Abstract
Multimorbidity, the presence of two or more diseases in a patient, is maybe the greatest health challenge for the aging populations of many high-income countries. One of the main drivers of multimorbidity is diabetes mellitus (DM) due to its large number of risk factors and complications. Yet, we currently have very limited understanding of how to quantify multimorbidity beyond a simple counting of diseases and thereby inform prevention and intervention strategies tailored to the needs of elderly DM patients. Here, we conceptualize multimorbidity as typical temporal progression patterns of multiple diseases, so-called trajectories, and develop a framework to perform a matched and sex-specific comparison between DM and non-diabetic patients. We find that these disease trajectories can be organized into a multi-level hierarchy in which DM patients progress from relatively healthy states with low mortality to high-mortality states characterized by cardiovascular diseases, chronic lower respiratory diseases, renal failure, and different combinations thereof. The same disease trajectories can be observed in non-diabetic patients, however, we find that DM patients typically progress at much higher rates along their trajectories. Comparing male and female DM patients, we find a general tendency that females progress faster toward high multimorbidity states than males, in particular along trajectories that involve obesity. Males, on the other hand, appear to progress faster in trajectories that combine heart diseases with cerebrovascular diseases. Our results show that prevention and efficient management of DM are key to achieve a compression of morbidity into higher patient ages. Multidisciplinary efforts involving clinicians as well as experts in machine learning and data visualization are needed to better understand the identified disease trajectories and thereby contribute to solving the current multimorbidity crisis in healthcare.
Highlights
Multimorbidity might well be one of the defining challenges for healthcare systems of high-income countries in the twenty-first century (Pearson-Stuttard et al, 2019)
We identify 250,498 diabetes mellitus (DM) patients in our study population and 500, 996 in the matched control group
We currently have limited understanding on how multimorbidity in DM patients differs from the general population in terms of disease trajectories and how they lead to highly multimorbid health states and high mortality
Summary
Multimorbidity might well be one of the defining challenges for healthcare systems of high-income countries in the twenty-first century (Pearson-Stuttard et al, 2019). Using electronic health records a typical trajectory toward T2D has been identified in which patients acquire hyperlipidemia, hypertension, impaired fasting glucose and DM, in that order (Oh et al, 2016) This and related research shows that multimorbidity is better understood in terms of typical disease trajectories, rather than a simple count of diagnoses. The situation changes drastically for elderly patients with multifactorial chronic disorders, including DM, that serve as risk factors for other diseases across the entire diagnostic spectrum (Chmiel et al, 2014) The existence of such disease networks is a direct consequence of the complex networks of physiological processes that underlie most diseases (Menche et al, 2015). We conclude this work by showing that our analysis and visualization system recovers meaningful diabetic disease trajectories, from early risk factors to late-stage complications and show how our work can be used to generate new hypotheses on sex-specific differences of these trajectories
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