Body mass index, follicle-stimulating hormone and their predictive value in in vitro fertilization

Predictors of success after in vitro fertilization

The last few decades have witnessed a rise in the global uptake of in vitro fertilization (IVF) treatment. To ensure optimal use of this technology, it is important for patients and clinicians to have access to tools that can provide accurate estimates of treatment success and understand the contribution of key clinical and laboratory parameters that influence the chance of conception after IVF treatment. The focus of this review was to identify key predictors of IVF treatment success and assess their impact in terms of live birth rates. We have identified 11 predictors that consistently feature in currently available prediction models, including age, duration of infertility, ethnicity, body mass index, antral follicle count, previous pregnancy history, cause of infertility, sperm parameters, number of oocytes collected, morphology of transferred embryos, and day of embryo transfer.

Infertility Improvement after Medical Weight Loss in Women and Men: A Review of the Literature

Infertility is a modern health problem. Obesity is another expanding health issue associated with chronic diseases among which infertility is also included. This review will focus on the effects of weight loss by medical therapy on fertility regarding reproductive hormonal profile, ovulation rates, time to pregnancy, implantation rates, pregnancy rates, normal embryo development, and live birth rates. We comprised medicine already used for weight loss, such as orlistat and metformin, and emerging medical treatments, such as Glucagon-Like Peptide-1 receptor agonists (GLP-1 RA). Their use is not recommended during a planned pregnancy, and they should be discontinued in such cases. The main outcomes of this literature review are the following: modest weight loss after medication and the duration of the treatment are important factors for fertility improvement. The fecundity outcomes upon which medical-induced weight loss provides significant results are the female reproductive hormonal profile, menstrual cyclicity, ovulation and conception rates, and pregnancy rates. Regarding the male reproductive system, the fertility outcomes that feature significant alterations after medically induced weight loss are as follows: the male reproductive hormonal profile, sperm motility, movement and morphology, weight of reproductive organs, and sexual function. The newer promising GLP-1 RAs show expectations regarding fertility improvement, as they have evidenced encouraging effects on improving ovulation rates and regulating the menstrual cycle. However, more human studies are needed to confirm this. Future research should aim to provide answers about whether medical weight loss therapies affect fertility indirectly through weight loss or by a possible direct action on the reproductive system.

Should women with high body mass index be denied fertility treatments?

Obesity is an emerging global epidemic with a negative impact on fertility. Almost all guidelines and policies have a stringent limit of body mass index (BMI) to access fertility services which has promoted a debate amongst fertility practitioners globally. Proponents of placing such a limit point to the negative impact of elevated BMI on the outcome of fertility treatment, its cost effectiveness and the risk it poses to the intending mother and unborn child. Opponents of placing a restriction base their arguments on the lack of conclusive, robust evidence regarding the variables along with the ethical dilemmas of promoting discrimination and stigmatization by denying a couple their basic right of parenthood. In this review, we analyse these medical and ethical dilemmas in the light of current evidence. The focus is on female infertility.

Artificial intelligence model to predict pregnancy and multiple pregnancy risk following in vitro fertilization-embryo transfer (IVF-ET)

OBJECTIVE

To decrease multiple pregnancy risk and sustain optimal pregnancy chance by choosing suitable number of embryos during transfer, this study aims to construct artificial intelligence models to predict the pregnancy outcome and multiple pregnancy risk after IVF-ET.

MATERIALS AND METHODS

From Jan 2010 to Dec 2019, 1507 fresh embryo transfer cycles contained 20 features were obtained. After eliminating incomplete records, 949 treatment cycles were included in the pregnancy model dataset and 380 cycles in the twin pregnancy model dataset. Six machine learning algorithms were used for model building based on the dataset which 70% of the dataset were randomly selected for training and 30% for validation. Model performances were quantified with the area under the receiver operating characteristic curve (AUC), accuracy, specificity, and sensitivity.

RESULTS

Models built with XGBoost performed best. The pregnancy prediction model produced accuracy of 0.716, sensitivity of 0.711, specificity of 0.719, and AUC of 0.787. The multiple pregnancy prediction model produced accuracy of 0.711, sensitivity of 0.649, specificity of 0.740, and AUC of 0.732.

CONCLUSIONS

The AI models provide reliable outcome prediction and could be a promising method to decrease multiple pregnancy risk after IVF-ET.

Nomogram prediction for the prediction of clinical pregnancy in Freeze-thawed Embryo Transfer

BACKGROUND

This study aimed to identify multiple endometrial receptivity related factors by applying non-invasive, repeatable multimodal ultrasound methods. Combined with basic clinical data, we further established a practical prediction model for early clinical outcomes in Freeze-thawed Embryo Transfer (FET).

METHODS

Retrospective analysis of clinical data of infertility patients undergoing FET cycle in our Center from January 2017 to September 2019. Receiver operating characteristic (ROC) curve and decision curve analyses were performed by 500 bootstrap resamplings to assess the determination and clinical value of the nomogram, respectively.

RESULTS

A total of 2457 FET cycles were included. We developed simple nomograms that predict the early clinical outcomes in FET cycles by using the parameters of age, BMI, type and number of embryos transferred, endometrial thickness, FI, RI, PI and number of endometrial and sub-endometrial blood flow. In the training cohort, the area under the ROC curve (AUC) showed statistical accuracy (AUC = 0.698), and similar results were shown in the subsequent validation cohort (AUC = 0.699). Decision curve analysis demonstrated the clinical value of this nomogram.

CONCLUSIONS

Our nomogram can predict clinical outcomes and it can be used as a simple, affordable and widely implementable tool to provide guidance and treatment recommendations for FET patients.

Female obesity and infertility: outcomes and regulatory guidance

Obesity has been associated with reduced fertility, although the dynamics and mechanisms which link excess weight to reduced fertility are not yet fully clarified. Obese women, especially those with central obesity, are less likely to conceive per cycle. Obese women suffer from perturbations of the hypothalamus-pituitary-ovary axis, disturbances of the menstrual cycle and are up to three times more likely to suffer from oligo/anovulation. A delicate hormonal balance regulates follicular development and the maturation of oocytes and it has been observed that obesity can alter the hormonal environment: adipocytes, in fact, are responsible for the production of a hormone called leptin (present in high quantities in obese women) which has been associated with reduced fecundity. In addition to compromising ovulation, obesity negatively affects the development and implantation of the endometrium. The expression of polycystic ovary syndrome (PCOS) is regulated, in part, by weight, so obese women with PCOS often have a more severe phenotype and higher subfertility rates. Furthermore, obesity impairs women's response to medically assisted procreation (MAP) treatments. The authors have set out to delineate a broad-ranging overview of obesity's impact on female fertility, by drawing upon sources spanning the 1994-2022 period. Assisted reproductive technology (ART) procedures are also discussed as they relate to obese patients. In addition the dynamics by which maternal obesity reportedly affects fetal, neonatal and child development have also been briefly enunciated.

