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

被引：2

作者：

Brzenczek, Cyril ^{[1
]}

Klopfenstein, Quentin ^{[1
]}

Hähnel, Tom ^{[2
,3
]}

Fröhlich, Holger ^{[2
,4
]}

Glaab, Enrico ^{[1
]}

Zelimkhanov, Gelani ^{[5
,7
]}

Wollscheid-Lengeling, Evi ^{[6
]}

Wilmes, Paul ^{[6
]}

Vilas Boas, Liliana ^{[7
]}

Vega, Carlos ^{[5
]}

Vaillant, Michel ^{[5
]}

Tsurkalenko, Olena ^{[5
]}

Trouet, Johanna ^{[5
]}

Ting Jiin Loo, Rebecca ^{[6
]}

Thiry, Elodie ^{[7
]}

Thien, Hermann ^{[5
]}

Theresine, Maud ^{[5
]}

Sokolowska, Kate ^{[5
]}

Soboleva, Ekaterina ^{[6
,7
]}

Soare, Ruxandra ^{[6
]}

Sharify, Amir ^{[5
]}

Severino, Raquel ^{[5
]}

Schwamborn, Jens ^{[6
]}

Schneider, Reinhard ^{[6
]}

Schmitz, Sabine ^{[6
]}

Satagopam, Venkata ^{[6
]}

Sapienza, Stefano ^{[6
]}

Rosales, Eduardo ^{[5
]}

Roomp, Kirsten ^{[6
]}

Roland, Olivia ^{[5
]}

Richard, Ilsé ^{[5
]}

Remark, Lucie ^{[5
]}

Reddy Bobbili, Dheeraj ^{[6
]}

Rawal, Rajesh ^{[6
]}

Rauschenberger, Armin ^{[6
]}

Pexaras, Achilleas ^{[5
]}

Perquin, Magali ^{[5
]}

Pavelka, Lukas ^{[5
,7
]}

Pauly, Laure ^{[5
,11
]}

Pauly, Claire ^{[5
,7
]}

Pachchek, Sinthuja ^{[6
]}

Gomes, Clarissa P. C. ^{[6
]}

Noor, Fozia ^{[5
]}

Niño Uribe, Maria Fernanda ^{[6
,7
]}

Nicolay, Jean-Paul ^{[17
]}

Nicolai, Beatrice ^{[7
]}

Nickels, Sarah ^{[6
]}

Nehrbass, Ulf ^{[5
]}

Nati, Romain ^{[7
]}

Munsch, Maeva ^{[5
]}

机构：

[1] Biomedical Data Science Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg

[2] Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany

[3] Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany

[4] Bonn-Aachen International Center for IT (b-it), University of Bonn, Bonn, Germany

[5] Luxembourg Institute of Health, Strassen, Luxembourg

[6] Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg

[7] Centre Hospitalier de Luxembourg, Strassen, Luxembourg

[8] Centre Hospitalier Emile Mayrisch, Esch-sur-Alzette, Luxembourg

[9] Laboratoire National de Santé, Dudelange, Luxembourg

[10] Association of Physiotherapists in Parkinson’s Disease Europe, Esch-sur-Alzette, Luxembourg

[11] Faculty of Science, Technology and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg

[12] Department of Epidemiology, CAPHRI School for Public Health and Primary Care, Maastricht University Medical Centre, Maastricht, Netherlands

[13] Private Practice, Ettelbruck, Luxembourg

[14] Parkinson Luxembourg Association, Leudelange, Luxembourg

[15] Luxembourg Center of Neuropathology, Dudelange, Luxembourg

[16] Department of Life Sciences and Medicine, University of Luxembourg, Esch-sur-Alzette, Luxembourg

[17] Private Practice, Luxembourg, Luxembourg

来源：

npj Digital Medicine | 2024年 / 7卷 / 01期

关键词：

81;

D O I：

10.1038/s41746-024-01236-z

中图分类号：

学科分类号：

摘要：

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.

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