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Revisiting the predictability of follicular fluid leptin and related adiposity measures for live birth in women scheduled for ICSI cycles: a prospective cohort study



Our research question is could follicular fluid (FF) leptin solely or contemporaneously with other clinical, biochemical, and sonographic adiposity measures predict the probability of having a live birth during ICSI cycles? This is a prospective cohort study that enrolled infertile women without polycystic ovary syndrome scheduled for ICSI. At baseline, women had an assessment of obesity using different metrics: clinical, serum biochemical, and sonographic. Clinical measures encompassed waist circumference and body mass index. Biochemical evaluation comprised an assessment of the homeostasis model for insulin resistance, visceral adiposity index, and lipid accumulation product. Preperitoneal and subcutaneous abdominal fat were measured using ultrasound and body fat index was calculated. On the day of oocyte retrieval, pooled FF was sampled to assess FF leptin. Our primary outcome was live birth after one fresh embryo transfer cycle.


Out of 91 women analyzed in this study, 28 have a live birth (30.8%). No difference in FF leptin concentration was found between women with and without live birth (mean ± SD; 20336 ± 8006 vs 18493 ± 6655 pg/ml; P = 0.2). None of the assessed adiposity markers was a predictor for live birth. Substantially, follicular fluid leptin was positively correlated with insulin resistance in women with and without live birth (r = 0.21, P = 0.04). In logistic regression analysis, the outcome of the prior cycle, the ability to have cryopreserved embryos, and the oocyte maturation index were the predictors for live birth in our study.


The present work could not find evidence that follicular fluid leptin, preperitoneal fat, and other evaluated adiposity measures could impact live births after ICSI cycles.


In reproductive-aged women, obesity has been linked, in addition to the metabolic health risks, to ovulatory dysfunction, menstrual irregularities, and suboptimal outcomes of variant fertility treatments [19, 40].

Clinical metrics utilized as crucial descriptors for obesity include body mass index (BMI) and waist circumference (WC). However, body mass index considers total body weight and height without referring to body fat or central (abdominal) obesity. Also, waist circumference, although could accurately identify women with central obesity, cannot precisely reflect preperitoneal fat [39].

Hence, different imaging tools for measuring preperitoneal fat have been proposed in reproductive-aged women. Ultrasound has been determined as a valid tool for the assessment of intraabdominal and preperitoneal fat compared to computerized tomography scan and magnetic resonance imaging [41, 48, 49]. Moreover, ultrasound is more familiar in the clinicians’ hands, inexpensive, and devoid of radiation hazards.

An earlier report demonstrated that preperitoneal fat thickness by ultrasound was superior to subcutaneous fat in predicting insulin resistance and other metabolic syndrome elements in the non-specific population [32].

At the level of the ovary, Ciavattini and colleagues found that increased preperitoneal fat as measured by ultrasound was associated with high reactive oxygen species in the follicular fluid and negatively correlated with oocyte and embryo quality [10]. Moreover, Obese anovulatory women with polycystic ovary syndrome (PCOS) who resume ovulation during a 6-month lifestyle program lost more visceral fat compared to the women who did not resume ovulation,despite similar subcutaneous fat loss in both groups [26].

Leptin is a product of the adipose tissue. It is a hormone that plays a key role in the regulation of the HPO axis to start puberty and maintain ovarian function [45].

In infertile women scheduled for assisted reproductive technology (ART), there are conflicting reports regarding the effect of follicular fluid (FF) leptin levels on IVF outcomes. Some suggested the deleterious effect of high leptin on embryo quality [38]. This controversy is evident in studies recruiting a non-PCOS infertile population. Heterogenous methodology and endpoints stand beyond these incongruent observations. These studies did not analyze or correlate the findings in relation to different adiposity measures such as the preperitoneal fat, body fat index, or visceral adiposity index. And, few of them reported a correlation with insulin resistance [27]. Neither body fat index (BFI) nor visceral adiposity index (VAI) have been assessed before in non-PCOS women for IVF.

The body fat index that compiled preperitoneal and subcutaneous fat in its calculation is a newly studied index in pregnant women as a predictability tool for gestational diabetes [6, 34, 46], while visceral adiposity index has been deemed to predict insulin resistance in PCOS women [37].

In the current study, the researches targeted infertile women without PCOS to revisit the hypothesis of whether FF leptin as an adiposity biomarker could predict intracytoplasmic sperm injection (ICSI) outcomes in these women. Moreover, we integrated other adiposity measures in our evaluation (clinical, biochemical as well as sonographic) that have been proposed to be leptin-related. The reason for excluding PCOS women is to avoid the confounding effect of the pathophysiological mechanisms of PCOS on the study outcomes.

Materials and methods

Study design and setting

Our study is a prospective cohort study, conducted at the Assisted Conception Unit, Department of Obstetrics and Gynecology, Women's Health Hospital, Assiut University, Egypt. The study was registered (NCT03778684, Recruitment was started in February 2019, and the study was completed on November 2022.