A systematic review of the quality of clinical prediction models in in vitro fertilisation

STUDY QUESTION

What are the best-quality clinical prediction models in IVF (including ICSI) treatment to inform clinicians and their patients of their chance of success?

SUMMARY ANSWER

The review recommends the McLernon post-treatment model for predicting the cumulative chance of live birth over and up to six complete cycles of IVF.

WHAT IS KNOWN ALREADY

Prediction models in IVF have not found widespread use in routine clinical practice. This could be due to their limited predictive accuracy and clinical utility. A previous systematic review of IVF prediction models, published a decade ago and which has never been updated, did not assess the methodological quality of existing models nor provided recommendations for the best-quality models for use in clinical practice.

STUDY DESIGN, SIZE, DURATION

The electronic databases OVID MEDLINE, OVID EMBASE and Cochrane library were searched systematically for primary articles published from 1978 to January 2019 using search terms on the development and/or validation (internal and external) of models in predicting pregnancy or live birth. No language or any other restrictions were applied.

PARTICIPANTS/MATERIALS, SETTING, METHODS

The PRISMA flowchart was used for the inclusion of studies after screening. All studies reporting on the development and/or validation of IVF prediction models were included. Articles reporting on women who had any treatment elements involving donor eggs or sperm and surrogacy were excluded. The CHARMS checklist was used to extract and critically appraise the methodological quality of the included articles. We evaluated models' performance by assessing their c-statistics and plots of calibration in studies and assessed correct reporting by calculating the percentage of the TRIPOD 22 checklist items met in each study.

MAIN RESULTS AND THE ROLE OF CHANCE

We identified 33 publications reporting on 35 prediction models. Seventeen articles had been published since the last systematic review. The quality of models has improved over time with regard to clinical relevance, methodological rigour and utility. The percentage of TRIPOD score for all included studies ranged from 29 to 95%, and the c-statistics of all externally validated studies ranged between 0.55 and 0.77. Most of the models predicted the chance of pregnancy/live birth for a single fresh cycle. Six models aimed to predict the chance of pregnancy/live birth per individual treatment cycle, and three predicted more clinically relevant outcomes such as cumulative pregnancy/live birth. The McLernon (pre- and post-treatment) models predict the cumulative chance of live birth over multiple complete cycles of IVF per woman where a complete cycle includes all fresh and frozen embryo transfers from the same episode of ovarian stimulation. McLernon models were developed using national UK data and had the highest TRIPOD score, and the post-treatment model performed best on external validation.

LIMITATIONS, REASONS FOR CAUTION

To assess the reporting quality of all included studies, we used the TRIPOD checklist, but many of the earlier IVF prediction models were developed and validated before the formal TRIPOD reporting was published in 2015. It should also be noted that two of the authors of this systematic review are authors of the McLernon model article. However, we feel we have conducted our review and made our recommendations using a fair and transparent systematic approach.

WIDER IMPLICATIONS OF THE FINDINGS

This study provides a comprehensive picture of the evolving quality of IVF prediction models. Clinicians should use the most appropriate model to suit their patients' needs. We recommend the McLernon post-treatment model as a counselling tool to inform couples of their predicted chance of success over and up to six complete cycles. However, it requires further external validation to assess applicability in countries with different IVF practices and policies.

STUDY FUNDING/COMPETING INTEREST(S)

The study was funded by the Elphinstone Scholarship Scheme and the Assisted Reproduction Unit, University of Aberdeen. Both D.J.M. and S.B. are authors of the McLernon model article and S.B. is Editor in Chief of Human Reproduction Open. They have completed and submitted the ICMJE forms for Disclosure of potential Conflicts of Interest. The other co-authors have no conflicts of interest to declare.

REGISTRATION NUMBER

N/A.

predicting and improving the probability of live birth for women undergoing frozen-thawed embryo transfer: a data-driven estimation and simulation model

BACKGROUND AND OBJECTIVE

Frozen-thawed embryo transfer (FET) is now widely used for the treatment of infertility. For many couples and clinicians, concerns over the probability and how to increase the chance of a successful birth are very common. Currently, there is not a single model to predict the live birth outcomes for FET. To estimate the probability of live birth (PLB) in FET and to provide advice on potential treatment options by a data-driven predictive (DDP) model.

METHODS

2,189 FET cycles from Jan 2012 to Dec 2015 were recruited in a single center. 815 cycles of FET outcomes were live births and 1,374 cycles of FET outcomes failed. To verify the consistency of the DDP model, we carried out 10-fold cross-validation, and the mean and standard deviation of the accuracy were measured. Moreover, the performance of this model was evaluated by the mean and standard deviation of receiver operating characteristic curve and area under the curve (AUC).

RESULTS

Nine dominant factors, including age, BMI, HOMA-IR, basal follicle stimulating hormone, basal luteinizing hormone, basal estradiol, endometrial thickness, the number of embryo transfers and the total number of embryos, were automatically extracted from 28 candidate factors. The accuracy of our prediction model is 76.9%±1.6%, and the AUC is 0.83. Then, the PLB is estimated by the random forest algorithm. On this basis, the DDP model can comprehensively traverse and dynamically visualize the impact of several factors on live birth outcomes. Finally, optimal suggestions for the treatment of patients before FET are attempted to be made by the genetic algorithm.

CONCLUSION

The DDP model can not only provide satisfactory performance for predicting live birth outcomes in FET but also offer a visual estimation and simulation tool for clinicians to make treatment plans.

