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Schaeffer E, Yilmaz R, St Louis EK, Noyce AJ. Ethical Considerations for Identifying Individuals in the Prodromal/Early Phase of Parkinson's Disease: A Narrative Review. JOURNAL OF PARKINSON'S DISEASE 2024:JPD230428. [PMID: 38995800 DOI: 10.3233/jpd-230428] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/14/2024]
Abstract
The ability to identify individuals in the prodromal phase of Parkinson's disease has improved in recent years, raising the question of whether and how those affected should be informed about the risk of future disease. Several studies investigated prognostic counselling for individuals with isolated REM sleep behavior disorder and have shown that most patients want to receive information about prognosis, but autonomy and individual preferences must be respected. However, there are still many unanswered questions about risk disclosure or early diagnosis of PD, including the impact on personal circumstances, cultural preferences and specific challenges associated with different profiles of prodromal symptoms, genetic testing or biomarker assessments. This narrative review aims to summarize the current literature on prognostic counselling and risk disclosure in PD, as well as highlight future perspectives that may emerge with the development of new biomarkers and their anticipated impact on the definition of PD.
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Affiliation(s)
- Eva Schaeffer
- Department of Neurology, University Hospital Schleswig-Holstein, Campus Kiel and Kiel University, Kiel, Germany
| | - Rezzak Yilmaz
- Department of Neurology, Ankara University School of Medicine, Ankara, Turkey
- Ankara University Brain Research Center, Ankara, Turkey
| | - Erik K St Louis
- Mayo Center for Sleep Medicine, Mayo Clinic, Rochester, MN, USA
- Division of Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN, USA
- Department of Medicine, Mayo Clinic, Rochester, MN, USA
- Department of Neurology, Mayo Clinic, Rochester, MN, USA
- Mayo Clinic Health System Southwest Wisconsin, La Crosse, WI, USA
| | - Alastair J Noyce
- Centre for Preventive Neurology, Wolfson Institute of Population Health, Queen Mary University of London, London, United Kingdom
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de Klerk GW, van Laar T, Meles SK. A retrospective study of the MDS criteria for prodromal Parkinson's disease in the general population. NPJ Parkinsons Dis 2024; 10:125. [PMID: 38926405 PMCID: PMC11208573 DOI: 10.1038/s41531-024-00739-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 06/12/2024] [Indexed: 06/28/2024] Open
Abstract
The Movement Disorder Society developed research criteria for the detection of the prodromal phase of Parkinson's disease (PD). Accurate identification of this phase is essential for early interventions. Therefore, we investigated the diagnostic value of these research criteria in the general population. Lifelines is an ongoing cohort study of 167,000 participants from the general population of the Northern Netherlands. 160 participants self-reported to have developed PD during three rounds of follow-up of five years each. Data were available to infer six out of eleven risk markers, and six out of twelve prodromal markers. We retrospectively compared the criteria in the prodromal stage of a group of 160 'converters' with 320 age- and sex-matched controls. The overall incidence rate of PD was 0.20 per 1.000 person-years (95% CI: 0.049-0.36), increasing with age and rates were higher in men. The median probability for prodromal PD in PD-converters was 1.29% (interquartile range: 0.46-2.9), compared to 0.83% (0.39-1.8) for controls (P = 0.014). The MDS set of criteria for prodromal PD had an ROC-AUC of 0.577, and was therefore not sufficient to adequately predict conversion to PD. We were unable to predict conversion to PD in the general population using a selection of the prodromal PD research criteria. Ancillary investigations are required to improve the diagnostic accuracy of the criteria, but most are precluded from large-scale use. Strategies, including olfactory tests or alpha-synuclein seeding amplification assays may improve the detection of prodromal PD in the general population.