Study participants

Infertile women indicated for intracytoplasmic sperm injection (ICSI) were eligible for enrollment if they were non-PCOS, aged between 18 and 35 years, anticipated normal responders, and had normal uterine cavities by transvaginal ultrasound. Non-PCOS women scheduled for ICSI comprised women with anovulation, unexplained infertility, tubal disease, and male factor. Women with PCOS, those administering metformin, diabetic women, and poor responders based on Bologna criteria [16] were not included in the study. The Rotterdam European Society for Human Reproduction and Embryology (ESHRE)/American Society for Reproductive Medicine (ASRM) criteria were used to define PCOS [43]. Only one fresh ICSI transfer cycle for each participant was analyzed for the study outcomes.

Sample size calculation

Based on a study by Llaneza-Suarez and colleagues [27] who determined a mean FF leptin concentration of 16.8 ng/mL (SD ± 6.0 ng/ml), and 11.5 ng/ml (SD ± 4.6 ng/ml) in non-PCOS women without live birth and with live birth respectively, 50 women were required as a sample size at 85% study power, two-sided significance level of 0.05, and effect size of 0.88. Owing to the large effect size, we were willing to test the hypothesis at a modest effect size of 0.65 at the same power and significance level, so the concluded sample size was 88 non-PCO women. Sample size calculation was done using G-Power software program.

Evaluated adiposity measures

Clinical (BMI and WC), biochemical (VAI, lipid accumulation product, insulin resistance, and follicular fluid leptin), and sonographic (BFI) obesity-related parameters were assessed.

Clinical measures

They were evaluated at baseline and comprised waist circumference and body mass index. Waist circumference was measured at the end of expiration by a tape applied to the skin of the participant at a plane perpendicular to the midline and passing through a point just above the top of the iliac crest [36].

For body mass index (BMI), weight and height were measured while the participant was standing and wearing neither more than one layer of light clothes nor shoes.

Biochemical measures in serum and follicular fluid

Before starting any ovarian stimulation, serum samples were taken following an overnight fast for assessment of serum glucose, serum insulin, and serum lipoproteins. Serum Glucose level was measured in mmol/L using ADVIA 1800 Chemistry Auto-Analyzer, Siemens Healthineers, USA. Serum fasting Insulin was measured in µIU/mL using the Bioscience Human Insulin ELISA Kit (Catalog number: 10801). Serum triglycerides and high-density lipoprotein cholesterol (HDL-C) levels were measured using ADVIA 1800 Chemistry Auto-Analyzer, Siemens Healthineers, USA.

On the day of oocyte retrieval, follicular fluid pooled from large follicles; 17 mm or more, containing cumulus-oocyte complex was selected for sampling. Fluids containing debris and blood were excluded. They were centrifugated at 1500 rpm for 5 min, then stored at − 80 °C until leptin measurement was performed using SinoGeneClon ELISA Kit (Catalog number: SG-10057). Follicular fluid leptin determination was by pg/ml. All biochemical tests were performed in the laboratory of Women’s Health Hospital, Assiut University, Egypt.

Sonographic measures

We measured in this study the abdominal subcutaneous and preperitoneal fat utilizing the methodology validated in the literature using ultrasound [10, 20, 34, 41, 48, 49]. The maximum preperitoneal and the minimum subcutaneous fat were the target measurements. They were conducted by the same researcher (The third author: AAM) using a SONOACE R5 ultrasound machine with CN2-8 curved abdominal transducer (Samsung Medison Co., LTD). A 4-month duration of capacity building for the researcher was achieved before the study proposal submission to IRB through coupling with level 3 experience sonographer in order to ensure and maximize the quality of the scans.

Calculations and benchmarking

Body mass index was calculated as weight (kg) divided by the square of height (m2). According to the World Health Organization (WHO), BMI was categorized as normal (18.5–24. /m2), overweight (25–29.9 kg/m2), or obese (30 and above kg/m2) [54].

To identify women with central obesity, a waist circumference equal to or more than 80 cm was used according to the International Diabetes Federation (IDF) and the report of WHO Expert Consultation on Obesity [1, 52, 53].

The homeostasis-model assessment for insulin resistance (HOMA-IR) was calculated using the equation: fasting insulin (µIU/mL) × glucose (mmol/L))/22.5. Participants were designated to be insulin resistant if HOMA-IR was equal to or more than 2.5 [7, 31].

Nassr et al [34] were the first to conclude and report on the body fat index formula. We utilized the same formula to calculate that new adiposity marker. It was calculated by multiplying pre-peritoneal fat (mm) and subcutaneous fat (mm), then dividing the product by height (cm) [34].

Sex-specific equations were employed to calculate the visceral adiposity index and lipid accumulation product [2, 51].

Cycle management, ovarian stimulation protocol, and embryo transfer

For each woman, personal and fertility data were reported, and the indication for ICSI was affirmed. Preparation of the patients and selection of the ovarian stimulation protocol followed the standardized protocols in ICSI practice and were individualized based on each patient’s characteristics. The number of transferred embryos and the day of embryo transfer was not uniform for all enrolled patients; nevertheless, the same clinician conducted all transfers.