Efficacy of an Internet-Based Intervention to Promote a Healthy Lifestyle on the Reproductive Parameters of Overweight and Obese Women: Study Protocol for a Randomised Controlled Trial

Background: Infertility is estimated to affect 15% of couples of reproductive age. Weight management problems (being obese or overweight) are among the problems that produce infertility, both in women seeking spontaneous pregnancy and in those undergoing assisted reproduction techniques. Over the last few decades, the prevalence of obesity has increased alarmingly in our society and is now considered one of the most important public health problems. The combination of diet and exercise to achieve weight loss are currently considered an effective intervention for the improvement of reproductive parameters in overweight or obese infertile women. In other population groups, it has been shown that Internet-based interventions are just as effective as traditional ones, and these cover a larger population with a good cost–benefit ratio. However, to the best of our knowledge, no studies so far have analysed any specific online interventions for this group of infertile women. Thus, the objective of this project will be to evaluate the effectiveness of an online program to promote a healthy lifestyle among women who are overweight or obese who also have a diagnosis of infertility and are on the waiting list for in vitro fertilisation treatment. Methods: This will be a randomised controlled clinical trial conducted in 94 women which will compare a self-administered Internet-based intervention promoting a healthy lifestyle in terms of diet and exercise (n = 47) to a control group that will receive standard medical care. The online program will comprise nine modules, will last for 3 months, and will be monitored every 3 months after the intervention until the final follow-up at 12 months. The main outcome will be the spontaneous pregnancy rate. Secondary outcomes will include changes in body composition, dietary and physical exercise habits, glycaemic profiles, lipid profiles, hormonal profiles, and patient quality of life related to their fertility problems. The data analysis will be done on an intention-to-treat basis. Discussion: The aim of this study is to increase our knowledge of the effectiveness of online interventions specifically adapted to infertile women who are overweight or obese in the promotion of healthy lifestyles.

Polycystic Ovary Syndrome: Impact of Lipotoxicity on Metabolic and Reproductive Health

Importance

Polycystic ovary syndrome (PCOS) is the most common endocrinopathy of reproductive-aged women. Women with PCOS are at increased risk of developing several metabolic and reproductive abnormalities, including metabolic syndrome. Underlying the combined metabolic and reproductive dysfunction is lipotoxicity, defined as the ectopic deposition of lipid in nonadipose tissue where it induces oxidative stress linked with insulin resistance and inflammation.

Objective

To examine what metabolic components underlie insulin resistance in PCOS, how lipotoxicity through insulin resistance impairs metabolism and reproduction in these women, and why evidence-based, individualized management is essential for their care.

Evidence Acquisition

PubMed search was performed using relevant terms to identify journal articles related to the subject. Relevant textbook chapters were also used.

Results

Polycystic ovary syndrome by Rotterdam criteria represents a complex syndrome of heterogeneous expression with variable adverse metabolic and reproductive implications. Women with classic PCOS are often insulin resistant and at greatest risk of developing metabolic syndrome with preferential fat accumulation and weight gain. Moreover, PCOS women may also have an altered capacity to properly store fat, causing ectopic lipid accumulation in nonadipose tissue, including the ovaries, where it can perpetuate insulin resistance and inflammation and harm the oocyte.

Conclusions and Relevance

A personalized approach to managing PCOS is essential to improve the health of all PCOS women through cost-effective prevention and/or treatment, to minimize the risk of pregnancy complications in those individuals wishing to conceive, and to optimize the long-term health of PCOS women and their offspring.

Effects of Body Mass Index and Biochemical Lipid Levels on Reproductive Outcomes during An Intracytoplasmic Sperm Injection: A Retrospective Study

Background

The aim of this study was to evaluate the impact of body mass index (BMI) and lipid profile on repro- ductive outcomes of women undergoing intracytoplasmic sperm injection (ICSI) cycles.

Materials and Methods

This retrospective observational study was conducted in the Center of Human Reproductive Physiopathology of University of Catania between April 2017 and March 2018 and enrolled 114 couples undergoing ICSI. Levels of total cholesterol, low-density lipoprotein-cholesterol (LDL-c), high-density lipoprotein-cholesterol (HDL-c) and triglycerides were determinate and, according to the BMI, samples were divided into the following groups: group A (BMI: 18.5-24.9 kg/m²); group B (BMI: 25-29.9 kg/m²); and group C (BMI >30 kg/m²). BMI and lipid profile associations with the number of oocytes and embryos retrieved, the oocytes and embryo quality, the fer- tilization rate as well as the percentage of miscarriages and pregnancies, were assessed. The statistical analysis was performed using Shapiro-Wilk test, analysis of variance (ANOVA) and Kruskal -Wallis method.

Results

Fertilization and pregnancy rates were lower in women with BMI>30 than in women with BMI: 25-29.9 and BMI: 18.5-24.9, despite the not altered levels of lipoprotein.

Conclusion

Our results demonstrated that an excess of adipose tissue in women undergoing ICSI was not directly related with altered biochemical lipid values. However, overweight and obese patients showed poor fertilization and pregnancy rate despite the not altered values of lipoprotein.

Making IVF more effective through the evolution of prediction models: is prognosis the missing piece of the puzzle?

Assisted reproductive technology has evolved tremendously since the emergence of in vitro fertilization (IVF). In the course of the recent decade, there have been significant efforts in order to minimize multiple gestations, while improving percentages of singleton pregnancies and offering individualized services in IVF, in line with the trend of personalized medicine. Patients as well as clinicians and the entire IVF team benefit majorly from 'knowing what to expect' from an IVF cycle. Hereby, the question that has emerged is to what extent prognosis could facilitate toward the achievement of the above goal. In the current review, we present prediction models based on patients' characteristics and IVF data, as well as models based on embryo morphology and biomarkers during culture shaping a complication free and cost-effective personalized treatment. The starting point for the implementation of prediction models was initiated by the aspiration of moving toward optimal practice. Thus, prediction models could serve as useful tools that could safely set the expectations involved during this journey guiding and making IVF treatment more effective. The aim and scope of this review is to thoroughly present the evolution and contribution of prediction models toward an efficient IVF treatment.

