Skip to main content
NCBI home page
Reproduction & Fertility logoLink to Reproduction & Fertility
. 2026 Sep 24;7(3):RAF260103. doi: 10.1530/RAF-26-0103

Cumulus cell DNA fragmentation in relation to oocyte yield and blastocyst quality

Georges Raad 1,✉, Youmna Mourad 2, Gaelle El Asmar 2, Zalihe Yarkiner 3, Fadi Fakih 4, Ghina Ghazeeri 1,*, Chadi Fakih 2,5,*
PMCID: PMC13650552  PMID: 42715118

Abstract

Abstract

The objective of this study was to evaluate whether cumulus cell (CC) DNA fragmentation is associated with oocyte yield and the probability of obtaining at least one good-quality day-5 blastocyst in intracytoplasmic sperm injection (ICSI) cycles. A prospective cohort study was conducted in 112 infertile couples undergoing ICSI between March 2024 and November 2025. CC DNA fragmentation was assessed using a chromatin dispersion assay and expressed as the percentage of fragmented cells. The primary outcome was the probability of obtaining at least one good-quality day-5 blastocyst (≥3BB). Higher CC DNA fragmentation was negatively correlated with the numbers of collected cumulus–oocyte complexes (COCs) and metaphase II oocytes (all P < 0.05). In adjusted negative binomial regression models, higher DNA fragmentation remained independently associated with fewer collected COCs (incidence rate ratio (IRR): 0.97, 95% confidence interval (CI): 0.96–0.98; P < 0.001) and fewer metaphase II oocytes (IRR: 0.97, 95% CI: 0.96–0.99; P = 0.001). However, no evidence of an association between CC DNA fragmentation and the probability of obtaining at least one good-quality blastocyst was observed after adjustment for female age, BMI and total motile sperm count. These findings suggest that higher CC DNA fragmentation is associated with reduced oocyte yield, whereas its association with blastocyst quality remains uncertain.

Lay summary

Eggs develop inside the ovary surrounded by supporting cells called cumulus cells. These cells help nourish the egg and may provide information about its quality. In this study, we investigated whether damage to the DNA of cumulus cells was associated with the outcomes of fertility treatment. We studied 112 couples undergoing intracytoplasmic sperm injection (ICSI) and measured the amount of DNA damage in cumulus cells collected during treatment. We found that higher levels of DNA damage were associated with fewer eggs being obtained. However, DNA damage in cumulus cells was not associated with the chance of obtaining a high-quality blastocyst. These findings suggest that cumulus cell DNA damage may not reflect embryo quality but could provide insight into egg development.

Keywords: cumulus cells, DNA fragmentation, oocytes, blastocyst, intracytoplasmic sperm injection

Introduction

Cumulus cells (CCs) represent the somatic compartment of the cumulus–oocyte complex (COC), which consists of the oocyte, the surrounding CCs and an extracellular matrix. These cells maintain direct communication with the oocyte and neighbouring follicular cells through intercellular junctions (Richani et al. 2021). Through this interaction, CCs play a central role in supporting oocyte growth and maturation by providing metabolic substrates and regulating redox balance. This metabolic cooperation partly compensates for the limited biosynthetic and metabolic capacity of the oocyte itself (Agarwal et al. 2014, Richani et al. 2021). However, the functional integrity of CCs may be altered by conditions such as oxidative stress, reproductive ageing or endometriosis, potentially compromising their ability to support optimal oocyte competence (Hsu et al. 2015, Lu et al. 2022, Debbarh et al. 2024).

Although CCs are routinely discarded during denudation for intracytoplasmic sperm injection (ICSI), their molecular, biochemical and morphological characteristics can serve as non-invasive indicators of oocyte quality (Massoud et al. 2024, Rohn et al. 2025). Among these characteristics, DNA fragmentation in CCs has been proposed as a potential marker of oocyte developmental competence (Bosco et al. 2017, Ruvolo et al. 2019, Rodrigues et al. 2024).

However, studies examining CC DNA integrity as a biomarker of oocyte competence have reported inconsistent results. Several investigations have suggested that increased CC DNA fragmentation or apoptosis is associated with reduced fertilisation rates and impaired embryo development (Høst et al. 2000, Bosco et al. 2017, Ruvolo et al. 2019, Baratas et al. 2022, Rodrigues et al. 2024). In contrast, other studies did not observe a significant relationship between CC DNA damage and embryological outcomes (Moffatt et al. 2002, Abu-Hassan et al. 2006, Tola et al. 2019). These discrepancies may reflect important methodological differences across studies. In particular, various assays have been used to assess DNA damage, including TUNEL, alkaline comet and chromatin dispersion techniques (Moffatt et al. 2002, Abu-Hassan et al. 2006, Baratas et al. 2022). Sample sizes have also been limited in several reports, in some cases including fewer than 30 patients (Høst et al. 2000, Ruvolo et al. 2019). In addition, embryo outcomes were assessed at different developmental stages ranging from early cleavage to day-5 blastocysts (Høst et al. 2000, Abu-Hassan et al. 2006, Bosco et al. 2017, Baratas et al. 2022). The clinical characteristics of the populations studied have also varied substantially, including women with conditions such as polycystic ovarian syndrome, ovulatory dysfunction or smoking exposure (Moffatt et al. 2002, Tola et al. 2019, Rodrigues et al. 2024). As a result, the predictive value of CC genomic integrity for embryo developmental competence remains uncertain, highlighting the need for well-designed studies using standardised outcome measures and robust statistical approaches.

Importantly, blastocyst morphological quality remains one of the most widely used indicators of embryo developmental competence. Moreover, good-quality blastocysts may be associated with higher implantation and live birth rates compared with lower-grade blastocysts (ESHRE Special Interest Group of Embryology and Alpha Scientists in Reproductive Medicine 2017, Cimadomo et al. 2023, Zou et al. 2023). From a clinical standpoint, evaluating this outcome at the cycle level is particularly relevant, as it informs patient counselling and helps manage expectations during medically assisted reproduction (MAR) (Hunkler & Widra 2024, Franasiak & Pirtea 2025, Wu et al. 2025). However, the potential relationship between CC DNA fragmentation and the chance of achieving a good-quality blastocyst has not been clearly investigated.

Therefore, the objective of this study was to evaluate whether CC DNA fragmentation is associated with the probability of obtaining at least one good-quality day-5 blastocyst. Secondary analyses examined its relationships with oocyte yield and subsequent embryological outcomes, including fertilisation and blastulation rates.

Materials and methods

Study design and setting

A total of 112 infertile couples undergoing ICSI were prospectively enrolled at a tertiary fertility centre in Beirut, Lebanon, between March 2024 and November 2025 (Fig. 1) (Supplementary material 1 (see section on Supplementary materials given at the end of the article)).

