Diagnosis of Parkinson's disease on the basis of clinical and genetic classification: a population-based modelling study

Mike A Nalls; Cory Y McLean; Jacqueline Rick; Shirley Eberly; Samantha J Hutten; Katrina Gwinn; Margaret Sutherland; Maria Martinez; Peter Heutink; Nigel M Williams; John Hardy; Thomas Gasser; Alexis Brice; T Ryan Price; Aude Nicolas; Margaux F Keller; Cliona Molony; J Raphael Gibbs; Alice Chen-Plotkin; Eunran Suh; Christopher Letson; Massimo S Fiandaca; Mark Mapstone; Howard J Federoff; Alastair J Noyce; Huw Morris; Vivianna M Van Deerlin; Daniel Weintraub; Cyrus Zabetian; Dena G Hernandez; Suzanne Lesage; Meghan Mullins; Emily Drabant Conley; Carrie A M Northover; Mark Frasier; Ken Marek; Aaron G Day-Williams; David J Stone; John P A Ioannidis; Andrew B Singleton; Parkinson's Disease Biomarkers Program and Parkinson's Progression Marker Initiative investigators

doi:10.1016/S1474-4422(15)00178-7

Diagnosis of Parkinson's disease on the basis of clinical and genetic classification: a population-based modelling study

Lancet Neurol. 2015 Oct;14(10):1002-9. doi: 10.1016/S1474-4422(15)00178-7. Epub 2015 Aug 10.

Authors

Mike A Nalls¹, Cory Y McLean², Jacqueline Rick³, Shirley Eberly⁴, Samantha J Hutten⁵, Katrina Gwinn⁶, Margaret Sutherland⁶, Maria Martinez⁷, Peter Heutink⁸, Nigel M Williams⁹, John Hardy¹⁰, Thomas Gasser¹¹, Alexis Brice¹², T Ryan Price¹, Aude Nicolas¹, Margaux F Keller¹³, Cliona Molony¹³, J Raphael Gibbs¹, Alice Chen-Plotkin³, Eunran Suh¹⁴, Christopher Letson¹, Massimo S Fiandaca¹⁵, Mark Mapstone¹⁶, Howard J Federoff¹⁵, Alastair J Noyce¹⁰, Huw Morris¹⁰, Vivianna M Van Deerlin¹⁴, Daniel Weintraub¹⁷, Cyrus Zabetian¹⁸, Dena G Hernandez¹, Suzanne Lesage¹², Meghan Mullins², Emily Drabant Conley², Carrie A M Northover², Mark Frasier⁵, Ken Marek¹⁹, Aaron G Day-Williams²⁰, David J Stone¹³, John P A Ioannidis²¹, Andrew B Singleton²²; Parkinson's Disease Biomarkers Program and Parkinson's Progression Marker Initiative investigators

Collaborators

Parkinson's Disease Biomarkers Program and Parkinson's Progression Marker Initiative investigators:
Dubois Bowman, Alice Chen-Plotkin, Ted Dawson, Richard Dewey, Dwight Charles German, Xuemei Huang, Vladislav Petyuk, Clemens Scherzer, David Vaillancourt, Andrew West, Jing Zhang, Kenneth Marek, Danna Jennings, Shirley Lasch, Caroline Tanner, Tanya Simuni, Christopher Coffey, Karl Kieburtz, Renee Wilson, Werner Poewe, Brit Mollenhauer, Douglas Galasko, Tatiana Foroud, Todd Sherer, Sohini Chowdhury, Mark Frasier, Catherine Kopil, Vanessa Arnedo, Cynthia Casaceli, John Seibyl, Susan Mendick, Norbert Schuff, Chelsea Caspell, Liz Uribe, Eric Foster, Katherine Gloer, Jon Yankey, Arthur Toga, Karen Crawford, Danielle Elise Smith, Paola Casalin, Giulia Malferrari, John Trojanowski, Les Shaw, Andrew Singleton, Cheryl Halter, David Russell, Stewart Factor, Penelope Hogarth, David Standaert, Robert Hauser, Joseph Jankovic, Matthew Stern, Lama Chahine, Shu-Ching Hu, Samuel Frank, Claudia Trenkwalder, Wolfgang Oertel, Irene Richard, Klaus Seppi, Eva Reiter, Holly Shill, Hubert Fernandez, Anwar Ahmed, Daniela Berg, Isabel Wurster, Zoltan Mari, David Brooks, Nicola Pavese, Paolo Barone, Stuart Isaacson, Alberto Espay, Dominic Rowe, Melanie Brandabur, James Tetrud, Grace Liang, Karen Marder, Jean-Christophe Corvol, Jose Felix Martí Masso, Eduardo Tolosa, Jan O Aasly, Nir Giladi, Leonidas Stefanis, Laura Leary, Cheryl Riordan, Linda Rees, Barbara Sommerfeld, Cathy Wood-Siverio, Alicia Portillo, Art Lenahan, Karen Williams, Stephanie Guthrie, Ashlee Rawlins, Sherry Harlan, Christine Hunter, Baochan Tran, Abigail Darin, Carly Linder, Gretchen Todd, Cathi-Ann Thomas, Raymond James, Cheryl Deeley, Courtney Bishop, Fabienne Sprenger, Diana Willeke, Sanja Obradov, Jennifer Mule, Nancy Monahan, Katharina Gauss, Kathleen Comyns, Deborah Fontaine, Christina Gigliotti, Arita McCoy, Becky Dunlop, Bina Shah, Susan Ainscough, Angela James, Rebecca Silverstein, Kristy Espay, Madelaine Ranola, Helen M Santana, Nelly Ngono, Elisabet Rezola, Delores Vilas Rolan, Bjorg Waro, Anat Mirlman, Maria Stamelou, Thomas Comery, Spyros Papapetropoulos, Bernard Ravina, Igor D Grachev, Jordan S Dubow, Michael Ahlijanian, Holly Soares, Suzanne Ostrowizki, Paulo Fontoura, Alison Chalker, David L Hewitt, Marcel van der Brug, Alastair D Reith, Peggy Taylor, Jan Egebjerg, Mark Minton, Andrew Siderowf, Pierandrea Muglia, Robert Umek, Ana Catafau