ABBREVIATIONS

IVF: In vitro fertilization; ART: assisted reproduction techniques; BMI: body mass index; OHSS: ovarian hyperstimulation syndrome; eSET: elective single embryo transfer; ESHRE: European Society of Human Reproduction and Embryology; mtDNA: mitochondrial DNA; nDNA: nuclear DNA; ICSI: intracytoplasmic sperm injection; MBR: multiple birth rates; LBR: live birth rates; SART: Society for Assisted Reproductive Technology Clinic Outcome Reporting System; AFC: antral follicle count; GnRH: gonadotrophin releasing hormone; FSH: follicle stimulating hormone; LH: luteinizing hormone; AMH: anti-Müllerian hormone; DHEA: dehydroepiandrosterone; PCOS: polycystic ovarian syndrome; NPCOS: non-polycystic ovarian syndrome; CE: cost-effectiveness; CC: clomiphene citrate; ORT: ovarian reserve test; EU: embryo-uterus; DET: double embryo transfer; CES: Cumulative Embryo Score; GES: Graduated Embryo Score; CSS: Combined Scoring System; MSEQ: Mean Score of Embryo Quality; IMC: integrated morphology cleavage; EFNB2: ephrin-B2; CAMK1D: calcium/calmodulin-dependent protein kinase 1D; GSTA4: glutathione S-transferase alpha 4; GSR: glutathione reductase; PGR: progesterone receptor; AMHR2: anti-Müllerian hormone receptor 2; LIF: leukemia inhibitory factor; sHLA-G: soluble human leukocyte antigen G.

Models Predicting Success of Infertility Treatment: A Systematic Review

BACKGROUND

Infertile couples are faced with problems that affect their marital life. Infertility treatment is expensive and time consuming and occasionally isn't simply possible. Prediction models for infertility treatment have been proposed and prediction of treatment success is a new field in infertility treatment. Because prediction of treatment success is a new need for infertile couples, this paper reviewed previous studies for catching a general concept in applicability of the models.

METHODS

This study was conducted as a systematic review at Avicenna Research Institute in 2015. Six data bases were searched based on WHO definitions and MESH key words. Papers about prediction models in infertility were evaluated.

RESULTS

Eighty one papers were eligible for the study. Papers covered years after 1986 and studies were designed retrospectively and prospectively. IVF prediction models have more shares in papers. Most common predictors were age, duration of infertility, ovarian and tubal problems.

CONCLUSION

Prediction model can be clinically applied if the model can be statistically evaluated and has a good validation for treatment success. To achieve better results, the physician and the couples' needs estimation for treatment success rate were based on history, the examination and clinical tests. Models must be checked for theoretical approach and appropriate validation. The privileges for applying the prediction models are the decrease in the cost and time, avoiding painful treatment of patients, assessment of treatment approach for physicians and decision making for health managers. The selection of the approach for designing and using these models is inevitable.

Serum dehydroepiandrosterone sulphate concentration is not a predictive factor in IVF outcomes before the first cycle of GnRH agonist administration in women with normal ovarian reserve

Objective

The aim of our study was to determine whether serum dehydroepiandrosterone sulphate (DHEAS) concentration and the models incorporating it could help clinicians to predict IVF outcomes in women with normal ovarian reserve undergoing their first long protocol.

Study Design

We performed a retrospective analysis of 459 women undergoing cycles of intracytoplasmic sperm injection (ICSI) for the first time in a long GnRH agonist protocol.

Results

Embryo transfer was performed in 407 women (88.7%). The fertilisation rate was 78.6%. The clinical pregnancy rate was 44.8% per started cycle and 50.6% per embryo transfer. Our univariate model revealed that the best predictors of clinical pregnancy were the number of mature oocytes, the number of embryos transferred and the number of good quality embryos, account for the clinical parameters that reflect ovarian reserve the best being AMH level and AFC. DHEAS did not predict clinical pregnancy (OR 1.001, 95% CI, 0.999–1.004). After adjusting for the number of embryos transferred and class of embryos in a multivariate model, the best predictors were age (OR 0.918, 95% CI, 0.867–0.972) and AFC (OR 1.022, 95% CI, 0.992–1.053). Serum DHEAS levels were positively correlated with AFC (r = 0.098, P<0.039) and testosterone levels (r = 0.371, P<0.001), as well as the number of mature oocytes (r = 0.109, P<0.019); serum DHEAS levels were negatively correlated with age (r = -0.220, P<0.001), follicle-stimulating hormone (FSH), (r = -0.116, P<0.015) and sex hormone-binding globulin (SHBG), (r = -0.193, P<0.001).

Conclusions

DHEAS concentration (in addition to the known factors of ovarian reserve) does not predict clinical pregnancy in women with normal ovarian reserve who are undergoing ICSI.

The impact of maternal body mass index on in vitro fertilization outcomes

OBJECTIVE

To examine the effect of body mass index on gonadotropin dose requirements for ovarian stimulation, as well as other clinical outcomes in women undergoing in vitro fertilization.

METHODS

We performed a retrospective cohort study involving 752 women undergoing a total of 951 IVF or IVF-ICSI cycles at a private fertility clinic between January 2007 and May 2011. The 951 treatment cycles were divided into three groups according to the weight of the women involved: normal weight (BMI < 25 kg/m², 461 cycles), overweight (25 ≤ BMI < 30 kg/m², 277 cycles), and obese (BMI ≥ 30 kg/m², 179 cycles). Total gonadotropin dose requirements and clinical IVF cycle outcomes (cycle cancellation, clinical pregnancy, and live birth) were compared between the three BMI groups. We performed multivariable analyses, adjusting for potential confounders such as age at cycle start, day 3 serum FSH level, smoking, presence of polycystic ovary syndrome, and duration of infertility.

RESULTS

There were no significant differences between the three BMI groups for any of the IVF cycle outcomes measured, including the total FSH dose required for ovarian stimulation. The likelihood of cycle cancellation, clinical pregnancy, and live birth were not significantly different between normal weight, overweight, and obese women.

CONCLUSION

Obese women did not require significantly higher doses of gonadotropins for ovarian stimulation than normal weight individuals. At our centre, female obesity did not significantly affect the clinical outcomes of IVF treatment. However, given the conflicting results of existing studies, the effect of maternal obesity on IVF outcomes remains unclear.

Distinct localisation of lipids in the ovarian follicular environment

Obesity is associated with decreased pregnancy rates due, in part, to compromised oocyte quality. The aim of the present cross-sectional study of 84 women undergoing oocyte aspiration was to: (1) compare insulin, lipids and glucose in follicular fluid with serum; (2) determine whether increased body mass index (BMI) and waist circumference, hyperinsulinaemia, dyslipidaemia or metabolic syndrome altered follicular fluid metabolites; and (3) determine relative lipid content in oocytes to reveal any influence of these parameters on oocyte quality and IVF outcomes. Insulin, glucose, triglyceride and free fatty acids were lower in follicular fluid than blood and not strictly correlated between compartments. Insulin, glucose and triglyceride positively correlated with increasing BMI and waist circumference in blood and follicular fluid. Insulin increased in follicular fluid in association with metabolic syndrome. Free fatty acid composition analysis showed saturated fatty acids, particularly palmitic and stearic acid, to be more prevalent in follicular fluid than blood. There were no associations between follicular fluid metabolites or oocyte lipid content and clinical outcomes; however, oocyte immaturity correlated with follicular fluid glucose and fatty acid levels, as well as metabolic syndrome. The present study confirms that the human ovarian follicular environment surrounding the oocyte exhibits a unique metabolite profile compared with blood, with distinct localisation of lipids within follicular fluid and oocytes.

