During the last two decades, research on multimorbidity, i.e., the coexistence of a number of chronic diseases in the same individual, has rapidly increased. As chronic conditions are strongly related to aging, the majority of studies focused on the elderly population. However, until now, only selected aspects of multimorbidity were investigated, such as prevalence and consequences of co-existing diseases. As chronicity is one of the major challenges in the healthcare of aging populations, understanding how chronic diseases distribute and co-occur in the older population is essential.
Recently, a quantitative assessment of multimorbidity (e.g., the evaluation of the coexistence of multiple chronic diseases in the same person, whichever the diseases are) has been performed in order to quantify the dimension of the problem at a population level and to grab the attention of health care providers. Nevertheless, the exclusive use of a quantitative approach to this phenomenon fails to catch the nature and dynamics of the different patterns of co-existing diseases, potentially leading to inadequate care management.
The choice to use the cluster disease approach provides the framework in content (a biological correlate to statistical findings). The hypothesis behind cluster medicine is that the picture of the health status of a population is better understood as a ‘system of disease’ instead of a ‘disease by disease’ perspective. This has serious implications for the concept of causality as instead of considering one cause for one disease, a complex system behind single and aggregated disease should be considered. First, a cluster disease approach can improve our understanding of how different diseases distribute and aggregate in the population and can allow for the identification of specific disease clusters, i.e., the co-occurrence of two or more specific chronic diseases in the same person. This approach can allow us to reveal an important and often neglected underlying biological basis. Genetic, environmental and psychosocial factors may increase a disease-related or a general susceptibility to disease, causing diseases to co-occur in late life.
Second, the development of clusters medicine (from a disease-by-disease perspective to a system-of-disease perspective) probably arises from the pathological perturbation of one or more genetic, proteomic, biological and environmental networks. A holistic approach that uses clinical and experimental data could be the best approach to explain this complexity (network medicine, from one cause-one disease to a complex system).
The main objective of the CLAIMM project is to identify patho-physiological and biological mechanisms as well as risk factors common to specific clusters of chronic diseases and conditions. This will include two actions: identifying and following over time disease clustering using databases with both incident and retrospective assessment of multiple health indicators and outcomes and exploring the mechanisms of specific clusters of diseases using both preclinical and clinical information
To analyze preclinical and clinical data using different and complementary explanatory techniques
To collect, in conformity to the personal expertise of the partners, new data considered fundamental in order to evaluate pathological mechanisms common to disease clusters.
To evaluate the effect of disease clusters on patient-centered outcomes
To develop concepts of comprehensive and continuous care for the most prevalent patterns of chronic disease combinations