Age and BMI as Major Factors Contributing to Follicular Fluid Oxidative/Inflammatory Biomarkers Levels and ART Outcome: A Cluster Analysis

Research Article

Austin J Reprod Med Infertil. 2022; 8(1): 1059.

Age and BMI as Major Factors Contributing to Follicular Fluid ‘Oxidative/Inflammatory Biomarkers Levels and ART Outcome: A Cluster Analysis

Costa L1,2, Oliveira P3, Oliveira JC4, Pires I2, Cabral M2, Figueiredo H2, Felgueira E2, Fonseca BM1* and Rebelo I1

1UCIBIO, REQUIMTE, Laboratório de Bioquímica, Departamento de Ciências Biológicas, Faculdade de Farmácia da Universidade do Porto, Portugal

2Unidade de Medicina da Reprodução Dra, Ingeborg Chaves, Centro Hospitalar de Vila Nova de Gaia/Espinho E.P.E, Portugal

3EPIUnit - Departamento de Estudo de Populações, Instituto de Ciências Biomédicas Abel Salazar, Universidade do Porto, Portugal

4Serviço de Química Clínica, Departamento de Patologia, Centro Hospitalar e Universitário do Porto, Portugal

*Corresponding author: Fonseca BM, Faculdade de Farmácia da Universidade do Porto, Departamento de Ciências Biológicas-Laboratório de Bioquímica, Rua de Jorge Viterbo Ferreira n.° 228, 4050-313 Porto, Portugal

Received: January 06, 2022; Accepted: February 15, 2022; Published: February 22, 2022


During the last decades, due to its incidence, infertility became a focus of study. Nowadays, it is estimated to affect between 8 and 12% of reproductiveage couples worldwide. Advanced maternal age and increased body mass index (BMI) were recognized as main factors responsible for the observed trend. However, it is still not clear which mechanisms underlie such evidence and whether the two factors interact. In this work, we combined data from serum hormone levels, follicular fluid biomarkers levels, patients’ intrinsic characteristics and IVF outcome from 225 patients enrolled in IVF cycles. Data were statistically analyzed, which naturally grouped patients into 4 different clusters, distinguishable by BMI and age. Here, we noted the impact of age mainly on follicular fluid biomarkers of oxidative status and of BMI on inflammation. A retrospective second analysis, based on the clusters resulted from the first one, included data from 904 IVF cycles, and the results confirmed the impact of age and obesity on IVF outcome. A logistic model revealed that unsuccess risk (defined as failure to achieve pregnancy after fresh embryo transfer) is 2.2 higher in older women (>35 years old), and 2.3 higher in obese women. There was no interaction effect between BMI and age, being the effects cumulative. Thus, although age cannot be changed, weight loss by itself may improve reproductive potential. Here, we confirm and reinforce the importance of maternal age and BMI for infertility and provide an up-to-date overview about the impact of these factors on female fertility.

Keywords: Fertility; Follicular fluid; Body Mass Index (BMI); Age; Assisted Reproductive Techniques


PCOS: Polycystic Ovary Syndrome; ART: Assisted Reproductive Technologies; BMI: Body Mass Index; E2: Estradiol; LH: Luteinizing Hormone; FSH: Follicle Stimulating Hormone; AMH: Anti-Müllerian Hormone; FF: Follicular Fluid; GCs: Granulosa Cells; IVF: In vitro Fertilization; CCO: Corona-Cumulus-Oocyte Complexes; FET: Fresh Embryo Transfer; ICSI: Intracytoplasmic Sperm Injection; ΒhCG: Human Chorionic Gonadotropin; CRP: C-Reactive Protein; TAS: Total Antioxidant Status; SOD: Superoxide Dismutase; AOPPs: Advanced Oxidation Protein Products; TH: Total Hydroperoxides


Infertility (and subfertility) is a global public health problem, estimated to affect between 8 and 12% of reproductive-aged couples worldwide [1,2], reaching almost 30% in the populations with higher prevalence [3]. The time of unwanted non-conception, female age and disease-related infertility are three major factors that influence the spontaneous conception [4,5]. Infertility can affect one or both elements of the couple [6]. Premature ovarian insufficiency/failure, polycystic ovary syndrome (PCOS), endometriosis, uterine fibroids and endometrial polyps are the most common causes of female infertility [6].

Despite enormous advances in Assisted Reproductive Technologies (ART), the success rates remain relatively low. Whereas much has been published about nonmodifiable risk factors associated with assisted reproductive outcome, such as female age and genetic factors [6,7], less attention has been devoted to modifiable behavioural risk factors that may also influence assisted reproductive outcome, such as smoking habits and Body Mass Index (BMI). Estradiol (E2), Luteinizing Hormone (LH), Follicle Stimulating Hormone (FSH) and, more recently, Anti-Müllerian Hormone (AMH) [8,9] are routinely measured to estimate female ovarian reserve and ovarian stimulation response, in order to program the more suitable stimulation protocol in ART. However, such measurements do not provide information about the oocyte potential to generate a good quality embryo, capable to implant and deliver a healthy new-born.