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Affiliation(s)
- Gijs W de Klerk
- Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands.
| | - Teus van Laar
- Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Sanne K Meles
- Department of Neurology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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3
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Kmiecik MJ, Micheletti S, Coker D, Heilbron K, Shi J, Stagaman K, Filshtein Sonmez T, Fontanillas P, Shringarpure S, Wetzel M, Rowbotham HM, Cannon P, Shelton JF, Hinds DA, Tung JY, Holmes MV, Aslibekyan S, Norcliffe-Kaufmann L. Genetic analysis and natural history of Parkinson's disease due to the LRRK2 G2019S variant. Brain 2024; 147:1996-2008. [PMID: 38804604 PMCID: PMC11146432 DOI: 10.1093/brain/awae073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 01/26/2024] [Accepted: 02/06/2024] [Indexed: 05/29/2024] Open
Abstract
The LRRK2 G2019S variant is the most common cause of monogenic Parkinson's disease (PD); however, questions remain regarding the penetrance, clinical phenotype and natural history of carriers. We performed a 3.5-year prospective longitudinal online study in a large number of 1286 genotyped LRRK2 G2019S carriers and 109 154 controls, with and without PD, recruited from the 23andMe Research Cohort. We collected self-reported motor and non-motor symptoms every 6 months, as well as demographics, family histories and environmental risk factors. Incident cases of PD (phenoconverters) were identified at follow-up. We determined lifetime risk of PD using accelerated failure time modelling and explored the impact of polygenic risk on penetrance. We also computed the genetic ancestry of all LRRK2 G2019S carriers in the 23andMe database and identified regions of the world where carrier frequencies are highest. We observed that despite a 1 year longer disease duration (P = 0.016), LRRK2 G2019S carriers with PD had similar burden of motor symptoms, yet significantly fewer non-motor symptoms including cognitive difficulties, REM sleep behaviour disorder (RBD) and hyposmia (all P-values ≤ 0.0002). The cumulative incidence of PD in G2019S carriers by age 80 was 49%. G2019S carriers had a 10-fold risk of developing PD versus non-carriers. This rose to a 27-fold risk in G2019S carriers with a PD polygenic risk score in the top 25% versus non-carriers in the bottom 25%. In addition to identifying ancient founding events in people of North African and Ashkenazi descent, our genetic ancestry analyses infer that the G2019S variant was later introduced to Spanish colonial territories in the Americas. Our results suggest LRRK2 G2019S PD appears to be a slowly progressive predominantly motor subtype of PD with a lower prevalence of hyposmia, RBD and cognitive impairment. This suggests that the current prodromal criteria, which are based on idiopathic PD, may lack sensitivity to detect the early phases of LRRK2 PD in G2019S carriers. We show that polygenic burden may contribute to the development of PD in the LRRK2 G2019S carrier population. Collectively, the results should help support screening programmes and candidate enrichment strategies for upcoming trials of LRRK2 inhibitors in early-stage disease.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | | | - Paul Cannon
- 23andMe, Inc., Research, Sunnyvale, CA 94086, USA
| | | | | | - Joyce Y Tung
- 23andMe, Inc., Research, Sunnyvale, CA 94086, USA
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Marini K, Seppi K, Kiechl S, Stockner H, Willeit P, Willeit J, Djamshidian A, Rungger G, Poewe W, Mahlknecht P. Comparison of different risk scores for Parkinson disease in a population-based 10-year study. Eur J Neurol 2023; 30:3347-3352. [PMID: 37422903 DOI: 10.1111/ene.15971] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 06/19/2023] [Accepted: 07/04/2023] [Indexed: 07/11/2023]
Abstract
BACKGROUND AND PURPOSE Different algorithms aiming to identify individuals at risk of Parkinson disease (PD) have been proposed. Comparative studies of these scores and their recent updates in the general elder population are needed. METHODS We have previously applied the "basic" PREDICT-PD algorithm, designed for remote screening, and the original and updated Movement Disorder Society (MDS) criteria for prodromal PD to the longitudinal population-based Bruneck study cohort. We have now additionally employed the "enhanced" PREDICT-PD algorithm (which includes motor assessment, olfaction, probable rapid eye movement sleep behaviour disorder status, pesticide exposure, and diabetes as additional factors). Risk scores were calculated based on comprehensive baseline assessments (2005) in 574 subjects aged 55-94 years (290 females), and cases of incident PD were identified at 5-year (n = 11) and 10-year follow-up (n = 9). We analysed the association of the different log-transformed risk scores with incident PD at follow-up (calculated per 1-SD unit change). RESULTS The enhanced PREDICT-PD algorithm was associated with incident PD over 10-years of follow-up, yielding higher odds for incident PD (odds ratio [OR] = 4.61, 95% confidence interval [CI] = 2.68-7.93, p < 0.001) compared with the basic PREDICT-PD score (OR = 2.38, 95% CI = 1.49-3.79, p < 0.001). The updated MDS prodromal criteria yielded a numerically higher OR of 7.13 (95% CI = 3.49-14.54, p < 0.001) in comparison with the original criteria as well as the enhanced PREDICT-PD algorithm, with overlapping 95% CIs. CONCLUSIONS The enhanced PREDICT-PD algorithm was significantly associated with incident PD. The consistent performance of both the enhanced PREDICT-PD algorithm and the updated MDS prodromal criteria compared to their original versions supports their use in PD risk screening.
