Improving the prognostic value of multimorbidity through the integration of selected biomarkers to the comprehensive geriatric assessment: An observational retrospective monocentric study.

Background: Multimorbidity (MM) burdens individuals and healthcare systems, since it increases polypharmacy, dependency, hospital admissions, healthcare costs, and mortality. Several attempts have been made to determine an operational definition of MM and to quantify its severity. However, the lack of knowledge regarding its pathophysiology prevented the estimation of its severity in terms of outcomes. Polypharmacy and functional impairment are associated with MM. However, it is unclear how inappropriate drug decision-making could affect both conditions. In this context, promising circulating biomarkers and DNA methylation tools have been proposed as potential mortality predictors for multiple age-related diseases. We hypothesize that a comprehensive characterization of patients with MM that includes the measure of epigenetic and selected circulating biomarkers in the medical history, in addition to the functional capacity, could improve the prognosis of their long-term mortality.

Methods: This monocentric retrospective observational study was conducted as part of a project funded by the Italian Ministry of Health titled "imProving the pROgnostic value of MultimOrbidity through the inTegration of selected biomarkErs to the comprehensive geRiatric Assessment (PROMOTERA)." This study will examine the methylation levels of thousands of CpG sites and the levels of selected circulating biomarkers in the blood and plasma samples of older hospitalized patients with MM ( = 1,070, age ≥ 65 years) recruited by the Reportage Project between 2011 and 2019. Multiple statistical approaches will be utilized to integrate newly measured biomarkers into clinical, demographic, and functional data, thus improving the prediction of mortality for up to 10 years.

Discussion: This study's results are expected to: (i) identify the clinical, biological, demographic, and functional factors associated with distinct patterns of MM; (ii) improve the prognostic accuracy of MM patterns in relation to death, hospitalization-related outcomes, and onset of new comorbidities; (iii) define the epigenetic signatures of MM; (iv) construct multidimensional algorithms to predict negative health outcomes in both the overall population and specific disease and functional patterns; and (v) expand our understanding of the mechanisms underlying the pathophysiology of MM.