Front. Endocrinol. Frontiers in Endocrinology Front. Endocrinol. 1664-2392 Frontiers Media S.A. 10.3389/fendo.2025.1601803 Original Research Impact of gonadotropin genetic profile and ovarian reserve on controlled ovarian stimulation: data from prospective cohort of the GENACOS trial ConfortiAlessandro 1 * † Writing – review & editing Conceptualization Investigation Validation Methodology Resources Visualization Writing – original draft SantiDaniele 2 † Software Writing – review & editing Conceptualization Supervision Data curation Formal analysis Writing – original draft Methodology Validation Visualization AllegraAdolfo 3 Data curation Writing – review & editing Conceptualization Investigation Supervision Writing – original draft Visualization Mignini RenziniMario 4 Data curation Writing – original draft Conceptualization Visualization Writing – review & editing MarinoAngelo 3 Writing – original draft Visualization Writing – review & editing Data curation Supervision BriganteClaudio 4 Data curation Visualization Writing – review & editing Writing – original draft IemmelloRoberta 4 Writing – review & editing Data curation Writing – original draft VanniValeria Stella 5 Data curation Writing – original draft Writing – review & editing RebecchiAgnese 6 Writing – review & editing Data curation Writing – original draft PriviteraLaura 5 Writing – original draft Writing – review & editing Data curation SperdutiSamantha 2 7 Writing – review & editing Writing – original draft Data curation Methodology CasariniLivio 2 7 Writing – original draft Visualization Data curation Methodology Supervision Writing – review & editing CarboneIlma Floriana 8 Writing – original draft Methodology Data curation Writing – review & editing SimoniManuela 2 7 Writing – review & editing Visualization Writing – original draft Validation Supervision AlviggiCarlo 9 Writing – review & editing Visualization Resources Validation Funding acquisition Conceptualization Supervision Methodology Writing – original draft PapaleoEnrico 5 Methodology Supervision Data curation Investigation Visualization Resources Conceptualization Validation Writing – original draft Writing – review & editing Funding acquisition Department of Neuroscience, Reproductive Science and Odontostomatology, University of Naples Federico II, Naples, Italy Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Modena, Italy Reproductive Medicine Unit, ANDROS Day Surgery Clinic, Palermo, Italy Biogenesi Reproductive Medicine Centre, Istituti Clinici Zucchi, Monza, Italy Gynecology/Obstetrics Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy Istituto Eugyn, Modena, Italy Center for Genomic Research, University of Modena and Reggio Emilia, Modena, Italy Unit of Obstetrics, Department of Woman, Child and Neonate, Mangiagalli Center, Fondazione IRCCS Ca’ Granda Ospedale Maggiore Policlinico, Milan, Italy Department of Public Health, University of Naples Federico II, Naples, Italy Correspondence: Alessandro Conforti, alessandro.conforti@unina.it

†These authors have contributed equally to this work

 22 08 2025 29 04 2026 2025 16 1601803 28 03 2025 25 07 2025 Copyright © 2025 Conforti, Santi, Allegra, Mignini Renzini, Marino, Brigante, Iemmello, Vanni, Rebecchi, Privitera, Sperduti, Casarini, Carbone, Simoni, Alviggi and Papaleo. 2025 Conforti, Santi, Allegra, Mignini Renzini, Marino, Brigante, Iemmello, Vanni, Rebecchi, Privitera, Sperduti, Casarini, Carbone, Simoni, Alviggi and Papaleo https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Introduction

Several studies indicate that a specific genotype profile could influence ovarian sensitivity to exogenous gonadotropin. However, most of the previous studies were observational and retrospective and thereby more prone to bias. The aim of this study was to evaluate the impact of gonadotropin single nucleotide polymorphisms (SNPs) on the outcomes of in-vitro fertilization (IVF) in infertile patients undergoing their first ovarian stimulation (OS) cycle.

 Method

A multicenter, longitudinal, prospective, interventional cohort study was carried out in four clinical centers of medically assisted reproduction from August 2016 to November 2018. Only expected normo-responder women, estimated through standardized-computerized antral follicle count (AFC), stimulated with a fixed 150 IU daily dose of recombinant follicle-stimulating hormone (FSH), were included. The study population consisted of infertile normo-gonadotropic patients, aged between 34 and 39, at their first OS, with normal ovarian reserve (AFC between 8 and 16) measured with 3D automated ultrasonography and undergoing standardized OS protocol.

 Results

One hundred nineteen patients were enrolled, and the following five SNPs were studied (FSHR c.-29G>A, FSHR p.N680S, FSHB c.-211G>T, LHCGR p.S312N, and LHβ “V-LH” p.W8R). Separate and multivariate analysis of investigated polymorphisms did not show any statistical impact on the number of oocytes retrieved. However, adopting an overdominant model, heterozygosis of FSHR p.N680S SNP was associated with significantly lower duration of OS compared with homozygotic women. Considering LHCGR p.S312N polymorphism, N allele carriers required a longer duration of OS in the codominant, dominant, and log-additive models. Multivariate analysis revealed that specific genotype combinations could affect the ovarian sensitivity. A significantly higher follicle-to-oocyte index (FOI) was observed when the S or N allele of both FSHR p.N680S and LHCGR p.S312N were combined (S allele combination: difference 0.18, CI 95% 0.04–0.33, p = 0.011; N allele combination: difference 0.18, CI 95% 0.01–0.34, p = 0.037; N allele combination).

 Discussion

Based on our results, the combination of specific genetic variants could impact ovarian sensitivity to gonadotropin. This research adds to the controversy in the literature regarding the effect of genetic variants in IVF and ovarian response.

 polymorphisms pharmacogenomic ovarian stimulation IVF/ICSI ART genetic variants The author(s) declare financial support was received for the research and/or publication of this article. This study was supported by unrestricted grant by Merck. section-at-acceptance Reproduction Introduction

The crucial stage in assisted reproductive technologies (ART) is ovarian stimulation (OS), wherein the ovaries are stimulated primarily to acquire the highest number of oocytes (1). Research indicates that the live birth rate consistently rises when retrieving between 8 and 15 oocytes in fresh ART cycles (2). Moreover, in combined fresh and frozen cycles, an amount of more than 15 oocytes notably enhances the likelihood of achieving at least one successful delivery (3). Instead, low or poor responders are usually associated with a low number of oocytes retrieved at the end of stimulation. According to European Society of Human Reproduction and Embryology (ESHRE) guidelines, poor responders are those with less than four oocytes retrieved at the end of the OS phase (4). This concept was stressed even in the last Patient-Oriented Strategies Encompassing IndividualizeD Oocyte Number (POSEIDON) criteria, in which ovarian sensitivity and age-related embryo/blastocyst aneuploidy rate were considered to be crucial for poor responsiveness (5, 6). If no previous ART cycles have been performed, the prediction of OS outcomes is based on both women’s age and ovarian reserve markers, such as antral follicle count (AFC) and anti-Mullerian hormone (7). However, AFC exhibits several limitations, mainly due to inter-operator interpretation issues (8–10). To address this drawback, the use of ultrasonographic automated three-dimensional (3D) follicular count (11, 12) in conjunction with automatic volume calculation (AVC) has been proposed as a reliable alternative to standard 2D evaluation. Many studies demonstrated that AVC significantly enhances inter-observer reliability of AFC, offering highly accurate assessments of both number and size of antral follicles when compared to traditional 2D evaluation (13, 14). Indeed, if appropriately standardized across centers, AVC may be used as a centralized test to assess ovarian reserve.

