Diagnosis of Parkinson's disease on the basis of clinical and genetic classification: a population-based modelling study
Open accessAbstract
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.
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Parkinson's disease is characterized by the accumulation of alpha-synuclein within neurons and the loss of neuronal cells.
“Parkinson’s disease (PD) is a debilitating neurodegenerative disorder characterized by the accumulation of alpha-synuclein (αSyn) within neurons and the loss of neuronal cells1.”
From Microglial inhibition alleviates alpha-synuclein propagation and neurodegeneration in Parkinson’s disease mouse model (2024) · cites this paper as [1]
The Introduction describes Parkinson’s disease as a complex progressive disease and discusses indicators of neurodegeneration. Our copy does not state that Parkinson’s disease is characterized by intracellular alpha-synuclein accumulation or neuronal cell loss; searches for those features found no supporting passage. (LLM, conf=0.78)
Experimental — accuracy not yet measuredThere were four significant genetic correlations between Parkinson's disease and other traits (FDR-adjusted P < 0.05).
“We found four significant genetic correlations (FDR-adjusted P < 0.05, Table S10) including positive correlations with intracranial volume and putamen volume (42), and negative correlations with current tobacco use and “academic qualifications: National Vocational Qualifications (NVQ) or Higher National Diploma (HND) or Higher National Certificate (HNC) or equivalent” (43).”
From Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies (2019) · cites this paper as [43]
Parkinson's disease had a negative genetic correlation with academic qualifications: National Vocational Qualifications (NVQ) or Higher National Diploma (HND) or Higher National Certificate (HNC) or equivalent.
“We found four significant genetic correlations (FDR-adjusted P < 0.05, Table S10) including positive correlations with intracranial volume and putamen volume (42), and negative correlations with current tobacco use and “academic qualifications: National Vocational Qualifications (NVQ) or Higher National Diploma (HND) or Higher National Certificate (HNC) or equivalent” (43).”
From Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies (2019) · cites this paper as [43]
Parkinson's disease had a negative genetic correlation with current tobacco use.
“We found four significant genetic correlations (FDR-adjusted P < 0.05, Table S10) including positive correlations with intracranial volume and putamen volume (42), and negative correlations with current tobacco use and “academic qualifications: National Vocational Qualifications (NVQ) or Higher National Diploma (HND) or Higher National Certificate (HNC) or equivalent” (43).”
From Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies (2019) · cites this paper as [43]
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“Parkinson ’ s disease (PD) is a debilitating neurodegenerative disorder characterized by the accumulation of alpha-synuclein ( α Syn) within neurons and the loss of neuronal cells 1 .”
BackgroundCan Biomarkers Help the Early Diagnosis of Parkinson’s Disease?
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Clinical and Imaging Progression in the PARS Cohort: Long‐Term Follow‐up
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New hopes for disease modification in Parkinson's Disease
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Association of inflammatory factors and aging in Parkinson's disease
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Mining genetic and transcriptomic data using machine learning approaches in Parkinson’s disease
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Genome-wide assessment of Parkinson's disease in a Southern Spanish population
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Combining Neuroimaging and Omics Datasets for Disease Classification Using Graph Neural Networks
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Magnetic resonance T 1w/ T 2w ratio: A parsimonious marker for P arkinson disease
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Olfactory Dysfunction in Familial and Sporadic Parkinson's Disease
Chase, B. A., & Markopoulou, K. (2020) · Frontiers in Neurology · doi:10.3389/fneur.2020.00447 · Cited by 40 · see citing works
Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer
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The Parkinson’s phenome—traits associated with Parkinson’s disease in a broadly phenotyped cohort
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Non-motor symptoms and striatal dopamine transporter binding in early Parkinson's disease
Liu, R., Umbach, D. M., Tröster, A. I., Huang, X., & Chen, H. (2020) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2020.02.001 · Cited by 37 · see citing works
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Immune-related biomarkers for Parkinson's disease
Abdi, I. Y., Ghanem, S. S., & El‐Agnaf, O. M. A. (2022) · Neurobiology of Disease · doi:10.1016/j.nbd.2022.105771 · Cited by 36 · see citing works
Xu, C., Qu, P.-F., Chai, W., Li, R., & Chen, J. (2019) · Journal of Orthopaedic Surgery and Research · doi:10.1186/s13018-019-1179-9 · Cited by 35 · see citing works
Progress in the genetic analysis of Parkinson’s disease
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A Machine Learning Approach to Parkinson’s Disease Blood Transcriptomics
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van den Heuvel, L., Dorsey, R., Prainsack, B., Post, B., Stiggelbout, A. M., Meinders, M. J., & Bloem, B. R. (2019) · Journal of Parkinson s Disease · doi:10.3233/jpd-191712 · Cited by 32 · see citing works
Predicting onset, progression, and clinical subtypes of Parkinson disease using machine learningPreprint
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Arnerić, S. P., Kern, V. D., & Stephenson, D. T. (2018) · Biochemical Pharmacology · doi:10.1016/j.bcp.2018.01.043 · Cited by 31 · see citing works
REM sleep behaviour disorder: an early window for prevention in neurodegeneration?
