Association of FSHR, LH, LHR, BMP15, GDF9, AMH, and AMHR polymorphisms with poor ovarian response in patients undergoing in vitro fertilization

Meireles, Arivaldo José Conceição; Bilibio, João Paolo; Lorenzzoni, Pânila Longhi; Conto, Emily de; Nascimento, Fábio Costa do; Cunha-Filho, João Sabino

doi:10.5935/1518-0557.20210004

Cited by 5 publications

(5 citation statements)

References 38 publications

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“…Similarly, AMH could evaluate the number of embryos effectively in patients with expected POR ( 31 ). Again, our conclusions that AMH is strongly associated with the pregnancy outcome in patients with expected POR, which is in line with the findings of other studies in the field ( 32 ).…”

Section: Discussionsupporting

confidence: 93%

Development and validation of a live birth prediction model for expected poor ovarian response patients during IVF/ICSI

et al. 2023

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BackgroundA number of live birth predictive model during assisted reproductive technology treatment have been available in recent years, but few targeted evaluating the chances of live birth in poor ovarian response(POR) patients. The aim of this study was to develop a nomogram based on POSEIDON criteria to predict live birth in patients with expected POR.MethodsThis retrospective cohort study using clinical data from 657 patients in POSEIDON Groups 3 and 4 (antral follicle count [AFC] ≤5 and AMH <1.2 ng/ml) in the Center for Reproductive Medicine, First Affiliated Hospital of Xinjiang Medical University, and Construction a nomogram model tResultsAmong 657 expected POR patients, 111 (16.89%) had live births, and 546 (83.11%) did not have live births. These were divided into a training set(n=438) and a validation set (n=219). Multivariate logistic regression analysis showed that the age (OR = 0.91, 95% CI: 0.86–0.97), BMI (OR = 1.98, 95% CI: 1.09–3.67), AMH (OR = 3.48, 95% CI: 1.45–8.51), normal fertilized oocytes (OR = 1.40, 95% CI: 1.21–1.63), and the basal FSH (OR = 0.89, 95% CI: 0.80–0.98) of the female were independent factors predicting live birth in patients with expected POR. Then, an individualized nomogram prediction model was built from these five factors. The area under the ROC curve of the live birth prediction model was 0.820 in the training set and 0.879 in the validation set.ConclusionWe have developed a nomogram combining clinical and laboratory factors to predict the probability of live birth in patients with an expected POR during IVF/ICSI, which can helpful for clinician in decision-making. However, the data comes from the same center, needs a prospective multicenter study for further in-depth evaluation and validation of this prediction model.

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Section: Discussionsupporting

confidence: 93%

Development and validation of a live birth prediction model for expected poor ovarian response patients during IVF/ICSI

et al. 2023

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“…However, we were unable to locate a previous study demonstrating the exact length of stimulation as a predictor of ovarian response. These findings regarding the average length of stimulation were inconsistent with those of studies conducted in Brazil and the UK [ 12 , 59 ]. Nonetheless, given their tight connection with the nature of the treatment protocol employed, these outcomes made sense.…”

Section: Discussioncontrasting

confidence: 81%

Ovarian response to controlled stimulation and its predictors in a limited-resource setting

Dermolo,

Ansa,

Siferih

2024

BMC Women's Health

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Background Infertility remains a serious health concern for Ethiopian women. Most of its treatment approaches entail controlled ovarian stimulation, the responses of which vary. However, there are no data on ovarian response to stimulation or its predictors in our situation. Thus, the current study aimed to assess the ovarian response to controlled stimulation and identify predictors. Methods A retrospective follow-up study was undertaken from April 1, 2021, to March 31, 2022, among patients who had first-cycle controlled ovarian stimulation at St.Paul’s Hospital Fertility Center in Addis Ababa, Ethiopia. Clinical data were extracted using a checklist. SPSS-26 for data analysis and Epidata-4.2 for data entry were employed. The binary logistic regression model was fitted. A p-value < 0.05 indicated a significant association. The ROC curve was used to determine cutoff values and identify accurate predictors. Results A total of 412 study participants were included in the final analysis. The patients had a mean age of 32.3 ± 5.1 years (range: 20 − 4). The good ovarian response rate was 67% (95% CI: 62.2–71.5). An anti-Mullerian hormone (AMH) concentration < 1.2ng/ml (AOR = 0.19, 95% CI (0.06–0.57)), an antral follicle count (AFC) < 5 (AOR = 0.16, 95% CI (0.05–0.56)), and an induction length < 10 days (AOR = 0.23, 95% CI (0.06–0.93)) were significantly associated with ovarian response. The prediction accuracies for the AFC and AMH concentrations were 0.844 and 0.719, respectively. The optimal cutoff point for prediction was 5.5 AFC, which had a sensitivity of 77.2% and a specificity of 72.8%. However, its positive and negative predictive values were 85.2% and 61.1%, respectively. For AMH, the optimal cutoff value was 0.71ng/mL, with a corresponding sensitivity and specificity of 65.2% and 66%. At this value, the positive and negative predictive values were 63.8% and 67.3%, respectively. Conclusion Only two-thirds of our patients achieved a good ovarian response. Induction duration, AMH concentration, and AFC were found to be predictors, with the AFC being the strongest predictor. Therefore, the AFC should be performed on all of our patients, and the AMH is selectively employed. Future research must verify the best cutoff points and investigate additional factors affecting ovarian response.

