Body mass index is not associated with sperm-zona pellucida binding ability in subfertile males

Lifestyle factors, such as weight and nutritional status may affect male fertility, including sperm fertilization ability. The objective of this retrospective study was to evaluate the association between body mass index （BMI） and sperm-zona pellucida binding ability assessed according to the zona binding （ZB） test, which has been described to be a relevant diagnostic tool for the prediction of in vitro fertilization （IVF） ability. Three hundred and six male patients from couples diagnosed with primary idiopathic or mild male factor infertility were included. Correlations between BMI and semen parameters according to ZB test indices were assessed, together with frequencies of positive and negative tests across the BMI categories. In this selected population, BMI was not related to conventional semen parameters or sperm quality assessed according to the ability of spermatozoa to bind to the zona pellucida. The previously described poor outcomes of IVF procedures in cases of male obesity could be due to other sperm defects, such as alterations of sperm capacitation or acrosome reaction. The link between male BMI and biological outcomes during IVF procedures, such as fertilization rates, should be further evaluated.

Machine learning approach to assess the association between anthropometric,metabolic,and nutritional status and semen parameters

Many lifestyle factors,such as nutritional imbalance leading to obesity,metabolic disorders,and nutritional deficiency,have been identified as potential risk factors for male infertility.The aim of this study was to evaluate the relationship between semen parameters and anthropometric,metabolic and nutritional parameters.Relationship was first assessed individually,then after the application of a previously constructed and validated machine learning score that allows their combination.Anthropometric,metabolic,antioxidant,micronutrient,and sperm parameters from 75 men suffering from idiopathic infertility from four infertility centers in France(Jean-Verdier ART Center Hospital,Bondy;North Hospital ART Center,Saint-Étienne;Navarre Polyclinic ART Center,Pau;and Cochin Hospital ART Center,Paris)between September 2009 and December 2013 were collected.After assessing standard correlation analysis,a previously built machine learning model,providing a score ranging from 0(the poorest)to 1(the most favorable),was calculated for each man in the study cohort.This machine learning model,which separates infertile/fertile men with unexplained infertility on the basis of their bioclinical signature,provides a more holistic evaluation of the influence of the considered markers(anthropometric,metabolic,and oxidative status).We observed a significant correlation of some anthropometric,metabolic,and nutritional disorders with some sperm characteristics.Moreover,an unfavorable machine learning score was associated with a high level of sperm DNA fragmentation.Favorable anthropometric,metabolic,and oxidative patterns,which may reflect an appropriate lifestyle,appear to positively impact overall health,in particular reproductive function.This study,consistent with previous publications,suggests that beyond semen quality parameters,in an essential assessment of male fertility,other key factors should be taken into account.In this regard,the application of emerging artificial intelligence techniques may provide a unique opportunity to integrate all these parameters and deliver personalized care.