Derek B Archer1, Trina Mitchell1, Roxana G Burciu2, Jing Yang3, Salvatore Nigro4, Aldo Quattrone4,5, Andrea Quattrone6, Andreas Jeromin7, Nikolaus R McFarland8,9, Michael S Okun8,9,10, David E Vaillancourt1,8,9,11. 1. Laboratory for Rehabilitation Neuroscience, Department of Applied Physiology and Kinesiology, University of Florida, Gainesville, Florida, USA. 2. Department of Kinesiology and Applied Physiology, College of Health Sciences, University of Delaware, Newark, Delaware, USA. 3. Department of Neurology, West China Hospital of Sichuan University, Chengdu, China. 4. Neuroscience Centre, Magna Graecia University, Catanzaro, Italy. 5. Neuroimaging Research Unit, Institute of Molecular Bioimaging and Physiology, National Research Council, Catanzaro, Italy. 6. Institute of Neurology, Department of Medical Sciences, Magna Graecia University, Catanzaro, Italy. 7. Quanterix, Corporation, Lexington, Massachusetts, USA. 8. Fixel Institute for Neurological Disease, College of Medicine, University of Florida, Gainesville, Florida, USA. 9. Department of Neurology, University of Florida, McKnight Brain Institute, Gainesville, Florida, USA. 10. Department of Neurosurgery, University of Florida, McKnight Brain Institute, Gainesville, Florida, USA. 11. J. Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA.
Abstract
OBJECTIVE: Accurate diagnosis is particularly challenging in Parkinson's disease (PD), multiple system atrophy (MSAp), and progressive supranuclear palsy (PSP). We compare the utility of 3 promising biomarkers to differentiate disease state and explain disease severity in parkinsonism: the Automated Imaging Differentiation in Parkinsonism (AID-P), the Magnetic Resonance Parkinsonism Index (MRPI), and plasma-based neurofilament light chain protein (NfL). METHODS: For each biomarker, the area under the curve (AUC) of receiver operating characteristic curves were quantified for PD versus MSAp/PSP and MSAp versus PSP and statistically compared. Unique combinations of variables were also assessed. Furthermore, each measures association with disease severity was determined using stepwise multiple regression. RESULTS: For PD versus MSAp/PSP, AID-P (AUC, 0.900) measures had higher AUC compared with NfL (AUC, 0.747) and MRPI (AUC, 0.669), P < 0.05. For MSAp versus PSP, AID-P (AUC, 0.889), and MRPI (AUC, 0.824) measures were greater than NfL (AUC, 0.537), P < 0.05. We then combined measures to determine if any unique combination provided enhanced accuracy and found that no combination performed better than the AID-P alone in differentiating parkinsonisms. Furthermore, we found that the AID-P demonstrated the highest association with the MDS-UPDRS (Radj 2 -AID-P, 26.58%; NfL,15.12%; MRPI, 12.90%). CONCLUSIONS: Compared with MRPI and NfL, AID-P provides the best overall differentiation of PD versus MSAp/PSP. Both AID-P and MRPI are effective in differentiating MSAp versus PSP. Furthermore, combining biomarkers did not improve classification of disease state compared with using AID-P alone. The findings demonstrate in the current sample that the AID-P and MRPI are robust biomarkers for PD, MSAp, and PSP.
OBJECTIVE: Accurate diagnosis is particularly challenging in Parkinson's disease (PD), multiple system atrophy (MSAp), and progressive supranuclear palsy (PSP). We compare the utility of 3 promising biomarkers to differentiate disease state and explain disease severity in parkinsonism: the Automated Imaging Differentiation in Parkinsonism (AID-P), the Magnetic Resonance Parkinsonism Index (MRPI), and plasma-based neurofilament light chain protein (NfL). METHODS: For each biomarker, the area under the curve (AUC) of receiver operating characteristic curves were quantified for PD versus MSAp/PSP and MSAp versus PSP and statistically compared. Unique combinations of variables were also assessed. Furthermore, each measures association with disease severity was determined using stepwise multiple regression. RESULTS: For PD versus MSAp/PSP, AID-P (AUC, 0.900) measures had higher AUC compared with NfL (AUC, 0.747) and MRPI (AUC, 0.669), P < 0.05. For MSAp versus PSP, AID-P (AUC, 0.889), and MRPI (AUC, 0.824) measures were greater than NfL (AUC, 0.537), P < 0.05. We then combined measures to determine if any unique combination provided enhanced accuracy and found that no combination performed better than the AID-P alone in differentiating parkinsonisms. Furthermore, we found that the AID-P demonstrated the highest association with the MDS-UPDRS (Radj 2 -AID-P, 26.58%; NfL,15.12%; MRPI, 12.90%). CONCLUSIONS: Compared with MRPI and NfL, AID-P provides the best overall differentiation of PD versus MSAp/PSP. Both AID-P and MRPI are effective in differentiating MSAp versus PSP. Furthermore, combining biomarkers did not improve classification of disease state compared with using AID-P alone. The findings demonstrate in the current sample that the AID-P and MRPI are robust biomarkers for PD, MSAp, and PSP.
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