Insights from the past decade suggest that women sharing similar demographic, anthropometric, and gonadotropin level profiles may exhibit markedly different ovarian responses (15, 16), even when possessing similar ovarian reserve levels. In other words, the “sensitivity” of follicles to exogenous gonadotropins may vary among infertile women (17, 18). For example, two women with the same AFC may have different follicular output rates (FORTs) or follicle-to-oocytes indexes (FOIs) (17), suggesting that a different percentage of follicles reaches maturity with a similar FSH dose as a feature linked to individual genetic background. Most of the studies focused on SNPs of the follicle-stimulating hormone (FSH) receptor gene (FSHR) (19–22), although the combination of different SNPs may lead to specific fertility phenotypes (23–25). However, data concerning the clinical application of pharmacogenomics in ART remain limited. Many of the previous studies aiming to elucidate the impact of gonadotropins and their receptor SNPs on OS outcome were observational and retrospective. Therefore, they were more susceptible to bias owing to the absence of a standardized OS protocol (16, 24).

The aim of this study is to evaluate the impact of gonadotropin genetics on the in-vitro fertilization (IVF) outcomes in infertile women treated with 150 IU FSH daily, undergoing their first OS cycle, and expected to be normo-responders, according to standardized computerized AFC.

 Method Study design

A multicenter, longitudinal, prospective, interventional, non-pharmacological cohort study was carried out. Women undergoing their first IVF cycle with normal ovarian reserve and attending four Italian ART centers, were enrolled.

Inclusion criteria were (i) Caucasian women, (ii) age between 34 and 39 years, (iii) body mass index (BMI) within 18 and 27 kg/m2, (iv) normal hypothalamic-pituitary-gonadal function, (v) basal serum FSH levels below 8 IU/L, (vi) normal ovarian reserve, established as AFC between 8 and 16, (vii) first OS cycle, and (viii) indication to either IVF or intracytoplasmic sperm injection (ICSI).

Exclusion criteria were (i) patients with ovarian cysts > 12 mm found at the first OS day, (ii) polycystic ovary syndrome (PCOS) determined by Rotterdam criteria (26), (iii) poor ovarian response according to ESHRE criteria, and (iv) endometriosis at stage III-IV of the American Society for Reproductive Medicine (ASRM) revised classification (27).

The study protocol consisted of four consecutive steps. In the first screening phase, the signature of the informed consent was collected. The first visit (V0) was performed at the time of enrollment, and anamnesis and a check for eligibility criteria were recorded. Patients screened but not eligible or not willing to participate were recorded on a separate document. The second phase represents the OS, consisting of three different visits. Visit 1 (V1) was scheduled after 2–3 days after the menstrual cycle, during which two blood samples were obtained; one was for hormone measurement, while the other one was conserved with ethylenediaminetetraacetic acid (EDTA) and sent at room temperature to the central laboratory (Unit of Endocrinology, Modena, Italy) for DNA analysis. Moreover, ultrasound evaluation was performed to calculate basal AFC. At V1, enrolled women started the treatment with 150 IU of recombinant FSH daily (Gonal-F®; Merck KGaA, Darmstadt, Germany). Visit 2 (V2) was performed after 6 days of FSH treatment, during which AFC and hormone assays were performed. At V2, women started with gonadotropin-releasing hormone (GnRH) antagonist (Cetrotide®, Merck KGaA). Visit 3 (V3) was performed when at least two follicles larger than 16 mm were detected by ultrasound. During V3, women were treated with 10,000 IU of human chorionic gonadotropin (hCG; Gonasi HP 10.000, IBSA Farmaceutici Italia S.r.l.). The trigger was induced using a GnRH agonist (Fertipeptil 0.2 mg/ml, Ferring Pharmaceuticals, Saint-Prex, Switzerland) to decrease ovarian hyperstimulation syndrome (OHSS) (28, 29) risk. The oocyte pick-ups were performed 36h after the triggering of oocyte maturation. The third study phase consisted of a visit (visit 4 – V4) in which the fresh embryo transfer was performed. In the case of OHSS risk and/or progesterone rise (≥ 2 ng/ml) on the day of hCG administration, all embryos were cryopreserved. Pregnancy was confirmed by determining serum hCGβ concentration 14 days after embryo transfer in all patients. When the pregnancy tested positive, a second test was performed 2 days later. The fourth study phase consisted of the follow-up. This was a medication-free phase in which pregnancy was followed, according to the clinical practice of each center, to evaluate chromosomal abnormalities, early preeclampsia, and the weight of the newborn. The visit 5 (V5) was performed at the end of the first trimester. The study has been approved by the ethics committee of each participating Center [GENACOS. Version 2.0–15 Jun 2018 (included Amendment n.1)]. The informed and written consent form was signed by all participants.

For ultrasound evaluation, AFC and AVC were performed using E8 expert HD live with sonoAVC, v-SRI, crossXbeam, HDflow, VCI with omiview +, and a high-resolution 4D transvaginal probe. All the clinical centers were trained by the scientific expert in AFC assessment with AVC according to the same standard parameters in order to standardize the method. A working instruction document about this procedure was delivered to each center.

 Study endpoints

The primary endpoint of the study was the number of retrieved oocytes. The primary endpoint was evaluated considering the allelic state of the c.-29G>A FSHR SNP (rs1394205).

Secondary outcomes were (v) duration of OS indicated as days of stimulation; (vi) number of mature oocytes; (vii) number of fertilized 2PN oocytes; (viii) number of embryos developed, transferred, and cryopreserved; (ix) implantation rate; (x) pregnancy rate per started cycle; (xi) pregnancy rate per embryo transfer; and (xii) cumulative ongoing pregnancy rate per started cycle. Ovarian sensitivity was also evaluated using follicular output rate (FORT) and follicle-to-oocytes indexes (FOI) (17).

All these parameters were evaluated in association with the following SNPs: FSHR c.-29G>A (rs1394205); FSHR c.2039A>G, p.N680S (rs6166); FSHB c.-211G>T, (rs10835638); LHCGR c.942G>A, p.S312N (rs2293275); LHCGR c.872A>G, p.N291S (rs12470652); LHβ genetic variant c.82T>C, p.W8R (rs1800447) and c.104T>C, p.I15T (rs34349826). These SNPs were selected considering literature evidence that showed an association of these genetic variants to ovarian response and IVF outcome (22, 30–32).

 Genotype analysis

All DNA analyses were performed after the OS, at the Unit of Endocrinology, Department of Biomedical, Metabolic and Neural Sciences (University of Modena and Reggio Emilia, Modena, Italy). Genomic DNA was extracted from white blood cells using the automated extractor EZ1 Advanced XL (Qiagen, Hilden, Germany) and quantified by a NanoDrop™ 2000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).

