{"paper_id":"433e8e2b-c6fd-464c-9eee-3ef6c31606f5","body_text":"1 \n \nTitle Page  1 \n 2 \nTitle:  3 \nAnti-Müllerian hormone does not predict cumulative pregnancy rate in non -infertile 4 \nwomen following four IUI cycles with donor sperm 5 \n 6 \nAuthors: 7 \nSonia Gayete-Lafuente MD, PhD 1,2, ORCID ID: 0000-0002-7375-5848 8 \nJosé Moreno-Sepulveda MD, PhD 1,3, ORCID ID: 0000-0002-4921-1162  9 \nJavier Sánchez-Álvarez MD1,4  10 \nMaria Prat MD5,6, ORCID ID: 0000-0002-7960-0994 11 \nAna Robles MD, PhD5,7 12 \nJuan José Espinós MD, PhD1,7,8, ORCID ID: 0000-0003-4838-8940 13 \nMiguel Ángel Checa MD, PhD5,6,7,8,9, ORCID ID: 0000-0002-0226-3416 14 \n 15 \n 16 \nAffiliations: 17 \n 18 \n1 Obstetrics and Gynecology Department, Autonoma University of Barcelona (UAB), 19 \nCampus of Bellaterra, Cerdanyola del Vallès, Spain 20 \n 21 \n2 Foundation for Reproductive Medicine, New York, USA 22 \n 23 \n3 Clínica de la Mujer Medicina Reproductiva, Viña del Mar, Chile 24 \n 25 \n4 Hospital Vall d’Hebron, Barcelona, Spain 26 \n 27 \n5 Hospital del Mar, Barcelona, Spain 28 \n 29 \n6 Faculty of Medicine and Life Sciences, Pompeu Fabra University (UPF), Barcelona, Spain 30 \n 31 \n7 Fertty Clinic, Barcelona, Spain  32 \n 33 \n8 Fertty Foundation, Barcelona, Spain 34 \n 35 \n9 Hospital del Mar Research Institute (IMIM), Barcelona, Spain 36 \n 37 \n 38 \n 39 \n 40 \nCorresponding author: Sonia Gayete-Lafuente MD, PhD 41 \nE-mail: sonia.gayete@gmail.com  42 \nPhone number: +1 (347) 777-4158 43 \nPermanent address: 330 E 15th St, Apt 3, 10003, New York, NY, USA 44 \nAcknowledgements: 45 \n 46 \nAuthor contributions and acknowledgements 47 \n 48 \n\n \n \n2 \n \nMAC conceived the idea and directed the study. SGL participated in the study design as well 49 \nas in the research plan and coordination, collected data and wrote the manuscript. JMS and JSA 50 \ncollected data, participated in the literature review, performed statistics and prepared the 51 \nfigures. MPO and ARC collected data. All authors had access to the data in the study and take 52 \nresponsibility for its integrity. All authors made significant contributions to this manuscript, 53 \nreviewed its final version and approved of this submission. We thank Ana Badia, nurse and 54 \nclinical coordinator, for their patient work and technical assistance.  55 \n 56 \nStatements and declarations 57 \n 58 \nCompeting interests and funding: The authors have no conflicts of interest to disclose. 59 \n 60 \n 61 \n  62 \n\n \n \n3 \n \nAnti-Müllerian hormone does not predict cumulative pregnancy rate in non-63 \ninfertile women following four IUI cycles with donor sperm 64 \n 65 \n 66 \nCapsule Summary (40 words)  67 \nThe cumulative clinical pregnancy rate after 4 cycles of ds -IUI in non-infertile women is not 68 \ncorrelated with AMH levels. Decreased AMH levels do not seem to reduce pregnancy rates 69 \nfollowing ds-IUI and should not limit patient access to this treatment. 70 \n 71 \n 72 \n 73 \nAbstract (250 words) 74 \n 75 \nPurpose: To evaluate the predictive value of serum AMH for clinical pregnancy in non -76 \ninfertile population undergoing intrauterine insemination with donor sperm (ds-IUI). 77 \n 78 \nMethods: This multicenter prospective study (ClinicalTrials.gov ID: NCT06263192) recruited 79 \nall non-infertile women undergoing ds-IUI from June 2020 to December 2022 in three different 80 \nfertility clinics in Spain and Chile. Indications for ds -IUI included severe 81 \noligoasthenoteratozoospermia, female partner, or single status. Clinical pregnancy rates were 82 \ncompared between women with AMH ≥1.1 and <1.1 ng/mL. The main outcome measure was 83 \nthe cumulative clinical pregnancy rate after up to 4 ds-IUI cycles. 