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Recurrent miscarriage as a complex phenotype: Harnessing large-scale clinical data to uncover underlying biological pathways

Project description

Biological insight into recurrent miscarriage

Recurrent miscarriage is the occurrence of three or more consecutive pregnancy losses and undoubtedly is mentally and physically devastating for couples. Maternal age, uterus abnormalities or immune reactions may be underlying causes. Delineating the complex causes of recurrent miscarriage would help develop novel treatments. To address this medical need, the RMCmplxPheno project funded by the Marie Skłodowska-Curie Actions programme proposes to study electronic health records to identify clinical phenotypes associated with recurrent miscarriage. Machine learning will be applied for the classification of patients, while genetic analyses will provide important insight into the biological mechanisms.

Objective

Recurrent miscarriage (RM) affects 1% of couples trying to conceive and has a wide range of negative physical and mental impacts yet still has few evidence-based, preventative treatments. RM is used as a catch-all term for an event with diverse underlying causes. Broad categorisation hampers research targeting specific causes that could identify potential therapeutic avenues. This generalisation compounds trauma for patients and the path to successful pregnancy is unclear.
As a MSCA fellow at the University of Copenhagen, I will generate deeper understanding of RM aetiology by identifying granular subgroups of RM and elucidating their underlying biology. To do so I will: 1) Establish novel phenotypes derived from clinical data in electronic health records, including ultrasound images, and use these phenotypes to identify clinical phenotypes driving current miscarriage classification systems. 2) Apply hypothesis-free unsupervised machine learning to clinical data to disentangle complex phenotypes of RM into clinically relevant subgroups. 3) Employ genetic analyses to characterise biological pathways underlying these RM subgroups and identify potential therapeutic avenues.
This fellowship will allow me to apply my skills and expertise in large-scale biomedical data analysis and genetics to a new field in which I will pursue a long-term career. In particular it will provide training in field specific scientific knowledge (obstetrics and gynaecology), cutting edge techniques (machine learning) and transferable skills towards scientific leadership (research management).
Taken together the outcomes of this interdisciplinary research will have ramifications for researchers, clinicians and patients. For researchers, a granular understanding of RM and its causes will enable discovery of novel therapeutic avenues. For clinicians, it would assist clinical decision making towards personalised treatments. For patients, alleviation of trauma through empowerment with information.

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2021-PF-01

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Coordinator

KOBENHAVNS UNIVERSITET
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 173 080,80
Address
NORREGADE 10
1165 KOBENHAVN
Denmark

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Region
Danmark Hovedstaden Byen København
Activity type
Higher or Secondary Education Establishments
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Total cost

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