 % CI, 1.39 –3.36). l\n\" Fig. 1 provides an\noverview of the distribution of the risk factors finally selected in\nthis case–control study.\nThe apparent AUC value of the final regression model was 0.72,\nwhile the bootstrap-validated and cross-validated AUCs were\nboth 0.68.\nDiscussion\n!\nCertain menstrual cycle characteristics, pregnancies, miscar-\nriages, and smoking were identified as risk factors associated\nwith endometriosis in this case –control study in Germany. Body\nmass index did not show any association.\nMost of the results are in line with previously published studies,\nalthough some are not. With regard to menstrual cycle character-\nistics, it has been hypothesized that increasing exposure to men-\nstrual bleeding is a risk factor for endometriosis [2, 38 –41], im-\nplying that early menarche, short cycles, longer duration of men-\nstrual bleeding, and fewer pregnancies are risk factors. The\npresent study confirms the duration of menstrual bleeding as a\nrisk factor, but showed conflicting results with regard to the\nlength of the menstrual cycle. Cases had a longer menstrual cycle,\nbut the categorization differed from some other studies, as most\nof the participants in this study had fairly regular cycles, with\nmost women having a cycle length of 28 days. Other studies have\nconstructed categories ranging from ≤ 24 days to ≥ 31 days, re-\nsulting in categories with very small sample sizes and wide con-\nfidence intervals [42]. Another study has reported no association\nand show differences depending on which controls were used for\nthe comparison [2]. A cohort study with data for cycle length in\n688 women showed no association between cycle length and en-\ndometriosis in the overall group of women, but did find an asso-\nciation in the group of women with no history of infertility [40].\nThe present study clearly shows an inverse association between\nthe number of pregnancies and endometriosis, and a positive as-\nsociation between the number of miscarriages and endometrio-\nsis. This has also been observed in most of the published studies\non the etiology of endometriosis. The Nurses ʼ Health Study II re-\nported that the risk of endometriosis declines with an increasing\nnumber of pregnancies. This effect was clearly evident in women\nwith no history of infertility and somewhat weaker in women\nwith previous or concurrent infertility [40]. The relative risk in\nwomen with no pregnancies was 1.4 (95 % CI, 1.2–1.6) in compar-\nison with women with two pregnancies. The effect size in the\npresent study is comparable to that in other case –control studies\n[41].\nThe present study is also more consistent with other reports de-\nscribing no association between endometriosis and BMI [2, 5, 61].\nHowever, there have been several studies reporting an associa-\nTable 2 Odds ratio (OR) for endometriosis cases and healthy controls in relation to patient characteristics (predictors).The unadjusted and adjusted OR, w ith 95 %\nconfidence intervals in brackets, and the corresponding p values are shown.\nPredictor Values OR unadjusted 1 p value OR adjusted 2 p value\nAge at menarche (years) ≤ 11 1.00 (reference) –– 3\n12 0.50 (0.29, 0.89) 0.02\n13 0.63 (0.36, 1.12) 0.12\n14 0.37 (0.21, 0.68) < 0.01\n≥ 15 0.44 (0.23, 0.87) 0.02\nMenstrual cycle length (days) ≤ 27 1.00 (reference) – 1.00 (reference) –\n28 0.88 (0.58, 1.34) 0.56 0.71 (0.43, 1.18) 0.19\n≥ 29 1.73 (1.09, 2.75) 0.02 2.33 (1.34, 4.04) < 0.01\nDuration of menstrual bleeding (days) ≤ 4 1.00 (reference) – 1.00 (reference) –\n5 0.71 (0.47, 1.10) 0.12 0.84 (0.48, 1.45) 0.53\n≥ 6 1.48 (0.96, 2.29) 0.08 1.86 (1.07, 3.24) 0.03\nNumber of pregnancies 0 1.00 (reference) – 1.00 (reference) –\n1 0.76 (0.49,1.18) 0.21 0.71 (0.40, 1.28) 0.25\n2 0.46 (0.29,0.72) < 0.001 0.48 (0.27, 0.88) 0.02\n≥ 3 0.30 (0.18,0.49) < 0.00001 0.17 (0.08, 0.40) < 0.0001\nNumber of live births 0 1.00 (reference) ––\n3\n1 0.61 (0.40,0.93) 0.02\n2 0.45 (0.29,0.70) < 0.001\n≥ 3 0.30 (0.15,0.60) < 0.001\nNumber of miscarriages 0 1.00 (reference) – 1.00 (reference) –\n≥ 1 0.87 (0.55,1.38) 0.54 2.76 (1.28, 5.93) < 0.01\nUse of contraceptive pill, ever no 1.00 (reference) –– 3\nyes 0.48 (0.29,0.78) < 0.01\nBody mass index (kg/m2) ≤ 20 1.00 (reference) –– 3\n20–25 1.01 (0.60, 1.70) 0.98\n25–30 1.02 (0.55, 1.92) 0.94\n> 30 0.90 (0.39, 2.11) 0.81\nSmoking no 1.00 (reference) – 1.00 (reference) –\nyes 2.09 (1.48,2.95) < 0.0001 2.16 (1.39, 3.36) < 0.001\n1 OR estimated with simple logistic regression models; one model per predictor.