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

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Asian Journal of Andrology 2024年第4期26卷 349-355页

作者： Guillaume Bachelot Antonin Lamaziere Sebastien Czernichow Celine Faure chrystelle racine Rachel Levy Charlotte Dupont Nutrition and Fertility(ALIFERT)Group Author Information Sorbonne University School of Medicine Saint-Antoine Research CenterINSERM UMR 93827 rue ChalignyParis 75012France Reproductive Biology Department-CECOS Tenon HospitalAP-HP.Sorbonne UniversityParis 75020France Clinical Metabolomics Department Saint-Antoine HospitalAP-HP27 rue ChalignyParis 75012France Nutrition Department Obesity Specialist CentreGeorges Pompidou European HospitalAP-HPParis 75015France 不详

Many lifestyle factors,such as nutritional imbalance leading to obesity,metabolic disorders,and nutritional deficiency,have been identified as potential risk factors for male *** aim of this study was to evaluate the relationship between semen parameters and anthropometric,metabolic and nutritional *** was first assessed individually,then after the application of a previously constructed and validated machine learning score that allows their ***,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 *** 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 *** 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 ***,an unfavorable machine learning score was associated with a high level of sperm DNA *** anthropometric,metabolic,and oxidative patterns,which may reflect an appropriate lifestyle,appear to positively impact overall health,in particular reproductive *** 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 *** this regard,the application of emerging artificial intelligence t

关键词： lifestyle machine learning metabolism nutrition sperm DNA fragmentation

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