Figure 1.

Figure 1

Open in a new tab

Study workflow. Schematic representation of the study workflow. Cumulus–oocyte complexes (COCs) were retrieved from 112 women undergoing ICSI. Cumulus cells (CCs) were collected after oocyte denudation and analysed for DNA fragmentation using a chromatin dispersion assay. Mature oocytes were injected with immobilised spermatozoa, and embryos were cultured to day 5 for blastocyst development and quality assessment. The exposure variable was normalised CC DNA fragmentation, and the primary outcome was good-quality blastocyst development.

Ethical approval

The study protocol was approved by the Institutional Ethics Committee of Al-Hayat Hospital (ETC-2024-15) and was conducted in accordance with the principles of the Declaration of Helsinki. Written informed consent was obtained from all participating couples undergoing ICSI for the use of CCs for research purposes and for the analysis of anonymised clinical and laboratory data.

Study population

Infertile couples undergoing ICSI during the study period were consecutively invited to participate after review of their medical records. On the day of ICSI, participants completed in-person interviews to provide demographic and clinical information, including age, body mass index (BMI, kg/m2), medical history and lifestyle factors such as smoking and alcohol consumption. Couples were eligible if they underwent ICSI using fresh gametes and had at least three retrieved COCs available for analysis. Cycles were excluded if any of the following conditions were present: maternal age > 38 years, BMI > 30 kg/m2, use of cryopreserved gametes, fewer than three retrieved COCs, ICSI using surgically retrieved sperm (testicular or epididymal) or sperm concentration <1 × 106/mL.

Ovarian stimulation

Women underwent controlled ovarian stimulation using a GnRH antagonist protocol. Human menopausal gonadotrophin (hMG; Merional, IBSA, Switzerland) was administered starting on stimulation day 2, and a daily dose of GnRH antagonist (Cetrotide, 0.25 mg, Merck Serono, Germany) was added on day 6. Ovulation was triggered with highly purified hCG (Choriomon, IBSA, Switzerland) when at least three leading follicles reached a diameter of 16 mm (Motawi et al. 2017). COCs were retrieved 35–36 h after ovulation induction via ultrasound-guided transvaginal aspiration under conscious anaesthesia (Maisuradze et al. 2025).

Denudation and CC preparation

Collected COCs were incubated in culture medium (Global for Fertilisation, LifeGlobal, USA) overlaid with Lite oil (LifeGlobal, USA) at 37°C under 5% O2 and 5% CO2 for 2 h before denudation (Wang et al. 2021). Denudation was performed enzymatically using hyaluronidase (FertiPro, Belgium), followed by gentle mechanical pipetting to remove residual CC (Balaban et al. 2011). The detached CCs were collected in HEPES-buffered medium (LifeGlobal, USA), centrifuged for 5 min at 447 g and gently resuspended before being used for vitality and DNA fragmentation assays (Bosco et al. 2017). All analyses were performed immediately on fresh cells without storage. Oocyte maturation rate was calculated as the number of metaphase II oocytes divided by the total number of retrieved COCs. Maturity was defined by the presence of a first polar body in the perivitelline space (Balaban et al. 2011, Lainas et al. 2019).

CC viability assessment

CC suspensions were gently mixed to ensure homogeneous cell distribution. An aliquot of the cell suspension was mixed with an equal volume of 0.4% trypan blue solution. A 10 μL aliquot of the mixture was then placed on an improved Neubauer haemocytometer for evaluation under light microscopy. Viable cells excluded the dye and appeared transparent, whereas non-viable cells absorbed trypan blue and appeared blue. At least 100 cells per sample were counted, and cell viability was calculated as the percentage of unstained cells among the total number of counted cells using the trypan blue exclusion method (Strober 2015, Raad et al. 2020).

CC DNA fragmentation assessment

CC DNA fragmentation was evaluated using a modified chromatin dispersion test (Ovoselect kit, Halotech DNA, Spain), according to the manufacturer’s instructions and previously described protocols (Barcena et al. 2015, Baratas et al. 2022). Briefly, CC suspensions were embedded in low-melting agarose and spread onto microscope slides pre-coated with agarose. After agarose solidification, slides were sequentially treated with lysis solution, dehydrated in graded ethanol solutions and stained according to the kit instructions before microscopic evaluation. Cells were examined under bright-field microscopy. According to the assay criteria, cells with intact DNA displayed a compact chromatin halo with a diameter equal to or smaller than that of the nuclear core. Cells with fragmented DNA exhibited a large, weakly stained halo surrounding the nuclear core. Highly fragmented cells showed markedly reduced nuclear cores with minimal residual halos (Fig. 1). At least 100 cells per sample were evaluated. The DNA fragmentation percentage (%) was calculated as the percentage of cells with fragmented DNA relative to the total number of analysed cells. The analytical performance of the assay was additionally explored using an experimental oxidative stress model in pooled CC samples exposed to hydrogen peroxide, demonstrating expected changes in vitality and DNA fragmentation (Supplementary materials 2 and 3; Supplementary Table 1).

Semen analysis

Male partners were instructed to provide semen samples following 2–7 days of sexual abstinence in accordance with World Health Organization guidelines (WHO 2021). On the day of oocyte retrieval, semen samples were produced by masturbation in a private laboratory room and incubated at 37°C until complete liquefaction. Basic semen analysis was performed according to the World Health Organization (WHO) 2021 recommendations and reported in accordance with the semen analysis methodology checklist proposed by Björndahl et al. (2016) (Supplementary material 4). Sperm concentration was determined using an improved Neubauer haemocytometer. Sperm motility was evaluated by examining at least 200 spermatozoa under light microscopy at 37°C and classified as progressive, non-progressive or immotile. Sperm morphology was assessed on stained smears prepared using the Spermoscan staining kit (Ral Diagnostics, France) (Yamdjeu et al. 2024), and morphological defects were evaluated according to Kruger’s strict criteria.

Sperm preparation

For sperm preparation prior to ICSI, liquefied semen was processed using a two-layer density gradient (50 and 90%, ISolate Sperm Separation Medium, Irvine Scientific, USA). Semen was layered over the gradient and centrifuged for 10 min at 161 g. The resulting sperm pellet was collected, washed in sperm washing medium (Irvine Scientific, USA) and centrifuged again for 5 min at 447 g. The final sperm suspension was used for the ICSI procedure.

