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ID 300: Cluster analysis of patients with pre-metabolic and metabolic syndrome to determine their association with pathological outcomes, to aid the generation of pre-emptive therapeutic targeting utilising artificial intelligence - approved subject to amending 4.2

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Organisation name

Imperial College Healthcare NHS Trust (ICHT) / Imperial College London (ICL)

Applicant name(s)

Funders/ Sponsors

Safe Projects

Project ID

ID 300

Lay summary

This document is a proposal for the delivery of a study which aims to establish the clinical and socio-economic factors linking MetS and its associated downstream co-morbidities.

Public benefit statement

Metabolic syndrome (MetS), variously known also as syndrome X, insulin resistance, etc., is defined by WHO as a pathologic condition characterized by abdominal obesity, insulin resistance, hypertension, and hyperlipidemia. Metabolic syndrome is a growing epidemic worldwide, with approximately 1 in every 5-adult depending on the country, thought to have metabolic syndrome, the incidence of which increases with age. It has been estimated that in people over 50 years of age, metabolic syndrome affects more than 40% of the population in the USA and nearly 30% in Europe. Metabolic syndrome is associated with an increased risk of multiple chronic diseases (e.g., cardiovascular disease, arthritis, chronic kidney disease, schizophrenia, several types of cancer) and of early death, resulting in an Page 4 of 22 ever-increasing burden on healthcare and economic resources as well as a profound impact on the personal and professional lives of patients. It is thus paramount to understand the clinical and socio-economic factors associated between exact MetS and its associated downstream co-morbidities. This will aid a deeper understanding of the subgroups of patients developing the various complications associated with metabolic syndrome and thus inform the planning for targeted initiatives to reduce the morbidity and mortality associated with downstream pathologies.

Other approval committees

Latest approval date

16/02/2023

Safe Data

Dataset(s) name

Safe Setting

Access type

TRE

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