Hoarding Disorder (HD), recognized as an independent illness in the Diagnostic and Statistical Manual of Mental Disorders for less than a decade, is a debilitating psychiatric disorder with profound socioeconomic impacts. Emerging data shows that hoarding severity correlates with substantial medical burden. Prevalence of clinically significant hoarding behavior is estimated to be between 2 and 4%, with a higher burden in the older population. However, it is believed that hoarding disorder is underdiagnosed. The parent R01MH117114 combines in-person clinical, neuropsychological, and medical frailty assessments with a unique epidemiologic resource, the online Brain Health Registry (BHR) 17, to assess the extent of disability in older adults suffering with hoarding symptoms. To date, over 24,000 subjects have taken hoarding-related questionnaires. In addition, 1554 participants have completed additional surveys performed for validation of hoarding symptoms. Moreover, the BHR includes longitudinal objective and subjective measures of cognition, as well as childhood and medical history. We will classify longitudinal trends of measures of hoarding symptomatology in a subpopulation of the BHR with clinical assessments of hoarding disorders and other psychopathologies. We will then project the whole BHR population to classify longitudinal trends. We will then apply statistical inference and techniques from artificial intelligence to identify predictors of various trends of hoarding symptomatology to find predictors of developing severe hoarding symptoms. Ongoing recruitment through the parent R01 will allow for validation of the predictions made through this work. Moreover, as there is a rapid increase in the number of psychiatric studies using web-based data collection methods rather than in-person clinical assessments, the importance of studying the temporal trends and fidelity of these data collection methods extends beyond the scope of the current study. The present study will provide a proof-of-concept approach for analyzing such data. The work will be carried out by a trained mathematician transitioning into systems medicine whose career goal is to establish an independent career in the field of computational psychiatry. The training provided through this grant will prepare the supplement candidate to submit an independent K01 award on the interactions between late life depression and hoarding disorder to the National Institute of Mental Health. This will be done by providing tailored mentoring by experts in hoarding disorder and systems medicine, access to unique datasets related to various psychopathologies, structured training in grant writing and responsible conduct in research, a structured course in psychopathology, and premier computational resources at the University of Florida.
Sara K. Nutley, Lyvia Bertolace, Luis Sordo Vieira, Binh Nguyen, Ashley Ordway, Heather Simpson, Jessica Zakrzewski, Monica R. Camacho, Joseph Eichenbaum, Rachel Nosheny, Michael Weiner, R. Scott Mackin, Carol A. Mathews. Internet-based hoarding assessment: the reliability and predictive validity of the internet-based Hoarding Rating Scale, Self-Report, Psychiatry Research (2020). doi:https://doi.org/10.1016/j.psychres.2020.113505