ICSI procedure and embryo culture

Prepared spermatozoa were placed in a 7% polyvinylpyrrolidone (PVP) solution (FujiFilm Irvine Scientific, USA) within the ICSI dish to facilitate sperm immobilisation prior to injection. Individual immobilised spermatozoa were microinjected into metaphase II oocytes in HEPES-buffered medium (LifeGlobal, CooperSurgical, Denmark) under a layer of Lite oil (LifeGlobal, CooperSurgical, Denmark). Following ICSI, injected oocytes were transferred to Oosafe culture dishes (SparMed, Denmark) containing Global Total LP medium (LifeGlobal, CooperSurgical, Denmark) overlaid with Lite oil and cultured in a G210 InviCell incubator (K-System, Germany) at 37°C under 5% O2 and 5% CO2. Embryos were maintained under uninterrupted culture conditions for 5 days and were assessed only at predefined observation time points.

Embryological outcome definitions

Embryological outcomes were defined according to internationally recognised ART laboratory indicators and embryo assessment recommendations (ESHRE Special Interest Group of Embryology and Alpha Scientists in Reproductive Medicine 2017, Coticchio et al. 2025a). Embryologists performing CC DNA fragmentation assessment were blinded to embryological outcomes, and embryologists assessing embryo development were blinded to CC DNA fragmentation results. These measurements were performed on the day of oocyte retrieval, prior to fertilisation assessment and embryo development evaluation, thereby ensuring that the laboratory assessments were conducted without knowledge of subsequent blastocyst development. CC vitality, CC DNA fragmentation and embryological outcomes were evaluated independently by two embryologists. The mean of the two readings was used; when a substantial discrepancy was observed, a third embryologist independently assessed the sample and the two closest readings were averaged.

Primary outcome

The primary outcome was the achievement of at least one good-quality blastocyst on day 5. Blastocyst morphology was assessed according to the Gardner and Schoolcraft grading system. Good-quality blastocysts were defined as those with a grade ≥3BB, corresponding to an expansion stage ≥3 and inner cell mass and trophectoderm grades of A or B. Cycles were classified as positive if at least one good-quality blastocyst was obtained and negative otherwise (ESHRE Special Interest Group of Embryology and Alpha Scientists in Reproductive Medicine 2017, Coticchio et al. 2025a).

Secondary outcomes

Secondary embryological outcomes included the oocyte maturation rate, defined as the proportion of metaphase II (MII) oocytes among the total number of retrieved oocytes; the normal fertilisation rate, defined as the proportion of injected MII oocytes exhibiting two pronuclei (2PN) approximately 17 ± 1 h after ICSI; and the blastulation rate, defined as the proportion of normally fertilised oocytes (2PN) that developed into blastocysts by day 5 of culture (116 ± 2 h post-insemination) (ESHRE Special Interest Group of Embryology and Alpha Scientists in Reproductive Medicine 2017, Coticchio et al. 2025a).

Sample size calculation

A priori sample size estimation was performed using G*Power 3.1. In the absence of robust preliminary data, the study was powered to detect a medium effect size using a two-sided α = 0.05 and 80% power, yielding a target sample size of 112 ICSI cycles. For the adjusted logistic regression model, the number of predictors was limited according to the events-per-variable principle; with 48 observed events, the primary model was restricted to four predictors. Female age, BMI and total motile sperm count were selected a priori as clinically relevant potential confounders representing key female and male factors that may influence ICSI outcomes (Morimoto et al. 2024, Ganer Herman et al. 2025, Crafa et al. 2026). Covariate selection was restricted to these variables to limit model overfitting given the number of observed primary outcome events. A minimum detectable effect analysis based on the final sample size, observed event proportion and distributions of CC DNA fragmentation and model covariates indicated that the study had 80% power to detect adjusted odds ratios of approximately ≤0.93 or ≥1.07 per 1-percentage-point increase in CC DNA fragmentation, corresponding to odds ratios of approximately ≤0.46 or ≥1.97 per 10-percentage-point increase. Therefore, smaller associations could not be excluded.

Statistical analysis

Statistical analyses were performed using SPSS version 26 (IBM Corp., USA). Data contained no missing values for the primary outcome or the main exposure variable. The distribution of continuous variables was assessed using the Shapiro–Wilk test. Continuous variables are presented as median (minimum–maximum) or mean ± standard deviation, as appropriate, whereas categorical variables are presented as number and percentage. All tests were two-sided, and statistical significance was defined as P < 0.05. CC DNA fragmentation was modelled as a continuous variable expressed in percentage points. Effect estimates were additionally expressed per 10-percentage-point increase to facilitate clinical interpretation and were calculated from the unrounded regression coefficients.

Associations between CC DNA fragmentation and CC vitality, number of COCs, MII oocytes, normally fertilised zygotes, blastocysts, good-quality blastocysts and embryological rates were assessed using Spearman rank correlation coefficients. Correlation estimates are reported as Spearman’s rho (ρ) with 95% confidence intervals (CIs). These correlations were considered exploratory, and their P-values were adjusted collectively for nine comparisons using the Benjamini–Hochberg false discovery rate procedure. Both unadjusted and adjusted P-values are reported, with statistical significance based on the adjusted P-values. CC DNA fragmentation levels were compared between cycles that achieved at least one good-quality day-5 blastocyst and those that did not using the Mann–Whitney U test.

To further evaluate the association between CC DNA fragmentation and oocyte yield, negative binomial regression models were fitted for the number of collected COCs and MII oocytes. Both unadjusted and adjusted models were performed, with female age and body mass index (BMI) included as covariates in adjusted analyses. Negative binomial regression was selected because Poisson models demonstrated overdispersion (Pearson χ2/df = 3.81 for collected COCs and 3.12 for MII oocytes). Results are reported as incidence rate ratios (IRRs) with 95% CIs.

The primary outcome, achievement of at least one good-quality day-5 blastocyst, was analysed using binary logistic regression. Unadjusted and adjusted models were fitted, with female age, female BMI and total motile sperm count included as covariates in adjusted analyses. Results are reported as odds ratios (ORs) with 95% CIs. Exploratory pathway-adjusted logistic regression models were subsequently performed by individually adding the number of collected COCs, MII oocytes, normal zygotes or blastocysts to the primary adjusted model.

Secondary outcomes, including fertilisation and blastulation rates, were analysed using generalised linear models with a binomial distribution and logit link function. Fertilisation rate was modelled as the number of 2PN zygotes out of the number of MII oocytes, whereas blastulation rate was modelled as the number of blastocysts out of the number of 2PN zygotes. Unadjusted models included CC DNA fragmentation as the sole predictor, whereas adjusted models additionally included female age, female BMI and total motile sperm count. Results are reported as ORs with 95% CIs.

Results

Population characteristics

Women who achieved at least one good-quality blastocyst were younger (30 vs 32 years, P = 0.03) than those who did not. Median CC DNA fragmentation was 14% (IQR: 10–18%; range: 4–49%). Cycles achieving at least one good-quality blastocyst were characterised by higher numbers of collected COCs, MII oocytes, normally fertilised zygotes and blastocysts, as well as higher maturation, fertilisation and blastulation rates (all P ≤ 0.025) (Table 1). Furthermore, the number of good-quality blastocysts was positively correlated with the number of collected COCs (ρ = 0.352, P < 0.001), MII oocytes (ρ = 0.431, P < 0.001) and blastocysts obtained (ρ = 0.656, P < 0.001).