Study endpoints

The primary endpoint of the study was the probability of having a live birth (LB) per aspirated cycle as defined by the delivery of a live baby at 28 weeks of gestation or more. This is considered the standard definition in Egypt. We followed the standards of the Core Outcome Measure for Infertility Trials (COMMIT) initiative in reporting the primary and secondary endpoints [13]. The Time frame for reporting the outcomes was one fresh embryo transfer cycle. Enrolled women were contacted at the time of pregnancy test and estimated delivery date.

Statistical analysis

Statistical analysis was performed with the use of SPSS statistical package version 26.0 (IBM Corp, Armonk, NY, USA). The Kolmogorov-Smirnov test was used to determine data distribution. Normally distributed data are presented as mean (SD) however, abnormally distributed variables are presented as median (interquartile range (IQR)). Comparisons were conducted between women with and without live birth. Also, comparisons were done between women with and without central obesity as well as among the common three indications of ICSI in our cohort; unexplained, male, and other factors in order to explore if there was any hidden effect of the indication of ICSI on the study variables and outcomes. Other factors encompass tubal disease, endometriosis, anovulation, and combined factors. Based on the comparisons, when appropriate, means were compared with the use of Student’s t test or one-way ANOVA, and medians of non-parametric variables were compared utilizing the Mann-Whitney U test or Kruskal-Wallis test. Correlation analysis was conducted to determine the correlation between the adiposity measures and ICSI cycle variables and outcomes. Predictive models were constructed using regression and receiver operating characteristic (ROC) curve analyses to evaluate the predictability of adiposity measures for cycle outcomes. P value of < 0.05 was considered to be statistically significant.


Ninety-one women were enrolled and completed the study. Their median (IQR) age was 30 (7) years with 54.9% of the cohort (n = 50) in their 30s. In our cohort, the common indications for ICSI were male and unexplained factors in 41.8% (n = 38) and 36.3% (n = 33) of women respectively. Other indications of ICSI included tubal (9.9%), anovulatory (4.4%), endometriosis (1.1%), and combined (6.6%) factors. History of ICSI was found in 25 (27.5%) women while the remainder were undergoing their first ICSI cycle. Antagonist protocol was chosen for about two-thirds of the participants (n = 60; 65.9%). Eleven women (12.1%) had normal BMI while 33 (36.3%) and 47 (51.6%) were overweight and obese respectively. Based on waist circumference 52 (57.1%) women had central obesity.

The total pregnancies were 31 (34.1%) ended in 28 live births: 25 term and 3 preterm births. There was one first-trimester miscarriage, one second-trimester miscarriage, and one ectopic pregnancy. Of live births, 11 cases were multiple pregnancies.

Based on the primary endpoint (live birth), comparisons between women with (n = 28) and without live birth (n = 63) are shown in Tables 1 and 2. Neither follicular fluid leptin concentration nor other adiposity measures were different between women with and without live birth.

Table 1 Baseline characteristics and adiposity measures of women with and without LB
Table 2 ICSI Cycle characteristics and outcomes in women with and without LB

In Receiver Operating characteristic curve (ROC) and logistic regression analyses, none of the evaluated adiposity measures (WC, BMI, BFI, VAI, LAP, HOMA-IR, and FF Leptin) in our study was a predictor for having a live birth. The area under the curve for WC, BMI, BFI, VAI, LAP, HOMA-IR, and Follicular fluid leptin was 0.57, 0.54, 0.55, 0.51, 0.53, 0.53, and 0.56, respectively.

Stepwise multivariable logistic regression analysis, showed that the outcome of the prior cycle, ability to have cryopreserved embryos, and the oocyte maturation index were the predictors for having live birth in our study (Table 3).

Table 3 Predictability of cycle parameters for having a live birth

Investigating the relation among adiposity markers indicated that follicular fluid leptin was only correlated with HOMA-IR (Spearman’s correlation coefficient r = 0.21, P = 0.04) (Fig. 1). This correlation is corroborated by the finding that HOMA-IR tends to be higher with increasing leptin tertile (P = 0.047) when categorizing leptin values of the studied cohort into 3 tertiles: the first tertile is < 15980.7 (n = 31 women), the second tertile is 15980.7 to 22130.9 (n = 30 women), and the third tertile is > 22130.9 (n = 30 women).

Fig. 1
figure 1

Scatter plot showing positive correlation between HOMA-IR and follicular fluid leptin (r = 0.21, P = 0.04). HOMA-IR: homeostasis-model assessment for insulin resistance

Loess regression with Epanechnikov kernel fitting was done to demonstrate the interaction of follicular fluid leptin and HOMA-IR on live birth (Fig. 2). The scatter plot indicates that leptin increases with increased insulin resistance both in women with and without live birth. However, surprisingly in both groups, this positive correlation was lost or even reversed when HOMA-IR approached 5 or more.

Fig. 2
figure 2

Loess fitting scatter plot showing the interaction of HOMA-IR and follicular fluid leptin on LB. HOMA-IR: homeostasis-model assessment for insulin resistance

Comparing women with central obesity to their counterparts shows that they were obese and overweight, respectively (median (IQR) 32 (6.9) vs 27.1 (6); P < 0.001). Both groups were comparable regarding insulin resistance, follicular fluid leptin concentration, ICSI cycle characteristics, and outcomes. Miscarriage cases occurred in women without central obesity while all preterm deliveries were reported in central obesity women. However, indeed, we found central obesity women more likely to have higher BFI (p < 0.001), VAI (p < 0.001), and LAP (p < 0.001) (Supplemental Tables S1 and S2).