To what extent does Anti-Mullerian Hormone contribute to a better prediction of live birth after IVF?

OBJECTIVE

We assessed the predictive value added by Anti-Mullerian Hormone (AMH) to currently validated live birth (LB) prediction models.

METHODS

Based on recent data from our center, we compared the external validity of the Templeton Model (TM) and its recent improvement (TMA) to select our model of reference. The added predictive value of AMH was assessed in testing the likelihood ratio significance and the Net Reclassification Index (NRI). The surrogate utility of AMH was tested by conducting an exploratory stepwise logistic regression.

RESULTS

Based on 715 cycles, the original TM had poor performances (auROC C = 0.61 [0.58, 0.66], improving by fitting TM to our data (C = 0.71[0.66, 0.75]. TMA fitting proved better (C = 0.76; 95 %CI: 0.71, 0.80) and was selected as model of reference. Adding AMH to TMA or TM had no effect on discrimination (C = 0.76; 95 %CI: 0.72, 0.80), the likelihood ratio test was significant (p = 0.023), but the NRI was not (6.7 %; p = 0.055). A stepwise exploratory logistic regression identified the effects of age, previous IVF resulting in LB, time trend and AMH, leading to a prediction model reduced to four predictors (C = 0.75 [0.70, 0.81]).

CONCLUSION

The added predictive value of AMH is limited. A possible surrogate/simplifying effect of AMH was found in eliminating 9/13 predictors from the model of reference. We conclude that whereas AMH does not add significant predictive value to the existing model, it contributes to simplifying the equation to reliable, easy to collect, and available in all databases predictors: age, AMH, time trend and female previous fertility history.

Body composition: A predictive factor of cycle fecundity

Objective

To study the effect of body composition on reproduction in women with unexplained infertility treated with a controlled ovarian hyperstimulation and intrauterine insemination programme.

Methods

This prospective observational study was conducted on 308 unexplained infertile women who were scheduled for a controlled ovarian hyperstimulation and intrauterine insemination programme and were grouped as pregnant and non-pregnant. Anthropometric measurements were performed using TANITA-420MA before the treatment cycle. Body composition was determined using a bioelectrical impedance analysis system.

Results

Body fat mass was significantly lower in pregnant women than in non-pregnant women (15.61±3.65 vs.18.78±5.97, respectively) (p=0.01). In a multiple regression analysis, body fat mass proved to have a stronger association with fecundity than the percentage of body fat, body mass index, or the waist/hip ratio (standardized regression coefficient≥0.277, t-value≥2.537; p<0.05). The cut-off value of fat mass, which was evaluated using the receiver operating characteristics curve, was 16.65 with a sensitivity of 61.8% and a specificity of 70.2%. Below this cut-off value, the odds of the pregnancy occurrence was found to be 2.5 times more likely.

Conclusion

Body fat mass can be predictive for pregnancy in patients with unexplained infertility scheduled for a controlled ovarian hyperstimulation and intrauterine insemination programme.

Anti-Müllerian hormone (AMH) is a strong predictor of live birth in women undergoing assisted reproductive technology

In the present study, we evaluated the clinical value of the following parameters: basal anti-Müllerian hormone (AMH), follicle-stimulating hormone (FSH), inhibin B and antral follicle count (AFC) in predicting live birth outcomes. The study involved 603 women undergoing in vitro fertilization (IVF) using the long protocol for controlled ovarian hyperstimulation (COH). Serum levels of AMH, FSH and inhibin B as well as AFC were measured on the first three days of the menstrual cycle prior to the beginning of stimulation. AMH was the only independent parameter that correlated with the chance of live birth. We found that live birth rates of 46.2% (patient age <35 years), 44.7% (35-37 years), 32.1% (38-39) and 15.3% (>39) were associated with concentrations of AMH>1.4 ng/ml. For the AMH range 0.6-1.4 ng/ml, the live birth rates were 29.3%, 12.5%, 5.6% and 2.7%, respectively, and for AMH concentrations below 0.6 ng/ml the rates were 7.1%, 8.3%, 0% and 5.8%, respectively. Independently of other parameters affecting the chance of live birth, the success rate was the highest when the AMH level was >2 ng/ml, significantly lower when the AMH concentration was about 1 ng/ml, and 0% when the AMH concentration was ∼0.1 ng/ml. In conclusion, this is the first report to demonstrate that AMH level correlated better than age, FSH or inhibin B concentrations or AFC with live birth outcome. Therefore, the basal serum concentration of AMH may become a new, substantial prognostic factor regarding live birth above and beyond other currently available predictors of IVF outcome.

The effects of being overweight and obese on female reproduction: a review

Obesity is a major international problem related to many reproductive health problems including polycystic ovary syndrome (PCOS). This article reviews the evidence of being overweight and its effect on female reproduction. The fecundity of obese women is lower than normal weight women, but there is no absolute consensus about the effect of obesity on infertility treatment. The obese patient might have oocyte, hormone, metabolic and endometrial dysfunction affecting reproduction. Insulin and leptin may be some of the answers explaining anovulation during obesity leading to infertility. Moreover, the follicular glucose and lipids which are important for oocyte development also increase in the obese patient and these might have an effect on oocyte quality because studies in mice have revealed that the obesity affects follicular cell stress and oocyte lipids. Overall, obesity affects female reproduction by disturbing the general body metabolism, hormone metabolism and the follicular environment.

Factors associated with failed treatment: an analysis of 121,744 women embarking on their first IVF cycles

Background

In-vitro fertilization (IVF) is the treatment of choice for unresolved infertility. It comprises a number of key steps, each of which has to be negotiated before the next is attempted, but the factors which are associated with failure at each stage have not been reported.

