ProfessorJonathan Valabhji

EU Underwrite - Innovate UK: Real-world evidence for decisions in diabetes (REDDIE) (2023-2026)

Randomised controlled trials are the cornerstone of evidence-based medicine. However, the digitisation of real-world data (RWD) including data from devices, wearables, and electronic health records in large national registries provides opportunities to demonstrate efficacy and safety of innovative technologiesincluding drugs, devices, diagnostics, and digital health. These data are particularly relevant to long-term conditions such as diabetes mellitus, where drugs, lifestyle interventions, and digital technologies often work together. To better utilise RWD in diabetes for regulatory decision making, a development of standards, guidance, and an assessment of the efficacy to effectiveness gap is needed. REDDIE (Real-World Evidence for Decisions in Diabetes) aims to explore how RWD can complement RCTs to improve efficacy, safety, and value for money of technologies to prevent and treat diabetes. The overall aim of REDDIE is to support the use of RWD in diabetes and health-related research, which will maximise Europe’s scientific expertise and know-how to benefit people with diabetes, resulting in safer, more efficient, and cost-effective interventions. We thus aim to engage with stakeholders such as regulatory and HTA authorities and co-develop evidentiary standards for the collection, assessment, and acceptability of RWD. We will then develop and validate state-of-the art modelling techniques using synthetic data derived from large national registries to better assess outcomes of interventions using RWD. We will use data from four large national registries to elucidate the gap between outcomes in RCTs and RWD studies, and understand the factors that affect this gap. Finally, we will test the ability of machine learning to facilitate the better use of RWD. REDDIE will generate standards for RWD use for the evaluation of medicines and other interventions by regulatory authorities and HTA bodies.

Research questions: Is there geographic and demographic variation in outcomes in people with type 2 diabetes (T2D) and diabetic foot ulcer disease (DFUD), such as cardiovascular disease (CVD), mortality, and amputation? Which risk factors explain the excess risk of CVD specific to DFUD? Which elements of previous interventions in people with DFUD are effective? Can a complex intervention aimed at medication optimisation and behaviour change be effective, cost-effective and sustainable in preventing CVD events in people with DFUD? Background: Individuals with T2D and DFUD (~50-60,000 in the UK) have CVD much in excess of those with T2D alone. CVD is the leading cause of death in this group, but interventions to reduce event rates have not been developed or tested. Traditional CVD interventions may not be appropriate for this high-risk population; for example, weight-bearing physical activity and some glucose lowering medications are contraindicated in DFUD. An evidence-based CVD prevention intervention for people with T2D and DFUD is a significant unmet patient need. Aim: Reduce CVD events (including mortality) in adults with T2D and current/previous DFUD. Objectives: Evaluate risk of worsening morbidity or mortality in DFUD Evaluate existing interventions to reduce CVD risk in DFUD Develop a multifactorial complex intervention to prevent CVD events in T2D and DFUD Evaluate the intervention s effectiveness, cost-effectiveness and sustainability Methods: There will be four workstreams (WSs), with PPI/E integrated throughout. WS1: Analyse routinely-collected primary and secondary care data to understand the healthcare needs, disease burden and sociodemographic/geographic factors associated with DFUD, and estimate the incidence of CVD-related events and mortality. WS2: Perform a systematic review, meta-analysis and mixed treatment comparison of existing interventions for people with DFUD. We can then use evidence about what has, and has not, worked well before to design our intervention. WS3: Develop a complex intervention to reduce CVD events in people with T2D and DFUD by combining patient and healthcare professional views (qualitative study), findings from WS1 and WS2, PPI/E input, and our in-depth experience. It will likely use one-to-one, group and online sessions to target medication optimisation and behaviour change, including seated exercise. We will estimate the expected economic benefit of intervention implementation. WS4: Perform a randomised controlled trial, informed by previous WSs, with internal feasibility study and process evaluation to test the intervention effectiveness (an extended Major Adverse Cardiovascular Event [MACE] composite as primary outcome) and cost-effectiveness for preventing CVD events. Participants (n=392) will be randomised 1:1 to intervention or control conditions. Delivery timelines: PPI: Pre-funding to post-funding (Deliverable: Month 63) WS1 and WS2: Months 1-12 WS3: Pre-funding to month 39 WS4: Months 4-63 (including setup) Dissemination: Primarily months 13-24, 40-52 and 60 onwards. Anticipated Impact and Dissemination: The primary impact, if achieved, will be a substantial reduction in CVD outcomes for those with DFUD. Academic and non-academic outputs will be disseminated through well-established methods, including press releases, social networks, open days, and relevant charities. Wider effects include improved disease self-management ability and quality of life. These benefits will likely translate to economic benefits for the NHS.