Unplanned subgroup post-hoc analysis according to the indication of ICSI showed that women in the male factor group had the lowest BMI (p = 0.04), and through borderline significance; the least preperitoneal fat thickness (p = 0.05), and the lowest fertilization rate (p = 0.06). All canceled transfers (n = 4) were also in the male factor group. Yet, follicular fluid leptin, the rest of the adiposity markers, and cycle parameters as well as outcomes did not differ among women in this subgroup analysis (Supplemental Tables S3 and S4).


Main findings

Employing a prospective cohort study design, this study examined the follicular fluid leptin, along with a group of feasible clinical, biochemical, and sonographic adiposity markers as predictors of ICSI cycle outcomes in infertile non-PCOS women. Follicular fluid leptin was the basis for the power analysis of the sample size. According to BMI, only 12.1% of women had normal BMI while the remainder were overweight and obese. None of the tested parameters (including follicular fluid leptin levels) could predict a live birth. Insulin resistance was the only adiposity marker that has been positively correlated to follicular fluid leptin.


Only FF leptin and insulin resistance were tested before in women without polycystic ovary syndrome. In the present work, we tested multiple markers that never were evaluated before in such a patient cohort particularly; preperitoneal fat, body fat index, and visceral adiposity index. Multiplicity of the evaluated markers was to seek explanations, detect superiority, and build combined predictors, in case of significance. The concept of testing multiple variants of predictors in overweight and obese women comes from observations that the sole utility of leptin is not adequate to construct the predictive model. Leptin to body mass index ratio has been reported to be superior to leptin alone as an IVF outcome predictor [8].

A dearth of literature reported live birth as a primary endpoint for the impact of FF leptin on ICSI. We followed our participants till delivery to reflect on live births concurring with the published standards for reporting infertility trials  [13, 21].


There are limitations that should be pointed out. First, the underrepresentation of normal BMI women in the enrolled cohort, which could affect the generalizability of conclusions and mitigate the discriminative threshold of the evaluated indices.

Second, follicular fluid sampling like other studies presents a limitation [24]. We used pooled follicular fluid which is still better than sampling just the first follicle. The ideal is to sample fluid from each follicle to conduct follicle-to-embryo tracking and sibling oocyte cohort analysis. However, it is difficult from the implementation point of view. Third, the study was powered for the detection of leptin predictability but not for other tested parameters. The lack of comparable studies with similar study designs presents a challenge in proposing assumptions during sample size calculation. Thus, the conclusions for the other parameters should be taken with caution. Fourth, although excluding PCOS women was justifiable, the performance of these indices to predict cycle outcomes in PCOS women remains elusive. Lastly, if we followed the outcomes after 2 or more embryo transfers, a difference in the outcomes would be disclosed.

Comment on the study adiposity measures

Leptin is a neuroendocrinal protein that exists in excess in obese women. Integrating follicular fluid leptin in our study is rationalized by the following: first, leptin is a reflection of the oocyte microenvironment; second, leptin receptors and m-RNA are expressed in granulosa cells [28], oocytes [4, 11] and pre-implantation embryos [4],third, leptin is depicted to regulate ovarian steroidogenesis, follicular growth and apoptosis, and oocyte maturation [12, 47], Fourth: controversial reports exist for the impact of FF leptin on cycle outcomes in the studied infertile cohort [3, 24, 30].

Our results concur with the results of a recent metanalysis evaluating [24] 11 observational studies (266 pregnant and 552 non-pregnant cases). This metanalysis indicated that pregnancy was unrelated to leptin levels in the follicular fluid. Highlighting the dilemma of methodological heterogenicity in follicular fluid sampling and cycle outcome reporting, different groups of researchers conveyed favorable cycle outcomes with low follicular fluid leptin concentrations. Mantzoros et al. [30] sampled fluid from the first aspiration of the dominant follicle and demonstrated low FF leptin in pregnant women (11.9 vs 17 ng/ml) within three ART cycles. However, this could bias their conclusions as comparable follicular fluid microenvironment among all follicles cannot be guaranteed. Another study by Anifandis et al. [3] did not explore the sampling details of follicular fluid and found women with peak estradiol range 1001–2000 (n = 53) yielded the highest pregnancy rate (35.8%) and had the lowest follicular fluid concentration (52 ng/ml). However, they did not test the predictability of follicular fluid leptin for clinical pregnancy after adjustment of peak serum estradiol. A study reporting live birth as an outcome found FF leptin sampled from the first dominant follicle predicted live birth at a concentration of 16 ng/ml with sensitivity and specificity of 78.3% and 54.2%, respectively [27]. Another group has addressed higher sensitivity and specificity of similar leptin concertation for the oocyte maturation when follicular fluid was pooled from the largest 3 follicles [23].