Methods and Findings

We analyzed anonymised national data on women undergoing their first fresh autologous IVF and intracytoplasmic sperm injection (ICSI) cycle in the United Kingdom between 2000 and 2007 to predict factors associated with overall lack of livebirth as well as the chance of non-progress at different stages of an IVF cycle. A total of 121,744 women were included in this analysis. Multivariable models underlined the importance of increased female age and duration of infertility, lack of previous pregnancy, and a diagnosis of tubal or male factor infertility in predicting the risk of not having a live birth in an IVF treatment. At each stage, a woman’s chance of proceeding to the next stage of IVF treatment is affected by increased age and duration of infertility. The intention to use intra-cytoplasmic sperm injection (ICSI) is associated with a decreased risk of treatment failure in women starting an IVF cycle (RR 0.93, 99% CI 0.92, 0.94) but this association is reversed at a later stage once fertilisation has been confirmed (RR=1.01, 99%CI 1.00, 1.03).

Conclusions

Female age is a key predictor of failure to have a livebirth following IVF as well as the risk of poor performance at each stage of treatment. While increased duration of infertility is also associated with worse outcomes at every stage, its impact appears to be less influential. Women embarking on ICSI treatment for male factor infertility have a lower chance of treatment failure but this does not appear to be due to increased chances of implantation of ICSI embryos.

Prediction models in in vitro fertilization; where are we? A mini review

Since the introduction of in vitro fertilization (IVF) in 1978, over five million babies have been born worldwide using IVF. Contrary to the perception of many, IVF does not guarantee success. Almost 50% of couples that start IVF will remain childless, even if they undergo multiple IVF cycles. The decision to start or pursue with IVF is challenging due to the high cost, the burden of the treatment, and the uncertain outcome. In optimal counseling on chances of a pregnancy with IVF, prediction models may play a role, since doctors are not able to correctly predict pregnancy chances. There are three phases of prediction model development: model derivation, model validation, and impact analysis. This review provides an overview on predictive factors in IVF, the available prediction models in IVF and provides key principles that can be used to critically appraise the literature on prediction models in IVF. We will address these points by the three phases of model development.

Liver X Receptors and female reproduction: when cholesterol meets fertility!

The role of cholesterol in female reproductive physiology has been suspected for a long time, while the molecular bases were unknown. Cholesterol is the precursor of ovarian steroid biosynthesis and is also essential for fertility. In the uterus, cholesterol is essential to achieve correct contractions at term, but an excessive uterine cholesterol concentration has been associated with contractility defects. Liver X Receptor (LXR) α and LXR β are nuclear receptors activated by oxysterols, oxidized derivatives of cholesterol. Since their discovery, the role of LXR in the control of cholesterol homeostasis has been widely described. Beyond their cholesterol-lowering role, more recent data have linked these nuclear receptors to various physiological processes. In particular, they control ovarian endocrine and exocrine functions, as well as uterine contractility. Their contribution to female reproductive cancers will also be discussed. This review will try to enlighten on the LXR as a molecular link between dietary cholesterol and reproductive diseases in women. In the future, a better comprehension of the various physiological processes regulated by the LXR will help to develop new ligands to prevent or to cure these pathologies in women.

Simple adaptations to the Templeton model for IVF outcome prediction make it current and clinically useful

STUDY QUESTION

What is the validity of the Templeton model (TM) in predicting live birth (LB) for a couple starting an IVF/ICSI cycle?

SUMMARY ANSWER

A centre-specific model based on the original predictors of the TM may reach a sufficient level of accuracy to be used in every day practice, with a few simple adaptations.

WHAT IS KNOWN AND WHAT THIS PAPER ADDS

The TM seems the best predictive model of LB in IVF. However, previous validations of the TM suggest a lack of discrimination and calibration which means that it is not used in regular practice. We confirm this finding, and argue that such results are predictable, and essentially due to a strong centre effect. We provide evidence that the TM constitutes a useful reference reflecting a high proportion of the patient-mix effect since the parameters of the model remain invariant among centres, but also across various cultures, countries and types of hospitals. The only difference was the intercept value, interpreted as the measurement of the global performance of one centre, in particular, for a population of reference.

STUDY DESIGN

The validity of the TM was tested by a retrospective analysis all IVF/ICSI cycles (n = 12 901) in our centre since 2000.

PARTICIPANTS, SETTING AND METHODS

All IVF/ICSI cycles were included in the analysis. The model discrimination was evaluated by C-statistics, calculated as the area under the curve of an ROC curve. The TM was then adjusted for our data and additional variables were assessed.

MAIN RESULTS AND THE ROLE OF CHANCE

Poor calibration and discrimination (C = 0.64) was observed in conformity with previous external validations. Fitting the TM to our centre constituted the first substantial improvement in prediction accuracy of discrimination (C = 0.69) and calibration. We identified an important linear time trend effect and the added value of three other predictors (FSH, smoking habits and BMI) that significantly improved the model (C = 0.71).

BIAS, CONFOUNDING AND OTHER REASONS FOR CAUTION

Bias due to missing data handling was assessed through sensitivity analyses.

GENERALIZABILITY TO OTHER POPULATIONS

Neither the TM nor any other models based on some centres are directly applicable to other centres. However, the TM constitutes a useful basis to build an accurate centre-specific model.

STUDY FUNDING/COMPETING INTEREST(S)

There were no commercial relationships (i.e. consultancies, patent-licensing agreements) that might pose a conflict of interest in connection with the submitted manuscript. The objective of this research was not directed toward any treatment effects.

Obesity and reproductive dysfunction in women

Overweight and obesity are significant and increasing health problems associated with increased risks of morbidity, quality of life, and metabolic and reproductive health consequences. In women, being overweight or obese is associated with impaired fertility and decreased chance of conception both in natural and assisted reproductive technology births. During pregnancy, overweight and obesity are associated with increased risk of adverse maternal and infant health outcomes. Attention to weight loss before conception may improve fertility and maternal and infant health outcomes during pregnancy.