The analysis of FSHR c.-29G>A, FSHR c.2039A>G leading to the amino acid change p.N680S at the protein level, as well as of FSHB c.-211G>T SNPs, was performed by high-resolution melting (HRM) technology on a CFX96 Real-time PCR detection system (Bio-Rad Laboratories Inc., Hercules, CA, USA), using the following primer pairs: forward (Fw) 5’-ATAATTATGCATCCATCCAC-3’ and reverse (Rev) 5’-GAGATCTGTGGAGGTTTT-3’ for FSHR c.-29G>A; Fw 5’-AACACCCATCCAAGGAAT-3’ and Rev 5’-ATGACTTAGAGGGACAAG-3’ for FSHR c.2039A>G; Fw 5’-GGTGTGCTACTGTATCAA-3’ and Rev 5’-AAAGTAGTCTAAACGCAGTA-3’ for FSHB SNP c.-211G>T.

Genotyping of LHCGR p.S312N and p.N291S was performed by Sanger sequencing analysis, as previously described (33, 34) using the following primer pairs: Fw 5’-AGGCCAATGTGGAAAGGAGAG-3’ and Rev 5’-TGCAACAGCTCCGTAACCAA-3’. The analysis of the LHβ V-LH variant (p.W8R and p.I15T) was performed as previously described (35). Briefly, the cDNA region containing the LHB variants was amplified by PCR, using the primer pair Fw 5’- GAAGCAGTGTCCTTGTCCCA-3’ and Rev 5’- GAAGAGGAGGCCTGAGAGTT-3’, and resulting in a 622 bp fragment. For each sample, PCR products corresponding to p.W8R and p.I15T SNPs were digested by the NcoI and the BseGI restriction enzymes (Thermo Scientific, US), respectively. The genotype was determined by the specific restriction fragments pattern observed by 3% agarose gel migration.

 Determination of sample size

Many studies evaluated FSHR gene promoter in-vitro activity (position c.-29G>A; rs1394205), revealing decreased transcriptional activity of 56 ± 8% than that of the G allele. These data were corroborated by a study in 100 women attending ART, where authors found reduced relative FSHR mRNA transcripts linked to FSHR c.-29G>A A homozygosity (36). In the dominant model, relative FSHR mRNA expression was of 0.12 for G/G allele, 0.07 for G/A, and 0.02 for A/A (36). Thus, power analysis was performed (by G*Power software, version 3.1.9.2) assuming a variation of 0.075 of relative FSHR mRNA expression produced by two genotypes. Differences between two independent means were considered, α error probability was set to 0.05 and allocation ratio to 0.89, considering previous observational study (23, 36). According to the statistical power of 80%, sample size was estimated in 184 patients. Considering a dropout rate of 10%, the total number was of at least 202 patients.

 Statistical methods analysis

The Kolmogorov–Smirnov test was used for evaluation of the parameters’ distribution. Mann–Whitney’s U test and Kruskal–Wallis test were used for comparison of not-normally distributed variables, whereas t-test and ANOVA univariate analyses were used for normally distributed ones. Pregnancy outcomes were evaluated using a logistic regression model adjusting for age, day of the transfer, and number of transferred embryos. Statistical analysis was performed using the “Statistical Package for the Social Sciences” software for Macintosh (version 20.0; SPSS Inc., Chicago, IL). P-values < 0.05 were considered as statistically significant.

Genotypic association tests were performed considering each SNP alone, assuming codominant, dominant, recessive, overdominant, or log-additive genetic models. Moreover, the haplotype obtained by SNP combination was generated and its impact on the endpoint was evaluated by multivariate analyses. Genotypic association analyses were performed using SNPstats. Linkage disequilibrium was evaluated using SNPStat.

 Results

A total of 119 patients were initially enrolled in the study. Eight patients withdrew from the study immediately after providing informed consent, before beginning OS. Five more patients dropped out after commencing OS. Finally, 106 women completed the OS protocol and underwent oocyte retrieval. Baseline characteristics of the participants are detailed in Table 1. Considering the difficulties in the recruitment process due to relatively strict inclusion criteria and the lack of significant findings regarding the primary outcome (number of oocytes retrieved), the enrollment was interrupted. This decision was carried out considering an internal post-hoc analysis of our data. Considering these issues, a post hoc analysis was performed, and, setting the effect size to 0.165, the statistical power was 71.7%.

Patients baseline characteristics.

Patients characteristics Results
Number of patients achieving pick up 106
Age (years) 36.3 (1.7)
Weight (kg) 59.0 (7.7)
Height (cm) 164 (6)
BMI (kg/m2) 21.9 (2.5)
Smoking status
 Current smoker 30 (28.3)
 Former smoker 1 (0.9)
 Non smoker 83 (70.9)
Infertility duration (years) 3.6 (2.2)
Causes of infertility
 Only female 18 (15.4)
 Only male 51 (43.6)
 Male and female 1 (0.8)
 Unexplained 47 (40.2)
Tubaric factor (n) 15 (12.8)
FSH IU/L 6.1 (1.7)
LH IU/L 4.1 (1.6)
AMH ng/ml 2.4 (1.0)
E2 pg/ml 48.4 (53.4)

Data are presented as mean (SD) for continuous data and number (percentage) for categorical ones.

Embryo transfer was performed in 76.2% of cycles, equally distributed between day 3 (36.2%) and day 5 (33.3%). Only 6.7% of embryo transfers were performed at day 2. In 21% of cycles started, all embryos were frozen due to fresh embryo transfer contraindications. OS outcomes are reported in Table 2.

Stimulation cycle outcomes.

Stimulation cycle characteristics Results
Number of patients 106
Follicle number at Visit 1 13.4 (2.5)
Ovulation induction ◼ HCG ◼ a-GnRH 95 (89.6)11 (10.4)
Number of follicles ≥ 16mm at induction phase 5.8 (2.5)
Number of retrieved oocytes at pick-up 8.4 (4.2)
Mature oocytes (%) 85.7 (64.0–100)
Number of fertilized oocytes 6.0 (3.2)
Fertilized oocytes (%) 75.0 (60.1–88.2)
Cycles without fertilized oocytes 1 (0.9)
Cycles without developed embryos 3 (2.9)
Cycles with fresh embryo transfer (ET) ◼ Day 2 ◼ Day 3 ◼ Day 5 80 (76.2)7 (6.7)38 (36.2)35 (33.3)
“Freeze all” cycles Due to OHSS risk Due to high progesterone levels Other causes 22 (21.0)12 (54.6)7 (31.8)3 (13.6)
Number of embryos obtained 2.8 (1.7)
Positive β-hCG, per fresh ET 49.4
Ongoing pregnancies, fresh ET 37.8
Cumulative positive β-Hcg, per pick-up 53.8
Cumulative ongoing pregnancies per pick-up 36.8

Data are presented as mean (SD) for continuous data and number (percentage) for categorical ones.

Seven different SNPs have been analyzed for patients recruited. All SNPs were in “Hardy-Weinberg equilibrium” except for LHCGR p.N291S (Table 3). SNPs with an allele frequency lower than 10%, or not in Hardy-Weinberg equilibrium, were removed from further analysis, and OS characteristics were compared considering the remaining five SNPs (FSHR c.-29G>A, FSHR p.N680S, FSHB c.-211G>T, LHCGR p.S312N, and LHβ V-LH p.W8R). One patient was FSHB c.-211 G>T T homozygous and achieved oocyte pick-up.

Patients’ genotype.

