How Hidden Markov Models Could Elucidate Multimorbidity in Aging

Listen to a blog summary of a research paper published by Aging in Volume 14, Issue 24, entitled, "12-year evolution of multimorbidity patterns among older adults based on Hidden Markov Models." _______________________________________ Multimorbidity is a term that refers to living with two or more chronic diseases at the same time, and the prevalence of this phenomenon increases with age. In addition, humans tend to evolve and transition into distinct patterns of multimorbidity. These still ill-defined patterns of multimorbidity may offer a window of opportunity for researchers. Since the aging population continues to grow in many parts of the world, researchers are motivated to better understand these patterns and how they evolve and transition over time in order to develop interventions and therapeutics for healthier aging. However, this is a challenging task for several reasons. “Multimorbidity is associated with a higher risk of polypharmacy and decreased quality of life, and challenges the decision-making of clinicians that lack effective guidelines for the management and treatment of patients with cohexisting complex diseases [4].” While researchers have investigated multimorbidity, not all studies are created equal—rendering meta-analyses largely incongruent (thus far). One reason the evolution of multimorbidity patterns is so challenging to study is because most study designs are not powered to account for the dynamic nature of multimorbidity in old age. Another reason is that various studies use different lists of diseases. (Some studies include ten conditions or less and others include 200+ conditions.) Finally, most statistical methods used to organize data are not able to properly handle the complexity of multimorbidity. “Exploring how multimorbidity patterns evolve throughout people’s lives and the time subjects remain within specific patterns is still an under-researched area [7, 8]. The understanding of how diseases cluster longitudinally in specific age groups would pave the way to the design of new prognostic tools, as well as new preventive and, eventually, therapeutic approaches.” Full blog - https://aging-us.org/2023/01/how-hidden-markov-models-can-help-elucidate-multimorbidity-in-aging/ DOI - https://doi.org/10.18632/aging.204395 Corresponding authors - Albert Roso-Llorach - aroso@idiapjgol.org, Amaia Calderón-Larrañaga - amaia.calderon.larranaga@ki.se Keywords - multimorbidity, older adults, longitudinal population-based study, aging, Hidden Markov Models About Aging-US Launched in 2009, Aging-US publishes papers of general interest and biological significance in all fields of aging research and age-related diseases, including cancer—and now, with a special focus on COVID-19 vulnerability as an age-dependent syndrome. Topics in Aging-US go beyond traditional gerontology, including, but not limited to, cellular and molecular biology, human age-related diseases, pathology in model organisms, signal transduction pathways (e.g., p53, sirtuins, and PI-3K/AKT/mTOR, among others), and approaches to modulating these signaling pathways. Please visit our website at https://www.Aging-US.com​​ and connect with us: SoundCloud - https://soundcloud.com/Aging-Us Facebook - https://www.facebook.com/AgingUS/ Twitter - https://twitter.com/AgingJrnl Instagram - https://www.instagram.com/agingjrnl/ YouTube - https://www.youtube.com/agingus​ LinkedIn - https://www.linkedin.com/company/aging/ Pinterest - https://www.pinterest.com/AgingUS/ For media inquiries, please contact media@impactjournals.com.