Follicular Fluid (FF) composition results from the contribution of blood plasma constituents, that cross the blood follicular barrier, and from Granulosa Cells (GCs) secretory activity [10]. Since FF provides the microenvironment for oocytes development [11-13], it represents an optimal source of non-invasive biochemical predictors of reproductive potential. Any dysregulation in FF composition can alter ovarian follicular dynamics and, thus, impair oocyte quality and fertility. Although the research in this area has progressed towards a more complex type of molecular analysis, no FF reliable biochemical predictors of oocyte quality have been determined so far, nor the main factors affecting FF composition.

In this work, we studied the routine parameters evaluated in the laboratory (E2, FSH, LH and AMH) together with biochemical parameters associated with antioxidant status and inflammatory response in FF. The main objective of this research study was to define groups with similar subjects, with respect to clinical and biochemical parameters by performing a cluster analysis. Therefore, we performed a statistical analysis to group similar observations into a number of clusters based on the observed values for each individual. Subsequently, i) a cluster analysis was performed using all the patient’s intrinsic, plasmatic and follicular fluid variables measured and ii) based on the cluster analysis results, we evaluated the clinical significance of the findings on ART outcome. This is an observational study with a retrospective analysis of past data.

Material and Methods

Patient recruitment

This study was approved by the Ethical Committee of the Hospital (Centro Hospitalar de Vila Nova de Gaia/Espinho, E.P.E) and by the National Data Protection Commission (authorization number 526/2017).

It was conducted in two samples (Group A and Group B) of women undergoing IVF at the Human Assisted Reproduction Unit Dra. Ingeborg Chaves, Centro Hospitalar de Vila Nova de Gaia/ Espinho, Portugal. Since the patients included in this study were enrolled in IVF at a Portuguese public hospital, due to the law, the maximum age allowed to be engaged in in vitro techniques was 40 years (excluded). Also, patients who underwent dual stimulation or fertility preservation cycles were excluded.

Group A consists of 225 patients enrolled in IVF cycles from March to December 2018. All female patients provided a written consent for the collection of their follicular fluid and cycle-related data, before entering the study.

Group B was used as a retrospective analysis of data belonging to 904 IVF cycles performed between January 2016 and August 2018.

As a result, there were some patients that were common to both groups A and B.

Controlled ovarian stimulation and follicular fluid sampling

Ovarian stimulation was performed accordingly to clinical evaluation. The dosage of gonadotrophins was based on the patient’s age, BMI, clinical history, early follicular phase serum AMH levels and antral follicle count. Follicular maturation was accessed by serial transvaginal ultrasound scan and estradiol measurements. When the follicles reached the appropriate number and size, final maturation was induced, and oocyte retrieval was performed transvaginally under ultrasound guidance and intravenous sedation.

During oocyte aspiration, FF was collected into tubes and emptied into petri dishes. Then, the aspirated fluid was examined under a stereomicroscope and an embryologist identified, isolated, and collected the Corona-Cumulus-Oocyte complexes (CCO) for IVF. Thereafter, the remaining FF was transferred to 50mL polypropylene tubes. FF that presented obvious blood contamination was rejected. These tubes were then kept at 37°C for a maximum of 2 hours and transported to the laboratory for sample processing. FF samples were centrifuged at 300g, for 10 min at 21°C and the supernatants were filtered (using 0.45μm filters), aliquoted and kept at -80°C until further analysis.

Evaluation of fertilization, embryo quality and IVF outcome

Approximately 18h after insemination (classical IVF or Intracytoplasmic Sperm Injection - ICSI) the embryologist checked for the presence of two pronucleus, to confirm oocyte fertilization. Embryo development was evaluated accordingly to cell number, size and presence of fragments or other structures.

Embryo transfer (1 or 2 embryos) occurred on day 3 or 5 of development. For this study, the number of good quality embryos corresponded to the number of transferred plus cryopreserved embryos. When Fresh Embryo Transfer (FET) was performed, 16 days after oocyte pick-up the patient performed a blood test to measure human Chorionic Gonadotropin (ΒhCG) to confirm pregnancy. After additional 15 days, the presence of an embryonic sack was confirmed by echography. After birth, delivery data were also recorded.

