ArticleFrontiers in physiology2020
Decompression of Multimorbidity Along the Disease Trajectories of Diabetes Mellitus Patients.
Article in Frontiers in physiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
12 citing papers in PubMed.
- Network physiology in space: vision and perspectives on exploring physiological networks during spaceflight for the benefit of life on earth.Frontiers in network physiology · 2026Article
- Dynamics of multimorbidity patterns in older population across socioeconomic areas.Communications medicine · 2025Article
- Charting the Pathways of Cardiometabolic Multimorbidity: A Systematic Review of Clinical Trajectories.Journal of clinical medicine · 2025Review
- Identifying time patterns in Huntington's disease trajectories using dynamic time warping-based clustering on multi-modal data.Scientific reports · 2025Article
- Essential New Complexity-Based Themes for Patient-Centered Diagnosis and Treatment of Dementia and Predementia in Older People: Multimorbidity and Multilevel Phenomenology.Journal of clinical medicine · 2024Review
- A software package for efficient patient trajectory analysis applied to analyzing bladder cancer development.PLOS digital health · 2023Article
- The long-term effects of Kerala Diabetes Prevention Program on diabetes incidence and cardiometabolic risk: a study protocol.BMC public health · 2023Article
- Understanding multimorbidity requires sign-disease networks and higher-order interactions, a perspective.Frontiers in systems biology · 2023Article
- Hierarchical management of chronic heart failure: a perspective based on the latent structure of comorbidities.ESC heart failure · 2022Article
- Developing a morphomics framework to optimize implant site-specific design parameters for islet macroencapsulation devices.Journal of the Royal Society, Interface · 2021Article
- Risk of Typical Diabetes-Associated Complications in Different Clusters of Diabetic Patients: Analysis of Nine Risk Factors.Journal of personalized medicine · 2021Article
- The New Field of Network Physiology: Building the Human Physiolome.Frontiers in network physiology · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.