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


RESULTS
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).


CONCLUSIONS
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.


BACKGROUND
National Institute on Aging, National Institute of Neurological Disorders and Stroke, and the Michael J Fox Foundation.
Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.
Mike A
MA
Nalls
23andMe, Mountain View, CA, USA.
Cory Y
CY
McLean
Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Jacqueline
J
Rick
Deptartment of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA.
Shirley
S
Eberly
The Michael J Fox Foundation for Parkinson's Research, New York, NY 10018, USA.
Samantha J
SJ
Hutten
National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
Katrina
K
Gwinn
National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.
Margaret
M
Sutherland
INSERM, UMR 1043, Centre de Physiopathologie de Toulouse-Purpan, Toulouse, France; Paul Sabatier University, Toulouse, France.
Maria
M
Martinez
Genome Biology of Neurodegenerative Diseases, German Center for Neurodegenerative Diseases, Tübingen, Germany.
Peter
P
Heutink
Institute of Psychological Medicine and Clinical Neurosciences, MRCCentre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK.
Nigel M
NM
Williams
Reta Lila Weston Institute, University College London Institute of Neurology, London, UK.
John
J
Hardy
Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.
Thomas
T
Gasser
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.
Alexis
A
Brice
Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.
T Ryan
TR
Price
Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.
Aude
A
Nicolas
Genetics and Pharmacogenomics, Merck Research Laboratories, West Point, PA, USA; Merck Research Laboratories, Boston, MA, USA.
Margaux F
MF
Keller
Genetics and Pharmacogenomics, Merck Research Laboratories, West Point, PA, USA; Merck Research Laboratories, Boston, MA, USA.
Cliona
C
Molony
Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.
J Raphael
JR
Gibbs
Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Alice
A
Chen-Plotkin
Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Eunran
E
Suh
Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.
Christopher
C
Letson
Department of Neurology, Georgetown University Medical Center, Washington, DC, USA.
Massimo S
MS
Fiandaca
Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.
Mark
M
Mapstone
Department of Neurology, Georgetown University Medical Center, Washington, DC, USA.
Howard J
HJ
Federoff
Reta Lila Weston Institute, University College London Institute of Neurology, London, UK.
Alastair J
AJ
Noyce
Reta Lila Weston Institute, University College London Institute of Neurology, London, UK.
Huw
H
Morris
Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.
Vivianna M
VM
Van Deerlin
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.
Daniel
D
Weintraub
Department of Neurology, Division of Neurogenetics, VA Puget Sound Health Care System, Seattle, WA, USA.
Cyrus
C
Zabetian
Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.
Dena G
DG
Hernandez
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.
Suzanne
S
Lesage
23andMe, Mountain View, CA, USA.
Meghan
M
Mullins
23andMe, Mountain View, CA, USA.
Emily Drabant
ED
Conley
23andMe, Mountain View, CA, USA.
Carrie A M
CA
Northover
The Michael J Fox Foundation for Parkinson's Research, New York, NY 10018, USA.
Mark
M
Frasier
Institute for Neurodegenerative Disorders, New Haven, CT, USA.
Ken
K
Marek
Precision Medicine, Biogen, Cambridge, MA, USA.
Aaron G
AG
Day-Williams
Genetics and Pharmacogenomics, Merck Research Laboratories, West Point, PA, USA; Merck Research Laboratories, Boston, MA, USA.
David J
DJ
Stone
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.
John P A
JP
Ioannidis
Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA. Electronic address: singleta@mail.nih.gov.
Andrew B
AB
Singleton
D000368:Aged; D015331:Cohort Studies; D018450:Disease Progression; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D010300:Parkinson Disease / Q000175:diagnosis* / Q000235:genetics; D062706:Prodromal Symptoms