MULTIPREVENT: Integrated screening for smoking-related multimorbidity using low-dose chest computed tomography.
Authors
Affiliations (2)
Affiliations (2)
- Medical University of Gdańsk, Maria Skłodowska-Curie str. 3A, Gdańsk, 80-210, Poland.
- Medical University of Gdańsk, Maria Skłodowska-Curie str. 3A, Gdańsk, 80-210, Poland. Electronic address: [email protected].
Abstract
Tobacco consumption, combined with individual genetic predispositions, contributes to an age-dependent risk not only for lung cancer but also for other non-communicable diseases (NCDs) such as cardiovascular disease (CVD), chronic obstructive pulmonary disease (COPD), osteoporosis, and diabetes. The MULTIPREVENT project aims to validate whether low-dose computed tomography (LDCT) of the chest, combined with simple biomarkers, functional tests, and genomic profiling, can serve as an effective tool for comprehensive health assessment and risk prediction of multimorbidity in adults. The study is based on a prospective epidemiological design involving 3000 participants from the MOLTEST-BIS lung cancer screening cohort (2016-2018). These participants, aged 50-79 years (during MOLTEST-BIS) and with a smoking history of at least 30 pack-years, will undergo two follow-up assessments in 2025-2027 and 2030-2032. Each follow-up includes LDCT, spirometry, standardized blood pressure measurement, anthropometric evaluation, biomarker assessment (lipid profile, lipoprotein(a), glycated haemoglobin), and health-related questionnaires. Genetic profiling will be performed using the Illumina Infinium Global Screening Arrays approach to identify inherited predispositions to major NCDs. All data, clinical, imaging (including radiomics), molecular, and genetic, will be integrated through machine learning algorithms to develop AI-based risk prediction models. The MULTIPREVENT study is expected to generate a wide range of scientific, clinical, and infrastructural results that will serve as a foundation for future public health initiatives in integrated prevention. By linking imaging and biochemical markers, genetic susceptibility, and clinical parameters within a longitudinal design, MULTIPREVENT will establish data-driven, AI-supported prevention strategies aimed at reducing morbidity and mortality among adults exposed to tobacco. The project will also serve as a model for population-based multimorbidity prevention programs.