Affiliations

¹ Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.
² 23andMe, Mountain View, CA, USA.
³ Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
⁴ Deptartment of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA.
⁵ The Michael J Fox Foundation for Parkinson's Research, New York, NY 10018, USA.
⁶ National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
⁷ INSERM, UMR 1043, Centre de Physiopathologie de Toulouse-Purpan, Toulouse, France; Paul Sabatier University, Toulouse, France.
⁸ Genome Biology of Neurodegenerative Diseases, German Center for Neurodegenerative Diseases, Tübingen, Germany.
⁹ Institute of Psychological Medicine and Clinical Neurosciences, MRCCentre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK.
¹⁰ Reta Lila Weston Institute, University College London Institute of Neurology, London, UK.
¹¹ Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.
¹² Sorbonne Université, UPMC Univ Paris 06, UM 1127, ICM, Paris, France; INSERM, U 1127, and CNRS, UMR 7225, Institut du Cerveau et de la Moelle Epinière, Paris, France; AP-HP, Hôpital de la Salpêtrière, Département de Génétique et Cytogénétique, Paris, France.
¹³ Genetics and Pharmacogenomics, Merck Research Laboratories, West Point, PA, USA; Merck Research Laboratories, Boston, MA, USA.
¹⁴ Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
¹⁵ Department of Neurology, Georgetown University Medical Center, Washington, DC, USA.
¹⁶ Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.
¹⁷ Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA; Department of Psychiatry, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
¹⁸ Department of Neurology, Division of Neurogenetics, VA Puget Sound Health Care System, Seattle, WA, USA.
¹⁹ Institute for Neurodegenerative Disorders, New Haven, CT, USA.
²⁰ Precision Medicine, Biogen, Cambridge, MA, USA.
²¹ Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, CA, USA; Department of Medicine, Stanford Prevention Research Center, Stanford, CA, USA; Department of Health Research and Policy, Stanford University School of Medicine, Stanford, CA, USA; Department of Statistics, Stanford University School of Humanities and Sciences, Stanford, CA, USA.
²² Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA. Electronic address: singleta@mail.nih.gov.

Abstract

Background: Accurate diagnosis and early detection of complex diseases, such as Parkinson's disease, has the potential to be of great benefit for researchers and clinical practice. We aimed to create a non-invasive, accurate classification model for the diagnosis of Parkinson's disease, which could serve as a basis for future disease prediction studies in longitudinal cohorts.

Methods: We developed a model for disease classification using data from the Parkinson's Progression Marker Initiative (PPMI) study for 367 patients with Parkinson's disease and phenotypically typical imaging data and 165 controls without neurological disease. Olfactory function, genetic risk, family history of Parkinson's disease, age, and gender were algorithmically selected by stepwise logistic regression as significant contributors to our classifying model. We then tested the model with data from 825 patients with Parkinson's disease and 261 controls from five independent cohorts with varying recruitment strategies and designs: the Parkinson's Disease Biomarkers Program (PDBP), the Parkinson's Associated Risk Study (PARS), 23andMe, the Longitudinal and Biomarker Study in PD (LABS-PD), and the Morris K Udall Parkinson's Disease Research Center of Excellence cohort (Penn-Udall). Additionally, we used our model to investigate patients who had imaging scans without evidence of dopaminergic deficit (SWEDD).

Findings: In the population from PPMI, our initial model correctly distinguished patients with Parkinson's disease from controls at an area under the curve (AUC) of 0·923 (95% CI 0·900-0·946) with high sensitivity (0·834, 95% CI 0·711-0·883) and specificity (0·903, 95% CI 0·824-0·946) at its optimum AUC threshold (0·655). All Hosmer-Lemeshow simulations suggested that when parsed into random subgroups, the subgroup data matched that of the overall cohort. External validation showed good classification of Parkinson's disease, with AUCs of 0·894 (95% CI 0·867-0·921) in the PDBP cohort, 0·998 (0·992-1·000) in PARS, 0·955 (no 95% CI available) in 23andMe, 0·929 (0·896-0·962) in LABS-PD, and 0·939 (0·891-0·986) in the Penn-Udall cohort. Four of 17 SWEDD participants who our model classified as having Parkinson's disease converted to Parkinson's disease within 1 year, whereas only one of 38 SWEDD participants who were not classified as having Parkinson's disease underwent conversion (test of proportions, p=0·003).

Interpretation: Our model provides a potential new approach to distinguish participants with Parkinson's disease from controls. If the model can also identify individuals with prodromal or preclinical Parkinson's disease in prospective cohorts, it could facilitate identification of biomarkers and interventions.

Funding: National Institute on Aging, National Institute of Neurological Disorders and Stroke, and the Michael J Fox Foundation.

Publication types

Research Support, N.I.H., Extramural
Research Support, N.I.H., Intramural
Research Support, Non-U.S. Gov't

MeSH terms

Aged
Cohort Studies
Disease Progression
Female
Humans
Male
Middle Aged
Models, Statistical*
Parkinson Disease / diagnosis*
Parkinson Disease / genetics
Prodromal Symptoms

Abstract

Publication types

MeSH terms

Grants and funding