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Health Informatics and Data Science


This workstream will harness routinely-collected linked health datasets and multiple disease registers to study the bi-directional relationship between SMI and cardiometabolic disease: a) To establish metabolic phenotypes, by SMI status, based on multiple metabolic markers, including body weight, blood pressure, cholesterol and glucose or HbA1c (glycated haemoglobin, a marker of longer-term glycaemic status than glucose); b) To relate metabolic phenotypes to incidence of cardiometabolic disease, including premature disease; and c) To determine how (in those with established cardiometabolic disease) metabolic marker phenotypes relate to cardiometabolic complications and the development of SMI. A common theme across these objectives is risk stratification to identify high-risk sub-groups.

This workstream corrects the current lack of research on metabolic patterns

over time in people with SMI. Findings will lead to novel insights into metabolic phenotypes in SMI and the identification of individuals and groups at high-risk of cardiometabolic disease and poor clinical outcomes. This will contribute to the development of improved risk prediction models for SMI, including for people with diabetes and SMI (building on risk models such as

EUROSCORE for diabetes, PRIMROSE and PSychMetric).

Power in Numbers







Team Members 

Olivia Walker

Editor in Chief

Dan Mitchell

Assistant Manager

Noah Patterson

Programming Editor

Tess Anderson

Art Director

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