Late fertility intentions increase over time in Austria, but chances to have a child at later ages remain low

Partnership trajectories preceding medically assisted reproduction

The number of people who undergo medically assisted reproduction (MAR) to conceive has increased considerably in recent decades. However, existing research into the demographics and the partnership histories of this growing subgroup is limited. Using unique data from Finnish population registers on nulliparous women born in Finland in 1971-77 (n = 21,129; ∼10 per cent of all women) who had undergone MAR treatment, we created longitudinal partnership histories from age 16 until first MAR treatment. We identified six typical partnership trajectories and used relative frequency sequence plots to investigate heterogeneity in partnership transitions within and between these groups. The majority of women (60.7 per cent) underwent MAR with their first partner, followed by women who underwent MAR in a second (21.5 per cent) or higher-order partnership (7.1 per cent), while 10.7 per cent underwent MAR without a partner. On average, women undergoing MAR were relatively young (with around half starting treatment before age 30) and were highly educated with high incomes.

Multi-omics and machine learning for the prevention and management of female reproductive health

Females typically carry most of the burden of reproduction in mammals. In humans, this burden is exacerbated further, as the evolutionary advantage of a large and complex human brain came at a great cost of women's reproductive health. Pregnancy thus became a highly demanding phase in a woman's life cycle both physically and emotionally and therefore needs monitoring to assure an optimal outcome. Moreover, an increasing societal trend towards reproductive complications partly due to the increasing maternal age and global obesity pandemic demands closer monitoring of female reproductive health. This review first provides an overview of female reproductive biology and further explores utilization of large-scale data analysis and -omics techniques (genomics, transcriptomics, proteomics, and metabolomics) towards diagnosis, prognosis, and management of female reproductive disorders. In addition, we explore machine learning approaches for predictive models towards prevention and management. Furthermore, mobile apps and wearable devices provide a promise of continuous monitoring of health. These complementary technologies can be combined towards monitoring female (fertility-related) health and detection of any early complications to provide intervention solutions. In summary, technological advances (e.g., omics and wearables) have shown a promise towards diagnosis, prognosis, and management of female reproductive disorders. Systematic integration of these technologies is needed urgently in female reproductive healthcare to be further implemented in the national healthcare systems for societal benefit.