Body mass index has been the benchmarking for decades in IVF/ICSI daily practice in pre-IVF/ICSI preparation of obese/overweight infertile women to achieve the optimum outcomes for the service stakeholders [15, 17]. The contradictory conclusions about the effect of body mass index and weight reduction on IVF/ICSI outcomes [14, 25, 33, 42, 44, 50] should encourage the clinicians to test other or new feasible adiposity predictors for the cycle outcomes such as visceral adiposity and body fat indices.

Obesity and abdominal preperitoneal fat alter the metabolic milieu of follicular fluid in IVF women by increasing lipid peroxides and reducing total antioxidant capacity [5, 10, 22, 35]. Reflection of the preperitoneal fat on FF leptin in not been addressed before. In our results, neither preperitoneal fat nor the new body fat index improved the predictability of FF leptin for cycle outcomes. Body fat index has been evaluated before in pregnant women and revealed to predict gestational diabetes at cut-offs of 0.5 and 0.88 [6, 34, 46].

The concept of preperitoneal fat measurement is warranted by evidence that it is a source of multiple inflammatory mediators contributing to insulin resistance and reproductive dysfunction [18, 29, 55]. The correlation between insulin resistance and leptin found in our study is observed in other studies [27] and is supported by the fact that insulin stimulates leptin secretion [9]. This correlation was unrelated to live birth in our women. Moreover, surprisingly, the increase in leptin with insulin resistance was lost at higher insulin resistance levels denoting that a phenomenon of desensitization could be exhibited by leptin receptors at high insulin resistance levels. Further research is needed to elucidate this finding.

According to the reference ‘The Vienna consensus: report of an expert meeting on the development of art laboratory performance indicators’ the benchmarking range for maturation index is 75–90%. Although statistically significant between women with and without live birth, the maturation index of both groups is still reaching the benchmarking range (84.7% in women with LB and 76.4% in women without LB; as shown in Table 2). Conducting the comparisons based on central obesity and ICSI indication, still all subgroups achieved a maturation index within the benchmarking range (as shown in Supplemental Tables S2 and S4)

For the fertilization index based on the Vienna consensus, the competency value should be ≥ 65, and the benchmark value should be ≥ 80. In the current study, the fertilization index is 75% and 66.7% in women with and without LB, respectively. We are above the competency value, however, we did not reach the benchmark value. The lowering of the overall fertilization rate could be attributed to including the male factor in the study that seemed to be of poor prognosis and achieved the lowest fertilization rate among the groups (63.6%) (below the competency value) (Supplemental Table S4).


The current work limited the inference to only non-PCOS women and could not find evidence that a correlation exists between follicular fluid leptin and live birth after ICSI cycles. Furthermore, prediction for live birth after IVF, could not be demonstrated when any of the tested parameters (clinical, biochemical, preperitoneal fat, and BFI) was used. Further research should be warranted to evaluate the plausibility of these indices in all BMI categories of women without PCOS. Further research that includes another arm of PCOS in the evaluation of variant adiposity markers in ART women is needed.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.


  1. Alberti KG, Zimmet P, Shaw J (2007) International Diabetes Federation: a consensus on Type 2 diabetes prevention. Diabetic Med 24(5):451–63

    Article  CAS  PubMed  Google Scholar 

  2. Amato MC, Giordano C, Galia M, Criscimanna A, Vitabile S, Midiri M, Galluzzo A, AlkaMeSy Study Group (2010) Visceral Adiposity Index: a reliable indicator of visceral fat function associated with cardiometabolic risk. Diabetes Care. 33(4):920–2

    Article  PubMed  PubMed Central  Google Scholar 

  3. Anifandis G, Koutselini E, Louridas K, Liakopoulos V, Leivaditis K, Mantzavinos T, Sioutopoulou D, Vamvakopoulos N (2005) Estradiol and leptin as conditional prognostic IVF markers. Reproduction 129(4):531–534

    Article  CAS  PubMed  Google Scholar 

  4. Antczak M, Van Blerkom J (1997) Oocyte influences on early development: the regulatory proteins leptin and STAT3 are polarized in mouse and human oocytes and differentially distributed within the cells of the preimplantation stage embryo. Mol Human Reprod 3(12):1067–86

    Article  CAS  Google Scholar 

  5. Bacchetti T, Morresi C, Vignini A, Tiano L, Orlando P, Montik N, Ciavattini A, Ferretti G (2019) HDL functionality in follicular fluid in normal-weight and obese women undergoing assisted reproductive treatment. J Assisted Reprod Genet 36:1657–64

    Article  Google Scholar 

  6. Benchahong, S., Sunsaneevithayakul, P., Boriboonhirunsarn, D. The association between body fat index and gestational diabetes mellitus: a prospective cohort study. Cureus. 2023; 15(5).

