Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson’s disease

Gelani Zelimkhanov; Carlos Vega; Michel Vaillant; Johanna Trouet; Hermann Thien; Maud Theresine; Kate Sokolowska; Amir Sharify; Stefano Sapienza; Olivia Roland; Ilsé Richard; Lucie Remark; Armin Rauschenberger; Achilleas Pexaras; Magali Perquin; Lukas Pavelka; Laure Pauly; Claire Pauly; Fozia Noor; Sarah Nickels; Ulf Nehrbass; Maeva Munsch; Saïda Mtimet; Michel Mittelbronn; Myriam Menster; Alexia Mendibide; Chouaib Mediouni; Deborah Mcintyre; Guilherme Marques; Tainá M. Marques; Victoria Lorentz; Ana Festas Lopes; Zied Landoulsi; Pauline Lambert; Rejko Krüger; Olga Kofanova; Jochen Klucken; Sonja Jónsdóttir; Alexander Hundt; Margaux Henry; Estelle Henry; Anne Marie Hanff; Jérôme Graas; Marijus Giraitis; Laura Georges; Manon Gantenbein; Carlos Gamio; Joëlle Fritz; Angelo Ferrari; Nancy De Bremaeker; Gessica Contesotto; Ibrahim Boussaad; Guy Berchem; Sibylle Béchet; Katy Beaumont; Roxane Batutu; Wim Ammerlaan; Myriam Alexandre; Gloria Aguayo; Geeta Acharya; on behalf of the NCER-PD Consortium; Enrico Glaab

doi:10.1038/s41746-024-01236-z

Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson’s disease

Gelani Zelimkhanov, Carlos Vega, Michel Vaillant, Johanna Trouet, Hermann Thien, Maud Theresine, Kate Sokolowska, Amir Sharify, Stefano Sapienza, Olivia Roland, Ilsé Richard, Lucie Remark, Armin Rauschenberger, Achilleas Pexaras, Magali Perquin, Lukas Pavelka, Laure Pauly, Claire Pauly, Fozia Noor, Sarah NickelsUlf Nehrbass, Maeva Munsch, Saïda Mtimet, Michel Mittelbronn, Myriam Menster, Alexia Mendibide, Chouaib Mediouni, Deborah Mcintyre, Guilherme Marques, Tainá M. Marques, Victoria Lorentz, Ana Festas Lopes, Zied Landoulsi, Pauline Lambert, Rejko Krüger, Olga Kofanova, Jochen Klucken, Sonja Jónsdóttir, Alexander Hundt, Margaux Henry, Estelle Henry, Anne Marie Hanff, Jérôme Graas, Marijus Giraitis, Laura Georges, Manon Gantenbein, Carlos Gamio, Joëlle Fritz, Angelo Ferrari, Nancy De Bremaeker, Gessica Contesotto, Ibrahim Boussaad, Guy Berchem, Sibylle Béchet, Katy Beaumont, Roxane Batutu, Wim Ammerlaan, Myriam Alexandre, Gloria Aguayo, Geeta Acharya, on behalf of the NCER-PD Consortium, Enrico Glaab^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Research › peer-review

Abstract

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.

Original language	English
Article number	235
Pages (from-to)	235
Journal	npj Digital Medicine
Volume	7
Issue number	1
DOIs	https://doi.org/10.1038/s41746-024-01236-z
Publication status	Published - 6 Sept 2024

Access to Document

10.1038/s41746-024-01236-zLicence: CC BY

Cite this

Zelimkhanov, G., Vega, C., Vaillant, M., Trouet, J., Thien, H., Theresine, M., Sokolowska, K., Sharify, A., Sapienza, S., Roland, O., Richard, I., Remark, L., Rauschenberger, A., Pexaras, A., Perquin, M., Pavelka, L., Pauly, L., Pauly, C., Noor, F., ... Glaab, E. (2024). Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson’s disease. npj Digital Medicine, 7(1), 235. Article 235. https://doi.org/10.1038/s41746-024-01236-z

@article{b9b22c40ef8d4c86a0986982014ed79c,

title = "Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson{\textquoteright}s disease",

abstract = "Parkinson{\textquoteright}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{\textquoteright}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.",

author = "Gelani Zelimkhanov and Carlos Vega and Michel Vaillant and Johanna Trouet and Hermann Thien and Maud Theresine and Kate Sokolowska and Amir Sharify and Stefano Sapienza and Olivia Roland and Ils{\'e} Richard and Lucie Remark and Armin Rauschenberger and Achilleas Pexaras and Magali Perquin and Lukas Pavelka and Laure Pauly and Claire Pauly and Fozia Noor and Sarah Nickels and Ulf Nehrbass and Maeva Munsch and Sa{\"i}da Mtimet and Michel Mittelbronn and Myriam Menster and Alexia Mendibide and Chouaib Mediouni and Deborah Mcintyre and Guilherme Marques and Marques, {Tain{\'a} M.} and Victoria Lorentz and Lopes, {Ana Festas} and Zied Landoulsi and Pauline Lambert and Rejko Kr{\"u}ger and Olga Kofanova and Jochen Klucken and Sonja J{\'o}nsd{\'o}ttir and Alexander Hundt and Margaux Henry and Estelle Henry and Hanff, {Anne Marie} and J{\'e}r{\^o}me Graas and Marijus Giraitis and Laura Georges and Manon Gantenbein and Carlos Gamio and Jo{\"e}lle Fritz and Angelo Ferrari and {De Bremaeker}, Nancy and Gessica Contesotto and Ibrahim Boussaad and Guy Berchem and Sibylle B{\'e}chet and Katy Beaumont and Roxane Batutu and Wim Ammerlaan and Myriam Alexandre and Gloria Aguayo and Geeta Acharya and {on behalf of the NCER-PD Consortium} and Enrico Glaab",

