Decompression of Multimorbidity Along the Disease Trajectories of Diabetes Mellitus Patients

Haug, N., Sorger, J., Gisinger, T., Gyimesi, M., Kautzky-Willer, A., Thurner, S., & Klimek, P. (2021). Decompression of Multimorbidity Along the Disease Trajectories of Diabetes Mellitus Patients. Frontiers in Physiology 11 10.3389/fphys.2020.612604.

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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.

Item Type: Article
Uncontrolled Keywords: comorbidity networks; data visualization; diabetes mellitus; disease trajectories; machine learning; multimorbidity; population aging
Research Programs: Ecosystems Services and Management (ESM)
Depositing User: Luke Kirwan
Date Deposited: 21 Jan 2021 12:40
Last Modified: 21 Jan 2021 12:40
URI: http://pure.iiasa.ac.at/17002

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