  7. Bo S, Musso G, Gambino R, Villois P, Gentile L, Durazzo M, Cavallo-Perin P, Cassader M (2012) Prognostic implications for insulin-sensitive and insulin-resistant normal-weight and obese individuals from a population-based cohort. Am J Clin Nutr 96(5):962–9

    Article  CAS  PubMed  Google Scholar 

  8. Brannian JD, Schmidt SM, Kreger DO, Hansen KA (2001) Baseline non-fasting serum leptin concentration to body mass index ratio is predictive of IVF outcomes. Hum Reprod 16(9):1819–26

    Article  CAS  PubMed  Google Scholar 

  9. Catteau A, Caillon H, Barrière P, Denis MG, Masson D, Fréour T (2016) Leptin and its potential interest in assisted reproduction cycles. Hum Reprod Update 22(3):320–341

    Article  CAS  PubMed  Google Scholar 

  10. Ciavattini A, Montik N, Clemente N, Santoni F, Moriconi L, Serri M, Barbadoro P, Sabbatinelli J, Vignini A (2017) Obesity and ultrasound-estimated visceral fat deposits in women undergoing assisted reproductive technology (ART) procedures. Gynecol Endocrinol 33(12):972–6

    Article  CAS  PubMed  Google Scholar 

  11. Cioffi JA, Van Blerkom J, Antczak M, Shafer A, Wittmer S, Snodgrass HR (1997) The expression of leptin and its receptors in pre-ovulatory human follicles. Mol Hum Reprod 3(6):467–72

    Article  CAS  PubMed  Google Scholar 

  12. Craig J, Zhu H, Dyce PW, Petrik J, Li J (2004) Leptin enhances oocyte nuclear and cytoplasmic maturation via the mitogen-activated protein kinase pathway. Endocrinology 145(11):5355–5363

    Article  CAS  PubMed  Google Scholar 

  13. Duffy JM, AlAhwany H, Bhattacharya S, Collura B, Curtis C, Evers JL, Farquharson RG, Franik S, Giudice LC, Khalaf Y, Knijnenburg JM (2020) Developing a core outcome set for future infertility research: an international consensus development study. Hum Reprod 35(12):2725–34

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Einarsson S, Bergh C, Friberg B, Pinborg A, Klajnbard A, Karlström PO, Kluge L, Larsson I, Loft A, Mikkelsen-Englund AL, Stenlöf K (2017) Weight reduction intervention for obese infertile women prior to IVF: a randomized controlled trial. Hum Reprod 32(8):1621–1630

    Article  PubMed  Google Scholar 

  15. ESHRE Guideline Group on Ovarian Stimulation, Bosch, E., Broer, S., Griesinger, G., Grynberg, M., Humaidan, P., Kolibianakis, E., Kunicki, M., La Marca, A., Lainas, G., Le Clef, N. ESHRE guideline: ovarian stimulation for IVF/ICSI. Hum Reprod Open. 2020; 2020(2): hoaa009.

  16. Ferraretti A, La Marca A, Fauser BCJM, Tarlatzis B, Nargund G, Gianaroli L (2011) ESHRE consensus on the definition of “poor response” to ovarian stimulation for in vitro fertilization: the Bologna criteria. Hum Reprod 26(7):1616–1624

    Article  CAS  PubMed  Google Scholar 

  17. Fertility NICE (2013) Assessment and treatment for people with fertility problems. National Institute for Health and Care Excellence, London

    Google Scholar 

  18. Fischer-Posovszky P, Wabitsch M, Hochberg Z (2007) Endocrinology of adipose tissue-an update. Hormone Metab Res 39(05):314–21

    Article  CAS  Google Scholar 

  19. Gonzalez MB, Robker RL, Rose RD (2022) Obesity and oocyte quality: significant implications for ART and emerging mechanistic insights. Biol Reprod 106(2):338–50

    Article  PubMed  Google Scholar 

  20. Hamagawa K, Matsumura Y, Kubo T, Hayato K, Okawa M, Tanioka K, Yamasaki N, Kitaoka H, Yabe T, Nishinaga M, Doi YL (2010) Abdominal visceral fat thickness measured by ultrasonography predicts the presence and severity of coronary artery disease. Ultrasound Med Biol 36(11):1769–75

    Article  PubMed  Google Scholar 

  21. Harbin Consensus Conference Workshop Group (2014) Improving the reporting of clinical trials of infertility treatments (IMPRINT): modifying the CONSORT statement. Hum Reprod 29:2075–2082

    Article  Google Scholar 

  22. Hauck AK, Bernlohr DA (2016) Oxidative stress and lipotoxicity. J Lipid Res 57(11):1976–1986

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Hong KJ, Lin JJ, Lin LH, Lai TH (2022) The intrafollicular concentration of leptin as a potential biomarker to predict oocyte maturity in in-vitro fertilization. Sci Rep 12(1):19573

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Jafarpour S, Khosravi S, Janghorbani M, Mansourian M, Karimi R, Ghiasi MR, Miraghajani M, Symonds ME, Farajzadeghan Z, Salehi R. Association, of serum and follicular fluid leptin and in vitro Fertilization, ICSI outcome, (2021) a systematic review and meta-analysis. J Gynecol Obstet Hum Reprod 50(6):101924

    Article  PubMed  Google Scholar 

  25. Kim J, Patounakis G, Juneau C, Morin S, Neal S, Bergh P, Seli E, Scott R (2021) The Appraisal of Body Content (ABC) trial: increased male or female adiposity does not significantly impact in vitro fertilization laboratory or clinical outcomes. Fertil Steril 116(2):444–452