note = "Grants and funding INTER/ERAPerMed/20/14599012/DIGIPD/Fonds National de la Recherche Luxembourg (National Research Fund) INTER/22/17104370/RECAST/Fonds National de la Recherche Luxembourg (National Research Fund) INTER/EJPRD22l1/7027921/PreDYT/Fonds National de la Recherche Luxembourg (National Research Fund) FNR/NCER13/BM/11264123/Fonds National de la Recherche Luxembourg (National Research Fund) INTER/ERAPerMed/20/14599012/DIGIPD/Fonds National de la Recherche Luxembourg (National Research Fund) Publisher Copyright: {\textcopyright} The Author(s) 2024.",

year = "2024",

month = sep,

day = "6",

doi = "10.1038/s41746-024-01236-z",

language = "English",

volume = "7",

pages = "235",

journal = "npj Digital Medicine",

issn = "2398-6352",

publisher = "Nature Publishing Group",

number = "1",

}

Zelimkhanov, G, Vega, C , Vaillant, M , Trouet, J , Thien, H , Theresine, M, Sokolowska, K, Sharify, A, Sapienza, S, Roland, O, Richard, I, Remark, L , Rauschenberger, A , Pexaras, A , Perquin, M , Pavelka, L, Pauly, L, Pauly, C, Noor, F, Nickels, S, Nehrbass, U, Munsch, M , Mtimet, S, Mittelbronn, M, Menster, M, Mendibide, A, Mediouni, C, Mcintyre, D, Marques, G , Marques, TM , Lorentz, V , Lopes, AF , Landoulsi, Z, Lambert, P, Krüger, R , Kofanova, O, Klucken, J, Jónsdóttir, S , Hundt, A, Henry, M, Henry, E , Hanff, AM , Graas, J , Giraitis, M , Georges, L , Gantenbein, M , Gamio, C , Fritz, J , Ferrari, A, De Bremaeker, N, Contesotto, G , Boussaad, I , Berchem, G , Béchet, S , Beaumont, K, Batutu, R, Ammerlaan, W , Alexandre, M , Aguayo, G , Acharya, G, on behalf of the NCER-PD Consortium & Glaab, E 2024, 'Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson’s disease', npj Digital Medicine, vol. 7, no. 1, 235, pp. 235. https://doi.org/10.1038/s41746-024-01236-z

TY - JOUR

T1 - Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson’s disease

AU - Zelimkhanov, Gelani

AU - Vega, Carlos

AU - Vaillant, Michel

AU - Trouet, Johanna

AU - Thien, Hermann

AU - Theresine, Maud

AU - Sokolowska, Kate

AU - Sharify, Amir

AU - Sapienza, Stefano

AU - Roland, Olivia

AU - Richard, Ilsé

AU - Remark, Lucie

AU - Rauschenberger, Armin

AU - Pexaras, Achilleas

AU - Perquin, Magali

AU - Pavelka, Lukas

AU - Pauly, Laure

AU - Pauly, Claire

AU - Noor, Fozia

AU - Nickels, Sarah

AU - Nehrbass, Ulf

AU - Munsch, Maeva

AU - Mtimet, Saïda

AU - Mittelbronn, Michel

AU - Menster, Myriam

AU - Mendibide, Alexia

AU - Mediouni, Chouaib

AU - Mcintyre, Deborah

AU - Marques, Guilherme

AU - Marques, Tainá M.

AU - Lorentz, Victoria

AU - Lopes, Ana Festas

AU - Landoulsi, Zied

AU - Lambert, Pauline

AU - Krüger, Rejko

AU - Kofanova, Olga

AU - Klucken, Jochen

AU - Jónsdóttir, Sonja

AU - Hundt, Alexander

AU - Henry, Margaux

AU - Henry, Estelle

AU - Hanff, Anne Marie

AU - Graas, Jérôme

AU - Giraitis, Marijus

AU - Georges, Laura

AU - Gantenbein, Manon

AU - Gamio, Carlos

AU - Fritz, Joëlle

AU - Ferrari, Angelo

AU - De Bremaeker, Nancy

AU - Contesotto, Gessica

AU - Boussaad, Ibrahim

AU - Berchem, Guy

AU - Béchet, Sibylle

AU - Beaumont, Katy

AU - Batutu, Roxane

AU - Ammerlaan, Wim

AU - Alexandre, Myriam

AU - Aguayo, Gloria

AU - Acharya, Geeta

AU - on behalf of the NCER-PD Consortium

AU - Glaab, Enrico

N1 - Grants and funding INTER/ERAPerMed/20/14599012/DIGIPD/Fonds National de la Recherche Luxembourg (National Research Fund) INTER/22/17104370/RECAST/Fonds National de la Recherche Luxembourg (National Research Fund) INTER/EJPRD22l1/7027921/PreDYT/Fonds National de la Recherche Luxembourg (National Research Fund) FNR/NCER13/BM/11264123/Fonds National de la Recherche Luxembourg (National Research Fund) INTER/ERAPerMed/20/14599012/DIGIPD/Fonds National de la Recherche Luxembourg (National Research Fund) 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.

UR - http://www.scopus.com/inward/record.url?scp=85203273723&partnerID=8YFLogxK

UR - https://pubmed.ncbi.nlm.nih.gov/39242660/

U2 - 10.1038/s41746-024-01236-z

DO - 10.1038/s41746-024-01236-z

M3 - Article

C2 - 39242660

AN - SCOPUS:85203273723

SN - 2398-6352

VL - 7

SP - 235

JO - npj Digital Medicine

JF - npj Digital Medicine

IS - 1

M1 - 235

ER -

Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson’s disease

Abstract

Access to Document

Other files and links

Fingerprint

Cite this