Table 1.

Baseline characteristics and embryological outcomes of the study population. Continuous variables are presented as median (minimum–maximum), and categorical variables are presented as n (%). Stastically significant (P<0.05) values are in bold.

Variable Total (n = 112) No GQB (n = 64) ≥1 GQB (n = 48) P-value
Women characteristics
 Age (years) 31 (20–39) 32.0 (20–39) 30 (20–39) 0.03
 BMI (kg/m2) 24.4 (17.3–31.2) 24.4 (17.3–29.8) 24.2 (17.6–31.2) 0.93
 PCOS (%) 15 (13.4) 6 (9.4) 9 (18.8) 0.14
 Tubal factor (%) 9 (8) 5 (7.8) 4 (8.3) 1.00
 Endometriosis (%) 6 (5.4) 4 (6.2) 2 (4.2) 0.69
 Uterine defect (%) 0 (0) 0 (0) 0 (0) 1.00
 Primary infertility (%) 78 (69.6) 38 (59.4) 40 (83.3) 0.006
 Female smoker (%) 49 (43.8) 29 (45.3) 20 (41.7) 0.70
Men characteristics
 Age (years) 35 (23–63) 35 (26–63) 33.5 (23–46) 0.08
 BMI (kg/m2) 27.7 (17.2–41.5) 27.7 (17.2–39.0) 27.3 (20.1–41.5) 0.42
 Varicocelectomy (%) 26 (23.2) 14 (21.9) 12 (25.0) 0.69
 Inguinal hernia repair (%) 6 (5.4) 1 (1.6) 5 (10.4) 0.08
 Male smoker (%) 74 (66.1) 39 (60.9) 35 (72.9) 0.18
Semen parameters
 Liquefaction time (min) 15.0 (5–50) 15.0 (5–50) 15.0 (10–35) 0.54
 Non-viscous (%) 26 (23.2) 19 (29.7) 7 (14.6) 0.06
 Volume (mL) 3 (0.9–10) 3 (1–7) 3 (0.9–10) 0.90
 Sperm concentration (×106/mL) 26 (1–150) 40 (1–150) 20 (1–100) 0.03
 Progressive motility (%) 20 (0–40) 20 (0–40) 19 (0–35) 0.19
 Non-progressive motility (%) 20 (2–50) 20 (2–35) 20 (2.0–50) 0.99
 Non-motile (%) 60 (30–97) 60 (30–97) 60 (30–96) 0.47
 Normal morphology (%) 3 (0–15) 3.5 (0–10) 3 (1–15) 0.25
Embryological outcomes
 COCs Collected (n) 8 (3–40) 7 (3–30) 11 (3–40) 0.003
 CC DNA fragmentation (%) 14 (4–49) 15 (4–49) 13.9 (4.3–46) 0.18
 MII oocytes (n) 6 (1–37) 5 (1–20) 7.5 (2–37) <0.001
 Maturation rate (%) 75 (12.5–100) 65.5 (12.5–100) 77.8 (42.9–100) 0.010
 Normally fertilised zygote (n) 4 (0–18) 3 (0–12) 6 (1–18) <0.001
 Fertilisation rate (%) 75 (0–100) 66.7 (0–100) 80 (28.6–100) 0.025
 Blastocysts (n) 2 (0–16) 1 (0.0–7) 4 (1.0–16) <0.001
 Blastulation rate (%) 50 (0–100) 46.4 (0–100) 71.4 (25–100) <0.001
Open in a new tab

GQB, good-quality blastocyst; BMI, body mass index; CC, cumulus cell; COC, cumulus–oocyte complex.

CC DNA fragmentation is negatively correlated with oocyte yield

In exploratory correlation analyses, higher CC DNA fragmentation was weakly associated with lower numbers of retrieved COCs (ρ = −0.27, unadjusted P = 0.003, FDR-adjusted P = 0.02) and MII oocytes (ρ = −0.24, unadjusted P = 0.008, FDR-adjusted P = 0.03) (Fig. 2A). These associations remained statistically significant after Benjamini–Hochberg correction. The inverse correlations with the numbers of normally fertilised zygotes (ρ = −0.20, unadjusted P = 0.03, FDR-adjusted P = 0.090) and blastocysts obtained (ρ = −0.19, unadjusted P = 0.04, FDR-adjusted P = 0.09) did not remain statistically significant after correction. No significant correlations were observed with the number of good-quality blastocysts, CC vitality, maturation rate, fertilisation rate or blastulation rate after correction for multiple comparisons (Fig. 2A and B). Consistent with these findings, CC DNA fragmentation levels did not differ significantly between cycles that achieved at least one good-quality blastocyst and those that did not (P = 0.18) (Fig. 2C, Table 1).

Figure 2.

Figure 2

Open in a new tab

Associations between cumulus cell DNA fragmentation, cumulus cell vitality and embryological outcomes. (A) Spearman correlation coefficients (ρ) between CC DNA fragmentation and developmental yield parameters, including the number of collected COCs, MII oocytes, normally fertilised zygotes (2PN), blastocysts and good-quality blastocysts (GQBs), as well as CC vitality. Red symbols indicate statistically significant correlations (P < 0.05). Error bars represent 95% confidence intervals obtained by bootstrap resampling. (B) Spearman correlation coefficients (ρ) between CC DNA fragmentation and embryological rates, including maturation, fertilisation and blastulation rates. The vertical dashed line indicates no correlation (ρ = 0). Error bars represent 95% confidence intervals obtained by bootstrap resampling. (A + B) The P-values for the nine correlations shown in panels A and B were adjusted collectively using the Benjamini–Hochberg false discovery rate procedure. Adjusted P-values are presented in the figure. (C) Comparison of CC DNA fragmentation levels between cycles that did not achieve a good-quality blastocyst (GQB) and cycles that achieved at least one GQB on day 5. Boxes represent the interquartile range, horizontal lines indicate medians, whiskers indicate the minimum and maximum values excluding outliers, and dots represent outliers. Comparisons were performed using the Mann–Whitney U test. N.S., not significant.