84 \n 85 \nResults: A total of 458 ds -IUI cycles were performed amongst 245 patients, of whom 108 86 \n(44.08%) achieved clinical pregnancy within 4 cycles, 60.2% of these occurring in the first 87 \nattempt and 84.2% after two attempts. We found no significant differences in AMH levels or 88 \nother parameters (such as age, BMI, FSH, AFC) between women who became pregnant and 89 \nthose who did not. Cumulative pregnancy rates and logistic regression analysis revealed that 90 \nAMH ≥1.1 ng/mL was not predictive of ds-IUI success. While a high positive correlation was 91 \nobserved between AFC and AMH (r=0.67, p<0.001), ROC curve analyses indicated that neither 92 \nof these ovarian reserve markers accurately forecasts cumulative ds -IUI outcomes in non -93 \ninfertile women. 94 \n 95 \nConclusions: The findings of this multicenter study suggest that AMH is not a reliable 96 \npredictor of pregnancy in non-infertile women undergoing ds-IUI. Even women with low AMH 97 \nlevels can achieve successful pregnancy outcomes, supporting the notion that diminished 98 \novarian reserve should not restrict access to ds-IUI treatments in eligible non-infertile women. 99 \n 100 \n 101 \nKeywords: 102 \nanti-Müllerian hormone, AMH, intrauterine insemination, IUI, predictive value, cumulative 103 \npregnancy rate 104 \n  105 \n\n \n \n4 \n \nIntroduction 106 \n  107 \nAnti-Müllerian hormone (AMH) is secreted by the granulosa cells of preantral and antral 108 \novarian follicles. Its serum levels peak at 20-25 years of age and gradually decrease after that, 109 \nalong with antral follicle counts (AFC), due to the decrease in ovarian reserve [1-4]. This age-110 \nrelated follicle loss accelerates from 35 and furthermore from 37 and 40 years of age, while 111 \nserum levels of AMH becomes undetectable and follicle -stimulating hormone (FSH) increase 112 \nuntil reaching menopausal ranges [5,6].  113 \n 114 \nSerum AMH level is a widely used marker of ovarian reserve and predictor of follicular 115 \nresponse to controlled ovarian stimulation (COS) [1,7-9], being <1.1 ng/ml a well-established 116 \ncut-off point for poor ovarian response (POR) [10]. AMH levels have been correlated with the 117 \novarian sensitivity index [9], the number of oocytes retrieved [7], and live birth rates following 118 \novarian stimulation and In Vitro Fertilization (IVF) by the most comprehensive and recent 119 \nmeta-analysis by Peigne et al [11].  However, the authors highlighted that data for AMH 120 \npredictive value is lacking after IUI or in women trying to conceive without ART [11]. Indeed, 121 \ndespite occasional references, it is important to avoid misusing AMH as a \"fertility test\" since 122 \nthe likelihood of becoming pregnant depends on many factors, with age being the most reliable 123 \npredictor [12-15].  124 \n 125 \nParticularly, pregnancy rates significantly decrease with advancing maternal age from 35 years 126 \ndue to oocyte quality deterioration related to impaired DNA integrity and meiosis competence, 127 \noxidative stress, and early apoptosis [16-27]. Based on this rationale, some authors raise doubt 128 \nabout the predictive value of AMH for natural conception and intrauterine insemination (IUI) 129 \n[28-33]. This skepticism arises from the fact that in these predominantly monofollicular cycles, 130 \nthe critical factor for achieving pregnancy is the quality of the ovulated oocyte rather than the 131 \nquantity of oocytes remaining in the ovary. In contrast, other authors report better pregnancy 132 \nrates following IUI in patients with high AMH levels [34-38], while observing poorer outcomes 133 \nin those with low AMH levels [39]. In fact, all the published studies to date are retrospective 134 \nand methodologically heterogeneous, and the only available meta -analysis is focused on 135 \nassessing the association between AMH and spontaneous pregnancy [40]. After IUI with donor 136 \nsperm (ds-IUI), it has been reported that the cumulative pregnancy rate can reach up to 60% in 137 \nappropriately selected, non-infertile women without advanced maternal age [41], remaining the 138 \nimpact of low AMH uncertain in this group. However, despite the lack of conclusive evidence, 139 \npresenting low AMH level is a common exclusion criterion for women to access this treatment 140 \nin several fertility centers. This often results in more complex and invasive treatments such as 141 \nIVF.  142 \n 143 \nThe objective of this study is to assess the predictive value of AMH for clinical pregnancy in 144 \nnon-infertile population undergoing ds -IUI. Determining this would allow clinicians to 145 \nestablish a more accurate prognosis of IUI cycles and optimize the indication of assisted 146 \nreproductive techniques (ART).  