TY - JOUR
T1 - Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson’s disease
AU - Brzenczek, Cyril
AU - Klopfenstein, Quentin
AU - Hähnel, Tom
AU - Fröhlich, Holger
AU - Glaab, Enrico
AU - Zelimkhanov, Gelani
AU - Wollscheid-Lengeling, Evi
AU - Wilmes, Paul
AU - Vilas Boas, Liliana
AU - Vega, Carlos
AU - Vaillant, Michel
AU - Tsurkalenko, Olena
AU - Trouet, Johanna
AU - Ting Jiin Loo, Rebecca
AU - Thiry, Elodie
AU - Thien, Hermann
AU - Theresine, Maud
AU - Sokolowska, Kate
AU - Soboleva, Ekaterina
AU - Soare, Ruxandra
AU - Sharify, Amir
AU - Severino, Raquel
AU - Schwamborn, Jens
AU - Schneider, Reinhard
AU - Schmitz, Sabine
AU - Satagopam, Venkata
AU - Sapienza, Stefano
AU - Rosales, Eduardo
AU - Roomp, Kirsten
AU - Roland, Olivia
AU - Richard, Ilsé
AU - Remark, Lucie
AU - Reddy Bobbili, Dheeraj
AU - Rawal, Rajesh
AU - Rauschenberger, Armin
AU - Pexaras, Achilleas
AU - Perquin, Magali
AU - Pavelka, Lukas
AU - Pauly, Laure
AU - Pauly, Claire
AU - Pachchek, Sinthuja
AU - Gomes, Clarissa P.C.
AU - Noor, Fozia
AU - Niño Uribe, Maria Fernanda
AU - Nicolay, Jean Paul
AU - Nicolai, Beatrice
AU - Nickels, Sarah
AU - Nehrbass, Ulf
AU - Nati, Romain
AU - Hanff, Anne Marie
AU - NCER-PD Consortium
N1 - Funding Information:
Bioinformatics analyses presented in this paper were carried out in part using the HPC facilities of the University of Luxembourg81 (see https://hpc.uni.lu). We would like to thank all participants of the Luxembourg Parkinson\u2019s Study for their important support of our research. Furthermore, we acknowledge the joint effort of the National Centre of Excellence in Research on Parkinson\u2019s Disease (NCER-PD) Consortium members from the partner institutions Luxembourg Centre for Systems Biomedicine, Luxembourg Institute of Health, Centre Hospitalier de Luxembourg, and Laboratoire National de Sant\u00E9 generally contributing to the Luxembourg Parkinson\u2019s Study as listed in the Consortia section. We acknowledge funding support by the Luxembourg National Research Fund (FNR) as part of the National Centre for Excellence in Research on Parkinson\u2019s disease (NCER-PD, grant no. FNR/NCER13/BM/11264123), the project PreDYT (INTER/EJPRD22l1/7027921/ PreDYT), the project RECAST (INTER/22/17104370/RECAST), and for the project DIGIPD (INTER/ERAPerMed20/14599012) as part of the European Union\u2019s Horizon 2020 Programme for Research and Innovation.
Funding Information:
Bioinformatics analyses presented in this paper were carried out in part using the HPC facilities of the University of Luxembourg (see https://hpc.uni.lu ). We would like to thank all participants of the Luxembourg Parkinson\u2019s Study for their important support of our research. Furthermore, we acknowledge the joint effort of the National Centre of Excellence in Research on Parkinson\u2019s Disease (NCER-PD) Consortium members from the partner institutions Luxembourg Centre for Systems Biomedicine, Luxembourg Institute of Health, Centre Hospitalier de Luxembourg, and Laboratoire National de Sant\u00E9 generally contributing to the Luxembourg Parkinson\u2019s Study as listed in the Consortia section. We acknowledge funding support by the Luxembourg National Research Fund (FNR) as part of the National Centre for Excellence in Research on Parkinson\u2019s disease (NCER-PD, grant no. FNR/NCER13/BM/11264123), the project PreDYT (INTER/EJPRD22l1/7027921/ PreDYT), the project RECAST (INTER/22/17104370/RECAST), and for the project DIGIPD (INTER/ERAPerMed20/14599012) as part of the European Union\u2019s Horizon 2020 Programme for Research and Innovation.
Publisher Copyright:
© The Author(s) 2024.
PY - 2024/9/6
Y1 - 2024/9/6
N2 - Parkinson’s disease (PD) presents diverse symptoms and comorbidities, complicating its diagnosis and management. The primary objective of this cross-sectional, monocentric study was to assess digital gait sensor data’s utility for monitoring and diagnosis of motor and gait impairment in PD. As a secondary objective, for the more challenging tasks of detecting comorbidities, non-motor outcomes, and disease progression subgroups, we evaluated for the first time the integration of digital markers with metabolomics and clinical data. Using shoe-attached digital sensors, we collected gait measurements from 162 patients and 129 controls in a single visit. Machine learning models showed significant diagnostic power, with AUC scores of 83–92% for PD vs. control and up to 75% for motor severity classification. Integrating gait data with metabolomics and clinical data improved predictions for challenging-to-detect comorbidities such as hallucinations. Overall, this approach using digital biomarkers and multimodal data integration can assist in objective disease monitoring, diagnosis, and comorbidity detection.
AB - Parkinson’s disease (PD) presents diverse symptoms and comorbidities, complicating its diagnosis and management. The primary objective of this cross-sectional, monocentric study was to assess digital gait sensor data’s utility for monitoring and diagnosis of motor and gait impairment in PD. As a secondary objective, for the more challenging tasks of detecting comorbidities, non-motor outcomes, and disease progression subgroups, we evaluated for the first time the integration of digital markers with metabolomics and clinical data. Using shoe-attached digital sensors, we collected gait measurements from 162 patients and 129 controls in a single visit. Machine learning models showed significant diagnostic power, with AUC scores of 83–92% for PD vs. control and up to 75% for motor severity classification. Integrating gait data with metabolomics and clinical data improved predictions for challenging-to-detect comorbidities such as hallucinations. Overall, this approach using digital biomarkers and multimodal data integration can assist in objective disease monitoring, diagnosis, and comorbidity detection.
U2 - 10.1038/s41746-024-01236-z
DO - 10.1038/s41746-024-01236-z
M3 - Article
SN - 2398-6352
VL - 7
JO - npj Digital Medicine
JF - npj Digital Medicine
IS - 1
M1 - 235
ER -