Quantification of the association between CC DNA fragmentation and oocyte yield

To confirm the observed correlations between CC DNA fragmentation and oocyte yield, negative binomial regression models were fitted for the number of collected COCs and MII oocytes (Supplementary Table 2). In unadjusted analyses, higher CC DNA fragmentation was associated with a lower number of collected COCs (IRR: 0.97, 95% CI: 0.96–0.99, P = 0.001) and MII oocytes (IRR: 0.97, 95% CI: 0.96–0.99, P = 0.007). These associations remained significant after adjustment for female age and BMI, with adjusted IRRs of 0.975 (95% CI: 0.96–0.98, P < 0.001) for collected COCs and 0.97 (95% CI: 0.96–0.99, P = 0.001) for MII oocytes. To facilitate interpretation, effect estimates were additionally expressed per 10-percentage-point increase in CC DNA fragmentation, a contrast within the observed range of 4–49%. The corresponding adjusted IRRs were 0.77 for collected COCs and 0.78 for MII oocytes, corresponding to approximately 22.3 and 21.5% lower expected counts, respectively.

No evidence of association between CC DNA fragmentation and the probability of obtaining a good-quality day-5 blastocyst

In logistic regression analyses, there was no evidence of an association between CC DNA fragmentation and the probability of achieving at least one good-quality day-5 blastocyst in either the unadjusted model (OR: 0.97, 95% CI: 0.92–1.01, P = 0.21) or the adjusted model (OR: 0.96, 95% CI: 0.92–1.01, P = 0.20) (Table 2). Among the covariates included in the adjusted model, only female age was independently associated with the outcome (OR: 0.90, 95% CI: 0.82–0.99, P = 0.03).

Table 2.

Logistic and binomial generalised linear models evaluating the association between CC DNA fragmentation and good-quality blastocyst development, fertilisation and blastulation outcomes. Values are odds ratios (ORs) with 95% confidence intervals (CIs). Stastically significant (P<0.05) values are in bold. The primary outcome, achievement of at least one good-quality day-5 blastocyst, was analysed using binary logistic regression. Fertilisation and blastulation rates were analysed using binomial generalised linear models with a logit link, modelling the number of 2PN zygotes out of the number of MII oocytes and the number of blastocysts out of the number of normal zygotes, respectively. Adjusted models included CC DNA fragmentation, female age, female BMI and total motile sperm count.

Outcome/variable Unadjusted OR (95% CI) P-value Adjusted OR (95% CI) P-value
Primary outcome
 Good-quality blastocyst on day 5
  CC DNA fragmentation (%) 0.97 (0.92–1.01) 0.21 0.96 (0.92–1.01) 0.20
  Female age (years) — — 0.90 (0.82–0.99) 0.03
  Female BMI (kg/m2) — — 1.04 (0.94–1.16) 0.40
  Total motile sperm count — — 0.99 (0.97–1.01) 0.39
Secondary outcome
 Fertilisation rate
  CC DNA fragmentation (%) 1.01 (0.99–1.04) 0.14 1.01 (0.99–1.03) 0.16
  Female age (years) — — 0.96 (0.92–0.99) 0.04
  Female BMI (kg/m2) — — 0.98 (0.94–1.03) 0.61
  Total motile sperm count — — 1 (0.99–1.009) 0.91
 Blastulation rate
  CC DNA fragmentation (%) 1.001 (0.97–1.02) 0.89 1.001 (0.97–1.02) 0.92
  Female age (years) — — 0.99 (0.95–1.03) 0.78
  Female BMI (kg/m2) — — 1.008 (0.96–1.05) 0.74
  Total motile sperm count — — 1 (0.99–1.01) 0.73
Open in a new tab

BMI, body mass index; CC, cumulus cell; MII, metaphase II; OR, odds ratio; CI, confidence interval.

In exploratory analyses, no evidence of an association between CC DNA fragmentation and the probability of obtaining at least one good-quality day-5 blastocyst was observed after additional adjustment for the number of collected COCs, MII oocytes, normal zygotes and blastocysts (Supplementary Table 3).

No evidence of association between CC DNA fragmentation and fertilisation or blastulation rates

CC DNA fragmentation was not significantly associated with fertilisation rate in either unadjusted (OR: 1.01, 95% CI: 0.99–1.04, P = 0.14) or adjusted analyses (OR: 1.01, 95% CI: 0.99–1.03, P = 0.16) (Table 2). Female age was independently associated with lower fertilisation odds (OR: 0.96, 95% CI: 0.92–0.99, P = 0.04). Likewise, CC DNA fragmentation was not associated with blastulation rate in either unadjusted (OR: 1.00, 95% CI: 0.97–1.02, P = 0.89) or adjusted analyses (OR: 1.00, 95% CI: 0.97–1.02, P = 0.92), and none of the covariates included in the adjusted model were significantly associated with the outcome (Table 2).

Discussion

The main finding of this prospective study is that higher cumulus cell DNA fragmentation was associated with reduced oocyte yield, as reflected by lower numbers of collected COCs and MII oocytes. However, no evidence of an association between CC DNA fragmentation and good-quality blastocyst development was observed after adjustment for female age, BMI and total motile sperm count.

Although the inverse correlations of CC DNA fragmentation with retrieved COC and MII oocyte numbers remained significant after correction for multiple comparisons, their magnitudes were weak (ρ = −0.27 and −0.24, respectively), indicating modest cycle-level associations rather than strong individual predictive value. Residual confounding by ovarian reserve, stimulation response or other unmeasured factors cannot be excluded. Similarly, the absence of evidence of an association with good-quality blastocyst development should not be interpreted as evidence of no association, as the confidence interval remained compatible with potentially meaningful effects and the minimum detectable effect analysis indicated that the study was powered primarily to detect relatively large effects. Smaller associations may therefore have remained undetected and require evaluation in larger studies.

The lack of a detectable association between CC DNA fragmentation and good-quality blastocyst development may reflect, at least partly, the multifactorial nature of embryo competence (Cimadomo et al. 2023). Beyond the follicular microenvironment, embryo developmental trajectory is shaped by intrinsic oocyte characteristics (e.g. mitochondrial activity and meiotic spindle integrity) (Keefe et al. 2015, Gonzalez et al. 2023), embryonic genome activation (Wong et al. 2010, Conti & Franciosi 2018), paternal contribution (Isacson et al. 2025) and laboratory culture conditions (Coticchio et al. 2025b).

In line with these findings, previous studies have explored a wide range of CC characteristics, including gene expression patterns, biochemical activity and apoptotic markers (Rohn et al. 2025). A recent systematic review that synthesised evidence from more than 60 studies reported that several molecular pathways in CCs, particularly those related to extracellular matrix organisation, cellular metabolism and apoptosis, are associated with oocyte quality and embryo developmental potential (Rohn et al. 2025). Among these, CC apoptosis and DNA fragmentation have been associated with oocyte maturation, fertilisation rates and embryo development in several studies (Høst et al. 2000, Bosco et al. 2017, Ruvolo et al. 2019, Baratas et al. 2022, Rodrigues et al. 2024, Rohn et al. 2025). However, despite extensive research, no CC-derived biomarker has yet demonstrated sufficient robustness for routine clinical use, highlighting the ongoing challenge of identifying reliable non-invasive indicators of oocyte competence (Moffatt et al. 2002, Abu-Hassan et al. 2006, Tola et al. 2019, Cimadomo et al. 2023).

