The fact that many people suffer from more than one disease, known as comorbidity, has become one of the main challenges for the 21st century aging population, as it mainly affects the elderly, decreasing treatment options and patients’ life expectancy while increasing health care costs.
A study, led by the Barcelona Supercomputing Center (BSC) and the CRCT and published in Nature Communications, suggests that specific patient characteristics (such as gene expression) can be important factors in the development of clinical strategies to treat and manage comorbidities. The work goes one step further by investigating comorbidities at the molecular level for hundreds of diseases and thousands of patients.
We know about comorbidity relations mainly from epidemiological studies, where the numbers of people simultaneously suffering from two or more diseases are registered and compared to the rest of the population. Those approaches provide a global overview on the higher or lower probability of developing a secondary disease when already suffering from a previous one. A well-known example of such relations is the higher risk of heart failure in patients with hypertension, described to be 4 times higher than in non-hypertensive patients. On the other hand, a lower than expected probability of developing lung cancer in patients suffering from Alzheimer’s disease has been described. The actual reasons behind many of those positive and negative correlations are largely unknown.
Even if at the population level Alzheimer’s disease patients have less chances of getting lung cancer, this doesn’t mean that no one with Alzheimer’s disease will develop lung cancer. In our study we see a lot of variability between patients at the gene expression level, which suggests that some specific group of Alzheimer’s disease patients might actually have the opposite tendency, being at a higher risk of developing lung cancer.
The methods we introduced might be used in the future to prevent comorbidities in specific patients
This work is a first step towards studying comorbidities at a patient level, proposing a paradigmatic change from the disease-centered to a patient-centered approach, in line with the general idea behind personalized medicine.
Discover the published article :
Nature Communications volume 11, Article number: 2854 (2020)
Interpreting molecular similarity between patients as a determinant of disease comorbidity relationships
Jon Sánchez-Valle, Héctor Tejero, José María Fernández, David Juan, Beatriz Urda-García, Salvador Capella-Gutiérrez, Fátima Al-Shahrour, Rafael Tabarés-Seisdedos, Anaïs Baudot, Vera Pancaldi & Alfonso Valencia
Key words :
- Comorbidity,
- Network medicine,
- personalized medicine,
- gene expression,
- bioinformatics
Contact :
Vera Pancaldi / Jon Sanchez / Nina Verstraete
Team 21
Mail : vera.pancaldi@inserm.fr
One picture :
Using the similarity of gene expression between patients we are able to identify patient groups that have particular tendencies to develop specific diseases through their lives. We confirm known comorbidities observed in epidemiological studies and find new ones. We collect all results in a web-portal http://disease-perception.bsc.es
