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The Lancet. Infectious Diseases Mar 2023
Topics: Humans; Mpox (monkeypox); Monkeypox virus; Disease Progression
PubMed: 36356608
DOI: 10.1016/S1473-3099(22)00691-0 -
Journal of the American College of... Feb 2022
Topics: Disease Progression; Humans; Pulmonary Disease, Chronic Obstructive
PubMed: 35177201
DOI: 10.1016/j.jacc.2021.11.049 -
Journal of the American Medical... Jan 2024The early stages of chronic disease typically progress slowly, so symptoms are usually only noticed until the disease is advanced. Slow progression and heterogeneous...
OBJECTIVE
The early stages of chronic disease typically progress slowly, so symptoms are usually only noticed until the disease is advanced. Slow progression and heterogeneous manifestations make it challenging to model the transition from normal to disease status. As patient conditions are only observed at discrete timestamps with varying intervals, an incomplete understanding of disease progression and heterogeneity affects clinical practice and drug development.
MATERIALS AND METHODS
We developed the Gaussian Process for Stage Inference (GPSI) approach to uncover chronic disease progression patterns and assess the dynamic contribution of clinical features. We tested the ability of the GPSI to reliably stratify synthetic and real-world data for osteoarthritis (OA) in the Osteoarthritis Initiative (OAI), bipolar disorder (BP) in the Adolescent Brain Cognitive Development Study (ABCD), and hepatocellular carcinoma (HCC) in the UTHealth and The Cancer Genome Atlas (TCGA).
RESULTS
First, GPSI identified two subgroups of OA based on image features, where these subgroups corresponded to different genotypes, indicating the bone-remodeling and overweight-related pathways. Second, GPSI differentiated BP into two distinct developmental patterns and defined the contribution of specific brain region atrophy from early to advanced disease stages, demonstrating the ability of the GPSI to identify diagnostic subgroups. Third, HCC progression patterns were well reproduced in the two independent UTHealth and TCGA datasets.
CONCLUSION
Our study demonstrated that an unsupervised approach can disentangle temporal and phenotypic heterogeneity and identify population subgroups with common patterns of disease progression. Based on the differences in these features across stages, physicians can better tailor treatment plans and medications to individual patients.
Topics: Adolescent; Humans; Carcinoma, Hepatocellular; Disease Progression; Liver Neoplasms; Chronic Disease; Osteoarthritis
PubMed: 38055638
DOI: 10.1093/jamia/ocad230 -
JACC. Cardiovascular Interventions Feb 2023
Topics: Humans; Percutaneous Coronary Intervention; Treatment Outcome; Arteries; Disease Progression; Veins
PubMed: 36858667
DOI: 10.1016/j.jcin.2023.01.002 -
Neurology Apr 2016
Topics: Biomarkers; Disease Progression; Humans; Parkinson Disease
PubMed: 26865515
DOI: 10.1212/WNL.0000000000002473 -
Deutsches Arzteblatt International Feb 2016
Topics: Disease Progression; Humans
PubMed: 26940782
DOI: 10.3238/arztebl.2016.0116a -
Journal of Diabetes and Its... Jul 2016
Topics: Causality; Disease Progression; Humans; Lipoprotein(a)
PubMed: 27118508
DOI: 10.1016/j.jdiacomp.2016.04.001 -
European Journal of Heart Failure Jul 2019
Topics: Biomarkers; Disease Progression; Heart Failure; Humans; Methylamines
PubMed: 30623560
DOI: 10.1002/ejhf.1409 -
Thorax May 2017
Topics: Disease Progression; Forced Expiratory Volume; Humans; Lung; Pulmonary Disease, Chronic Obstructive
PubMed: 28292852
DOI: 10.1136/thoraxjnl-2016-209666 -
International Journal of Molecular... Apr 2022The terminal stage of many chronic inflammatory diseases is organ fibrosis [...].
The terminal stage of many chronic inflammatory diseases is organ fibrosis [...].
Topics: Disease Progression; Fibrosis; Humans; Liver Cirrhosis; Neoplasms
PubMed: 35409286
DOI: 10.3390/ijms23073924