Title: Artificial Intelligence to improve Cardiometabolic Risk Evaluation using CT (ACRE-CT)
Funder: Department of Health and Social Care

Abstract: Obesity, diabetes and cardiometabolic disease are global health economic burdens. Depot-specific adipose tissue (AT) inflammation, particularly in visceral AT, is an indicator of cardiometaboilic risk and a potential therapeutic target. However, visceral AT inflammation is not identifiable by any current blood or imaging biomarkers and does not correlate with simple indicators of obesity. This major gap in our diagnostic approach gives incomplete or even misleading information, wastes tests and drug treatments, and fails to take full advantage of powerful new drugs, that cannot be optimally targeted. We discovered new AI imaging biomarkers, derived from routine clinical CT scans, that provide highly quantitative readouts of depot-specific AT inflammation, validated against the pivotal molecular signatures of cardiometabolic risk, from transcriptional analysis of AT biopsies. Caristo Diagnostics has developed a clinically-applicable technology, FatHealth, that quantifies cardiometabolic risk from routine CT scans, acquired in everyday clinical practice. We will work with collaborating Universities and NHS Hospitals to develop FatHealth into a carefully tested commercial product. In the next 18 months we will analyse 20,000 CT scans (from existing NHS imaging resources) to refine the technical aspects of FatHealth across different CT platforms and scan types and validate the ability of FatHealth to stratify cardiometabolic risks using detailed metabolic phenotyping. We will develop an automated, regulatory-approved, web-based portal to deliver FatHealth for use in healthcare systems. We will work with clinical NHS organisations and patient groups to evaluate the clinical effectiveness of FatHealth in people and patients at risk of diabetes. We have formed collaborations with GPs in Primary Care and with the Academic Health Science Networks (AHSNs). FatHealth will fit seamlessly into current clinical pathways to transform cardiometabolic risk assessment, by generating new data from analysis of CT scans that are already widely performed in the NHS and other healthcare systems globally.

Lay abstract: The diabetes global epidemic affects 8% of the world's population and causes spiralling healthcare costs (~&pound;800 billion). Fat tissue (called adipose tissue) can become inflamed. This occurs differently in various parts of the body. This inflamed tissue can be a critical factor in the major complications of diabetes, such as cardiovascular disease (CVD). But not all fat tissue is considered 'harmful'. The risk factor relates to where in the body it is situated, and the biological characteristics of the fat. Even in people who are not obese, inflamed fat hidden in certain locations can still be a risk factor. Currently, none of the routine scans or tests can identify inflammation in fat tissue. This study will focus on how best to detect this tissue using routine scans. Our new method, called FatHealth, detects fat tissue inflammation using new artificial intelligence techniques applied to routine computed tomography ( CT ) scans. FatHealth can identify people who may be at risk of developing diabetes, and also people with diabetes who are at high risk of death from cardiovascular disease. This new method is better than other diagnostic tests for this purpose. Caristo Diagnostics is a new technology company associated with the University of Oxford. It will collaborate with other institutions to transform FatHealth into a commercial product. The intention is to analyse 20,000 CT scans to train its AI algorithm. We plan to develop an automated web-based system for rapid and reliable AI analysis of CT scans to deliver accurate risk predictions. We will work with clinical NHS organisations and patient groups to evaluate the effectiveness of the FatHealth test in patients at risk of diabetes. We will conduct a clinical study that compares the results of FatHealth to the current method for diagnosing diabetes and pre-diabetes (the Oral Glucose Tolerance Test). Recruitment: Patients with a BMI>25kg/m2 and no prior diagnosis of diabetes and matched control patients having CT scans (to assess the lungs or the heart) will be identified from medical records. Approach will either be made by letter, asking whether they would be happy to participate in the study, or in person during their hospital visit after they had their scan. Visits: The patients will be invited for a single hospital visit, where a blood sample will be taken along with an oral glucose tolerance test. This entails drinking a glass of glucose in water, and blood glucose and insulin then being measured at 1, 2 and 3 hours. By the end of the project, our intention is to have FatHealth adopted into routine use, thereby saving the NHS considerable costs and improving the lives of many at-risk patients.

Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;7.3 MANAGEMENT AND DECISION MAKING;4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: ORAL AND GASTROINTESTINAL;CANCER AND NEOPLASMS;STROKE;CARDIOVASCULAR;METABOLIC AND ENDOCRINE