Daniele Santi1, Elisa Magnani2, Marco Michelangeli3, Roberto Grassi3, Barbara Vecchi3, Gioia Pedroni4, Laura Roli5, Maria Cristina De Santis5, Enrica Baraldi5, Monica Setti6, Tommaso Trenti5, Manuela Simoni2. 1. Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Italy; Department of Medicine, Endocrinology, Metabolism and Geriatrics, Azienda Ospedaliero-Universitaria of Modena, Italy. Electronic address: daniele.santi@unimore.it. 2. Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Italy; Department of Medicine, Endocrinology, Metabolism and Geriatrics, Azienda Ospedaliero-Universitaria of Modena, Italy. 3. Hopenly S.r.l., Vignola, Italy. 4. Department of Biomedical, Metabolic and Neural Sciences, University of Modena and Reggio Emilia, Italy. 5. Department of Laboratory Medicine and Anatomy Pathology, Azienda USL of Modena, Italy. 6. Service of Clinical Engineering, Azienda Ospedaliero-Universitaria of Modena, Italy.
Abstract
BACKGROUND: Male fertility is progressively declining in many developed countries, but the relationship between male infertility and environmental factors is still unclear. OBJECTIVES: To assess the influence of environmental temperature and air pollution on semen parameters, using a big-data approach. METHODS: A big data analysis of parameters related to 5131 men, living in a province of Northern Italy and undergoing semen analyses between January 2010 and March 2016 was performed. Ambient temperature was recorded on the day of analysis and the 90 days prior to the analysis and the average value of particulate matter (PM) and NO2 in the year of the test. All data were acquired by geocoding patients residential address. A data warehouse containing 990,904,591 data was generated and analysed by multiple regressions. RESULTS: 5573 semen analyses were collected. Both maximum and minimum temperatures registered on the day of collection were inversely related to total sperm number (p < .001), non-progressive motility (NPrM) (p < .005) and normal forms (p < .001). Results were confirmed considering temperature in the 30 and 60 days before collection, but not in the 90 days before collection. Total sperm number was lower in summer/autumn (p < .001) and was inversely related with daylight duration (p < .001). PM10 and PM2.5 were inversely related to PrM (p < .001 and p < .005) and abnormal forms (p < .001). CONCLUSIONS: This is the first evaluation of the relationship between male fertility-related parameters and environment using a big-data approach. A seasonal change in semen parameters was found, with a fluctuation related to both temperature and daylight duration. A negative correlation between air pollution and semen quality is suggested. Such seasonal and environmental associations should be considered when assessing changes of male fertility-related parameters over time.
BACKGROUND: Male fertility is progressively declining in many developed countries, but the relationship between male infertility and environmental factors is still unclear. OBJECTIVES: To assess the influence of environmental temperature and air pollution on semen parameters, using a big-data approach. METHODS: A big data analysis of parameters related to 5131 men, living in a province of Northern Italy and undergoing semen analyses between January 2010 and March 2016 was performed. Ambient temperature was recorded on the day of analysis and the 90 days prior to the analysis and the average value of particulate matter (PM) and NO2 in the year of the test. All data were acquired by geocoding patients residential address. A data warehouse containing 990,904,591 data was generated and analysed by multiple regressions. RESULTS: 5573 semen analyses were collected. Both maximum and minimum temperatures registered on the day of collection were inversely related to total sperm number (p < .001), non-progressive motility (NPrM) (p < .005) and normal forms (p < .001). Results were confirmed considering temperature in the 30 and 60 days before collection, but not in the 90 days before collection. Total sperm number was lower in summer/autumn (p < .001) and was inversely related with daylight duration (p < .001). PM10 and PM2.5 were inversely related to PrM (p < .001 and p < .005) and abnormal forms (p < .001). CONCLUSIONS: This is the first evaluation of the relationship between male fertility-related parameters and environment using a big-data approach. A seasonal change in semen parameters was found, with a fluctuation related to both temperature and daylight duration. A negative correlation between air pollution and semen quality is suggested. Such seasonal and environmental associations should be considered when assessing changes of male fertility-related parameters over time.