147 \n 148 \n 149 \n\n \n \n5 \n \nMethods 150 \n 151 \nThis multicenter prospective observational study evaluated the correlation between AMH 152 \nlevels and pregnancy rates in non -infertile women undergoing ds -IUI. Participants were 153 \nrecruited from June 2020 to December 2022 from three centers: Hospital del Mar and  Fertty 154 \nClinic (Barcelona, Spain) and Women's Reproductive Medicine Clinic (Viña del Mar, Chile). 155 \nThe study includes women aged 25 -42 years undergoing ds -IUI due to partner’s severe 156 \noligoasthenoteratozoospermia, female partner or single status, were eligible for the study. 157 \nPatients with a BMI ≥ 30 kg/m2, ovarian cysts, endometriosis, or ovulation dysfunction were 158 \nexcluded. In all participants, baseline measures included: age (years), pregnancy history, AMH 159 \n(ng/mL), cycle day -2-5 FSH (IU/L), and cycle day -2-5 AFC by transvaginal ultrasound 160 \n(TVUS). Patients underwent ovulation induction with low doses of gonadotropins (from 37.5 161 \nto 75 IU/day) from the second to the fifth day of the cycle until the follicles reached a diameter 162 \nof 17 to 20 mm. Follicle growth was monitored by transvaginal ultrasound (TVUS) every 2 -3 163 \ndays, and ds-IUI was performed 36 hours after triggering ovulation with subcutaneous injection 164 \nof 250 µg of HCG (Ovitrelle®, Merk). Skilled Gynecologists specialized in Reproductive 165 \nEndocrinology and Infertility from each center managed patient care and performed the ds -166 \nIUIs. Patients repeated subsequent ds -IUI cycles until achieving a live birth or up to 4 cycles 167 \nwhen IVF was indicated.  168 \n 169 \nIn all cases, serum AMH levels were determined using the commercial automated 170 \nimmunoassay Elecsys® test on a Cobas measurement system by Roche Diagnostics. This test 171 \noffers a measurement interval of 0.01 -23 ng/mL and an inter -day imprecision of <5%, which 172 \nis remarkably more accurate than preceding ELISA tests. 173 \n 174 \nSperm for the Spanish centers was supplied by the CEFER Reproduction Institute sperm bank 175 \n(Spain), while at Women's Reproductive Medicine Clinic, it was obtained from the National 176 \nSperm Bank of Chile via California Cryobank (USA).  177 \n 178 \nPer each complete ds-IUI cycle, total number of follicles reaching at least 17 mm in diameter 179 \non the trigger day and clinical pregnancy rate were registered. Reproductive outcomes were 180 \ncompared between women with AMH ≥1.1 ng/mL and <1.1 ng/mL. The main outcome 181 \nmeasure was the cumulative clinical pregnancy rate after up to 4 consecutive ds -IUI. Clinical 182 \npregnancy was defined according to the latest version of the international ART terminology 183 \nconsensus [42].  184 \n 185 \nStatistical analysis 186 \n 187 \nParticipant demographics, clinical characteristics, and outcomes were summarized using 188 \ndescriptive statistics. Quantitative variables were reported as means and range or standard 189 \ndeviation (SD).  190 \n 191 \nCumulative clinical pregnancy rates of patients presenting AMH ≥1.1 and <1.1 ng/mL were 192 \nrepresented using Kaplan-Meier, and both curves were compared by means of the log-rank test 193 \nor the model of Cox regression. A sub-analysis was also conducted comparing pregnancy rates 194 \n\n \n \n6 \n \nwith AMH ≥1.1 and <1.1 ng/mL in patients in different age groups using the Chi -square test. 195 \nStatistical significance was established for p-value <0.05.  