Name ID (Reference minor allele frequency) Allele1/ allele2 Frequency allele 1 Frequency allele 2 Homo- zygous allele 1 Hetero-zygous Homo- zygous allele 2 Hardy Weinberg equilibrium
FSHR c.-29G/A rs1394205(A 28.9) G/A 70.9 29.1 59 (50.4) 48 (41.0) 10 (8.6) yes
FSHR p.N680S rs6166(G 44.9) A/G 53.8 46.2 36 (30.89 54 (46.1) 27 (23.1) yes
FSHB c.-211 G > T rs10835638(T 14.3) G/T 85.5 14.5 85 (72.7) 30 (25.6) 2 (1.7) yes
LHB p.W8R rs1800447(G 9.5) A/G 92.3 7.7 99 (84.6) 18 (15.4) 0 (0.0) yes
LHB p.I15T rs34349826(G 8.9) A/G 92.3 7.7 99 (84.6) 18 (15.4) 0 (0.0) yes
LHCGR p.N291S rs12470652(C 4.6) T/C 96.6 3.4 110 (94.0) 6 (5.1) 1 (0.9) no
LHCGR p.S312N rs2293275(A 36.0) G/A 63.7 36.3 49 (41.9) 51 (43.6) 17 (14.5) yes

Allele 1, major allele. Values are expressed in percent or numbers (percentage).

The reference minor allele frequency (percentage) is referred to the aggregate “European” population indexed in the 1000 Genomes database, and accessed through the National Center for Biotechnology Information SNP database (https://www.ncbi.nlm.nih.gov/snp).

 Number of oocytes retrieved; metaphase II oocytes; 2PN oocytes

The total number of oocytes retrieved did not change considering the genotype in the codominant, dominant, recessive, overdominant, or log-additive genetic models (Table 4). Similarly, multivariate analyses did not detect any specific association between haplotype and number of oocytes retrieved (Table 5).

Genotypic association tests performed considering each SNP alone for the number of retrieved oocytes.

Model Genotype n Response mean (SE) Difference (95% CI) P-value
FSHR c.-29G > A
Codominant GG 59 7.64 (0.61) 0.00 0.380
GA 48 7.12 (0.70) −0.52 (−2.31 to 1.27)
AA 10 9.4 (1.25) 1.76 (−1.39 to 4.90)
Dominant GG 59 7.64 (0.61) 0.00 0.880
GA-AA 58 7.52 (0.62) −0.13 (−1.83 to 1.58)
Recessive GG-GA 107 7.41 (0.46) 0.00 0.200
AA 10 9.4 (1.25) 1.99 (−1.04 to 5.02)
Overdominant GG-AA 69 7.9 (0.55) 0.00 0.380
GA 48 7.12 (0.7) −0.77 (−2.50 to 0.95)
FSHR p.N680S
Codominant NN 36 7.42 (0.77) 0.00 0.970
NS 54 7.67 (0.59) 0.25 (−1.74 to 2.24)
SS 27 7.63 (1.06) 0.21 (−2.15 to 2.57)
Dominant NN 36 7.42 (0.77) 0.00 0.800
NS-SS 81 7.65 (0.53) 0.24 (−1.61 to 2.09)
Recessive NN-NS 90 7.57 (0.47) 0.00 0.950
SS 27 7.63 (1.06) 0.06 (−1.96 to 2.09)
Overdominant NN-SS 63 7.51 (0.63) 0.00 0.860
NS 54 7.67 (0.59) 0.16 (−1.55 to 1.87)
FSHB c.-211G > T
Codominant GG 85 7.52 (0.49) 0.00 0.900
GT 30 7.67 (0.88) 0.15 (−1.82 to 2.12)
TT 2 9 (9) 1.48 (−5.14 to 8.11)
Dominant GG 85 7.52 (0.49) 0.00 0.810
GT-TT 32 7.75 (0.92) 0.23 (−1.68 to 2.15)
Recessive GG-GT 115 7.56 (0.43) 0.00 0.670
TT 2 9 (9) 1.44 (−5.13 to 8.02)
Overdominant GG-TT 87 7.55 (0.5) 0.00 0.910
GT 30 7.67 (0.88) 0.11 (−1.84 to 2.07)
LHB V-LH p.W8R
Codominant WW 99 7.34 (0.48) 0.00 0.200
WR 18 8.89 (1) 1.55 (−0.80 to 3.89)
LHCGR p.S312N
Codominant CC 49 7.51 (0.67) 0.00 0.900
TC 51 7.49 (0.7) −0.02 (−1.87 to 1.83)
TT 17 8.06 (0.91) 0.55 (−2.06 to 3.15)
Dominant CC 49 7.51 (0.67) 0.00 0.890
TC-TT 68 7.63 (0.57) 0.12 (−1.61 to 1.85)
Recessive CC-TC 100 7.5 (0.48) 0.00 0.650
TT 17 8.06 (0.91) 0.56 (−1.86 to 2.98)
Overdominant CC-TT 66 7.65 (0.55) 0.00 0.850
TC 51 7.49 (0.7) −0.16 (−1.88 to 1.56)

Multivariate analysis performed considering the ovarian stimulation parameters as dependent variables, and the allele combination as independent ones.