Several biological mechanisms may hypothetically explain the observed association between CC DNA fragmentation and oocyte yield. CC genomic integrity is normally maintained by oocyte-secreted factors that sustain surrounding CCs in an anti-apoptotic state (Hussein et al. 2005, Liu et al. 2021), intrinsic antioxidant defence systems that buffer reactive oxygen species (ROS) (Agarwal et al. 2014) and mechanisms supporting metabolic homeostasis (Rohn et al. 2025). Disturbances in the follicular microenvironment may disrupt CC genomic integrity by promoting oxidative stress and DNA damage (von Mengden et al. 2020). Factors such as ovarian ageing, inflammatory signalling, endometriosis, cigarette smoking and suboptimal hormonal conditions during controlled ovarian stimulation have been associated with increased oxidative stress within the follicular environment, which may compromise both CC function and oocyte competence (Ruvolo et al. 2007, Agarwal et al. 2014, Requena et al. 2016, von Mengden et al. 2020). Elevated oxidative stress may subsequently induce CC death (Glamočlija et al. 2005, Stringer et al. 2023). It may therefore be hypothesised that damage or loss of CCs could reduce the metabolic support available to the oocyte and potentially affect its developmental competence (Tatone & Amicarelli 2013, Raad et al. 2024). Taken together, the results of this study are clinically relevant, given that oocyte yield is a key determinant of cumulative live birth rates in MAR and that identifying follicular biomarkers capable of predicting ovarian response remains an active area of research (Polyzos et al. 2018, Fanton et al. 2023).

Several limitations should be acknowledged. First, the study was conducted at a single centre, which may limit the generalisability of the findings. Second, cumulus cells from all COCs within each cycle were pooled for DNA fragmentation assessment rather than maintained separately and linked to their corresponding oocytes and embryos. Subsequently, the analysis was performed at the cycle level and does not allow determination of whether DNA fragmentation in the cumulus cells surrounding an individual oocyte is associated with that oocyte’s subsequent developmental competence. Future studies using paired oocyte-level assessment are needed to provide more granular insight into this relationship. Formal inter-observer reliability could not be quantified because individual embryologist readings were not retained separately. Moreover, although blastocyst quality is an established surrogate of embryo competence, clinical outcomes such as implantation or live birth were not evaluated. The adjusted analyses were restricted to a limited number of prespecified covariates because of the sample size and number of primary outcome events. Consequently, residual confounding by unmeasured or unaccounted-for clinical and biological factors cannot be excluded. In particular, the absence of established ovarian reserve markers such as anti-Müllerian hormone and antral follicle count limits our ability to determine whether the observed association between CC DNA fragmentation and oocyte yield is independent of underlying ovarian reserve. The restrictive eligibility criteria, including maternal age ≤ 38 years, BMI ≤ 30 kg/m2, exclusion of severe male-factor infertility and retrieval of at least three COCs, may have restricted the range of patient characteristics and embryological outcomes, potentially reducing the ability to detect associations with blastocyst quality and limiting generalisability. Nevertheless, ovarian reserve may still have varied among the included women.

Conclusion

In conclusion, higher CC DNA fragmentation was associated with reduced oocyte yield, whereas no evidence of an association with fertilisation rate, blastulation rate or the probability of obtaining a good-quality day-5 blastocyst was observed. Future studies incorporating established ovarian reserve markers and paired oocyte-level analyses are needed to clarify these relationships.

Supplementary materials

Declaration of interest

The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported.

Funding

No external funding was obtained for this study. This work was supported by Al Hadi Laboratory and Medical Centre.

Author contribution statement

GR was responsible for conceptualisation, methodology, investigation, formal analysis, visualisation and writing the original draft. YM contributed to the investigation and resources and reviewed and edited the manuscript. GEA was responsible for investigation, data curation and reviewed and edited the manuscript. ZY contributed to formal analysis, validation and reviewed and edited the manuscript. FF contributed to investigation and reviewed and edited the manuscript. GG was responsible for supervision and validation and reviewed and edited the manuscript. CF contributed to conceptualisation, supervision and resources and reviewed and edited the manuscript.

Data availability

The de-identified data supporting the findings of this study are available from the corresponding author on reasonable request.

