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

被引：177

作者：

Nalls, Mike A. ^{[1
]}

McLean, Cory Y. ^{[2
]}

Rick, Jacqueline ^{[3
]}

Eberly, Shirley ^{[4
]}

Hutten, Samantha J. ^{[5
]}

Gwinn, Katrina ^{[6
]}

Sutherland, Margaret ^{[6
]}

Martinez, Maria ^{[7
,8
]}

Heutink, Peter ^{[9
]}

Williams, Nigel M. ^{[10
]}

Hardy, John ^{[11
]}

Gasser, Thomas ^{[12
]}

Brice, Alexis ^{[13
,14
,15
]}

Price, T. Ryan ^{[1
]}

Nicolas, Aude ^{[1
]}

Keller, Margaux F. ^{[16
,17
]}

Molony, Cliona ^{[16
,17
]}

Gibbs, J. Raphael ^{[1
]}

Chen-Plotkin, Alice ^{[3
]}

Suh, Eunran ^{[18
]}

Letson, Christopher ^{[1
]}

Fiandaca, Massimo S. ^{[19
]}

Mapstone, Mark ^{[20
]}

Federoff, Howard J. ^{[19
]}

Noyce, Alastair J. ^{[11
]}

Morris, Huw ^{[11
]}

Van Deerlin, Vivianna M. ^{[18
]}

Weintraub, Daniel ^{[3
,21
]}

Zabetian, Cyrus ^{[22
]}

Hernandez, Dena G. ^{[1
]}

Lesage, Suzanne ^{[13
,14
,15
]}

Mullins, Meghan ^{[2
]}

Conley, Emily Drabant ^{[2
]}

Northover, Carrie A. M. ^{[2
]}

Frasier, Mark ^{[5
]}

Marek, Ken ^{[23
]}

Day-Williams, Aaron G. ^{[24
]}

Stone, David J. ^{[16
,17
]}

Ioannidis, John P. A. ^{[25
]}

Singleton, Andrew B. ^{[1
]}

机构：

[1] NIA, NIH, Neurogenet Lab, Bethesda, MD 20892 USA

[2] 23andMe, Mountain View, CA USA

[3] Univ Penn, Perelman Sch Med, Dept Neurol, Philadelphia, PA 19104 USA

[4] Univ Rochester, Dept Biostat & Computat Biol, Rochester, NY 14627 USA

[5] Michael J Fox Fdn, Parkinsons Res, New York, NY USA

[6] NINDS, NIH, Bethesda, MD 20892 USA

[7] INSERM, UMR 1043, Ctr Physiopathol Toulouse Purpan, Toulouse, France

[8] Univ Toulouse 3, F-31062 Toulouse, France

[9] German Ctr Neurodegenerat Dis, Genome Biol Neurodegenerat Dis, Tubingen, Germany

[10] Cardiff Univ, MRC Ctr Neuropsychiat Genet & Gen, Inst Psychol Med & Clin Neurosci, Cardiff CF10 3AX, S Glam, Wales

[11] UCL, Inst Neurol, Reta Lila Weston Inst, London, England

[12] Univ Tubingen, Hertie Inst Clin Brain Res, Dept Neurodegenerat Dis, Tubingen, Germany

[13] Sorbonne Univ, UPMC Univ Paris 06, 1127, Paris, France

[14] CNRS, INSERM, UMR 7225,U 1127, Inst Cerveau & Moelle Epiniere, Paris, France

[15] Hop La Pitie Salpetriere, AP HP, Dept Genet & Cytogenet, Paris, France

[16] Merck Res Labs, Genet & Pharmacogen, West Point, PA USA

[17] Merck Res Labs, Boston, MA USA

[18] Univ Penn, Perelman Sch Med, Dept Pathol & Lab Med, Ctr Neurodegenerat Dis Res, Philadelphia, PA 19104 USA

[19] Georgetown Univ, Med Ctr, Dept Neurol, Washington, DC 20007 USA

[20] Univ Rochester, Med Ctr, Dept Neurol, Rochester, NY 14642 USA

[21] Univ Penn, Perelman Sch Med, Dept Psychiat, Philadelphia, PA 19104 USA

[22] VA Puget Sound Hlth Care Syst, Div Neurogenet, Dept Neurol, Seattle, WA USA

[23] Inst Neurodegenerat Disorders, New Haven, CT USA

[24] Biogen, Precis Med, Cambridge, MA USA

[25] Stanford Univ, Meta Res Innovat Ctr Stanford METRICS, Stanford, CA 94305 USA

来源：

LANCET NEUROLOGY | 2015年 / 14卷 / 10期

关键词：

MUTATIONS; IDENTIFICATION; METAANALYSIS;

D O I：

10.1016/S1474-4422(15)00178-7

中图分类号：

R74 [神经病学与精神病学];

学科分类号：

100204 [神经病学];

摘要：

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.

引用

页码：1002 / 1009

页数：8

共 20 条

[1]

Olfactory function in restless legs syndrome [J].