Integrative analyses of multimodal clinical, neuroimaging, genetic, and transcriptomic data identify subtypes and potential treatments for heterogeneous Parkinson’s disease progression

The Parkinson’s disease (PD) is a heterogeneous neurodegenerative disease, of which the etiological and pathological mechanisms remain unclear to date. PD has been associated with diverse movement dysfunctions and non-motor symptoms (i.e., symptom heterogeneity) and progression patterns of these symptoms differ from patient to patient (i.e., progression heterogeneity). To address these, the present investigation aims at comprehensively considering full progression course of early PDs to identify subtypes, each of which can reflect unique PD progression pattern. We retrospectively analyzed the Parkinson’s Progression Markers Initiative (PPMI) and the Parkinson Disease Biomarkers Program (PDBP) as the development and validation cohorts, respectively. An unsupervised deep learning model was built to model progression trajectories in diverse clinical manifestations and cerebrospinal fluid (CSF) biomarkers to produce a representation vector for each patient, encoding his/her symptom progression profile. Then by performing clustering analysis on the patients’ representation vectors, we identified three subtypes with distinct PD progression patterns in the PPMI cohort: Subtype I, mild baseline severity and mild symptom progression; mild baseline severity and moderate progression; and Subtype III, rapid symptom progression. Replication in the PDBP validation cohort demonstrated reproducibility of the subtypes. After that, we explored demographic factors, CSF biomarkers, neuroimaging biomarkers in brain regional atrophy, and genetic factors of the subtypes. Last, to enhance usability of the subtypes, predictive model of subtypes that relies on data at baseline and 1-year follow-up was trained. In conclusion, the identified subtypes revealed significant symptom progression patterns of PDs. Patients with similar baseline severities can even suffer from different progression pattern, leading to distinct prognosis. Demographic factors, biomarkers, and genetic components of the subtypes suggested distinct biological mechanisms and pathways potentially leading to those progression patterns. Our findings may benefit pathophysiological study, clinical practice, and clinical trials to advance PD. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement The work was supported by MJFF 14858, 14858.01, 15914, NSF 1750326, NIH RF1AG072449. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The institutional review board approved the PPMI protocol in all participating sites. The institutional review board approved the PDBP protocol in all participating sites. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes Data from PPMI can be download from the official web site () via request. Data from the PDBP can be download from AMP-PD platform () via request.