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URPP Dynamics of Healthy Aging

Stress Management 50+ / Resilience I


Swiss Perimenopause Study

In this study, changes and relationships between biopsychosocial variables in 135 healthy women aged 40-55 in the perimenopause were examined from the perspective of healthy aging. Within this longitudinal study, the comparison of hormone profiles, the examination of genetic dispositions, and the assessment of dispositional and variable psychosocial constructs are made to receive a better understanding of this multi-faceted time in women’s lives. Particular attention is paid to hormonal and (epi-)genetic changes, specifically the methylation of estrogen-receptor-genes. Our prior studies showed that gene modification through methylation is involved in female healthy aging as well as depressive symptoms.
A follow-up study of this cohort a year after the initial data collection will allow an even more in-depth, longitudinal perspective on the menopausal transition.

Further information can be found  here.

Project status: ongoing (2018-2024)
Contact: Jessica Grub, Martina Piraccini and Prof. Dr. Ulrike Ehlert



Biopsychosocial factors of healthy aging - Women’s health study 40+

In this study, we investigated a total of 245 healthy women aged 40 to 73. The study was based on a biopsychosocial model of health and healthy aging. The results of this study support the assumption of an interplay between age-related changes in biological factors (e.g. hormones) and psychosocial aspects (e.g. relationship quality and optimism) on health in middle-aged and older women.  A follow up investigation of this sample will target the characterisation of longitudinal changes in hormon status and psychosocial factors in healthy aging women.

Project status: ongoing (2017-2024)
Contact: Serena Fiaccoand Prof. Dr. Ulrike Ehlert



Men’s vitality and exhaustion predicted by their hormonal profile

Using a prior data set assessed during the first working period of this URPPP we plan to classify vital exhausted and vital men according to their hormone profile by using the most advanced machine learning techniques. In collaboration with our international colleagues, who are leaders in the field of machine learning and network science (Prof. Cannistraci; Tsinghua University, Beijing, China), the latest approaches will be evaluated to correctly classify men based on their hormone profile obtained from a saliva sample. In case of a successful classification performance, a commercial test could be developed from this in a further procedure.

Project status: ongoing (2021-2024)
Contact: Dr. Andreas Walther and Prof. Dr. Ulrike Ehlert

Weiterführende Informationen

Bild von Prof. Dr. Ulrike Ehlert

Head of Research Group

Prof. Dr. Ulrike Ehlert

Principal Investigator