Professor Anna Murray, University of Exeter Medical School
Professor Debbie Lawlor, University of Bristol
Dr Andrew Wood, University of Exeter Medical School
Dr Katherine Ruth, University of Exeter Medical School
Reproductive ageing in women is associated with hormonal changes, debilitating symptoms and increased risk of disease. In this project the student will characterise the transition to menopause using large datasets, aiming to identify novel biological pathways involved in the process.
As life-expectancy increases, more women will spend a large proportion of their lives in a post-reproductive state, i.e. after menopause. The transition to menopause is often accompanied by numerous health issues and symptoms, such as sleep disturbance, hot flushes, weight gain and depression, which can be debilitating and last for several years. In addition, post-menopausal women are at increased risk of heart disease, type 2 diabetes and osteoporosis. Hormone replacement therapy (HRT) has been used to treat symptoms and reduce the likelihood of diseases such as osteoporosis, but health scares over increased risk of hormone-responsive cancer in women taking HRT have resulted in decreased usage in the last two decades. The student will investigate the genetic basis of menopausal symptoms to understand the biological processes governing this important transition during female ageing. The project will use innovative analysis tools to mine big data sets and will develop methods that can be applied to other medical research. This work could lead to new treatments/lifestyle modifications to alleviate symptoms or reduce disease risk in older women. For the first time, we will use objective measures of symptoms to study the health impact of the transition to menopause in large numbers of individuals. The supervisory team have access to data from hundreds of thousands of individuals from studies, including UK Biobank, ALSPAC, Women’s Genome Health Study and the Exeter 10,000 study. The project will have the following objectives:
• Define sleep patterns from activity data – over 100,000 individuals in UK Biobank have wrist-worn activity monitor data. The student will use these data to classify sleep patterns, to better understand the nature of sleep disturbance during menopause.
• Quantify temperature changes during sleep to identify hot flushes/night sweats – night sweats are the most frequently reported symptom of menopause. Temperature data from the activity monitors will be used to identify night sweats in large numbers of individuals for the first time.
• Classify HRT usage – the current best treatment for menopausal symptoms is HRT, however fewer women now take HRT following studies demonstrating increased risks of breast cancer with prolonged HRT use. We will investigate the effect of HRT usage on symptoms.
• Analyse the association of genetic variation with menopausal symptoms – once menopausal symptoms have been classified we will use the genetic data to search for genetic associations.
• Determine functional pathways – output from genome-wide association studies will be used to search for causative genes and functional pathways, which will inform us about the underlying biology that determines menopausal symptoms.
• Investigate the effect of menopausal symptoms on health outcomes – we will use the objective menopausal symptoms data and genetic data to investigate how menopausal symptoms influence disease outcomes.
To apply, please complete the application form at https://cardiff.onlinesurveys.ac.uk/gw4-biomed-mrc-dtp-student-2019
by 5pm Friday 23 November 2018.