[Obesity and female reproduction]

Weight, fat mass and obesity have been shown to play a major role in female reproduction. Obese women have a greater risk than nonobese women of infertility and they fail to become pregnant in both natural and assisted conception cycles. This cannot be explained only by their lack of ovulation. There are several potential mechanisms. On one hand, the endometrium seems to be partially responsible for this low fecundity in obese women. On the other hand, the oocyte seems to be implied. In a model of obese mouse, maternal obesity prior to conception is associated with altered mitochondria in mouse oocytes and an increased generation of reactive oxygen species (ROS). Furthermore, compared with controls, obese mice have significantly more decreased embryonic IGF-IR staining, smaller fetuses and smaller pups. In this model, all weaned pups have been fed with a regular diet. At 13 weeks, pups delivered from obese mice were significantly larger, and these pups demonstrated early development of a metabolic-type syndrome. These findings suggest that maternal obesity has adverse effects as early as the oocyte and preimplantation embryo stages and that these effects may contribute to lasting morbidity in offspring, underscoring the importance of optimal maternal weight and nutrition before conception.

Anti-Müllerian hormone-based prediction model for a live birth in assisted reproduction

Prediction of assisted reproduction treatment outcome has been the focus of clinical research for many years, with a variety of prognostic models describing the probability of an ongoing pregnancy or a live birth. This study assessed whether serum anti-Müllerian hormone (AMH) concentrations may be incorporated into a model to enhance the prediction of a live birth in women undergoing their first IVF cycle, by analysing a database containing clinical and laboratory information on IVF cycles carried out between 2005 and 2008 at the Mother-Infant Department of University Hospital, Modena. Logistic regression was used to examine the association of live birth with baseline patient characteristics. Only AMH and age were demonstrated in regression analysis to predict live birth, so a model solely based on these two criteria was generated. The model permitted the identification of live birth with a sensitivity of 79.2% and a specificity of only 44.2%. In the prediction of a live birth following IVF, a distinction, however moderate, can be made between couples with a good and a poor prognosis. The success of IVF was found to mainly depend on maternal age and serum AMH concentrations, one of the most relevant and valuable markers of ovarian reserve.

Predictive factors in in vitro fertilization (IVF): a systematic review and meta-analysis

BACKGROUND

Various models have been developed for the prediction of pregnancy after in vitro fertilization (IVF). These models differ from one another in the predictors they include. We performed a systematic review and meta-analysis to identify the most relevant predictors for success in IVF.

METHODS

We systematically searched MEDLINE and EMBASE for studies evaluating IVF/ICSI outcome. Studies were included if they reported an unconditional odds ratio (OR) or whenever one could be calculated for one or more of the following factors: age, type of infertility, indication, duration of infertility, basal FSH, number of oocytes, fertilization method, number of embryos transferred and embryo quality.

RESULTS

Fourteen studies were identified. A summary OR could be calculated for five factors. We found negative associations between pregnancy and female age [OR: 0.95, 95% confidence interval (CI): 0.94-0.96], duration of subfertility (OR: 0.99, 95% CI: 0.98-1.00) and basal FSH (OR: 0.94, 95% CI: 0.88-1.00). We found a positive association with number of oocytes (OR 1.04, 95% CI: 1.02-1.07). Better embryo quality was associated with higher pregnancy chances. No significant association was found for the type of infertility and fertilization method. A summary OR for IVF indication and number of embryos transferred could not be calculated, because studies reporting on these used different reference categories.

CONCLUSIONS

Female age, duration of subfertility, bFSH and number of oocytes, all reflecting ovarian function, are predictors of pregnancy after IVF. Better quality studies are necessary, especially studies that focus on embryo factors that are predictive of success in IVF.

Impact of overweight and underweight on IVF treatment in Chinese women

OBJECTIVE

To investigate the effects of body mass index (BMI) on ovarian stimulation and the outcome of IVF or ICSI treatment in Asian population who have different characteristics of BMI from white people.

DESIGN

In this retrospective study, the first fresh cycles of 1107 Chinese women were classified by BMI: underweight (BMI <18.5 kg/m(2)), normal weight (BMI: 18.5-23.9 kg/m(2)) and overweight (BMI >or= 24 kg/m(2)). Dose and days of ovarian simulation, cancelled cycles and number of oocytes collected, fertilisation and embryo utilisation rates and pregnancy outcome were compared among BMI groups.

RESULTS

It showed that overweight women required more ampoules of gonadotrophin (P = 0.002) and had lower peak oestradiol concentration (P = 0.001), increased cycle cancellation due to insufficient follicle development (P = 0.018) and a higher miscarriage rate (10.5 vs. 5.4%, P = 0.018, OR = 2.006 (95% CI: 1.09-3.69, P = 0.025)) compared with normal weight women. However, no differences were found in clinical pregnancy and live-birth rates. Underweight compared with normal weight women showed no differences in ovarian stimulation and IVF outcome although the clinical pregnancy rate was lower (31.1 vs. 37.3%).

CONCLUSION

At a lower cutoff point of BMI in Chinese women, overweight is associated with increased miscarriage risks and impaired response to ovarian stimulation after IVF and ICSI.

The adverse effects of obesity on conception and implantation

Whilst many multiparous women are obese (body mass index >30 kg/m(2)), obesity has been associated with impaired fecundity; however, the mechanism which links obesity to reduced fertility remains to be fully elucidated. Obese women, particularly those with central obesity, are less likely to conceive per cycle. Obese women suffer perturbations to the hypothalamic-pituitary-ovarian axis, menstrual cycle disturbance and are up to three times more likely to suffer oligo-/anovulation. A fine hormonal balance regulates follicular development and oocyte maturation, and it has been observed that obesity can alter the hormonal milieu. Leptin, a hormone produced by adipocytes, is elevated in obese women, and raised leptin has been associated with impaired fecundity. Obesity impairs ovulation but has also been observed to detrimentally affect endometrial development and implantation. The expression of polycystic ovary syndrome (PCOS) is regulated, in part, by weight, and so obese women with PCOS often have a more severe phenotype and experience more subfertility. Obesity also impairs the response of women to assisted conception treatments. Weight loss through lifestyle modification or bariatric surgery has been demonstrated to restore menstrual cyclicity and ovulation and improve the likelihood of conception. In this article, we will discuss the effect of obesity upon key reproductive mechanisms and its relation to fertility treatments.

Prediction models in reproductive medicine: a critical appraisal

BACKGROUND

Prediction models have been developed in reproductive medicine to help assess the chances of a treatment-(in)dependent pregnancy. Careful evaluation is needed before these models can be implemented in clinical practice.

METHODS

We systematically searched the literature for papers reporting prediction models in reproductive medicine for three strategies: expectant management, intrauterine insemination (IUI) or in vitro fertilization (IVF). We evaluated which phases of development these models had passed, distinguishing between (i) model derivation, (ii) internal and/or external validation, and (iii) impact analysis. We summarized their performance at external validation in terms of discrimination and calibration.

