Endocrine Abstracts

Infertility affects 15%–25% of couples, and male reproductive issues related primarily to semen quality play a role in many cases. Furthermore, impaired semen quality has been linked to adverse long-term health outcomes. A key pillar in the evaluation of men from infertile couples is the traditional semen analysis, which by and large has remained unchanged for decades, including assessment of semen volume, sperm concentration, motility and morphology. Though, the process of fertilization (in vivo) is much more complex that what is covered with these parameters. At present, most treatment options bypass natural reproduction barriers by enhancing ovulation and/or increasing the chance of oocyte/sperm interaction, whereas only few treatments target the underlying cause of infertility. The causes of male infertility are multifactorial involving both genetic factors and exposures in fetal life and adulthood. However, the interplay between and relative contributions of health behavior, psychosocial, environmental, genetic, endocrine, metabolic, immunologic, and epigenetic factors as well as pathogenic processes at different stages of life are still poorly understood. Despite the availability of increasingly advanced diagnostic and therapeutic techniques, approximately 30% of infertile couples do not obtain a live birth after fertility treatment. Reaching beyond the traditional semen analysis and implementing artificial intelligence methods is essential to further improve our ability to a) study risk factors for male (and female) infertility, b) identify new biomarkers to diagnose subfertility and predict fecundity and response to treatment, and c) identify subgroups of infertility patients at risk of long-term health impairment. With the establishment of the ReproUnion Biobank and Infertility Cohort (RUBIC), including 5000 well-characterized infertile Danish and Swedish couples, we hope to create a framework for this approach in a multidisciplinary research environment. An unresolved issue is, however, how to apply the artificial intelligence methods and develop the new interdisciplinary collaborations needed.