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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.LN

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.

Authors+Show Affiliations

Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.23andMe, Mountain View, CA, USA.Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.Deptartment of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA.The Michael J Fox Foundation for Parkinson's Research, New York, NY 10018, USA.National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.INSERM, UMR 1043, Centre de Physiopathologie de Toulouse-Purpan, Toulouse, France; Paul Sabatier University, Toulouse, France.Genome Biology of Neurodegenerative Diseases, German Center for Neurodegenerative Diseases, Tübingen, Germany.Institute of Psychological Medicine and Clinical Neurosciences, MRCCentre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, UK.Reta Lila Weston Institute, University College London Institute of Neurology, London, UK.Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.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.Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.Genetics and Pharmacogenomics, Merck Research Laboratories, West Point, PA, USA; Merck Research Laboratories, Boston, MA, USA.Genetics and Pharmacogenomics, Merck Research Laboratories, West Point, PA, USA; Merck Research Laboratories, Boston, MA, USA.Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.Department of Neurology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.Department of Neurology, Georgetown University Medical Center, Washington, DC, USA.Department of Neurology, University of Rochester Medical Center, Rochester, NY, USA.Department of Neurology, Georgetown University Medical Center, Washington, DC, USA.Reta Lila Weston Institute, University College London Institute of Neurology, London, UK.Reta Lila Weston Institute, University College London Institute of Neurology, London, UK.Center for Neurodegenerative Disease Research, Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA.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.Department of Neurology, Division of Neurogenetics, VA Puget Sound Health Care System, Seattle, WA, USA.Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA.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.23andMe, Mountain View, CA, USA.23andMe, Mountain View, CA, USA.23andMe, Mountain View, CA, USA.The Michael J Fox Foundation for Parkinson's Research, New York, NY 10018, USA.Institute for Neurodegenerative Disorders, New Haven, CT, USA.Precision Medicine, Biogen, Cambridge, MA, USA.Genetics and Pharmacogenomics, Merck Research Laboratories, West Point, PA, USA; Merck Research Laboratories, Boston, MA, USA.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.Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA. Electronic address: singleta@mail.nih.gov.No affiliation info available

Pub Type(s)

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

Language

eng

PubMed ID

26271532

Citation

Nalls, Mike A., et al. "Diagnosis of Parkinson's Disease On the Basis of Clinical and Genetic Classification: a Population-based Modelling Study." The Lancet. Neurology, vol. 14, no. 10, 2015, pp. 1002-9.
Nalls MA, McLean CY, Rick J, et al. Diagnosis of Parkinson's disease on the basis of clinical and genetic classification: a population-based modelling study. Lancet Neurol. 2015;14(10):1002-9.
Nalls, M. A., McLean, C. Y., Rick, J., Eberly, S., Hutten, S. J., Gwinn, K., Sutherland, M., Martinez, M., Heutink, P., Williams, N. M., Hardy, J., Gasser, T., Brice, A., Price, T. R., Nicolas, A., Keller, M. F., Molony, C., Gibbs, J. R., Chen-Plotkin, A., ... Singleton, A. B. (2015). Diagnosis of Parkinson's disease on the basis of clinical and genetic classification: a population-based modelling study. The Lancet. Neurology, 14(10), 1002-9. https://doi.org/10.1016/S1474-4422(15)00178-7
Nalls MA, et al. Diagnosis of Parkinson's Disease On the Basis of Clinical and Genetic Classification: a Population-based Modelling Study. Lancet Neurol. 2015;14(10):1002-9. PubMed PMID: 26271532.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Diagnosis of Parkinson's disease on the basis of clinical and genetic classification: a population-based modelling study. AU - Nalls,Mike A, AU - McLean,Cory Y, AU - Rick,Jacqueline, AU - Eberly,Shirley, AU - Hutten,Samantha J, AU - Gwinn,Katrina, AU - Sutherland,Margaret, AU - Martinez,Maria, AU - Heutink,Peter, AU - Williams,Nigel M, AU - Hardy,John, AU - Gasser,Thomas, AU - Brice,Alexis, AU - Price,T Ryan, AU - Nicolas,Aude, AU - Keller,Margaux F, AU - Molony,Cliona, AU - Gibbs,J Raphael, AU - Chen-Plotkin,Alice, AU - Suh,Eunran, AU - Letson,Christopher, AU - Fiandaca,Massimo S, AU - Mapstone,Mark, AU - Federoff,Howard J, AU - Noyce,Alastair J, AU - Morris,Huw, AU - Van Deerlin,Vivianna M, AU - Weintraub,Daniel, AU - Zabetian,Cyrus, AU - Hernandez,Dena G, AU - Lesage,Suzanne, AU - Mullins,Meghan, AU - Conley,Emily Drabant, AU - Northover,Carrie A M, AU - Frasier,Mark, AU - Marek,Ken, AU - Day-Williams,Aaron G, AU - Stone,David J, AU - Ioannidis,John P A, AU - Singleton,Andrew B, AU - ,, Y1 - 2015/08/10/ PY - 2015/05/04/received PY - 2015/06/23/revised PY - 2015/07/07/accepted PY - 2015/8/15/entrez PY - 2015/8/15/pubmed PY - 2015/12/23/medline SP - 1002 EP - 9 JF - The Lancet. Neurology JO - Lancet Neurol VL - 14 IS - 10 N2 - 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. SN - 1474-4465 UR - https://www.unboundmedicine.com/medline/citation/26271532/Diagnosis_of_Parkinson's_disease_on_the_basis_of_clinical_and_genetic_classification:_a_population_based_modelling_study_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S1474-4422(15)00178-7 DB - PRIME DP - Unbound Medicine ER -