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Affiliation(s)
- Kathrin Marini
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
| | - Klaus Seppi
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
| | - Stefan Kiechl
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
- VASCage, Research Centre on Vascular Ageing and Stroke, Innsbruck, Austria
| | - Heike Stockner
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
| | - Peter Willeit
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Johann Willeit
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
| | - Atbin Djamshidian
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
| | | | - Werner Poewe
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
| | - Philipp Mahlknecht
- Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
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Nagy AV, Leschziner G, Eriksson SH, Lees A, Noyce AJ, Schrag A. Cognitive impairment in REM-sleep behaviour disorder and individuals at risk of Parkinson's disease. Parkinsonism Relat Disord 2023; 109:105312. [PMID: 36827949 DOI: 10.1016/j.parkreldis.2023.105312] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 01/14/2023] [Accepted: 01/30/2023] [Indexed: 02/05/2023]
Abstract
BACKGROUND Mild cognitive impairment (MCI) is commonly present at the time of Parkinson's Disease (PD) diagnosis, but its prevalence amongst individuals at increased risk of PD is unclear. METHODS Cognition was assessed using the Montreal Cognitive Assessment (MoCA) in 208 participants in the PREDICT-PD study, and 25 participants with REM-sleep behaviour disorder (RBD). Prevalence of MCI level I was determined in all participants, and level II MCI in the RBD sub-group. RESULTS Total MoCA scores were worse in the higher risk than the lower risk group defined as those below the 15th percentile of risk (p = 0.009), and in the RBD group compared to all healthy participants (p < 0.001). The prevalence of MCI level I was 12.8% in the lower-risk, 21.9% in the higher-risk (within the highest 15th percentile) and 64% in RBD participants; 66% of RBD participants had MCI level II with multi-domain MCI, but particularly attention and memory deficits. CONCLUSIONS Cognitive impairment is increased in different groups at higher risk of PD, particularly in the subgroup formally diagnosed with RBD.
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Affiliation(s)
- A V Nagy
- Department of Clinical and Behavioural Neurosciences, University College London Queen Square Institute of Neurology, United Kingdom
| | - G Leschziner
- Sleep Disorders Centre and Department of Neurology, Guy's and St Thomas' NHS Foundation Trust, Dept of Basic and Clinical Neuroscience, Institute of Psychology, Psychiatry and Neuroscience, King's College London, United Kingdom
| | - S H Eriksson
- National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, United Kingdom
| | - A Lees
- Rita Lila Weston Institute of Neurological Studies, University College London Queen Square Institute of Neurology, United Kingdom
| | - A J Noyce
- Preventive Neurology Unit, Wolfson Institute of Population Health, Queen Mary University of London, United Kingdom
| | - A Schrag
- Department of Clinical and Behavioural Neurosciences, University College London Queen Square Institute of Neurology, United Kingdom.
