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The relationship between COVID-19 and cardio-metabolic health risks

2024/11

background

Covid-19 has been more common and more hazardous among those with pre-existing obesity, hypertension, diabetes and other chronic diseases, and short-term studies have reported an excess in stroke events among those exposed to infection from Covid-19. However, the determinants and mediators of such associations are largely unknown, as are the long-term cardio-metabolic consequences associated with Covid-19. In addition, while the benefits of flu-vaccination for preventing cardiovascular events is well established, the consequences of Covid-19 related levels of immunoglobulins (Ig) acquired by natural or vaccine-induced immunity to subsequent cardiovascular risk is unknown. Hypothesis-generating clinical trials and observational studies suggest that levels of immunoglobulins are associated with lower risk of myocardial infarction, heart failure and stroke.

This project aims to assess associations between Covid-19 infection, vaccination and specific immunity-acquired characteristics with cardio-metabolic consequences in a large contemporary UK population. The UK Biobank is a prospective cohort of 0.5 million adults with a wealth of information on lifestyle, medical history, biochemistry, metabolomics and proteomic assays, genetics, morbidity and mortality. GP data on clinical events are available, as well as Covid-19 serology, including antibody negative cases, IgG positive cases and IgM positive cases.

The aims of the project are to examine:

  1. the associations between pre-existing cardio-metabolic risk factors and Covid-19 infection
  2. the associations between Covid-19 and subsequent non-fatal and fatal cardio-metabolic events
  3. potential novel mediators and pathways of such associations among individuals with different Covid-19 related characteristics.

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

The student will gain experience in chronic disease epidemiology, population health and analysis of large-scale prospective data. They will develop skills in conducting systematic literature reviews, analytical techniques, research planning, statistical programming, data analysis, and presentation skills. The student will be supported to publish peer-reviewed papers during their DPhil.

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING

Training in advanced statistics, epidemiological methods, genetic epidemiology, statistical programming, and scientific writing might be provided as needed. Attendance at seminars, workshops and courses provided by NDPH and University will also be encouraged. The candidate will have the opportunity to present their research work at relevant international/national conferences. 

PROSPECTIVE  STUDENT

Candidates should have a Master's degree in genetic/epidemiology, medical statistics or a closely related discipline. Proficiency in conducting epidemiological analyses with R, Stata, Python or SAS is essential.

Supervisors

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