Integrating digital gait data with metabolomics and clinical data to predict outcomes in Parkinson’s disease
Authors: Brzenczek Cyril , Klopfenstein Quentin , Hähnel Tom , Fröhlich Holger , Glaab Enrico ,   On behalf of the NCER-PD Consortium, Acharya Geeta , Aguayo Gloria , Alexandre Myriam , Ali Muhammad , Ammerlann Wim , Arena Giuseppe , Bassis Michele , Batutu Roxane , Beaumont Katy , Béchet Sibylle , Berchem Guy , Bisdorff Alexandre , Boussaad Ibrahim , Bouvier David , Castillo Lorieza , Contesotto Gessica , De Bremaeker Nancy , Dewitt Brian , Diederich Nico , Dondelinger Rene , Ramia Nancy E. , Ferrari Angelo , Frauenknecht Katrin , Fritz Joëlle , Gamio Carlos , Gantenbein Manon , Gawron Piotr , Georges Laura , Ghosh Soumyabrata , Giraitis Marijus , Glaab Enrico , Goergen Martine , Gómez De Lope Elisa , Graas Jérôme , Graziano Mariella , Groues Valentin , Grünewald Anne , Hammot Gaël , Hanff Anne-Marie , Hansen Linda , Heneka Michael , Henry Estelle , Henry Margaux , Herbrink Sylvia , Herzinger Sascha , Hundt Alexander , Jacoby Nadine , Jónsdóttir Sonja , Klucken Jochen , Kofanova Olga , Krüger Rejko , Lambert Pauline , Landoulsi Zied , Lentz Roseline , Lopes Ana Festas , Lorentz Victoria , Marques Tainá M. , Marques Guilherme , Martins Conde Patricia , May Patrick , Mcintyre Deborah , Mediouni Chouaib , Meisch Francoise , Mendibide Alexia , Menster Myriam , Minelli Maura , Mittelbronn Michel , Mtimet Saïda , Munsch Maeva , Nati Romain , Nehrbass Ulf , Nickels Sarah , Nicolai Beatrice , Nicolay Jean-Paul , Niño Uribe Maria Fernanda , Noor Fozia , Gomes Clarissa P. C. , Pachchek Sinthuja , Pauly Claire , Pauly Laure , Pavelka Lukas , Perquin Magali , Pexaras Achilleas , Rauschenberger Armin , Rawal Rajesh , Reddy Bobbili Dheeraj , Remark Lucie , Richard Ilsé , Roland Olivia , Roomp Kirsten , Rosales Eduardo , Sapienza Stefano , Satagopam Venkata , Schmitz Sabine , Schneider Reinhard , Schwamborn Jens , Severino Raquel , Sharify Amir , Soare Ruxandra , Soboleva Ekaterina , Sokolowska Kate , Theresine Maud , Thien Hermann , Thiry Elodie , Ting Jiin Loo Rebecca , Trouet Johanna , Tsurkalenko Olena , Vaillant Michel , Vega Carlos , Vilas Boas Liliana , Wilmes Paul , Wollscheid-Lengeling Evi , Zelimkhanov Gelani 
Published: September 2024

AbstractParkinson’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.