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CCU022: Genomics of multimorbidity and CVD associated with susceptibility to COVID-19 infection and complications

Safe People

Organisation name

University of Cambridge

Organisation sector

Academic Institute

Applicant name(s)

Michael InouyeHoward Tang

Sub-licence arrangements (if any)?

No

Safe Projects

Project ID

CCU002

Lay summary

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new virus responsible for the COVID-19 pandemic, which has caused many deaths worldwide. We are worried that some long-term (“chronic”) diseases, such as heart disease and diabetes, can worsen COVID-19 infection and lead to increased risk of death. Also, these diseases often occur together in groups, and a person can have multiple diseases. When this happens, we call this “multimorbidity”. However we do not fully understand why this happens, nor how they may be linked to COVID-19. We therefore wish to study electronic health records (EHR) from hospitals and doctor’s clinics, as well as large-scale electronic databases of genetic and biological data, that cover people from a broad range of social and ethnic backgrounds from across the UK. Our team has expertise in using advanced computer-assisted techniques that can effectively make sense of such complex data, to find patterns and results beyond what is possible with traditional methods. Ultimately, we aim to identify groups of individuals with cardiovascular-related multimorbidities and assess their response to COVID-19 infection, and explore how genetic, biological, and social factors interact to change this response. Our results may help us better identify people who are at greatest risk of disease, and find new possibilities to treat or prevent the disease.

Public benefit statement

Ultimately, we aim to identify groups of individuals with cardiovascular-related multimorbidities and assess their response to COVID-19 infection, and explore how genetic, biological, and social factors interact to change this response. Our results may help us better identify people who are at greatest risk of disease, and find new possibilities to treat or prevent the disease.

Latest approval date

27/05/2021

Safe Data

Dataset(s) name
Data sensitivity level

De-Personalised

Safe Setting

Access type

TRE