Mechanisms of Multimorbidity – from Populations to Cells

The overarching goal of my proposal is to deliver multimorbidity research that challenges and re-designs current thinking by combining agnostic, i.e. hypothesis-free discovery approaches with targeted hypothesis testing, bespoke experimental follow-up and validation. My proposal is to consider and integrate data across all diseases in diverse populations and at different life stages with focused clinical challenges and in-depth molecular investigations. By building and leading a team that delivers data-driven, solution-oriented multimorbidity science, I will address the following synergistic objectives structured to deliver my 3 main aims: 1) systematically map disease co-occurrence and estimate the prognostic impact of having multiple diseases, 2) accelerate early detection by identifying those at risk of developing disease clusters with the highest prognostic impact, and 3) enable precision prevention by understanding the underlying molecular mechanisms, i.e. shared causes driving the development of multiple diseases using multi-omic approaches at single-cell resolution and across the whole spectrum of common and rare diseases. The data opportunities and methods brought together to deliver this work reflect the reality of the ethnic and pathological diversity present in modern society with profound inequalities and thereby enable translatable solutions to clinical challenges that matter globally. The agnostic investigations of this proposal will identify and map previously unsuspected connections across diseases, organs and pathologies; a fundamentally different, data-driven computational approach that is complemented by scientifically prioritised, pre-specified investigations, and enables their parallel pursuit through the results it generates. I have provided scientific leadership for each of these currently separate areas, brought together here for the first time, and the transdisciplinary nature of my proposal reflects the reality of my professional profile.