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Elephants, Parkinson's Disease, and Proof‐of‐Concept Clinical Trials
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Disease-modifying therapies for Parkinson disease: lessons from multiple sclerosis
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Ten Years of the International Parkinson Disease Genomics Consortium: Progress and Next Steps
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Variation in Recent Onset Parkinson’s Disease: Implications for Prodromal Detection
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Progress toward an integrated understanding of Parkinson’s disease
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Parkinson's Disease Biomarkers: Where Are We and Where Do We Go Next?
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What have we learned from genome-wide association studies (GWAS) in Parkinson's disease?
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Olfactory Function in Latino Farmworkers
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Vertebrate food products as a potential source of prion-like α-synuclein
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Genetic Analysis of LRRK2 R1628P in Parkinson’s Disease in Asian Populations
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The Birth of the Modern Era of Parkinson’s Disease Genetics
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- Authors
- 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., Suh, E., Letson, C., Fiandaca, M. S., Mapstone, M., Federoff, H. J., Noyce, A. J., Morris, H., Van Deerlin, V. M., Weintraub, D., Zabetian, C., Hernandez, D. G., Lesage, S., Mullins, M., Conley, E. D., Northover, C. A. M., Frasier, M., Marek, K., Day-Williams, A. G., Stone, D. J., Ioannidis, J. P. A., & Singleton, A. B.
- Affiliations
- National Institutes of Health (US)
- 23andMe (United States) (US)
- University of Pennsylvania (US)
- University of Rochester Medicine (US)
- Michael J. Fox Foundation (US)
- Université Toulouse III - Paul Sabatier (FR)
- German Center for Neurodegenerative Diseases (DE)
- Cardiff University (GB)
- UCL Queen Square Institute of Neurology (GB)
- Hertie Institute for Clinical Brain Research (DE)
- Centre National de la Recherche Scientifique (FR)
- Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
- Georgetown University Medical Center (US)
- VA Puget Sound Health Care System (US)
- Biogen (United States) (US)
- Stanford Medicine (US)
- Corresponding author
- Singleton, Andrew B
- ORCID
- Nalls, Mike A
- McLean, Cory Y
- Eberly, Shirley
- Hutten, Samantha J
- Gwinn, Katrina
- Sutherland, Margaret
- Martinez, Maria
- Heutink, Peter
- Williams, Nigel M
- Hardy, John
- Gasser, Thomas
- Brice, Alexis
- Nicolas, Aude
- Keller, Margaux F
- Molony, Cliona
- Gibbs, J Raphael
- Suh, Eunran
- Fiandaca, Massimo S
- Mapstone, Mark
- Federoff, Howard J
- Noyce, Alastair J
- Morris, Huw
- Weintraub, Daniel
- Zabetian, Cyrus
- Hernandez, Dena G
- Lesage, Suzanne
- Frasier, Mark
- Ioannidis, John P A
- Singleton, Andrew B
- Venue
- The Lancet Neurology
- Publisher
- Elsevier BV
- Published
- 2015-10
- Volume
- 14
- Issue
- 10
- Pages
- 1002-1009
- ISSN
- 1474-4422 (ISSN-L)
- PMC
- PMC4575273
- OpenAlex
- W2098399146
- Cited by
- 204
- References
- 21
- Citations by year
- 2026: 11 · 2025: 14 · 2024: 15 · 2023: 10 · 2022: 16 · 2021: 15 · 2020: 29 · 2019: 21 · 2018: 21 · 2017: 28 · 2016: 19 · 2015: 5
- FWCI
- 17.16
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- The Lancet Neurology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- UCL Discovery (University College London) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- ORCA Online Research @Cardiff (Cardiff University) (submittedVersion)
- DZNE Pub (submittedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion)
- Funders
- NINDS (U01NS082134, U01NS082157, U01NS082151, U01NS082137, U01NS082148, U01NS082133, grant P50NS053488 and P50 NS062684)
- Parkinson's UK (Grants 8047)
- Medical Research Council UK (G0700943, G1100643)
- Topics
- Health Sciences › Medicine › Neurology › Parkinson's Disease Mechanisms and Treatments
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Medicine › Neurology › Neurological disorders and treatments
- Keywords
- Parkinson's disease, non-invasive diagnosis, olfactory function, genetic risk, family history (medicine), imaging biomarkers, dopaminergic deficit, Parkinson's Progression Markers Initiative, external validation, early detection, disease prediction, clinical classification, Parkinson's disease biomarker
- MeSH terms
- Aged, Female, Humans, Male, Middle Aged, Parkinson Disease, Models, Statistical, Cohort Studies, Disease Progression, Prodromal Symptoms
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