196 \n 197 \nMultivariate logistic regression analysis was performed, including age, BMI, FSH, AFC and 198 \nAMH, and correlation coefficients, adjusted odds ratio (OR) with 95% CI and receiver 199 \noperating characteristic (ROC) curve analysis were estimated.  200 \n 201 \nT-student test was performed to assess differences between patients who achieved clinical 202 \npregnancy in a ds-IUI cycle and those who did not.  203 \n 204 \nThe STATA software package version 18.0 (SPSS, Chicago, IL) was used for statistical 205 \nanalysis. Statistical significance was set at p<0.05. 206 \n 207 \nEthics 208 \n  209 \nThis project was approved by the Institutional Review Board and Ethics Committee at Hospital 210 \ndel Mar in Barcelona (Spain) (IRB Protocol ID 2020/9445) and registered at ClinicalTrials.gov 211 \n(ID NCT06263192 ).  212 \n 213 \nNeither the data collection, its analysis nor its results implied any change in the clinical 214 \nmanagement of ds-IUI for the patients included in the study. 215 \n 216 \n 217 \nResults 218 \n 219 \nA total of 458 ds -IUI cycles were performed amongst 245 patients, of whom 108 (44.08%) 220 \nachieved a clinical pregnancy within 4 ds-IUI cycles. Patient baseline characteristics are shown 221 \nin Table 1. 222 \n 223 \nNoteworthy, out of the 108 clinical pregnancies achieved through ds -IUI, 91 occurred within 224 \nthe first two ds -IUI cycles (84.2%), with 65 of them occurring after the first ds -IUI attempt 225 \n(60.2%). The number of clinical pregnancies remarkably decreased after the third and fourth 226 \ncycles of ds-IUI (12 cases and 5 pregnancies, respectively). 227 \n 228 \nPatients who achieved clinical pregnancy did not exhibit statistically significant differences in 229 \nAMH levels compared to those who did not become pregnant (Figure 1), nor did they show 230 \ndifferences in other parameters such as age, BMI, FSH, and AFC (Table 2). 231 \n 232 \nThe cumulative clinical pregnancy rates for women with AMH ≥1.1 and <1.1 ng/mL are 233 \npresented through the Kaplan -Meier estimator (Figure 2). The log -rank test (Mantel -Cox) 234 \nshows no statistically significant differences in cumulative clinical pregnancy rate between both 235 \ngroups (1.06; p-value 0.302). Sub-analyses of patients in different age groups with AMH ≥1.1 236 \nng/mL and <1.1 ng/mL revealed similar findings, with no significant differences in pregnancy 237 \nrates (Supplemental Tables 1 and 2). 238 \n 239 \n\n \n \n7 \n \nPearson correlation determined that there was a high positive correlation between AFC and 240 \nAMH (r= 0.67; p.value <0.001).  241 \n 242 \nLogistic regression analysis examining the influence of age, BMI, FSH, AFC, and AMH on 243 \ncumulative clinical pregnancy rates is presented in Table 3. The comprehensive model was 244 \nreliable, being significantly correlated to pregnancy outcomes in the study population (Chi2= 245 \n12.45, p-value 0.029).   246 \n 247 \nROC curve analyses for AMH and AFC predicting ds -IUI pregnancy outcomes demonstrate 248 \nareas under the curve (AUC) of 0.554 and 0.562, respectively (Figure 3 and Supplemental 249 \nFigure 1), indicating that using AMH or AFC to predict ds-IUI success in non-infertile women 250 \nwould not provide accurate guidance. Additional ROC curve analyses revealed that neither age, 251 \nFSH or BMI predict pregnancy following ds-IUI in the study population (Supplemental Figures 252 \n2 to 4).  253 \n 254 \nThe rates of clinical pregnancy and treatment failure in women with AMH ≥1.1 ng/mL were 255 \n45.1% and 54.8%, respectively. In contrast, these rates were 40.6% and 59.3% in women with 256 \nAMH <1.1 ng/mL. Logistic regression analysis examining the influence of serum AMH on 257 \npregnancy rates showed that AMH ≥1.1 ng/mL is not a definitive predictive factor for clinical 258 \npregnancy following ds-IUI in non-infertile women (OR 0.83; 0.46-1.51) (Table 4). 