haplotype n FSHR c.-29G>A FSHR c.2039A>G (p.N680S) FSHB c.-211G>T V-LHβ c.82T>C (p.W8R) LHCGR c.942G>A (p.S312N) Frequency Difference (95% CI) P-value
Oocytes retrieved
1 G N G W S 0.2235 0.00 —
2 G S G W S 0.1825 0.82 (−1.2 to 2.83) 0.430
3 G S G W N 0.0997 1.31 (−0.92 to 3.54) 0.250
4 A N G W S 0.0937 1.87 (−0.88 to 4.62) 0.180
5 G N G W N 0.0759 2.19 (−0.95 to 5.34) 0.170
6 A N G W N 0.0687 0.77 (−2.57 to 4.11) 0.650
7 A S G W N 0.0613 −2.31 (−5.38 to 0.75) 0.140
8 G S T W S 0.0521 −0.54 (−3.49 to 2.41) 0.720
9 A S G W S 0.0494 0.82 (−2.02 to 3.66) 0.570
10 G N T W N 0.0411 −0.72 (−4.14 to 2.7) 0.680
Mature oocytes
1 G N G W S 0.2299 0.00 —
2 G S G W S 0.1415 0.82 (−1.01 to 2.65) 0.380
3 A N G W S 0.1134 0.51 (−1.45 to 2.47) 0.610
4 G S G W N 0.1108 −0.25 (−2.28 to 1.79) 0.810
5 G N G W N 0.066 1.33 (−0.84 to 3.51) 0.230
6 A N G W N 0.0564 1.63 (−0.75 to 4.01) 0.180
7 A S G W S 0.0481 2.26 (0.04–4.47) 0.048
8 G S T W S 0.0445 −0.03 (−2.55 to 2.49) 0.980
9 A S G W N 0.0276 −2.54 (−6.18 to 1.1) 0.170
10 G N T W N 0.0223 3.56 (−0.01 to 7.12) 0.052
FOI
1 G N G W S 0.2299 0.00 —
2 G S G W S 0.1376 0.18 (0.04–0.33) 0.011
3 A N G W S 0.1133 0.13 (−0.03 to 0.28) 0.100
4 G S G W N 0.1042 0.02 (−0.13 to 0.18) 0.780
5 G N G W N 0.0736 0.18 (0.01–0.34) 0.037
6 A N G W N 0.0508 0.13 (−0.08 to 0.35) 0.220
7 G S T W S 0.0486 0.11 (−0.09 to 0.31) 0.280
8 A S G W S 0.0471 −0.01 (−0.19 to 0.17) 0.940
9 A S G W N 0.0308 −0.19 (−0.44 to 0.05) 0.120
10 G S G R S 0.0249 0.2 (−0.05 to 0.46) 0.120
Cumulative FSH dose
1 G N G W S 0.2249 0.00 –
2 G S G W S 0.1444 5.5 (−47.01 to 58.01) 0.840
3 G S G W N 0.1055 119.82 (53.45–186.19) < 0.001
4 A N G W S 0.0996 −22.77 (−75.91 to 30.37) 0.400
5 G N G W N 0.0733 150.03 (88.61–211.45) < 0.001
6 A N G W N 0.0535 82.90 (60.60–105.20) < 0.001
7 A S G W S 0.0527 113.01 (92.29–133.74) < 0.001
8 G N T W S 0.0334 206.51 (195.29–217.72) < 0.001
9 G S T W S 0.0325 −22.21 (−27.84 to −16.59) < 0.001
10 A S G W N 0.0309 −104.09 (−109.19 to −98.99) < 0.001
Ovarian stimulation duration
1 G N G W S 0.2144 0.00 —
2 G S G W S 0.1471 0.33 (−0.21 to 0.87) 0.230
3 A N G W S 0.1052 0.14 (−0.49 to 0.78) 0.660
4 G S G W N 0.0947 0.18 (−0.44 to 0.8) 0.570
5 G N G W N 0.0748 1.34 (0.76–1.93) < 0.001
6 A N G W N 0.0611 0.04 (−0.73 to 0.8) 0.930
7 A S G W S 0.0443 0.6 (−0.13 to 1.33) 0.110
8 G S T W S 0.0402 0.09 (−0.71 to 0.89) 0.830
9 G N T W S 0.0360 1.74 (0.74–2.74) < 0.001
10 A S G W N 0.0340 1.33 (0.4–2.25) 0.005
Follicle output rate (FORT)
1 G N G W S 0.2445 0.00 –
2 G S G W S 0.1337 0.16 (−0.03 to 0.35) 0.096
3 A N G W S 0.1038 0.26 (0.05–0.47) 0.016
4 G S G W N 0.0937 −0.02 (−0.25 to 0.21) 0.850
5 G N G W N 0.0632 0.30 (0.06–0.54) 0.015
6 G S T W S 0.0528 −0.03 (−0.29 to 0.23) 0.810
7 A S G W N 0.0515 −0.12 (−0.39 to 0.16) 0.410
8 A S G W S 0.0496 −0.11 (−0.35 to 0.14) 0.380
9 A N G W N 0.0459 0.27 (−0.02 to 0.55) 0.070
10 G N T W N 0.0354 −0.21 (−0.53 to 0.10) 0.190
2PN oocytes
1 G N G W C 0.2442 0.00 —
2 G S G W C 0.1355 −0.01 (−1.85 to 1.83) 0.990
3 A N G W C 0.1134 0.41 (−1.33 to 2.15) 0.640
4 G S G W T 0.1042 0.27 (−.58 to 2.12) 0.770
5 G N G W T 0.0653 0.35 (−2.1 to 2.8) 0.780
6 G S T W C 0.0473 0.77 (−1.42 to 2.96) 0.490
7 A N G W T 0.0464 0.89 (−1.54 to 3.32) 0.470
8 A S G W C 0.0426 1.52 (−0.61 to 3.65) 0.160
9 A S G W T 0.0406 −2.67 (−5.42 to 0.08) 0.059
10 G S G R C 0.0269 −0.93 (−3.67 to 1.82) 0.510

Only the ten most frequent allele combinations are shown.

p<0.05 are indicated with bold.

A significantly higher number of mature oocytes was retrieved when T homozygous (FSHB c.-211G>T) was expressed in codominant and recessive models (Table 6). Multivariate analysis confirmed the impact of haplotype on mature oocytes retrieved (Table 5).

Genotypic association tests performed considering each SNP alone for the number of mature oocytes retrieved.

Model Genotype n Response mean (SE) Difference (95% CI) P-value
FSHR c.-29G>A
Codominant GG 53 6.04 (0.45) 0.00 0.430
GA 43 6.16 (0.54) 0.13 (−1.28 to 1.53)
AA 10 7.6 (1.35) 1.56 (−0.80 to 3.92)
Dominant GG 53 6.04 (0.45) 0.00 0.560
GA-AA 53 6.43 (0.51) 0.40 (−0.94 to 1.73)
Recessive GG-GA 96 6.09 (0.35) 0.00 0.200
AA 10 7.6 (1.35) 1.51 (−0.76 to 3.77)
Overdominant GG-AA 63 6.29 (0.44) 0.00 0.860
GA 43 6.16 (0.54) −0.12 (−1.48 to 1.24)
FSHR p.N680S
Codominant NN 32 6.44 (0.62) 0.00 0.820
NS 52 6.02 (0.51) −0.42 (−1.97 to 1.13)
SS 22 6.45 (0.69) 0.02 (−1.89 to 1.93)
Dominant NN 32 6.44 (0.62) 0.00 0.700
NS-SS 74 6.15 (0.41) −0.29 (−1.74 to 1.16)
Recessive NN-NS 84 6.18 (0.39) 0.00 0.740
SS 22 6.45 (0.69) 0.28 (−1.37 to 1.92)
Overdominant NN-SS 54 6.44 (0.46) 0.00 0.530
NS 52 6.02 (0.51) −0.43 (−1.76 to 0.91)
FSHβ c.-211G>T
Codominant GG 77 6.05 (0.39) 0.00 0.035
GT 28 6.43 (0.64) 0.38 (−1.10 to 1.85)
TT 1 15 (0) 8.95 (2.22–15.68)
Dominant GG 77 6.05 (0.39) 0.00 0.380
GT-TT 29 6.72 (0.69) 0.67 (−0.82 to 2.16)
Recessive GG-GT 105 6.15 (0.33) 0.00 0.011
TT 1 15 (0) 8.85 (2.15–15.54)
Overdominant GG-TT 78 6.17 (0.4) 0.00 0.740
GT 28 6.43 (0.64) 0.26 (−1.25 to 1.78)
LHβ V-LH p.W8R
Codominant WW 88 6.4 (0.39) 0.00 0.180
WR 18 5.44 (0.63) −0.95 (−2.72 to 0.82)
LHCGR p.S312N
Codominant SS 45 6.13 (0.58) 0.00 0.960
NS 45 6.29 (0.5) 0.16 (−1.30 to 1.61)
NN 16 6.38 (0.73) 0.24 (−1.77 to 2.25)
Dominant SS 45 6.13 (0.58) 0.00 0.800
NS-NN 61 6.31 (0.41) 0.18 (−1.17 to 1.53)
Recessive SS-NS 90 6.21 (0.38) 0.00 0.860
NN 16 6.38 (0.73) 0.16 (−1.70 to 2.03)
Overdominant SS-NN 61 6.2 (0.46) 0.00 0.890
SS 45 6.29 (0.5) 0.09 (−1.26 to 1.44)

p<0.05 are indicated with bold.