RESULTS

We identified 36 papers reporting on 29 prediction models. There were 9 models for the prediction of treatment-independent pregnancy, 3 for the prediction of pregnancy after IUI and 17 for the prediction of pregnancy after IVF. All of the models had completed the phase of model derivation. For six models, the validity of the model was assessed only in the population in which it was developed (internal validation). For eight models, the validity was assessed in populations other than the one in which the model was developed (external validation), and only three of these showed good performance. One model had reached the phase of impact analysis.

CONCLUSIONS

Currently, there are three models with good predictive performance. These models can be used reliably as a guide for making decisions about fertility treatment, in patients similar to the development population. The effects of using these models in patient care have to be further investigated.

Evidence that obesity alters the quality of oocytes and embryos

Infertility is more common in overweight and obese women, with reproductive impairments occurring at many levels of the hypothalamic-ovarian-uterine axis. These impairments lead primarily to longer times to conception and decreased pregnancy rates and have resulted in increasing numbers of overweight and obese women seeking assisted reproduction technologies, such as in vitro fertilization or IVF. Even after undertaking IVF procedures obese women have decreased pregnancy rates compared to moderate weight women, suggesting there may be intrinsic differences in the oocytes of these patients. Definitive data is lacking however, and thus the effect of obesity on oocyte quality remains one of the biggest controversies in reproductive medicine. This review summarizes the studies to date which have yielded information about the effects of obesity on human oocyte quality and pre-implantation embryo development. In addition recent results from our laboratory which clearly demonstrate that diet-induced obesity in mice impairs oocyte developmental competence are discussed.

Body mass index: impact on IVF success appears age-related

BACKGROUND

The objective of this study was to examine the effect of BMI on IVF outcomes.

METHODS

This was a retrospective analysis of all patients undergoing IVF from 1st January 2005 to 1st March 2006 in a large private practice using a single IVF laboratory. The patients underwent standard protocols for controlled ovarian hyperstimulation and embryology parameters. The main outcome measure was clinical pregnancy rate.

RESULTS

A total of 2167 fresh, non-donor IVF cycles were queried, but to minimize bias, only the first treatment cycle for each patient was analyzed (n = 1273). The data were examined by multiple regression models that included BMI and Age as main effects plus a BMI x Age interaction. When examined as a main effect, BMI did not appear to have a major effect on IVF outcome, but there was a significant BMI x Age interaction. At younger ages, a high BMI had a pronounced negative influence on fertility, but this effect diminished as the patient age increased. Clinical pregnancy rates decreased with increasing BMI and increasing Age.

CONCLUSIONS

In younger patients undergoing IVF, BMI has a significant negative impact on fertility that diminishes as patients reach their mid thirties. After Age 36, BMI has a minimal impact on fertility.

Effect of overweight and obesity on assisted reproductive technology—a systematic review

Obesity is known to be associated with sub-optimal reproductive performance but its direct effect on the outcome of assisted reproduction techniques (ART) is less clear. This present study aimed to perform a systematic review of the available evidence to assess the effects of obesity on the outcome of ART. A number of observational studies were identified. Interpretation of the results was compromised by variations in the methods used to define overweight and obese populations and inconsistencies in the choice and definition of outcome measures. Compared with women with a BMI of 25 kg/m(2) or less, women with a BMI > or = 25 kg/m(2) have a lower chance of pregnancy following IVF [odds ratio (OR) 0.71, 95% CI: 0.62, 0.81], require higher dose of gonadotrophins (weighed mean differences 210.08, 95% CI: 149.12, 271.05) and have an increased miscarriage rate (OR 1.33, 95% CI: 1.06, 1.68). There is insufficient evidence on the effect of BMI on live birth, cycle cancellation, oocyte recovery and ovarian hyperstimulation syndrome. Further studies with clear entry criteria and uniform reporting of outcomes are needed to investigate the true impact of weight on the outcome of ART.

Association of obesity with treatment outcomes in ovulatory infertile women undergoing superovulation and intrauterine insemination

OBJECTIVE

To evaluate the association between obesity and the outcome of superovulation and intrauterine insemination (IUI) in infertile ovulatory women.

DESIGN

Retrospective chart review.

SETTING

University-based infertility clinic.

PATIENT(S)

Three hundred thirty-three ovulatory women, grouped by body mass index (BMI) categories, who received superovulation and IUI for treatment of infertility.

INTERVENTION(S)

None.

MAIN OUTCOME MEASURE(S)

PRIMARY OUTCOME

cycle fecundity.

SECONDARY OUTCOMES

total dose of gonadotropins, serum level of E(2), and number of follicles >or=17 mm on the day of hCG injection.

RESULT(S)

Adjusted cycle fecundity was not different among BMI groups (underweight: 0.14 [95% CI: {0.07, 0.29}], normal weight: 0.12 [95% CI: {0.09, 0.16}], overweight: 0.17 [95% CI: {0.12, 0.24}], and obese: 0.14 [95% CI: {0.08, 0.23}]). Adjusted total gonadotropin dose (IU/cycle) was greater in obese women than in underweight or normal-weight women. Although the numbers of large follicles were not different, E(2) levels (pg/mL) were lower in obese women than in normal-weight and overweight women.

CONCLUSION(S)

Our sample of ovulatory infertile women demonstrated that treatment-related cycle fecundity is unaffected by obesity. We conclude that obese, infertile ovulatory women require a greater dose of gonadotropins to achieve similar levels of superovulation than normal, underweight or overweight women.

Role of lifestyle modification in the management of polycystic ovary syndrome

Overweight and obesity are common findings in polycystic ovary syndrome (PCOS). Obesity-particularly central obesity-is strongly indicated as a cause of insulin resistance, a central feature of PCOS. The prevalence of obesity is reaching epidemic proportions in many developed countries, and this increase is of particular concern in adolescent women. Obesity worsens both the metabolic and endocrine profiles in PCOS and may decrease the response to treatment. In the short term, weight reduction improves both metabolic and endocrine aspects of PCOS as well as clinical markers such as ovulation. However, extreme non-surgical efforts to lose weight are rarely successful and are associated with high rates of weight regain. Lifestyle modification with modest weight loss goals of 5-10% appear to be equally effective in restoring fertility and may be more compatible with long-term success; however, further research is needed.