References

  1. Abu-Hassan D, Koester F, Shoepper B, et al. 2006. Comet assay of cumulus cells and spermatozoa DNA status, and the relationship to oocyte fertilization and embryo quality following ICSI. Reprod Biomed Online 12 447–452. ( 10.1016/S1472-6483(10)61997-9) [DOI] [PubMed] [Google Scholar]
  2. Agarwal A, Durairajanayagam D & du Plessis SS. 2014. Utility of antioxidants during assisted reproductive techniques: an evidence based review. Reprod Biol Endocrinol 12 112. ( 10.1186/1477-7827-12-112) [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Balaban B, Brison D, Calderón G, et al. 2011. The istanbul consensus workshop on embryo assessment: proceedings of an expert meeting. Hum Reprod 26 1270–1283. ( 10.1093/humrep/der037) [DOI] [PubMed] [Google Scholar]
  4. Baratas A, Gosálvez J, de la Casa M, et al. 2022. Cumulus cell DNA damage as an index of human oocyte competence. Reprod Sci 29 3194–3200. ( 10.1007/s43032-021-00817-7) [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Barcena P, López-Fernández C, García-Ochoa C, et al. 2015. Detection of DNA damage in cumulus cells using a chromatin dispersion assay. Syst Biol ;Reprod Med 61 277–285. ( 10.3109/19396368.2015.1063739) [DOI] [PubMed] [Google Scholar]
  6. Björndahl L, Barratt CLR, Mortimer D, et al. 2016. “How to count sperm properly”: checklist for acceptability of studies based on human semen analysis. Hum Reprod 31 227–232. ( 10.1093/humrep/dev305) [DOI] [PubMed] [Google Scholar]
  7. Bosco L, Chiarelli R, Roccheri MC, et al. 2017. Relationship between apoptosis and survival molecules in human cumulus cells as markers of oocyte competence. Zygote 25 583–591. ( 10.1017/S0967199417000429) [DOI] [PubMed] [Google Scholar]
  8. Cimadomo D, Rienzi L, Conforti A, et al. 2023. Opening the black box: why do euploid blastocysts fail to implant? A systematic review and meta-analysis. Hum Reprod Update 29 570–633. ( 10.1093/humupd/dmad010) [DOI] [PubMed] [Google Scholar]
  9. Conti M & Franciosi F. 2018. Acquisition of oocyte competence to develop as an embryo: integrated nuclear and cytoplasmic events. Hum Reprod Update 24 245–266. ( 10.1093/humupd/dmx040) [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Coticchio G, Ahlstrom A, Arroyo G, et al. 2025a. The istanbul consensus update: a revised ESHRE/ALPHA consensus on oocyte and embryo static and dynamic morphological assessment. Hum Reprod 40 989–1035. ( 10.1093/humrep/deaf021) [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Coticchio G, Cimadomo D & Rienzi L. 2025b. Do we still need embryologists? Reprod Biomed Online 50 104790. ( 10.1016/j.rbmo.2024.104790) [DOI] [PubMed] [Google Scholar]
  12. Crafa A, Cannarella R, Navarro-Gomezlechon A, et al. 2026. Does total motile sperm count predict embryo quality and ICSI success in young normal-weight women? A retrospective multicentre study. Reprod Biomed Online 52 105209. ( 10.1016/j.rbmo.2025.105209) [DOI] [PubMed] [Google Scholar]
  13. Debbarh H, Jamil M, Jelloul H, et al. 2024. Evaluation of oxidative and nitrosative stress markers related to inflammation in the cumulus cells and follicular fluid of women undergoing intracytoplasmic sperm injection: a prospective study. Int J Fertil Steril 18 108–114. ( 10.22074/IJFS.2023.559526.1342) [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. ESHRE Special Interest Group of Embryology and Alpha Scientists in Reproductive Medicine 2017. The Vienna consensus: report of an expert meeting on the development of ART laboratory performance indicators. Reprod Biomed Online 35 494–510. ( 10.1016/J.RBMO.2017.06.015) [DOI] [PubMed] [Google Scholar]
  15. Fanton M, Cho JH, Baker VL, et al. 2023. A higher number of oocytes retrieved is associated with an increase in fertilized oocytes, blastocysts, and cumulative live birth rates. Fertil Steril 119 762–769. ( 10.1016/j.fertnstert.2023.01.001) [DOI] [PubMed] [Google Scholar]
  16. Franasiak J & Pirtea P. 2025. Addressing the EVERYTHING; expectation in infertility treatments: messaging to patients about embryo diagnostics in assisted reproductive technology. Fertil Steril 124 893–895. ( 10.1016/j.fertnstert.2025.05.161) [DOI] [PubMed] [Google Scholar]
  17. Ganer Herman H, Feferkorn I, Dahan MH, et al. 2025. A meta-analysis and systematic review of advanced maternal age patients in IVF. Hum Reprod Update 31 626–642. ( 10.1093/humupd/dmaf020) [DOI] [PubMed] [Google Scholar]
  18. Glamočlija V, Vilović K, Saraga-Babić M, et al. 2005. Apoptosis and active caspase-3 expression in human granulosa cells. Fertil Steril 83 426–431. ( 10.1016/j.fertnstert.2004.06.075) [DOI] [PubMed] [Google Scholar]
  19. Gonzalez XV, Almutlaq A & Gupta SS. 2023. Systematic review of mRNA expression in human oocytes: understanding the molecular mechanisms underlying oocyte competence. J Assist Reprod Genet 40 2283–2295. ( 10.1007/s10815-023-02906-9) [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Høst E, Mikkelsen AL, Lindenberg S, et al. 2000. Apoptosis in human cumulus cells in relation to maturation stage and cleavage of the corresponding oocyte. Acta Obstet Gynecol Scand 79 936–940. ( 10.1034/j.1600-0412.2000.079011936.x) [DOI] [PubMed] [Google Scholar]
  21. Hsu AL, Townsend PM, Oehninger S, et al. 2015. Endometriosis may be associated with mitochondrial dysfunction in cumulus cells from subjects undergoing in vitro fertilization-intracytoplasmic sperm injection, as reflected by decreased adenosine triphosphate production. Fertil Steril 103 347–352.e1. ( 10.1016/j.fertnstert.2014.11.002) [DOI] [PubMed] [Google Scholar]
  22. Hunkler K & Widra E. 2024. Expectant management? How to communicate realistic outcomes and the possibility of failure. Fertil Steril 122 991–992. ( 10.1016/j.fertnstert.2024.06.008) [DOI] [PubMed] [Google Scholar]
  23. Hussein TS, Froiland DA, Amato F, et al. 2005. Oocytes prevent cumulus cell apoptosis by maintaining a morphogenic paracrine gradient of bone morphogenetic proteins. J Cell Sci 118 5257–5268. ( 10.1242/jcs.02644) [DOI] [PubMed] [Google Scholar]
  24. Isacson S, Karlsson K, Zalavary S, et al. 2025. Small RNA in sperm–paternal contributions to human embryo development. Nat Commun 16 6571. ( 10.1038/s41467-025-62015-2) [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Keefe D, Kumar M & Kalmbach K. 2015. Oocyte competency is the key to embryo potential. Fertil Steril 103 317–322. ( 10.1016/j.fertnstert.2014.12.115) [DOI] [PubMed] [Google Scholar]
  26. Lainas GT, Lainas TG, Sfontouris IA, et al. 2019. Is oocyte maturation rate associated with triptorelin dose used for triggering final oocyte maturation in patients at high risk for severe ovarian hyperstimulation syndrome. Hum Reprod 34 1770–1777. ( 10.1093/humrep/dez105) [DOI] [PubMed] [Google Scholar]
  27. Liu M, Zhang K & Xu T. 2021. The role of BMP15 and GDF9 in the pathogenesis of primary ovarian insufficiency. Hum Fertil 24 325–332. ( 10.1080/14647273.2019.1672107) [DOI] [PubMed] [Google Scholar]
  28. Lu X, Liu Y, Xu J, et al. 2022. Mitochondrial dysfunction in cumulus cells is related to decreased reproductive capacity in advanced-age women. Fertil Steril 118 393–404. ( 10.1016/j.fertnstert.2022.04.019) [DOI] [PubMed] [Google Scholar]