Similarly, 2PN oocytes were significantly higher when the FSHB c.-211G>T T allele was present, both in codominant and recessive models (Table 7). However, no specific SNPs or haplotypes able to describe 2PN variation were detected by multivariate analysis (Table 5). Considering that only a very small number of women expressed allele T, these findings might be fortuitous.

Genotypic association tests performed considering each SNP alone for the number of 2PN oocytes retrieved.

Model Genotype n Response mean (SE) Difference (95% CI) P-value
FSHR c.-29G>A
Codominant GG 52 6.04 (0.43) 0.00 0.230
GA 43 5.67 (0.5) −0.36 (−1.65 to 0.92)
AA 10 7.6 (1) 1.56 (−0.59 to 3.72)
Dominant GG 52 6.04 (0.43) 0.00 0.999
GA-AA 53 6.04 (0.46) −0.00 (−1.23 to 1.23)
Recessive GG-GA 95 5.87 (0.33) 0.00 0.100
AA 10 7.6 (1) 1.73 (−0.34 to 3.79)
Overdominant GG-AA 62 6.29 (0.4) 0.00 0.330
GA 43 5.67 (0.5) −0.62 (−1.86 to 0.63)
FSHR p.N680S
Codominant NN 31 6.23 (0.57) 0.00 0.510
NS 52 5.69 (0.46) −0.53 (−1.96 to 0.89)
SS 22 6.59 (0.65) 0.37 (−1.39 to 2.12)
Dominant NN 31 6.23 (0.57) 0.00 0.700
NS-SS 74 5.96 (0.37) −0.27 (−1.61 to 1.08)
Recessive NN-NS 83 5.89 (0.36) 0.00 0.360
SS 22 6.59 (0.65) 0.70 (−0.81 to 2.20)
Overdominant NN-SS 53 6.38 (0.43) 0.00 0.270
NS 52 5.69 (0.46) −0.69 (−1.91 to 0.54)
FSHB c.-211G>T
Codominant GG 76 5.86 (0.33) 0.00 0.036
GT 28 6.25 (0.69) 0.39 (−0.96 to 1.75)
TT 1 14 (0) 8.14 (1.98–14.31)
Dominant GG 76 5.86 (0.33) 0.00 0.350
GT-TT 29 6.52 (0.72) 0.66 (−0.71 to 2.03)
Recessive GG-GT 104 5.96 (0.31) 0.00 0.012
TT 1 14 (0) 8.04 (1.90–14.17)
Overdominant GG-TT 77 5.96 (0.35) 0.00 0.680
GT 28 6.25 (0.69) 0.29 (−1.10 to 1.68)
LHβ V-LH p.W8R
Codominant WW 88 6.07 (0.35) 0.00 0.830
WR 17 5.88 (0.66) −0.19 (−1.85 to 1.48)
LHCGR p.S312N
Codominant SS 44 5.89 (0.52) 0.00 0.900
NS 45 6.2 (0.5) 0.31 (−1.03 to 1.65)
NN 16 6 (0.56) 0.11 (−1.73 to 1.96)
Dominant SS 44 5.89 (0.52) 0.00 0.680
NS-NN 61 6.15 (0.39) 0.26 (−0.98 to 1.51)
Recessive SS-NS 89 6.04 (0.36) 0.00 0.960
NN 16 6 (0.56) −0.04 (−1.76 to 1.67)
Overdominant SS-NN 60 5.92 (0.4) 0.00 0.660
SS 45 6.2 (0.5) 0.28 (−0.96 to 1.52)

p<0.05 are indicated with bold.

 Ovarian sensitivity indexes: FOI and FORT

FOI was significantly influenced by FSHB c.-211G>T (Table 8). In particular, FOI was higher in FSHB c.-211G>T T homozygous patients, both in codominant and recessive models (Table 8). Even in this circumstance the rarity of allele T should be taken into account in the interpretation of data.

Genotypic association tests performed considering each SNP alone for the follicle on oocytes index (FOI).

Model Genotype n Response mean (SE) Difference (95% CI) P-value
FSHR c.-29G>A
Codominant GG 53 0.65 (0.04) 0.00 0.850
GA 42 0.61 (0.05) −0.03 (−0.15 to 0.09)
AA 10 0.63 (0.08) −0.02 (−0.22 to 0.18)
Dominant GG 53 0.65 (0.04) 0.00 0.590
GA-AA 52 0.62 (0.04) −0.03 (−0.14 to 0.08)
Recessive GG-GA 95 0.63 (0.03) 0.00 0.990
AA 10 0.63 (0.08) −0.00 (−0.19 to 0.19)
Overdominant GG-AA 63 0.65 (0.04) 0.00 0.590
GA 42 0.61 (0.05) −0.03 (−0.15 to 0.08)
FSHR p.N680S
Codominant NN 32 0.61 (0.04) 0.00 0.510
NS 51 0.62 (0.04) 0.01 (−0.12 to 0.14)
SS 22 0.7 (0.07) 0.09 (−0.07 to 0.25)
Dominant NN 32 0.61 (0.04) 0.00 0.610
NS-SS 73 0.64 (0.04) 0.03 (−0.09 to 0.15)
Recessive NN-NS 83 0.62 (0.03) 0.00 0.240
SS 22 0.7 (0.07) 0.08 (−0.06 to 0.22)
Overdominant NN-SS 54 0.65 (0.04) 0.00 0.630
NS 51 0.62 (0.04) −0.03 (−0.14 to 0.09)
FSHB c.-211G>T
Codominant GG 76 0.64 (0.03) 0.00 0.027
GT 28 0.59 (0.06) −0.04 (−0.17 to 0.08)
TT 1 1.38 (0) 0.75 (0.18–1.31)
Dominant GG 76 0.64 (0.03) 0.00 0.810
GT-TT 29 0.62 (0.06) −0.02 (−0.14 to 0.11)
Recessive GG-GT 104 0.63 (0.03) 0.00 0.009
TT 1 1.38 (0) 0.76 (0.20 to 1.32)
Overdominant GG-TT 77 0.65 (0.03) 0.00 0.420
GT 28 0.59 (0.06) −0.05 (−0.18 to 0.07)
LHβ V-LH p.W8R
Codominant WW 87 0.62 (0.03) 0.00 0.180
WR 18 0.72 (0.07) 0.10 (−0.05 to 0.25)
LHCGR p.S312N
Codominant SS 45 0.64 (0.05) 0.00 0.990
NS 44 0.63 (0.04) −0.01 (−0.13 to 0.11)
NN 16 0.63 (0.06) −0.01 (−0.18 to 0.16)
Dominant SS 45 0.64 (0.05) 0.00 0.880
NS-NN 60 0.63 (0.03) −0.01 (−0.12 to 0.10)
Recessive SS-NS 89 0.63 (0.03) 0.00 0.920
NN 16 0.63 (0.06) −0.01 (−0.17 to 0.15)
Overdominant SS-NN 61 0.63 (0.04) 0.00 0.940
SS 45 0.64 (0.05) 0.00

p<0.05 are indicated with bold.