    Article  PubMed  Google Scholar 

  26. Kuchenbecker WK, Groen H, van Asselt SJ, Bolster JH, Zwerver J, Slart RH, Vd Jagt EJ, Muller Kobold AC, Wolffenbuttel BH, Land JA, Hoek A (2011) In women with polycystic ovary syndrome and obesity, loss of intra-abdominal fat is associated with resumption of ovulation. Hum Reprod 26(9):2505–2512

    Article  PubMed  Google Scholar 

  27. Llaneza-Suarez, D., Llaneza, P., Gonza´lez, C., De-La-Fuente, P., Garcı´a-Ochoa, C., Garrido, P., Castan˜o´n, V., Pe´rez-Lo´pez, F.R. Assessment of follicular fluid leptin levels and insulin resistance as outcome predictors in women undergoing in vitro fertilization intracytoplasmic sperm injection. Fertil Steril. 2014; 102:1619 –1625

  28. Löffler S, Aust G, Köhler U, Spanel-Borowski K (2001) Evidence of leptin expression in normal and polycystic human ovaries. Mol Hum Reprod 7(12):1143–9

    Article  PubMed  Google Scholar 

  29. Lumeng CN, Saltiel AR (2011) Inflammatory links between obesity and metabolic disease. J Clin Investig 121(6):2111–7

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Mantzoros CS, Cramer DW, Liberman RF, Barbieri LR (2000) Predictive value of serum and follicular fluid leptin concentrations during assisted reproductive cycles in normal women and in women with the polycystic ovarian syndrome. Hum Reprod 15(3):539–44

    Article  CAS  PubMed  Google Scholar 

  31. Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC (1985) Homeostasis model assessment: insulin resistance and beta cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28:412–9

    Article  CAS  PubMed  Google Scholar 

  32. Meriño-Ibarra E, Artieda M, Cenarro A, Goicoechea J, Calvo L, Guallar A, Civeira F (2005) Ultrasonography for the evaluation of visceral fat and the metabolic syndrome. Metabolism 54(9):1230–1235

    Article  PubMed  Google Scholar 

  33. Mutsaerts MA, Van Oers AM, Groen H, Burggraaff JM, Kuchenbecker WK, Perquin DA, Koks CA, Van Golde R, Kaaijk EM, Schierbeek JM, Oosterhuis GJ (2016) Randomized trial of a lifestyle program in obese infertile women. N Engl J Med 374(20):942–1953

    Article  Google Scholar 

  34. Nassr AA, Shazly SA, Trinidad MC, El-Nashar SA, Marroquin AM, Brost BC (2018) Body fat index: a novel alternative to body mass index for prediction of gestational diabetes and hypertensive disorders in pregnancy. Eur J Obstet Gynecol Reprod Biol 228:243–248

    Article  PubMed  Google Scholar 

  35. Nasiri N, Moini A, Eftekhari-Yazdi P, Karimian L, Salman-Yazdi R, Zolfaghari Z, Arabipoor A (2015) Abdominal obesity can induce both systemic and follicular fluid oxidative stress independent from polycystic ovary syndrome. Eur J Obstet Gynecol Reprod Biol 184:112–116

    Article  CAS  PubMed  Google Scholar 

  36. National Institutes of Health, The Practical Guide Identification, Evaluation, and Treatment of Overweight and Obesity in Adults, 2000

  37. Oh JY, Sung YA, Lee HJ (2013) The visceral adiposity index as a predictor of insulin resistance in young women with polycystic ovary syndrome. Obesity 21(8):1690–1694

    Article  CAS  PubMed  Google Scholar 

  38. Polyzos NP, Ayoubi JM, Pirtea P (2022) General infertility workup in times of high assisted reproductive technology efficacy. Fertil Steril 118(1):8–18

    Article  PubMed  Google Scholar 

  39. Ponti F, De Cinque A, Fazio N, Napoli A, Guglielmi G, Bazzocchi A (2020) Ultrasound imaging, a stethoscope for body composition assessment. Quant Imaging Med Surg 10(8):1699–1722

    Article  PubMed  PubMed Central  Google Scholar 

  40. Practice Committee of the American Society for Reproductive Medicine. Obesity and reproduction: a committee opinion. Fertil Steril. 2021;116(5):1266-1285. Epub 2021 Sep 25. PMID: 34583840.

  41. Ribeiro-Filho FF, Faria AN, Kohlmann O Jr, Ajzen S, Ribeiro AB, Zanella MT, Ferreira SR (2001) Ultrasonography for the evaluation of visceral fat and cardiovascular risk. Hypertension 38(3):713–717

    Article  CAS  PubMed  Google Scholar 

  42. Rittenberg V, Seshadri S, Sunkara SK, Sobaleva S, Oteng-Ntim E, El-Toukhy T (2011) Effect of body mass index on IVF treatment outcome: an updated systematic review and meta-analysis. Reprod Biomed Online 23(4):421–439

    Article  PubMed  Google Scholar 

  43. Rotterdam ESHRE/ASRM-Sponsored PCOS consensus workshop group (2004) Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome (PCOS). Hum Reprod 19(1):41–7. (PMID: 14688154)

