Acrosome reaction and chromatin integrity as additional parameters of semen analysis to predict fertilization and blastocyst rates - Pamela Tello-Mora; Leticia Hernández-Cadena; Jeimy Pedraza; Esther López-Bayghen; Betzabet Quintanilla-Vega (BMC, 2018)
Abstract Background Traditional semen parameters have shown little to none predictive value for fertilization and blastocyst viability for a successful pregnancy. Therefore, the purpose of this study was to explore the usefulness of incorporating the acrosome reaction (AR) and chromatin integrity to conventional semen analysis to individually predict the fertile potential of sperm samples. Methods A cross-sectional study was conducted in 69 participants undergoing IVF using oocyte donation. Semen samples were collected and evaluated for: AR [spontaneous (sAR) and induced (iAR)] by flow cytometry using anti-CD46-FITC, Acrosome Response to an Ionophore Challenge (ARIC), chromatin integrity by Sperm Chromatin Structure Assay (DNA Fragmentation Index-%DFI and High DNA Stainability-%HDS), WHO semen analysis, fertilization and blastocyst rates. Results The participant age was 40.0 ± 6.1 years (66% were normozoospermic). Sperm morphology, sAR, iAR, and ARIC were associated with the fertilization (β = 3.56, R2 = 0.054; β = − 5.92, R2 = 0.276; β = 1.83, R2 = 0.150; and β = 2.10, R2 = 0.270, respectively, p < 0.05). A logit model was developed to calculate the probability of fertilization (≥ 60%) for each participant, using the sperm morphology and ARIC as independent variables, followed by ROC analysis to determine a cutoff probability of 0.65 (specificity = 80.6%, sensitivity = 63.2%). %DFI was inversely associated with the viable blastocyst rate (β = − 1.77, R2 = 0.057, p = 0.003), by the logit model and ROC analysis, a cutoff probability of 0.70 (specificity = 80.6%, sensitivity = 72.3%) was obtained to predict blastocyst viability (≥ 40%). There was no difference in the results with normozoospermic samples (n = 46). Conclusions The incorporation of ARIC and %DFI allowed to obtain predictive models for high fertilization and blastocyst rates in an individualized way, being promising tools to improve the diagnosis of male fertility potential for research or assisted reproduction, even in men with unknown infertility.