\n2 OR estimated by a multifactorial logistic regression model with variable selection, as described in the statistical methods section. ORs are adjust ed for all other predictors.\n3 The predictors age at menarche, number of live births, use of contraceptive pills ever, and body mass index were dropped during the variable selection process.\n1076\nBurghaus S et al. Risk Factors for … Geburtsh Frauenheilk 2011; 71: 1073 –1079\nOriginal Article\n\n\ntion between low BMI and endometriosis [36, 40, 62, 63]. In the\nNursesʼ Health Study II cohort, physical activity was not strongly\nassociated with the endometriosis risk [44].\nIn the present study, smoking was a clear risk factor in both the\nunivariate analysis and the multivariate model. As with all the\nother risk factors described, published studies have both identi-\nfied and also failed to identify associations between smoking\nand endometriosis. Three larger case –control studies reported\nno difference between cases and controls with regard to smoking\nstatus [61, 62, 64]. Two earlier case–control studies also reported\nno relationship [2, 36]. The present study showed a positive asso-\nciation between smoking (past and current) and endometriosis.\nThe Nursesʼ Health Study II reported a more complex relationship\nbetween endometriosis and smoking status. In women who re-\nported never having had an infertility problem, there was a pos-\nitive correlation between smoking status and endometriosis,\nwhereas the association was inverse in the group of patients with\nan infertility history [65].\nThis study has several limitations and strengths. In endometrio-\nsis case –control studies, not only the selection of controls but\nalso the selection of cases is critical with regard to the possible\nresults. Case selection in a hospital-based study may be biased\nby the variety of health care that is provided in the hospital con-\ncerned. Women seeking help for pelvic pain might increase the\nnumbers of cases of pain-inducing endometriosis, and a hospital\nwith specialized health care for infertility patients might have\nlarger numbers of patients in that subgroup. In the present study,\nthe hospital is a center for all types of treatment, so that a bias for\none of these groups seems unlikely. No previous abdominal sur-\ngery was required for the controls, in order to exclude any bias\nregarding the reason for surgery. This group might have larger\nnumbers of women with less pelvic pain. However, a clear limita-\ntion is that the study is not population-based, so that any selec -\ntion bias could influence the results.\nOne strength of the study is the robust selection of the variables\nduring the bootstrap validation procedure, which yielded stable\nvariable selection results. The final model predicted case –control\nstatus quite well, with a validated AUC of 0.68.\nIn conclusion, the findings of the present study show both consis-\ntencies and inconsistencies with other published studies for al-\nmost every risk factor. It is difficult for both patients and physi-\ncians to use the available information [66]. Only the number of\npregnancies and the duration of menstrual bleeding appear to\nbe consistent throughout the published studies and in this Ger-\nman case –control study. Assessing risk estimates in the popula-\ntion as determined in a hospital setting might improve our\nunderstanding of the nature of the condition for treatment of en-\ndometriosis patients and might also help identify differences be-\ntween clinical varieties of endometriosis.\nDuration of menstrual bleeding (days)\nNumber of miscarriages Number of pregnancies Smoking status\nMenstrual cycle length (days)\nNumber o\nfp a t i e n t s( % )\nNumber o\nfp a t i e n t s( % )\nNumber o\nfp a t i e n t s( % )\nNumber o\nfp a t i e n t s( % )\nNumber o\nf patients (%)\n≤ 4\n00 2 ≥ 3N o\n≤ 4≥ 6 ≥ 65\n≥ 11 Y e s\n5\n50\n40\n30\n20\n10\n0\n100\n80\n60\n40\n20\n0\n60\n50\n40\n30\n20\n10\n0\n80\n60\n40\n20\n0\n50\n40\n30\n20\n10\n0\nControl\nCase\nControl\nCase\nControl\nCase\nControl\nCase\nControl\nCase\nFig. 1 Distribution of risk factors between cases and controls.\n1077\nBurghaus S et al. 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