en.Wedoany.com Reported - Researchers Yuichiro Yada and Honda Naoki from the Nagoya University Graduate School of Medicine have developed a computational framework called DiSPAH to model chronic disease progression in orthogonal dimensions, addressing the heterogeneity among patients. This framework can simultaneously track the order of functional deterioration (progression pathways) and the speed at which patients pass through disease stages (progression rate).
The frustrating variability in chronic disease progression among different patients not only poses clinical challenges but also hinders the development of effective treatment plans, clinical trial design, and patient counseling. Using amyotrophic lateral sclerosis (ALS) as an example, the researchers constructed the DiSPAH model. This model employs a continuous-time hidden Markov model to probabilistically infer underlying disease states based on clinical features observed during patients' irregular hospital visits. Transitions between these latent states reflect the diversity of progression pathways. To simulate differences in progression speed, the research team introduced a patient-specific speed parameter that time-scales the transition rates.
The researchers trained and validated the tool using two datasets of patients with limb-onset ALS, a form of the disease where symptoms begin in the arms or legs rather than in muscles controlling speech and swallowing. Data from 264 patients in the AnswerALS dataset were used to train the model, while 2,565 patients from the PRO-ACT cohort were used to validate the results. The system identified six distinct disease progression patterns, with some patients showing slow motor function decline with minimal impact on speech or breathing, while others experienced rapid deterioration. Yada stated in a press release that subtle differences between patients also emerged; for example, in some patients, gross motor functions such as walking declined before fine motor skills like writing, while the opposite occurred in others. These six patterns were identified in one dataset and largely replicated in a second, larger dataset, indicating that they capture common progression pathways of limb-onset ALS. Importantly, progression speed and decline patterns were independent of each other; a patient might follow a severe pattern at a slow speed or a milder pattern at a fast speed, whereas previous tools could not measure both dimensions simultaneously.
In terms of early insights, DiSPAH can, to some extent, predict a patient's progression speed and approximate progression pattern based solely on basic functional assessments and certain genetic mutation information obtained at the first visit. These early predictions hold potential value for patient care, allowing physicians to plan treatments, prepare patients and families, and optimize clinical trial design by grouping participants according to disease progression patterns. The researchers also found that patients carrying the C9orf72 mutation exhibited faster disease progression than those without the mutation, consistent with previous reports linking C9orf72 mutations to shortened ALS survival. Analysis of motor neuron data cultured in the lab from patients' own stem cells showed that faster ALS progression may be associated with cellular protein management issues and signs of cellular stress. The research team stated that jointly modeling progression pathways and speed can improve predictions of heterogeneous disease courses, providing tools for personalized care and research in ALS and other chronic diseases. They plan to extend the tool to all types of ALS patients, improve its reliability, and ultimately apply it to other chronic diseases such as Alzheimer's disease and Parkinson's disease.
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