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Schrag A, Bohlken J, Dammertz L, Teipel S, Hermann W, Akmatov MK, Bätzing J, Holstiege J. Widening the Spectrum of Risk Factors, Comorbidities, and Prodromal Features of Parkinson Disease. JAMA Neurol 2023; 80:161-171. [PMID: 36342675 PMCID: PMC9641600 DOI: 10.1001/jamaneurol.2022.3902] [Citation(s) in RCA: 20] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Importance The prodromal phase of Parkinson disease (PD) may last for more than 10 years. Recognition of the spectrum and occurrence of risk factors, comorbidities, and prodromal features of PD can increase understanding of the causes and development of the disease and help identify individuals at risk. Objective To identify the association of a subsequent diagnosis of PD with a range of risk factors and prodromal features, including lifestyle factors, comorbidities, and potential extracerebral manifestations of PD. Design, Setting, and Participants This was a case-control study using insurance claims of outpatient consultations of patients with German statutory health insurance between January 1, 2011, and December 31, 2020. Included were patients with incident diagnosis of PD without a previous diagnosis of parkinsonism or dementia and controls matched 1:2 for age, sex, region, and earliest year of outpatient encounter. Exposures Exposures were selected based on previous systematic reviews, case-control and cohort studies reporting on risk factors, comorbidities, and prodromal features of PD. Main Outcomes and Measures Previously postulated risk factors and prodromal features of PD, using the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) coding. Results A total of 138 345 patients with incident PD (mean [SD] age, 75.1 [9.8] years; 73 720 male [53.3%]) and 276 690 matched controls (mean [SD] age, 75.1 (9.8) years; 147 440 male [53.3%]) were identified. Study participants were followed up for a mean (SD) of 6.0 (2.0) years. Consistent with previous reports, risk factors and prodromal features associated with PD included traumatic brain injury, odds ratio (OR), 1.62; 95% CI, 1.36-1.92; alcohol misuse, OR, 1.32; 95% CI, 1.21-1.44; hypertension, OR, 1.29; 95% CI, 1.26-1.31; anosmia, OR, 2.16; 95% CI, 1.59-2.93; and parasomnias (including RBD), OR, 1.62; 95% CI, 1.42-1.84. In addition, there were associations with restless legs syndrome (OR, 4.19; 95% CI, 3.91-4.50), sleep apnea (OR, 1.45; 95% CI, 1.37-1.54), epilepsy (OR, 2.26; 95% CI, 2.07-2.46), migraine (OR, 1.21; 95% CI, 1.12-1.29), bipolar disorder (OR, 3.81; 95% CI, 3.11-4.67), and schizophrenia (OR, 4.48; 95% CI, 3.82-5.25). The following diagnoses were also found to be associated with PD: sensory impairments beyond anosmia, such as hearing loss (OR, 1.14; 95% CI, 1.09-1.20) and changes of skin sensation (OR, 1.31; 95% CI, 1.21-1.43). There were also positive associations with skin disorders (eg, seborrheic dermatitis, OR, 1.30; 95% CI, 1.15-1.46; psoriasis, OR, 1.13; 95% CI, 1.05-1.21), gastrointestinal disorders (eg, gastroesophageal reflux, OR, 1.29; 95% CI, 1.25-1.33; gastritis, OR, 1.28; 95% CI, 1.24-1.33), conditions with a potential inflammatory component (eg, seronegative osteoarthritis, OR, 1.21; 95% CI, 1.03-1.43), and diabetes types 1 (OR, 1.32; 95% CI, 1.21-1.43) and 2 (OR, 1.24; 95% CI, 1.20-1.27). Associations even 5 to 10 years before diagnosis included tremor (odds ratio [OR], 4.49; 95% CI, 3.98-5.06), restless legs syndrome (OR, 3.73; 95% CI, 3.39-4.09), bipolar disorder (OR, 3.80; 95% CI, 2.82-5.14), and schizophrenia (OR, 4.00; 95% CI, 3.31-4.85). Conclusions and Relevance Results of this case-control study suggest that the associations found between PD and certain risk factors, comorbidities, and prodromal symptoms in a representative population may reflect possible early extrastriatal and extracerebral pathology of PD. This may be due to shared genetic risk with PD, medication exposure, or direct causation, or represent pathophysiologically relevant factors contributing to the pathogenesis of PD.
