In a recent study published in npj Digital Medicine, researchers at the Complexity Science Hub analyzed a comprehensive dataset of 44 million hospitals in Austria. They identified critical points where disease trajectories diverge significantly—with serious consequences for patients and healthcare providers.
The world population is aging at an increasing rate. According to the World Health Organization (WHO), in 2023, one in six people was over 60 years old. By 2050, the number of people over 60 is expected to double to 2.1 billion.
As age increases, the risk of multiple, often chronic diseases occurring at the same time – known as multimorbidity – increases significantly,” he explains.
Elma Dervic from the Complexity Science Hub (CSH)
Given the demographic change we are facing, this poses several challenges. On the one hand, multimorbidity reduces the quality of life of those affected. On the other hand, this demographic shift creates a huge additional burden on health care and social systems.
Identify typical disease trajectories
“We wanted to know what typical disease trajectories occur in multimorbid patients from birth to death and what critical moments in their lives significantly shape the further course. This provides clues for very early and personalized prevention strategies,” explains Dervic.
Together with researchers from the Medical University of Vienna, Dervic analyzed all hospital stays in Austria between 2003 and 2014, a total of about 44 million. To make sense of this massive amount of data, the team built multi-layer networks. A layer represents each ten-year age group, and each diagnosis is represented by nodes within these layers.
Using this method, researchers were able to identify associations between different diseases among different age groups -? for example, how often obesity, hypertension and diabetes occur together at ages 20-29 and which diseases have a higher risk of occurring after them in the 30s, 40s or 50s.
The team identified 1,260 different disease trajectories (618 in women and 642 in men) over a 70-year period. “On average, one of these disease trajectories includes nine different diagnoses, highlighting how common multimorbidity really is,” Dervic emphasizes.
Critical moments
Specifically, 70 trajectories have been identified where patients showed similar diagnoses in their younger years, but later developed significantly different clinical profiles. “If these trajectories, despite similar starting conditions, differ significantly later in life in terms of severity and corresponding required hospitalizations, this is a critical time that plays an important role in prevention,” says Dervic.
Men with sleep disorders
The model, for example, shows two typical trajectories for men between the ages of 20 and 29 who suffer from sleep disorders. In trajectory A, metabolic diseases such as diabetes mellitus, obesity and lipid disorders appear years later. Movement disorders, among other conditions, occur in the B pathway. This suggests that organic sleep disturbances could be an early indicator for the risk of developing neurodegenerative diseases such as Parkinson’s disease.
“If someone suffers from sleep disorders at a young age, that can be a critical event that prompts the attention of doctors,” explains Dervic. The study results show that patients on trajectory B spend nine fewer days in hospital at age 20, but 29 more days in hospital at age 30, and also suffer from more additional diagnoses. As sleep disorders become more prevalent, distinguishing their disease course is important not only for sufferers but also for the health care system.
Women with high blood pressure
Similarly, when adolescent girls between the ages of ten and nineteen have high blood pressure, their trajectory also varies. While some develop additional metabolic diseases, others develop chronic kidney disease in their twenties, leading to increased mortality at a young age. This is of particular clinical importance as childhood hypertension is increasing worldwide and is closely linked to the increasing prevalence of childhood obesity.
There are specific trajectories that deserve special attention and should be watched closely, according to the study authors. “With this knowledge derived from real-life data, doctors can monitor various diseases more intensively and implement targeted, personalized preventive measures decades before serious problems arise,” explains Dervic. In this way, they not only reduce the burden on healthcare systems, but also improve the quality of life of patients.
Source:
Journal Reference:
Dervić, E., et al. (2024). Unraveling cradle-to-grave disease trajectories from multilevel comorbidity networks. npj Digital Medicine. doi.org/10.1038/s41746-024-01015-w.