Serum and follicular fluid measurements

Early follicular phase serum LH, FSH, E2 and AMH were measured in the hospital laboratory. Concerning follicular fluid, quantification of C-reactive protein (CRP), Total Antioxidant Status (TAS), Superoxide Dismutase (SOD) and Glutathione were performed automatically using Randox commercial kits, following manufactures’ instructions. Advanced Oxidation Protein products (AOPPs) and Total Hydroperoxides (TH) were measured using inhouse spectrophotometry methods.

Retrospective analysis

To explore the clinical relevance of the groups that resulted from the cluster analysis, the data from 904 cycles from January 2016 to August 2018 were collected from FileMaker Pro 4.0 database. The collected data included basal serum FSH, LH, E2 and AMH levels; mean IVF attempt; infertility time; duration of ovarian stimulation; number of collected oocytes; percentage of mature (MII) oocytes; fertilization rate; number of two-pronuclear (2PN) zygotes; percentage of FET; reason for no FET; number of transferred embryos; implantation rate per FET; percentage of live birth per FET; number of newborns; weeks of gestation; and birth weight.

Statistical analysis

Cluster Analysis was carried so that groups with similar subjects, with respect to the study variables, could be defined. The different clusters were compared by an Analysis of Variance (ANOVA), and when the assumptions of normality or homogeneity of variance were not observed, by the non-parametric test of Kruskal-Wallis. Comparisons between groups were based on the Tukey HSD test or on the non-parametric Mann-Whitney test. In the case of categorical variables, Chi-square or Fisher´s exact test were used. A logistic regression model was developed taking as response variable the unsuccessful of the fertility treatment and independent variables age and BMI in classes. All analysis was carried out in IBM SPSS Statistic 25. Significance was assessed for p<0.05.


Group A: cluster analysis

The mean age of the studied population was 34.84 (3.46). 2.2% of women presented low BMI, 64.9% had normal BMI, 24.9% were overweight and 8% were obese. The reason for fertility treatment was exclusively female subfertility in 27.5% of the couples and 27.8% male-only infertility. A total of 17.5% suffered from an idiopathic unexplained infertility cause. There was no significant difference in success rates between IVF and ICSI treatments.

A total number of 225 IVF cycles were grouped into clusters using three sets of variables: i) BMI and age; ii) fertilization rate, percentage of mature oocytes, percentage of obtained quality embryos per oocyte; iii) all clinical and biochemical parameters. First, a hierarchical cluster analysis was performed for each set of variables to determine the number of clusters to be considered. From the analysis of the dendrograms, a number of clusters between 3 and 5 seemed to capture the different assemblage groups. Subsequently, for each set, a k-means cluster analysis was performed, using 3, 4 and 5 centroids. From this approach, it was observed that, when using the set with the variables BMI and age, the results were very similar to those obtained with the set using all variables. The clusters obtained based on the 3 variables that we considered to reflect the success of the treatment (fertilization rate, percentage of mature oocytes and percentage of obtained quality embryos per oocyte) did not lead to groups that distinguished themselves with respect to age and BMI, as well as to the variables measured in the follicular fluid.

The groups obtained considering two (BMI and age) and five variables (BMI, age, fertilization rate, percentage of mature oocytes and percentage of obtained quality embryos per oocyte) led to similar results regarding serum hormone measurements and treatment success variables. Therefore, we concluded that the two determining variables in the definition of clusters were BMI and age. Considering the importance of these two variables (BMI and age) on cluster definition, three sets of clusters were considered: patients grouped in three, four and five clusters. The different cluster arrangements were compared with respect to the observed biochemical parameters.

We observed that the analysis with three and four clusters produced nearly/almost identical results in serum measurements, with a significant difference for FSH using three clusters, and for FSH and AMH using four clusters. The same differences were observed using five clusters. Regarding follicular fluid measurements, the grouping into three or four clusters showed significant differences for AOPP, TAS and CRP. Due to these findings, we kept four clusters. In addition, the division into four clusters seems to have a natural meaning, distinguishing between a) higher and lower BMI and b) young and older women. Taking into account the classification in four clusters, patients are distinguished with the symbols “+” and “-” corresponding to a higher (+) / lower (-) BMI and to an older (+) or younger (-) age.

The main characteristics and patient distribution between clusters of group A sample are presented in Table 1. Concerning serum variables, a significant difference was found in AMH values between cluster 4 for clusters 1 and 3 and between cluster 2 for clusters 1 and 3. As expected, it seems that age is the factor that justifies this difference in AMH levels, with cluster 4 being the one with the highest value. For FSH, we also found significant differences between groups. Although cluster 4 presents a higher FSH level, there is only a significant difference between cluster 1 and 2. Thus, as in the case of AMH, age seems to be the differentiating factor. For E2 and LH serum variables, no significant differences were found between clusters.