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“…Studies related to LHR and FSHR genes have been conducted on Bubalus bubalis Linnaeus 1758 (Sosa et al 2016;Kathiravan et al 2019), Bos taurus Linnaeus 1758, Bos indicus Linnaeus 1758, and crossbreed cattle (Andreas et al 2014;Faraj et al 2021), Ovis aries Linnaeus 1758 breed (Du et al 2019;He et al 2021), and human (Meireles et al 2021;Atoum et al 2022). Several studies on SNP have reported that there is a polymorphism of the LHR gene in Holstein cattle in Azerbaijan (Abdi et al 2017), Pasundan cattle (Said et al 2021), Holstein cattle in Honduras (Cañizares-Martinez et al 2021), and one of them at the site of exon 11 (Hastings et al 2006;Marson et al 2008 (Putra et al 2020), and Holstein bulls (Nikitkina et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Identification polymorphism of LHR and FSHR genes in Indonesian Holstein dairy cattle associated with productive and reproductive traits

et al. 2023

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Abstract. Setyorini YW, Kurnianto E, Sutopo, Sutiyono. 2023. Identification polymorphism of LHR and FSHR genes in Indonesian Holstein dairy cattle associated with productive and reproductive traits. Biodiversitas 24: 2898-2905. Luteinizing and follicle-stimulating hormones and their receptors play an important role in the reproductive system’s hormonal activity and physiological function. This study aimed to identify the genotype variation of the Luteinizing Hormone Receptor (LHR) gene exon 11 and the Follicle-stimulating Hormone Receptor (FSHR) gene exon 10 and to elucidate the associating between polymorphism with the milk and reproductive traits in dairy cattle. A total of 100 samples of deoxyribonucleic acid (DNA) from Indonesian Holstein dairy cattle were used in this study. Polymerase Chain Reaction Restriction Fragment Length Polymorphism (PCR-RFLP) was performed for genotyping of the Single Nucleotide Polymorphism (SNP) of the LHR and FSHR genes with the HhaI and AluI restriction enzymes, respectively. The collected phenotype data were milk production, quality, and reproductive trait. In addition, the general linear model procedure in the SAS program was carried out to investigate the association of genotypes with productive and reproductive traits. The results showed that the LHR gene in the population of this study was monomorphic, while the FSHR gene was polymorphic with three genotypes, namely CC, CG, and GG. The sequences of the FSHR gene indicated the presence of a mutation at nucleotide number 2037 that substitutes cytosine for guanine. Cows with the CC genotype showed better parameter service per conception (P<0.05) than the other genotypes but did not correlate with other reproductive parameters and milk traits. Therefore, it was concluded that SNP g.2037C>G polymorphism at FSHR gene exon 10 was identified in the Indonesian Holstein Dairy cattle population and associated with service per conception parameters but not with milk trait.

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Association of FSHR, LH, LHR, BMP15, GDF9, AMH, and AMHR polymorphisms with poor ovarian response in patients undergoing in vitro fertilization

Cited by 5 publications

References 38 publications

Development and validation of a live birth prediction model for expected poor ovarian response patients during IVF/ICSI

Development and validation of a live birth prediction model for expected poor ovarian response patients during IVF/ICSI

Ovarian response to controlled stimulation and its predictors in a limited-resource setting

Identification polymorphism of LHR and FSHR genes in Indonesian Holstein dairy cattle associated with productive and reproductive traits

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