259 \n 260 \n 261 \nDiscussion 262 \n 263 \nThis multicenter study showed for the first time that AMH levels do not predict cumulative 264 \npregnancy rates following multiple ds -IUI cycles in non -infertile women. Additionally, we 265 \nfound that ovarian reserve markers were comparable between non -infertile women who 266 \nachieved pregnancy following ds-IUI and those who did not.  267 \n 268 \nOur comprehensive logistic regression model was globally significant, indicating that the 269 \nvariables of age, BMI, FSH, AFC, and AMH have a collective impact on the probability of 270 \npregnancy. However, none of the studied variables proved to be an independent predictor of 271 \ncumulative clinical pregnancy rate in non-infertile women undergoing ds-IUI. This result may 272 \nbe due to several reasons, including the size of the sample or the variability of the data. While 273 \nwe identified a correlation between AMH and AFC in our study cohort, we observed a non -274 \nsignificant trend suggesting higher AFC, but not AMH, in women who obtained pregnancy 275 \ncompared to those who did not. This contrast may be attributed to inter -observer variations in 276 \nAFC measurements or perhaps to a potentially higher incidence of favorable bifollicular cycles 277 \nin patients with higher AFC prior to IUI. Nonetheless, no significant differences in pregnancy 278 \noutcomes based on ovarian reserve were found and, therefore, none of these plausible 279 \nhypothetical effects seem remarkable, if present.  280 \n 281 \nThe selection of women without anatomical -functional ovarian abnormalities or diagnosis of 282 \nfemale infertility along with the use of donor semen allowed us to adequately assess the effect 283 \n\n \n \n8 \n \nof ovarian reserve on the probability of pregnancy after IUI in non -infertile populations. The 284 \nprospective nature of the study provided a longitudinal perspective for cumulative effects of 285 \nexposures, controlled data collection and the ability to measure incidence and multiple 286 \noutcomes, without recall bias from participants. Being performed at centers in different 287 \ncontinents, the study included patients from different ethnic backgrounds and socioeconomic 288 \nlevels, granting robustness and a greater external validity.  289 \n 290 \nLimitations of the study included the potential influence of undiagnosed polycystic ovary 291 \nsyndrome (PCOS) in women with exceptionally high AMH values [43]. However, this effect 292 \nseems minimal, if any, given the exclusion of patients with menstrual irregularities and the 293 \npresence of 11 participants with AFC >25, and only 4 among them >30. Another limitation is 294 \nthe limited representation of women with low AMH levels (<1.1 ng/mL, 43 patients; <0.5 295 \nng/mL, 15 patients), which may be attributed to the general practice in our centers of not 296 \noffering IUI to women with low ovarian reserve or those over the age of 42. In addition, the 297 \nvariability in length and total dosage of rFSH exposure before ds-IUI could have influenced the 298 \noutcomes, although this seems unlikely. Only exposure to excessively high gonadotropin levels 299 \nhas been reported to decrease oocyte quality and pregnancy likelihood with IVF treatments [44-300 \n46], but low daily doses were used in all cases in our study. 301 \n 302 \nThere is still extensive controversy regarding the relationship between ovarian reserve and 303 \nfertility, while the association between AMH levels and success rates in natural conception and 304 \nIUI varies greatly across published studies. Although no studies assessed cumulative pregnancy 305 \nafter ds-IUI in non -infertile population to date, some authors have reported poor predictive 306 \nvalue of AMH for natural pregnancy and IUI outcome [12,28 -34], in line with our findings. 