Our Global Health Research Network is a partnership between research intensive organisations in Bangladesh, India, Pakistan, Sri Lanka and the UK, and key collaborators in Australia and Singapore. Our primary ambitions are to: i. improve understanding of the mechanisms underlying the high burden of Type-2 diabetes (T2D) and cardiovascular disease (CVD) in South Asians, and ii. improve prevention and control of these major diseases through personalised, health systems and policy-based interventions, that are scalable, sustainable, effective and equitable. Our research comprises four interconnected themes. Theme 1 (Population health) will determine cardiovascular and metabolic outcomes at 4 years, for the ~150,000 South Asian men and women in our Surveillance study (baseline evaluation 2018-21). Using face-face and electronic methods, we will identify ~6,800 cases of incident T2D, and ~3,100 of incident CVD. We will complete molecular phenotyping of baseline samples, and use the data to investigate how lifestyle (diet and physical activity), environmental, genomic and molecular factors drive the increased risk of T2D and CVD amongst South Asians. Theme 2 (Digital Interventions) will test whether digital intervention with i. AI driven education or ii. gamification for behaviour change reduces weight (primary endpoint) and improves quantitative measures of cardiometabolic health, amongst South Asian men and women aged 30-60 years with overweight or obesity (BMI>23kg/m2), compared to usual care over 2 years. We embed the trial within the Surveillance study (Theme 1), bringing rich baseline data for participants, and evaluation in a wide range of South Asian settings. Theme 3 (Training and care co-ordination) focuses on strengthening primary healthcare systems. We connect health surveillance to computational guidelines, care pathway engines, training and digital health interventions, linking seamlessly with work in Themes 1 and 2. We collaborate with WHO to advance international standards for computational guidelines and decision support, and develop modular digital solutions for care co-ordination and care pathway delivery for T2D and CVD, that are scalable, evidence based, interoperable and generalisable. Theme 4 (Policy and environmental interventions) addresses community- and policy-based actions for health promotion and prevention of T2D and CVD in South Asians. In partnership with Health Promotion Bureau of Sri Lanka, we evaluate 'whole of society' approaches to intervention that target the demand and supply-side factors identified through our policy and environmental mapping (Theme 1). Evaluation at national scale will provide powerful evidence to inform future health policy in South Asian settings. Capacity building. The research themes will be supported by a programme of capacity building at our collaborating Centres of Excellence in genetic and environmental epidemiology, as well as in digital health, health systems, health policy and implementation science research. Our programme of global health research will improve understanding of T2D and CVD in South Asians, including underlying mechanisms, approaches to identification of high-risk individuals, and scalable strategies for personalised and community wide prevention and control strategies. Results will contribute to transformational change of health promotion and disease prevention amongst South Asians, who represent ~25% of the world's population.

Multimorbidity is the presence of two or even several more major health condition at the same time within a single person. It is a growing problem in the United Kingdom in part of the combination of increasing lifespans with unhealthy lifestyles. Although multimorbidity is most common in older age, poor diet, physical inactivity, and obesity in young adulthood and middle-age are likely important causes of the tendency to multimorbidity. We suspect that 3 conditions in particular - diabetes, hypertension, and depression - cause a large portion of multimorbidity because of the way that they work together to affect so many systems of the body. If this is the case, then programmes for focused lifestyle interventions could make a big difference in preventing multimorbidity. Unfortunately, it is difficult to know how these conditions in young and middle-age affect multimorbidity and what works to prevent it because no studies track population across many stage of life, while measuring the impact of interventions. This research progamme will tackle these questions with 3 parts. First, it will use long-term data from the UK and London populations to uncover the most common combinations of diseases occurring and whether there are particular steps and pathways in their formation. After finding those combinations and estimating how rapidly they develop at different times in life, we will construct a new computer-based model, called a "life-course simulation model" that can identify the optimal times in life, combinations of behaviors, risk factors, and diseases that cause the greatest illness over life. The computer model will also examine the effect of different ways of preventing the accumulation of multimorbidity, such as using focused support to change lifestyle in people at risk for hypertension, diabetes, and depression. The third part of the research programme will use these data and the computer model to measure the effect of two National Health Service initiatives that support people at risk of diabetes or with diabetes to change lifestyle diabetes. Since lifestyle behaviors are also crucial to hypertension, depression, and other conditions, these "natural experiments" may have a big effect on multimorbidity as well. This work will require a team with expertise in diverse areas - including medical care, epidemiology, behaviour change, mathematics, and computer modeling. This study will answer important questions about what types of conditions are causing the most multimorbidity and what are the best ways to act to prevent them. The computer model and research that results will give doctors, health planners, and the public new way to improve health in communities for the years to come. The work will be first-of-its-kind in the way that it assesses chronic conditions as they form in combination from young adulthood to older adulthood. It will also be new in the way it uses computer models and natural experiments in combination to find out what works best to reduce multimorbidity.