Biological embedding of social adversity and cardiometabolic risk in severe mental illness: a longitudinal multi-omic study in East London

People living with severe mental illness (SMI), including schizophrenia, bipolar disorder and severe depression, experience substantially earlier cardiovascular disease and premature mortality. These inequalities are not explained solely by diagnosis or medication. Social adversity prevalent in East London -such as deprivation, discrimination, insecure housing, unemployment and loneliness - further affect immune and metabolic biology, accelerating cardiometabolic harm. However, the biological pathways linking adversity to poorer physical-health outcomes in SMI remain insufficiently understood.
This PhD will use the Social Health Hub longitudinal cohort and biobank to identify immune–metabolic signatures through which social adversity may become biologically embedded. East London is a core, actively recruiting and biosampling site within this national study, so participants recruited locally will contribute directly to the analyses. The student will use harmonised social, clinical and cardiometabolic data; analyse longitudinal plasma proteomic and metabolomic data; and prioritise the most plausible pathways for causal follow-up using genetic summary data. The central aim is to establish whether biological information improves identification of people with SMI at highest risk beyond routine clinical and socioeconomic measures.
Year 1 will focus on PPIE-informed refinement of the adversity framework, data and sample-quality checks, a preregistered analysis plan, and baseline analyses. In Year 2, the student will analyse longitudinal immune–metabolic change and cardiometabolic risk. Year 3 will complete incremental prediction, causal prioritisation, targeted validation, translation with clinical and lived-experience partners, publications and thesis submission.
The student will gain practical training in biomarker science, longitudinal epidemiology, reproducible R-based data science, multi-omic integration, causal inference, biobanking governance and public involvement. The project will generate an evidence base for more equitable cardiometabolic monitoring and prevention in people with SMI.