In multivariate analysis, two different haplotypes were associated with FOI (p = 0.005), which was higher when the S allele in both FSHR p.N680S and LHCGR p.S312N were combined (Table 5). Similar results were found when FOI was considered for the N allele in both FSHR p.N680S and LHCGR p.S312N combined (Table 5).

The FORT was not significantly different among SNP haplotypes, irrespective of the genetic model considered (Supplementary Table S1). However, in the multivariate analysis, significant influence of haplotypes generated by the five SNPs was detected (p = 0.003) in association with FORT, representing 10.3% of the entire cohort (Table 5).

 Days of stimulation

OS duration was influenced by three genotypes (Table 9). In the overdominant model, FSHR p.N680S heterozygosis showed a significantly reduced OS compared to other genotypes (Table 9), while FSHB c.-211G>T “T” and LHCGR p.N312S “S” alleles were linked to prolonged stimulation in codominant, dominant, and overdominant models (Table 9).

Genotypic association tests performed considering each SNP alone for the duration of ovarian stimulation (OS).

Model Genotype n Response mean (SE) Difference (95% CI) P-value
FSHR c.-29G>A
Codominant GG 53 7.98 (0.18) 0.00 0.560
GA 44 8.07 (0.18) 0.09 (−0.41 to 0.58)
AA 10 7.60 (0.27) −0.8 (−1.22 to 0.46)
Dominant GG 53 7.98 (0.18) 0.00 0.999
GA-AA 54 7.98 (0.16) 0.00 (−0.47 to 0.47)
Recessive GG-GA 97 8.02 (0.13) 0.000 0.310
AA 10 7.60 (0.27) −0.42 (−1.23 to 0.38)
Overdominant GG-AA 63 7.92 (0.16) 0.00 0.650
GA 44 8.07 (0.18) 0.15 (−0.33 to 0.62)
FSHR p.N680S
Codominant NN 32 8.16 (0.21) 0.00 0.110
NS 52 7.73 (0.17) −0.43 (−0.96 to 0.11)
SS 23 8.30 (0.26) 0.15 (−0.51 to 0.80)
Dominant NN 32 8.16 (0.21) 0.00 0.340
NS-SS 75 7.91 (0.15) −0.25 (−0.76 to 0.26)
Recessive NN-NS 84 7.89 (0.13) 0.00 0.160
SS 23 8.30 (0.26) 0.41 (−0.16 to 0.98)
Overdominant NN-SS 55 8.22 (0.16) 0.00 0.041
NS 52 7.73 (0.17) −0.49 (−0.95 to −0.03)
FSHB c.-211G>T
Codominant GG 77 7.83 (0.14) 0.00 0.030
GT 29 8.38 (0.23) 0.55 (0.33–1.07)
TT 1 8.00 (0.00) 0.17 (−2.24 to 1.05)
Dominant GG 77 7.83 (0.14) 0.00 0.044
GT-TT 30 8.37 (0.23) 0.54 (0.02–1.05)
Recessive GG-GT 106 7.98 (0.12) 0.00 0.990
TT 1 8.00 (0.00) 0.02 (−2.43 to 2.46)
Overdominant GG-TT 78 7.83 (0.14) 0.00 0.042
GT 29 8.38 (0.23) 0.55 (0.03–1.07)
LHβ V-LH p.W8R
Codominant WW 89 8.02 (0.12) 0.00 0.450
WR 18 7.78 (0.36) −0.24 (−0.87 to 0.38)
LHCGR p.S312N
Codominant SS 45 7.56 (0.15) 0.00 0.007
NS 46 8.24 (0.19) 0.68 (0.19–1.17)
NN 16 8.44 (0.34) 0.88 (0.20–1.56)
Dominant SS 45 7.56 (0.15) 0.00 0.002
NS-NN 62 8.29 (0.16) 0.73 (0.28–1.19)
Recessive SS-NS 31 7.90 (0.13) 0.00 0.110
NN 16 8.44 (0.34) 0.54 (−0.12 to 1.19)
Overdominant SS-NN 61 7.79 (0.15) 0.00 0.061
SS 46 8.24 (0.19) 0.45 (−0.02 to 0.92)

p<0.05 are indicated with bold.

Multivariate analysis confirmed the impact of haplotypes generated by the five SNPs (p = 0.009), revealing that the combination of FSHR c.-29G>A G allele, FSHR p.N680S N allele, FSHB c.-211G>T G allele, LHβ V-LH p.W8R W allele, and LHβ and LHCGR p.S312N N allele was associated with longer stimulation (Table 5). Overall, this combination was expressed in 7.4% of the study population.

 Number of embryos developed, transferred and cryopreserved

No significant difference was observed among SNP haplotypes (Supplementary Table S2) in both separate and multivariate analyses, irrespective to the genetic model considered.

 Implantation rate; pregnancy rate per started cycle; pregnancy rate per embryo transfer; cumulative ongoing pregnancy rate per started cycle

No significant difference was observed among SNP haplotypes, irrespective to the genetic model considered. Given the limited sample size, multivariate analysis was not carried out.

 Discussion

Our study evaluated prospectively the potential role of five SNPs of gonadotropin and their receptor genes on the response to OS in normo-responder women undergoing ART. Our analysis does not clearly identify a genetic haplotype associated with the number of oocytes retrieved after standard antagonist COS protocol. However, SNPs or combinations of them could impact OS parameters. In particular, FSHR p.N680S and LHCGR p.N312S SNPs influence the duration of the stimulation per se. Concerning the LHβ V-LH variant (p.W8R and p.I15T), we did not detect any relevant findings, likely due to reduced sample size and ethnicity-related issues, opposite to what was previously demonstrated (37). Moreover, although the evidence is limited, the FSHB c.-211G>T SNP influences metaphase II oocytes, FOI, and OS duration. The most interesting findings are observed when multiple genotype analyses are performed, stressing the importance of multiple genetic analyses instead of focusing on a single SNP as performed in the previous papers (19, 21, 38). In detail, multivariate analysis revealed higher FOI and FORT together with specific LHCGR p.S312N and FSHR p.N680S haplotypes. Although this result is limited, it explains the variability of FOI and FORT in almost 10% of our cohort of patients.