307 \nThis is also supported by the recent meta-analysis by Lin et al., which included eleven studies 308 \n(n=4,388 women) and was aimed to study the utility of AMH in predicting pregnancy. The 309 \nauthors found low AMH levels not to be associated with reduced fertility following IUI in 310 \ndifferent age groups [40], demonstrating the limited capability of AMH to predict fertility when 311 \nno COS is needed. Yet, a few authors reported better pregnancy rates after IUI in patients with 312 \nhigh AMH levels [34-38], and worse outcomes in patients with low AMH levels [39]. However, 313 \nthese works present several limitations. Many only consider the first attempt of IUI and are 314 \nheterogeneous in its methods, lacking accurate control of confounding variables, especially 315 \nregarding the study populations, which include male factor or different infertility diagnoses 316 \nand/or treatment indications. In comparison, our study presents less risk of bias by being 317 \nprospectively monitored and strictly including women not older than 42 years, without female 318 \ninfertility factors and using sperm from donors for their IUIs. Additionally, in most studies, low 319 \nAMH levels were strongly associated with advanced and very advanced maternal age, hence 320 \nthe poorer pregnancy rates could have been due to age and not necessarily to AMH. In fact, 321 \nadvanced maternal age is an established independent negative prognostic factor for clinical 322 \npregnancy and live birth [47,48], including the following IUI [49]. While our results did not 323 \ndemonstrate significantly higher cumulative clinical pregnancy rates in younger patients, it’s 324 \nnoteworthy that the women who conceived following ds -IUI tended to be younger (p=0.057). 325 \nHence, increasing our sample size could potentially lead to achieving statistical significance.  326 \n 327 \n\n \n \n9 \n \nOn the other hand, diminished ovarian reserve (DOR) does not necessarily correlate with poor 328 \nreproductive outcomes, despite the numerous controversial theories attempting to explain 329 \npossible oocyte quality impairment associated with DOR [50 -52]. These hypotheses suggest 330 \npotential underlying mechanisms such as ovulation of higher -quality oocytes earlier in life, 331 \ndecreased ovarian support for folliculogenesis during IVF and reduced euploidy rates in DOR. 332 \nYet, these theories remain unproven and are currently debated, with our findings not supporting 333 \nthem. Instead, extensive research suggests that coexisting factors with DOR, rather than the 334 \ncondition itself, impact oocyte performance and embryo quality. Extensive studies by the 335 \nPOSEIDON group and others show how reproductive outcomes are not directly affected by 336 \nlow ovarian reserve but by a range of possible coexisting factors [53 -56]. Even young women 337 \nwho have undergone chemotherapy, experiencing DOR due to a gonadotoxic insult, seem to 338 \nmaintain age -appropriate oocyte competence [57 -58]. Therefore, current evidence does not 339 \nsupport the existence of specific biochemical or molecular mechanisms in DOR compromising 340 \noocyte quality. 341 \n 342 \nOur study emphasizes the inappropriateness of directly inferring a poor pregnancy prognosis 343 \nto women with low ovarian reserve and therefore automatically dismissing the potential 344 \neffectiveness of IUI in selected cases, indicating IVF at the outset given its high success rates 345 \nin fertility clinics. In fact, DOR is a criterion for exclusion for access to IUI in many public 346 \nprograms over the world. However, in practice, many other factors must be taken into account 347 \nwhen indicating ART techniques on an individual basis. IVF presents greater reproductive 348 \nefficiency per cycle than IUI in infertile and elderly maternal populations and has the advantage 349 \nof being able to freeze additional embryos. However, non-infertile women younger than 38-40 350 \nyears of age with a male, female or single infertile partner can benefit from starting ART with 351 \nIUIs regardless of their ovarian reserve, as these treatments are less complex, less invasive, and 352 \nless expensive. As these are essentially monofollicular cycles, the prognosis of this technique 353 \nwill depend on oocyte quality, and therefore presenting low AMH should not be used as an 354 \nexclusion criterion for non -infertile women seeking ds -IUI. Indeed, conversely, it could be 355 \nargued that in women with a low ovarian reserve (AFC 2 -3 or poor response criteria), the 356 \nindication for IUI becomes more advisable because IVF would offer little probability of 357 \nobtaining additional embryos for freezing, providing limited added benefit. Especially in 358 \nyounger patients without female infertility factors, whose oocyte quality is anticipated to be 359 \nhigh, IUI should not be dismissed solely based on DOR since their outcomes may be 360 \ncomparable to those with normal ovarian reserve.  361 \n 362 \nFurther studies are essential to validate our findings, ensuring a comprehensive interpretation 363 \nof ovarian markers and a consequent accurate prognosis and indications of ART in each clinical 364 \ncase. Moreover, notably, prospects for a novel trend in ART centered around oocyte -based 365 \napproaches are emerging [59]. It seems crucial to investigate the influence of aging and 366 \nmolecular environment on oocyte quality, potentially being the main predictor for pregnancy 367 \nsuccess in the absence of COS. While clinicians should consider the results of this study when 368 \nindicating ds-IUI, we believe that a deeper understanding of the mechanisms underlying oocyte 369 \ncompetence will enhance overall reproductive outcomes in the future.  370 \n 371 \n\n \n \n10 \n \n 372 \nConclusions 373 \n 374 \nAMH is not a reliable predictor of pregnancy in non-infertile women undergoing ds-IUI. Even 375 \nwomen with significantly low ovarian reserve can achieve successful outcomes after ds -IUI, 376 \nwhich may be primarily influenced by oocyte quality. The findings of this multicenter study 377 \nsupport the idea that low AMH levels should not limit access of non-infertile women to ds-IUI. 378 \n 379 \n 380 \n  381 \n\n \n \n11 \n \nReferences 382 \n 383 \n1. Broer SL, Dólleman M, Opmeer BC, Fauser BC, Mol BW, Broekmans FJ. AMH and AFC as 384 \npredictors of excessive response in controlled ovarian hyperstimulation: a meta-analysis. Hum Reprod 385 \nUpdate. 2011;17(1):46-54.  386 \n 387 \n2. 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Reprod 607 \nFertil Dev. 2019;32(2):7-10.  608 \n609 \n\n \n \n16 \n \nTables 610 \n 611 \nTable 1. Description of baseline patient characteristics 612 \n 613 \nPatients (n) 245 \nds-IUI cycles (n) 458 \nAge (years) 34.28 ± 3.86 \nBMI (kg/m2) 25.01 ± 4.26 \nFSH (IU/L) 7.23 ± 3.03 \nAFC (n) 13.23 ± 7.06 \nAMH (ng/mL) 2.6 ± 2.09 \nMonofollicular cycles (n) 353 \nBifollicular cycles (n) 105 \n1st ds-IUI cycle (n) 245 \n2nd ds-IUI cycle (n) 123 \n3rd ds-IUI cycle (n) 57 \n4th ds-IUI cycle (n) 34 \n 614 \nValues expressed as total number (n) or as mean ± SD. 615 \nds-IUI= donor sperm intrauterine insemination; BMI= Body Mass Index; FSH= follicle -616 \nstimulating hormone; AFC= antral follicle count; AMH= Anti-Müllerian hormone. 617 \n 618 \n 619 \nTable 2. Differences in clinical parameters among patients who achieved pregnancy after 620 \nup to 4 ds-IUI compared to those who did not 621 \n 622 \n \nPregnancy \n(n= 108) \nNo Pregnancy \n(n= 137) p-value \nAge (years) 33.78 ± 3.85 34.67 ± 3.83 0.057 \nBMI (kg/m2) 25.12 ± 4.15 24.93 ± 4.35 0.768 \nFSH (UI/L) 7.02 ± 2.54 7.41 ± 3.41 0.48 \nAFC (n) 14.22 ± 7.7 12.42 ± 6.42 0.071 \nAMH (ng/mL) 2.67 ± 1.83 2.55 ± 2.28 0.649 \n 623 \nValues expressed as mean ± SD. 624 \nds-IUI= donor sperm intrauterine insemination; BMI= Body Mass Index; FSH= follicle -625 \nstimulating hormone; AFC= antral follicle count; AMH= Anti-Müllerian hormone. 626 \n 627 \n 628 \n\n \n \n17 \n \nTable 3. Logistic regression analysis examining the association between patient 629 \ncharacteristics and cumulative clinical pregnancy outcome 630 \n 631 \n Univariate logistic regression  Multivariate logistic regression \n OR 95% CI p-value Adjusted OR 95% CI p-value \nAge (years) 0.94 0.88 - 1.00 0.059 0.87 0.75 - 1.01 0.068 \nBMI (kg/m2) 1.01 0.94 - 1.08 0.767 1.11 0.98 - 1.25 0.102 \nFSH (UI/L) 0.96 0.85 - 1.08 0.478 1.19 0.96- 1.46 0.107 \nAFC (n) 1.04 1.00 - 1.08 0.074 1.07 0.98 - 1.17 0.142 \nAMH (ng/ml) 1.03 0.91 - 1.16 0.648 0.77 0.53 - 1.12 0.177 \n 632 \nOR= Odds Ratio; 95% CI= 95% Confidence Interval. 