A study by ENDVOC researchers using the COVICAT cohort identifies risk profiles for long COVID
A new study led by ENDVOC researchers at the Germans Trias i Pujol Research Institute (IGTP) and the Barcelona Institute for Global Health (ISGlobal) offers a fresh perspective: the risk of developing long COVID is not only determined by which diseases a person had before infection, but also by the order in which they appeared and how they interact. This approach reveals previously undetected risk profiles.
The study draws on data from more than 10,000 participants in the GCAT (Genomes for Life) cohort, which has gathered clinical and genetic information from the Catalan population for over 15 years. Linking these data to the prospective COVICAT study launched in 2020, the team reconstructed individual health trajectories (meaning the temporal sequence of chronic diseases) to analyse their influence on long COVID.
The importance of disease sequence
While most previous studies focused solely on the presence of pre-existing conditions, this work shows that the sequence and interaction of diseases over time can be key to predicting long COVID risk.
“It is not enough to know which diseases a person has. The order in which they appear can significantly influence risk, especially among women,” explains Natàlia Blay, first author of the study.
Disease sequences provided more accurate predictions than analysing single conditions. For example, anxiety followed by depression carries a different risk than the reverse order. Of 162 disease trajectories analysed, 38 were linked to a significantly higher risk of long COVID. The most frequent involved mental health, neurological, respiratory (such as asthma), and metabolic or digestive conditions (such as hypertension, obesity or reflux).
Some disease trajectories increased risk regardless of the severity of the initial COVID infection, suggesting that long COVID cannot be explained solely by the acute episode. The researchers note that future analysis using artificial intelligence could further improve risk prediction by uncovering complex patterns in large longitudinal datasets.
“This work shows that long COVID results from a prior health trajectory rather than a single factor. It also highlights the value of longitudinal data like GCAT for identifying population health patterns that support more preventive and personalised public health strategies,” says Rafael de Cid, ENDVOC researcher at IGTP and director of GCAT.
Reference:
Blay N, Farré X, Garcia-Aymerich J, Castaño-Vinyals G, Kogevinas M, de Cid R. Pre-pandemic disease trajectories and genetic insights into long COVID susceptibility. BMC Medicine 23, 590 (2025). DOI: doi.org/10.1186/s12916-025-04427-x
Read also: our document on Long Covid and Post-Acute Infectious Syndromes: Evidence, Impact and Policy Directions.
