Patient clustering based on temporal patterns of comorbidities may be a future tool for understanding the processes that cause depression

Both patients and doctors increasingly recognize that chronic diseases rarely occur in isolation but are often accompanied by other conditions, known as comorbidities. This observation suggests that these diseases may share common or interconnected causes. Identifying the most important direct relationships between these diseases, however, is challenging, despite the potential benefits for accurate diagnosis and personalized treatment. A systemic analytical approach that considers the timing and types of comorbidities in patients could provide significant insights.

Major depressive disorder (MDD) is associated with a wide range of possible symptoms, underpinned by divergent etiological factors, making the choice of effective treatment challenging. Therefore, identifying and understanding different subtypes of depression, potentially through their links to comorbidities is crucial.

In our study within the TRAJECTOME consortium, we categorized patients based on the time sequence in which depression and its related mental and physical comorbidities (such as cardiovascular diseases, respiratory disorders, neurological conditions, etc.) appeared. We hypothesized that shared genetic risks between these conditions could be more effectively identified and that mapping these risks could help unravel the biological processes underlying depression. We analysed the health data of 1.2 million European participants using advanced bioinformatics methods developed for this purpose. Then, in the next step, we examined the genetic and other risk factors of the identified clusters.

Our results suggest that depression can be best characterized by dividing patients into seven distinct clusters, each differing significantly in terms of the comorbid physical and mental conditions that occur alongside depression, their frequency, and the age at which they appear. Moreover, these clusters also varied in terms of genetic and non-genetic risk factors (e.g., stress, certain personality traits, physiological or behavioural factors). For example, healthier groups were characterized by lower body mass index, blood pressure, higher education levels, and less frequent smoking.
In four of these clusters, diseases tended to develop later in life (between 45-65 years), and depression was less common. These groups were notably associated with beneficial genetic variants related to the immune system and a low genetic risk for depression. However, even between these clusters, there were distinct differences: one cluster was more prone to high blood pressure, kidney, and cerebral vascular diseases, while another had higher occurrences of hypothyroidism and lipid metabolism disorders.

Among the other three clusters, which had a higher prevalence of depression and various comorbidities, two were marked by the presence of genetic variants associated with depression, while the third showed a predominance of genes linked to inflammatory diseases. In two of these clusters, diseases appeared at a younger age, but with different patterns: one cluster was more affected by pain-related and psychiatric disorders, while the other was predominantly troubled by allergic diseases and migraines. The third group experienced a later onset of diseases (after age 45), but these diseases occurred in large numbers, and included particularly respiratory infections and stress-related psychological disorders.

Recognizing these patterns provides valuable insights into identifying patient subgroups with distinct biological backgrounds. Our findings could contribute to more effective prevention of depression and its comorbidities, as well as to more targeted and thus more effective treatment strategies in the future.
The original article was published in Nature Communication and can be read here:
https://www.nature.com/articles/s41467-024-51467-7