633 \nBMI= Body Mass Index; FSH= follicle -stimulating hormone; AFC= antral follicle count; 634 \nAMH= Anti-Müllerian hormone; ds-IUI= donor sperm intrauterine insemination. 635 \n 636 \n 637 \nTable 4. Cumulative pregnancy outcome after up to 4 ds -IUI in patients with AMH ≥1.1 638 \nng/mL vs AMH <1.1 ng/mL 639 \n 640 \n \nAMH ≥1.1 ng/mL \n(n= 186) \nAMH <1.1 ng/mL  \n(n= 59) \nOR  \n(95% CI, p-value) \nNo Pregnancy  102 35 0.83  \n(0.46 - 1.51, 0.546) Pregnancy 84 24 \n 641 \nOR= Odds Ratio; 95% CI= 95% Confidence Interval. 642 \nAMH= Anti-Müllerian hormone; ds-IUI= donor sperm intrauterine insemination. 643 \n 644 \n 645 \n  646 \n\n \n \n18 \n \nFigures 647 \n 648 \nFigure 1. Association between AMH and cumulative clinical pregnancy outcome after up 649 \nto 4 ds-IUI  650 \n 651 \n 652 \np-value=0.62. 653 \n 654 \nAMH= Anti-Müllerian hormone; ds-IUI= donor sperm intrauterine insemination. 655 \n 656 \n 657 \nFigure 2. Cumulative clinical pregnancy rate up to 4 ds -IUI in women with serum AMH 658 \nlevels ≥1.1 and <1.1 ng/mL 659 \n 660 \n 661 \n 662 \nLog-rank test (Mantel-Cox)= 1.06; p-value 0.302. 663 \n\n\n \n \n19 \n \n 664 \nAMH= Anti-Müllerian hormone; ds-IUI= donor sperm intrauterine insemination. 665 \n 666 \n 667 \nFigure 3. ROC curve analysis of AMH for cumulative clinical pregnancy rate after up to 668 \n4 ds-IUI  669 \n 670 \nAUC= 0.554 671 \n 672 \nROC= Receiving Operating Characteristic; AUC= Area Under the Curve.  673 \nAMH= Anti-Müllerian hormone; ds-IUI= donor sperm intrauterine insemination. 674 \n 675 \n  676 \n\n\n \n \n20 \n \nSupplemental Material 677 \n 678 \nSupplemental Table 1. Comparison of cumulative pregnancy rate up to 4 ds-IUI in women 679 \nwith AMH ≥1.1 and <1.1 ng/mL in different age groups  680 \n 681 \nWomen aged <35 years \n AMH ≥1.1 (n=18) AMH <1.1 (n=109) Chi2 (df, p-value) \nNo Pregnancy 10 53 0.3, (1, 0.586) \n Pregnancy 8 56 \n 682 \nWomen aged ≥35 years \n AMH ≥1.1 (n=41) AMH <1.1 (n=77) Chi2 (df, p-value) \nNo Pregnancy 25 49 0.08, (1, 0.776) \n Pregnancy 16 28 \n 683 \nWomen aged <38 years \n AMH ≥1.1 (n=36) AMH <1.1 (n=149) Chi2 (df, p-value) \nNo Pregnancy 17 81 0.59, (1, 0.441) \n Pregnancy 19 68 \n 684 \nWomen aged ≥38 years \n AMH ≥1.1 (n=23) AMH <1.1 (n=37) Chi2 (df, p-value) \nNo Pregnancy 18 21 02.88, (1, 0.09) \n Pregnancy 5 16 \n 685 \nChi2= Chi square; df= degrees of freedom. 686 \nds-IUI= donor sperm intrauterine insemination; AMH= Anti-Müllerian hormone. 687 \n\n \n \n21 \n \nSupplemental Figure 1. ROC curve analysis of AFC for the cumulative pregnancy rate 688 \nafter up to 4 ds-IUI 689 \n 690 \nAUC= 0.562 691 \n 692 \nROC= Receiving Operating Characteristic; AUC= Area Under the Curve.  693 \nAFC= antral follicle count; ds-IUI= donor sperm intrauterine insemination. 694 \n 695 \n 696 \nSupplemental Figure 2. ROC curve analysis of age for cumulative pregnancy rate after 697 \nup to 4 ds-IUI 698 \n 699 \n\n\n \n \n22 \n \nAUC= 0.578 700 \n 701 \nROC= Receiving Operating Characteristic; AUC= Area Under the Curve.  702 \nds-IUI= donor sperm intrauterine insemination. 703 \n 704 \n 705 \nSupplemental Figure 3. ROC curve analysis of FSH for cumulative pregnancy rate after 706 \nup to 4 ds-IUI 707 \n 708 \nAUC= 0.509 709 \n 710 \nROC= Receiving Operating Characteristic; AUC= Area Under the Curve.  711 \nFSH= follicle-stimulating hormone; ds-IUI= donor sperm intrauterine insemination. 712 \n 713 \n 714 \n\n\n \n \n23 \n \nSupplemental Figure 4. ROC curve analysis of BMI for cumulative pregnancy rate after 715 \nup to 4 ds-IUI 716 \n 717 \nAUC= 0.536 718 \n 719 \nROC= Receiving Operating Characteristic; AUC= Area Under the Curve.  720 \nBMI= Body Mass Index; ds-IUI= donor sperm intrauterine insemination. 721","source_license":"Public-Domain","license_restricted":false}