    Article  Google Scholar 

  44. Sermondade N, Huberlant S, Bourhis-Lefebvre V, Arbo E, Gallot V, Colombani M, Fréour T (2019) Female obesity is negatively associated with live birth rate following IVF: a systematic review and meta-analysis. Hum Reprod Update 25(4):439–451

    Article  CAS  PubMed  Google Scholar 

  45. Silvestris E, De Pergola G, Rosania R, Loverro G (2018) Obesity as disruptor of the female fertility. Reprod Biol Endocrinol 16:1–13

    Article  Google Scholar 

  46. Singh, D., Mittal, P., Bachani, S., Mukherjee, B., Mittal, M.K., Suri, J. Ultrasonographic Assessment of Body Fat Index for Prediction of Gestational Diabetes Mellitus and neonatal complications. J Obstet Gynaecol Can. 2023; S1701-2163(23)00449-8. Epub ahead of print. PMID: 37437777.

  47. Sirotkin, A.V., Mlynček, M., Makarevick, A.V., Florkovičová, I., Hetényi, L. Leptin affects proliferation-, apoptosis-and protein kinase A-related peptides in human ovarian granulosa cells. Physiological Research. 2008; 57(3).

  48. Stolk RP, Wink O, Zelissen PMJ, Meijer R, Van Gils APG, Grobbee DE (2001) Validity and reproducibility of ultrasonography for the measurement of intra-abdominal adipose tissue. Int J Obes 25(9):1346–1351

    Article  CAS  Google Scholar 

  49. Suzuki R, Watanabe S, Hirai Y, Akiyama K, Nishide T, Matsushima Y, Murayama H, Ohshima H, Shinomiya M, Shirai K, Saito Y (1993) Abdominal wall fat index, estimated by ultrasonography, for assessment of the ratio of visceral fat to subcutaneous fat in the abdomen. Am J Med 95(3):309–314

    Article  CAS  PubMed  Google Scholar 

  50. Tang K, Guo Y, Wu L, Luo Y, Gong B, Feng L (2021) A non-linear dose-response relation of female body mass index and in vitro fertilization outcomes. J Assist Reprod Genet 38:931–939

    Article  PubMed  PubMed Central  Google Scholar 

  51. Taverna MJ, Martínez-Larrad MT, Frechtel GD, Serrano-Ríos M (2011) Lipid accumulation product: a powerful marker of metabolic syndrome in healthy population. Eur J Endocrinol 164(4):559–67

    Article  CAS  PubMed  Google Scholar 

  52. World Health Organization. Obesity: preventing and managing the global epidemic: report of a WHO consultation. (TRS 894). Geneva, World Health Organization (WHO), 2000

  53. World Health Organization (2011) Waist circumference and waist-hip ratio: report of a WHO expert consultation. World Health Organization, Geneva

    Google Scholar 

  54. World Health Organization. Global status report on noncommunicable diseases. World Health Organization, 2014.

  55. Xu H, Barnes GT, Yang Q, Tan G, Yang D, Chou CJ, Sole J, Nichols A, Ross JS, Tartaglia LA, Chen H (2003) Chronic inflammation in fat plays a crucial role in the development of obesity-related insulin resistance. J Clin Investig 112(12):1821–30

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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The authors would like to thank Prof. Dina Habib for her role in the capacity building for the second author AAM before the study start. Also, we would like to thank Prof. Ehab Soud for his sincere linguistic revision of the manuscript.


This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations



(1) The conception and design of the study or acquisition of data, or analysis and interpretation of data: Ahmed Abdelmagied, Mohammed K. Ali, Safwat Abdel-Rady, Ahmed A. Abdel-Alleem, Alaa Makhlouf, Azza Abo Elfadl. (2) Drafting the article or revising it critically for important intellectual content: Ahmed Abdelmagied, Mohammed K. Ali. (3) Final approval of the version to be submitted: Ahmed Abdelmagied, Alaa Makhlouf, Ahmed A. Abdel-Aleem, Safwat A. Mohamed, Azza Abo Elfadl, Mohammed K. Ali.

Corresponding author

Correspondence to Alaa A. Makhlouf.

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Ethics approval and consent to participate

The Institutional Review Board (IRB) of the Faculty of Medicine, Assiut University approved the study on January 22, 2019 (IRB approval number: 17200286). Every patient was informed about the steps of the study and written informed consent was obtained from each patient.

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Not applicable.

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The authors declare that they have no competing interests.

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Supplementary Information

Additional file 1:

 Supplementary Table S1. Baseline characteristics and adiposity measures of the study groups based on waist circumference. Supplementary Table S2. Cycle characteristics and outcomes of the studied groups based on waist circumference. Supplementary Table S3. Characteristics and adiposity measures of women based on ICSI indication. Supplementary Table S4. ICSI cycle parameters and outcomes of women based on ICSI indication.

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Abdelmagied, A., Makhlouf, A.A., Abdel-Aleem, A.A. et al. Revisiting the predictability of follicular fluid leptin and related adiposity measures for live birth in women scheduled for ICSI cycles: a prospective cohort study. Middle East Fertil Soc J 29, 6 (2024).

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