  29. Maisuradze N, Maisuradze N, Pylypenko N, et al. 2025. P-718 optimal timing from trigger to oocyte retrieval for maximizing mature oocyte yield: a two-year multicenter analysis of 1,534 cycles. Hum Reprod 40 deaf097.1024. ( 10.1093/humrep/deaf097.1024) [DOI] [Google Scholar]
  30. Massoud G, Spann M, Vaught KC, et al. 2024. Biomarkers assessing the role of cumulus cells on IVF outcomes: a systematic review. J Assist Reprod Genet 41 253–275. ( 10.1007/s10815-023-02984-9) [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Moffatt O, Drury S, Tomlinson M, et al. 2002. The apoptotic profile of human cumulus cells changes with patient age and after exposure to sperm but not in relation to oocyte maturity. Fertil Steril 77 1006–1011. ( 10.1016/s0015-0282(02)02951-5) [DOI] [PubMed] [Google Scholar]
  32. Morimoto A, Rose RD, Smith KM, et al. 2024. Granulosa cell metabolism at ovulation correlates with oocyte competence and is disrupted by obesity and aging. Hum Reprod 39 2053–2066. ( 10.1093/humrep/deae154) [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Motawi TMK, Rizk SM, Maurice NW, et al. 2017. The role of gene polymorphisms and AMH level in prediction of poor ovarian response in Egyptian women undergoing IVF procedure. J Assist Reprod Genet 34 1659–1666. ( 10.1007/s10815-017-1013-4) [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Polyzos NP, Drakopoulos P, Parra J, et al. 2018. Cumulative live birth rates according to the number of oocytes retrieved after the first ovarian stimulation for in vitro fertilization/intracytoplasmic sperm injection: a multicenter multinational analysis including ∼15,000 women. Fertil Steril 110 661–670.e1. ( 10.1016/j.fertnstert.2018.04.039) [DOI] [PubMed] [Google Scholar]
  35. Raad G, Bazzi M, Tanios J, et al. 2020. Optimization of the cell aggregates method for isolation and purification of human granulosa cells from follicular fluid. Int J Fertil Steril 13 339–345. ( 10.22074/ijfs.2020.5790) [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Raad G, Tanios J, Serdarogullari M, et al. 2024. Mature oocyte dysmorphisms may be associated with progesterone levels, mitochondrial DNA content, and vitality in luteal granulosa cells. J Assist Reprod Genet 41 795–813. ( 10.1007/s10815-024-03053-5) [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Requena A, Cruz M, Agudo D, et al. 2016. Type of gonadotropin during controlled ovarian stimulation affects the endocrine profile in follicular fluid and apoptosis rate in cumulus cells. Eur J Obstet Gynecol Reprod Biol 197 142–146. ( 10.1016/j.ejogrb.2015.12.018) [DOI] [PubMed] [Google Scholar]
  38. Richani D, Dunning KR, Thompson JG, et al. 2021. Metabolic co-dependence of the oocyte and cumulus cells: essential role in determining oocyte developmental competence. Hum Reprod Update 27 27–47. ( 10.1093/humupd/dmaa043) [DOI] [PubMed] [Google Scholar]
  39. Rodrigues B, Sousa V, Esteves F, et al. 2024. Cumulus cell DNA damage linked to fertilization success in females with an ovulatory dysfunction phenotype. Front Cell Dev Biol 12 1448733. ( 10.3389/fcell.2024.1448733) [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Rohn MCH, Simeone JM, Doctorman S, et al. 2025. The functional role of cumulus cells and their influence on oocyte quality: a systematic review. Reprod Sci 32 2877–2902. ( 10.1007/s43032-025-01940-5) [DOI] [PubMed] [Google Scholar]
  41. Ruvolo G, Bosco L, Pane A, et al. 2007. Lower apoptosis rate in human cumulus cells after administration of recombinant luteinizing hormone to women undergoing ovarian stimulation for in vitro fertilization procedures. Fertil Steril 87 542–546. ( 10.1016/j.fertnstert.2006.06.059) [DOI] [PubMed] [Google Scholar]
  42. Ruvolo G, Roccheri MC, Luparello C, et al. 2019. DNA fragmentation index, pAKT and pERK1/2 in cumulus cells are related to oocyte competence in patients undergoing in vitro fertilization programme. Zygote 27 350–354. ( 10.1017/S0967199419000248) [DOI] [PubMed] [Google Scholar]
  43. Stringer JM, Alesi LR, Winship AL, et al. 2023. Beyond apoptosis: evidence of other regulated cell death pathways in the ovary throughout development and life. Hum Reprod Update 29 434–456. ( 10.1093/humupd/dmad005) [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Strober W 2015. Trypan blue exclusion test of cell viability. Curr Protoc Immunol 111 A3.B.1-A3.B.3. ( 10.1002/0471142735.ima03bs111) [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Tatone C & Amicarelli F. 2013. The aging ovary – the poor granulosa cells. Fertil Steril 99 12–17. ( 10.1016/j.fertnstert.2012.11.029) [DOI] [PubMed] [Google Scholar]
  46. Tola EN, Koşar PA, Karatopuk DU, et al. 2019. Effect of DNA damage of cumulus oophorus cells and lymphocytes analyzed by alkaline comet assay on oocyte quality and intracytoplasmic sperm injection success among patients with polycystic ovary syndrome. J Obstet Gynaecol Res 45 609–618. ( 10.1111/jog.13868) [DOI] [PubMed] [Google Scholar]
  47. von Mengden L, Klamt F & Smitz J. 2020. Redox biology of human cumulus cells: basic concepts, impact on oocyte quality, and potential clinical use. Antioxid Redox Signal 32 522–535. ( 10.1089/ars.2019.7984) [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Wang X, Xiao YL, Sun ZY, et al. 2021. Effect of the time interval between oocyte retrieval and ICSI on embryo development and reproductive outcomes: a systematic review. Reprod Biol Endocrinol 19 34. ( 10.1186/s12958-021-00717-0) [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. WHO 2021. WHO Laboratory Manual for The Examination and Processing of Human Semen, 6th edn. Geneva, Switzerland. [Google Scholar]
  50. Wong CC, Loewke KE, Bossert NL, et al. 2010. Non-invasive imaging of human embryos before embryonic genome activation predicts development to the blastocyst stage. Nat Biotechnol 28 1115–1121. ( 10.1038/nbt.1686) [DOI] [PubMed] [Google Scholar]
  51. Wu S, Wang X, Liu Y, et al. 2025. Predictive models for live birth outcomes following fresh embryo transfer in assisted reproductive technologies using machine learning. J Transl Med 23 1004. ( 10.1186/s12967-025-07045-6) [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Yamdjeu KP, Moukouma C, Bolanga BR, et al. 2024. Spermocytogram: Comparison between the papanicolaou staining and modified May Grünwald giemsa staining. Eur J Med Health Res 2 50–54. ( 10.59324/ejmhr.2024.2(4).06) [DOI] [Google Scholar]
  53. Zou H, Kemper JM, Hammond ER, et al. 2023. Blastocyst quality and reproductive and perinatal outcomes: a multinational multicentre observational study. Hum Reprod 38 2391–2399. ( 10.1093/humrep/dead212) [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

RAF-26-0103_supplementary_material_1.pdf (296.7KB, pdf)
RAF-26-0103_supplementary_materials.pdf (336.2KB, pdf)
RAF-26-0103_supplementary_tables.pdf (124.4KB, pdf)

Data Availability Statement

The de-identified data supporting the findings of this study are available from the corresponding author on reasonable request.

Articles from Reproduction & Fertility are provided here courtesy of Bioscientifica Ltd.

RESOURCES