R01 MH117114-03S2: Predicting Populations At-Risk of Developing Pathological Hoarding

Luis Sordo Vieira, PhD
Parent: R01 MH117114
Carol A. Mathews, MD
Robert Scott Mackin, PhD
Jeremiah M. Scharf, MD, PhD

Project Summary

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.


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 (2020) Internet-based hoarding assessment: The reliability and predictive validity of the internet-based Hoarding Rating Scale, Self-Report. Psychiatry Research 294:113505. PMID: 33070108 PMCID: PMC8080473 DOI: 10.1016/j.psychres.2020.113505

Luis Sordo Vieira, Reinhard C. Laubenbacher (2022) Computational models in systems biology: standards, dissemination, and best practices. Current Opinion in Biotechnology 75:102702. PMID: 35217296 PMCID: PMC9177621 DOI: 10.1016/j.copbio.2022.102702

Luis Sordo Vieira, Binh Nguyen, Sara K. Nutley, Lyvia Bertolace, Ashley Ordway, Heather Simpson, Jessica Zakrzewski, Marie E. Jean Gilles, Rachel Nosheny, Michael Weiner, R. Scott Mackin, Carol A. Mathews (2022) Self-reporting of psychiatric illness in an online patient registry is a good indicator of the existence of psychiatric illness. Journal of Psychiatric Research 151:34–41 PMID: 35436704 DOI: 10.1016/j.jpsychires.2022.03.022

Luis Sordo Vieira, Andrea Guastello, Binh Nguyen, Sara K. Nutley, Ashley Ordway, Heather Simpson, Jessica Zakrzewski, Christian Archer, Na Liu, Marie E. Jean Gilles, Rachel Nosheny, Michael Weiner, R. Scott Mackin, Carol A. Mathews (2022) Identifying psychiatric and neurological comorbidities associated with hoarding disorder through network analysis. Journal of Psychiatric Research 156:16–24. PMID: 36219904 DOI: 10.1016/j.jpsychires.2022.09.037