Philip Pallmann, Madeleine M. Lowery, Laura Mills, Grzegorz Witkowski, Astri Arnesen, Monica Busse, Esther Cubo, Hans H. Jung, Cheney Drew, Philippa Morgan-Jones, Nigel Kirby, Bernhard Landwehrmeyer, and Beth Ann Griffin
Background The course of Huntington’s disease (HD) is believed to be modulated by lifestyle and genetic factors. However, we do not understand how the interplay of these affects disease progression. An efficient method of measuring lifestyle factors involves the use of digital monitoring devices, but their long-term use in clinical HD populations has not yet been explored. Aim Investigate the use of digital technologies in a longitudinal observational study to inform our understanding of the contribution of multi-domain lifestyle and genetic factors in the progression of HD. Methods We plan to recruit 300-450 people with early to mid-stage HD to a 12-month observational study measuring aspects of physical activity, nutrition and sleep. Participants with existing genome wide association study (GWAS) data will be preferentially recruited. Assessment of dietary, sleep and physical activity habits will be performed at baseline and 12-month follow-up Clinical measures will be obtained from the corresponding annual Enroll-HD assessment (within 8 weeks of the DOMINO-HD assessment). Each participant will wear a Fitbit for the duration of the study. Lifestyle, genetic and clinical data will be linked and propensity score weighting methodology will be applied to examine the causal effect of the multi-domain lifestyle and genetic measures on HD progression. Results The start of recruitment was delayed by 10 months due to Covid-19. As of 1st July 2021, we have recruited 36 participants across 5 clinical sites, with recruitment planned to continue until March 2022. Conclusion Successful collection of longitudinal lifestyle data, combined with functional clinical measures and genetic factors will allow, for the first time, the investigation of causal relationships between environmental and genetic modifiers with HD progression. We can then use the information generated to design lifestyle interventions aimed at improving quality of life and prognosis in HD.