Over the past years, our understanding of the role of SNPs in gonadotropins and their receptor genes, as well as their implications in the modulation of female reproduction, has significantly expanded. Nevertheless, the impact of SNPs on ART remains a subject of debate. Numerous studies involving women undergoing ART have attempted to assess whether the FSHR genotype could predict ovarian response and/or determine the starting FSH dose. This approach could lead to personalized OS, resulting in decreased incidence of hyper and poor response (4, 29), thereby conserving resources and preventing dropouts. The FSHR p.N680S SNP is one of the most investigated genetic variants in the IVF context (30). FSHR p.N680S S homozygosity has been correlated with reduced sensitivity to exogenous FSH (22), while the N allele with increased sensitivity to FSH (39). An in vitro study demonstrated that the pattern of FSH-dependent intracellular pathways are modulated according to the FSHR p.N680S genotype, and higher cAMP levels were achieved in homozygous asparagine than serine carriers (39, 40). From a clinical point of view, the reduced ovarian sensitivity is typically interpreted as the requirement of a higher total dose of gonadotropins used during OS (38, 41, 42), lower peak estradiol levels on the day of hCG administration (36, 43), a lower number of retrieved oocytes at pickup (16, 44), and higher basal FSH levels (16, 45). It was even suggested that the p.N680S N allele could be linked with higher risk of OHSS (39) and increased risk of progesterone rise (46) during OS, although these results should be confirmed by further studies. The evidence collected so far does not demonstrate any effect on pregnancy rate (16, 45). Our findings are consistent with the previous studies, confirming that the FSHR p.N680S N variant is linked to better duration of stimulation and ovarian sensitivity, rather than pregnancy and cumulative pregnancy rate. Notably, we detected that ovarian sensitivity, measured by the FOI index, is significantly high in women displaying specific combinations of FSHR p.N680S and LHCGR p.S312N SNPs. This observation supports that both LHCGR and FSHR play an important role during follicular recruitment and growth (47, 48). In contrast with previous studies, we observed for the first time that even serine carriers of FSHR p.N680S have better FOI than heterozygotes, only when they are also associated with serine in the LHCGR p.S312N. Instead, FORT was better in FSHR p.N680S N carriers. In other words, these data may suggest that the expression of the LHCGR p.S312N S variant could mitigate the reduced FOI but not the FORT index in the same way. This phenomenon might mean that LH actions might be more pronounced in oocyte output and maturation than follicular recruitment. The literature so far focuses on the role of these polymorphisms only in terms of the crude number of eggs retrieved that is not necessarily related to a good or a worse ovarian sensitivity (17). For instance, even a retrieval of a suboptimal number of eggs (3) (i.e., 4–9 eggs retrieved) could reflect a good ovarian sensitivity if the AFC is consistent (17). It is plausible that combined genetic variants could modulate the ovarian sensitivity rather than the number of eggs per se, stressing the importance of further and larger sample-sized study studies evaluating FOI and FORT as primary outcomes.

Previous studies demonstrated that the FSHR c.-29G>A SNP modulates the promoter transcriptional activity (36), and the G homozygosity was significantly associated with a higher number of collected oocytes (21, 24). More recently, it was observed that FSHR c.-29G>A homozygous carriers have reduced ovarian response (44), although other studies did not find any correlations (49, 50). In the present study, we did not detect any effect exerted by this polymorphism per se on OS. Rather, cumulative FSH dosage, FOI, FORT, and stimulation duration are modulated when the FSHR c.-29G>A SNP is combined with other genetic variants, suggesting that the evaluation of a single SNP could not have enough clinical relevance in predicting OS outcomes. Indeed, one of the main strengths of our investigation resides in the effort to analyze, prospectively, multiple genetic combinations associated with specific OS outcomes. Furthermore, for the first time we assessed parameters strictly related to ovarian sensitivity, such as FOI and FORT. Both could be effectively used to assess the efficiency of ovarian response in order to allow better tailoring of the treatment (51). The influence of genetic analysis on OS outcome could open a new scenario in personalization of protocols, avoiding unexpected reduced responses that affect a large number of women candidates for IVF (52). Of course, we recognized that such analysis required very large number of observations that certainly was not achieved by this multicentered study. In addition, with the purpose to limit biases, AFC evaluation was standardized among centers and a fixed FSH starting dose was maintained constant throughout the stimulation period. In this way, we overcome any possible limitation linked to adjustments in the FSH dose or prolonged treatment, which could mask the impact of genotypes on ovarian response (53, 54). Another important limitation is that the sample size was estimated only considering FSHR c.-29G>A, a genetic variant limiting the robustness of our findings regarding the other SNPs analyzed.

 Conclusion

In order to elucidate the role of gene SNPs in OS outcomes, we conducted a study in which the women’s setting was strictly established a priori to consider normo-responder women. In particular, the ovarian reserve has been considered among inclusion criteria, and the AFC was assessed using a reliable, standardized, automated method along with centralized AMH assessment. Furthermore, OS was administered with a fixed standard dose of 150 IU FSH daily.

Our study confirmed the presence of an association between specific genotype variants and OS outcomes. Indeed, separate analysis of gonadotropin and receptor SNPs revealed a mild association with OS outcomes. However, combined multivariate analysis of polymorphisms showed greater impact on the efficacy of OS outcomes, suggesting the potential role of these SNPs in the decision-making process of ART settings, regarding useful clinical quality indicators such as FOI (51). Obviously, multivariate analyses require a very large sample size to be considered as robust, and our findings should be corroborated by further studies. In the future, increasing use of real-world data and the support of artificial intelligence could offer the opportunity to dramatically increase the number of observations and thereby ameliorate the research in the field of pharmacogenomics, focusing more on the combination of genetic variants instead of single analysis.

Unfortunately, the clinical utility of a pharmacogenomic approach falls outside the scope of this prospective association analysis, despite a possible effect on ART outcome is being suggested. We hope that our findings could contribute in the future to the development of robust randomized controlled trials to demonstrate the clinical utility of a pharmacogenomic approach, focusing on FOI or FORT rather than endpoints such as pregnancy rate, which are affected by confounding factors, that is, the male factor.

 Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

 Ethics statement

The studies involving humans were approved by Ethical committee Federico II University. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.

 Author contributions

AC: Writing – review & editing, Conceptualization, Investigation, Validation, Methodology, Resources, Visualization, Writing – original draft. DS: Software, Writing – review & editing, Conceptualization, Supervision, Data curation, Formal Analysis, Writing – original draft, Methodology, Validation, Visualization. AA: Data curation, Writing – review & editing, Conceptualization, Investigation, Supervision, Writing – original draft, Visualization. MR: Data curation, Writing – original draft, Conceptualization, Visualization, Writing – review & editing. AM: Writing – original draft, Visualization, Writing – review & editing, Data curation, Supervision. CB: Data curation, Visualization, Writing – review & editing, Writing – original draft. RI: Writing – review & editing, Data curation, Writing – original draft. VV: Data curation, Writing – original draft, Writing – review & editing. AR: Writing – review & editing, Data curation, Writing – original draft. LP: Writing – original draft, Writing – review & editing, Data curation. SS: Writing – review & editing, Writing – original draft, Data curation, Methodology. LC: Writing – original draft, Visualization, Data curation, Methodology, Supervision, Writing – review & editing. IC: Writing – original draft, Methodology, Data curation, Writing – review & editing. MS: Writing – review & editing, Visualization, Writing – original draft, Validation, Supervision. CA: Writing – review & editing, Visualization, Resources, Validation, Funding acquisition, Conceptualization, Supervision, Methodology, Writing – original draft. EP: Methodology, Supervision, Data curation, Investigation, Visualization, Resources, Conceptualization, Validation, Writing – original draft, Writing – review & editing, Funding acquisition.

 Conflict of interest

The authors declare that this study received funding from Merck KGaA. The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article or the decision to submit it for publication.

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

 Correction note

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Edited by: Jung Ryeol Lee, Seoul National University, Republic of Korea

Reviewed by: Sezcan Mumusoglu, Anatolia IVF Center, Türkiye

Durai Sellegounder, Buck Institute for Research on Aging, United States

YD Mao, Nanjing Medical University, China