Author:
de Girolamo Giovanni,Andreassen Ole A.,Bauer Michael,Brambilla Paolo,Calza Stefano,Citerà Nicholas,Corcoy Rosa,Fagiolini Andrea,Garcia-Argibay Miguel,Godin Ophélia,Klingler Florian,Kobayashi Nene F.,Larsson Henrik,Leboyer Marion,Matura Silke,Martinelli Alessandra,De la Peña-Arteaga Víctor,Poli Roberto,Reif Andreas,Ritter Philipp,Rødevand Linn N.,Magno Marta,Caselani Elisa, ,Bayas Maximilian,Bellivier Frank,Álvarez Narcís Cardoner,Carmellini Pietro,Cevoli Federico,Clemens Julia,Courtet Philippe,Consoli Lorena,Delvecchio Giuseppe,Dobrosavljevic Maja,Etain Bruno,Friedrichsen Hendrik,Kelemen Adrienne,Koukouna Despoina,Mato Eugenia,Mauricio Dídac,Miranda-Olivos Romina,Möbius Lisa,Moltrasio Chiara,Mohn-Haugen Caroline,Nuss Isabelle,Olie Emilie,Pelletier Agnes,Rahman Zillur,Rampi Davide,Repple Jonathan,Resmini Eugenia,Schneider Julia,Toffol Elena
Abstract
Abstract
Background
BIPCOM aims to (1) identify medical comorbidities in people with bipolar disorder (BD); (2) examine risk factors and clinical profiles of Medical Comorbidities (MC) in this clinical group, with a special focus on Metabolic Syndrome (MetS); (3) develop a Clinical Support Tool (CST) for the personalized management of BD and medical comorbidities.
Methods
The BIPCOM project aims to investigate MC, specifically MetS, in individuals with BD using various approaches. Initially, prevalence rates, characteristics, genetic and non-genetic risk factors, and the natural progression of MetS among individuals with BD will be assessed by analysing Nordic registers, biobanks, and existing patient datasets from 11 European recruiting centres across 5 countries. Subsequently, a clinical study involving 400 participants from these sites will be conducted to examine the clinical profiles and incidence of specific MetS risk factors over 1 year. Baseline assessments, 1-year follow-ups, biomarker analyses, and physical activity measurements with wearable biosensors, and focus groups will be performed. Using this comprehensive data, a CST will be developed to enhance the prevention, early detection, and personalized treatment of MC in BD, by incorporating clinical, biological, sex and genetic information. This protocol will highlight the study's methodology.
Discussion
BIPCOM's data collection will pave the way for tailored treatment and prevention approaches for individuals with BD. This approach has the potential to generate significant healthcare savings by preventing complications, hospitalizations, and emergency visits related to comorbidities and cardiovascular risks in BD. BIPCOM's data collection will enhance BD patient care through personalized strategies, resulting in improved quality of life and reduced costly interventions. The findings of the study will contribute to a better understanding of the relationship between medical comorbidities and BD, enabling accurate prediction and effective management of MetS and cardiovascular diseases.
Trial registration: ISRCTN68010602 at https://www.isrctn.com/ISRCTN68010602. Registration date: 18/04/2023.
Funder
Fondazione Regionale per la Ricerca Biomedica
Norges Forskningsråd
Sächsisches Staatsministerium für Wissenschaft und Kunst
Departament de Salut, Generalitat de Catalunya
Sweden’s Innovation Agency
Agence Nationale de la Recherche
German Federal Ministry of Education and Research
Publisher
Springer Science and Business Media LLC
Cited by
1 articles.
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1. Probabilistic Model of Patient Classification Using Bayesian Model;International Journal of Reliable and Quality E-Healthcare;2024-07-17