Identification of Parkinson’s disease PACE subtypes and repurposing treatments through integrative analyses of multimodal data, 2024, Chang Su et al

Abstract
Parkinson’s disease (PD) is a serious neurodegenerative disorder marked by significant clinical and progression heterogeneity.

This study aimed at addressing heterogeneity of PD through integrative analysis of various data modalities. We analyzed clinical progression data (≥5 years) of individuals with de novo PD using machine learning and deep learning, to characterize individuals’ phenotypic progression trajectories for PD subtyping.

We discovered three pace subtypes of PD exhibiting distinct progression patterns: the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate.

We found cerebrospinal fluid P-tau/α-synuclein ratio and atrophy in certain brain regions as potential markers of these subtypes. Analyses of genetic and transcriptomic profiles with network-based approaches identified molecular modules associated with each subtype. For instance, the PD-R-specific module suggested STAT3, FYN, BECN1, APOA1, NEDD4, and GATA2 as potential driver genes of PD-R.

It also suggested neuroinflammation, oxidative stress, metabolism, PI3K/AKT, and angiogenesis pathways as potential drivers for rapid PD progression (i.e., PD-R).

Moreover, we identified repurposable drug candidates by targeting these subtype-specific molecular modules using network-based approach and cell line drug-gene signature data. We further estimated their treatment effects using two large-scale real-world patient databases; the real-world evidence we gained highlighted the potential of metformin in ameliorating PD progression.

In conclusion, this work helps better understand clinical and pathophysiological complexity of PD progression and accelerate precision medicine.

LINK

 

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A really cool new approach to use big data to characterize phenotypic progression trajectories for Parkinson's subtyping. Great money figure! Do you have an 'inching, moderate or rapid subtype' of Parkinson's progression? Is metformin the next big re-purposable drug for Parkinson's? Su, Bian, Wang and colleagues introduce us to PACE subtyping in

. Key Points: - The challenge to Parkinson's disease as the authors point out is significant clinical and progression heterogeneity. - They employed integrative analysis of various data modalities. - Cool was their look at clinical progression data (≥5 years) of individuals with 'de novo' Parkinson's (new diagnosis). - Machine learning and deep learning were applied.

My take: Check out the 'money figure' comparing staging methods including PACE. There were three PACE subtypes which emerged and revealed distinct progression patterns: 'the Inching Pace subtype (PD-I) with mild baseline severity and mild progression speed; the Moderate Pace subtype (PD-M) with mild baseline severity but advancing at a moderate progression rate; and the Rapid Pace subtype (PD-R) with the most rapid symptom progression rate.' There was also some preliminary analysis done on biomarkers, genetics and transcriptomic profiles. The authors hint that neuroinflammation, oxidative stress, metabolism, PI3K/AKT, and angiogenesis may be pathways to explore as potential drivers for rapid Parkinson's progression.

The authors go on to provide us a list of possible 're-purposable drug candidates for targeting these subtype-specific molecular modules using network-based approach and cell line drug-gene signature data.' Metformin emerged from their data as a possibly for ameliorating progression. Should there be a trial? Will this approach advance the field or are we still struggling to get out from under the heaps of data we have collected?