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Affiliation(s)
- Anette Schrag
- Department of Clinical and Movement Neurosciences, University College London, London, United Kingdom
| | - Jens Bohlken
- Institut für Sozialmedizin, Arbeitsmedizin und Public Health der Medizinischen Fakultät der Universität Leipzig, Leipzig, Germany
| | - Lotte Dammertz
- Central Research Institute of Ambulatory Health Care in Germany, Department of Epidemiology and Healthcare Atlas, Berlin, Germany
| | - Stefan Teipel
- Deutsches Zentrum für Neurodegenerative Erkrankungen Rostock/Greifswald, Rostock, Germany,Department of Psychosomatic Medicine, University of Rostock, Rostock, Germany
| | - Wiebke Hermann
- Department of Neurology, University of Rostock, Rostock, Germany
| | - Manas K. Akmatov
- Central Research Institute of Ambulatory Health Care in Germany, Department of Epidemiology and Healthcare Atlas, Berlin, Germany
| | - Jörg Bätzing
- Central Research Institute of Ambulatory Health Care in Germany, Department of Epidemiology and Healthcare Atlas, Berlin, Germany
| | - Jakob Holstiege
- Central Research Institute of Ambulatory Health Care in Germany, Department of Epidemiology and Healthcare Atlas, Berlin, Germany
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Li J, Mestre TA, Mollenhauer B, Frasier M, Tomlinson JJ, Trenkwalder C, Ramsay T, Manuel D, Schlossmacher MG. Evaluation of the PREDIGT score’s performance in identifying newly diagnosed Parkinson’s patients without motor examination. NPJ Parkinsons Dis 2022; 8:94. [PMID: 35906250 PMCID: PMC9338052 DOI: 10.1038/s41531-022-00360-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 07/11/2022] [Indexed: 11/17/2022] Open
Abstract
Several recent publications described algorithms to identify subjects with Parkinson’s disease (PD). In creating the “PREDIGT Score”, we previously developed a hypothesis-driven, simple-to-use formula to potentially calculate the incidence of PD. Here, we tested its performance in the ‘De Novo Parkinson Study’ (DeNoPa) and ‘Parkinson’s Progression Marker Initiative’ (PPMI); the latter included participants from the ‘FOllow Up persons with Neurologic Disease’ (FOUND) cohort. Baseline data from 563 newly diagnosed PD patients and 306 healthy control subjects were evaluated. Based on 13 variables, the original PREDIGT Score identified recently diagnosed PD patients in the DeNoPa, PPMI + FOUND and the pooled cohorts with area-under-the-curve (AUC) values of 0.88 (95% CI 0.83–0.92), 0.79 (95% CI 0.72–0.85), and 0.84 (95% CI 0.8–0.88), respectively. A simplified version (8 variables) generated AUC values of 0.92 (95% CI 0.89–0.95), 0.84 (95% CI 0.81–0.87), and 0.87 (0.84–0.89) in the DeNoPa, PPMI, and the pooled cohorts, respectively. In a two-step, screening-type approach, self-reported answers to a questionnaire (step 1) distinguished PD patients from controls with an AUC of 0.81 (95% CI 0.75–0.86). Adding a single, objective test (Step 2) further improved classification. Among seven biological markers explored, hyposmia was the most informative. The composite AUC value measured 0.9 (95% CI 0.88–0.91) in DeNoPa and 0.89 (95% CI 0.84–0.94) in PPMI. These results reveal a robust performance of the original PREDIGT Score to distinguish newly diagnosed PD patients from controls in two established cohorts. We also demonstrate the formula’s potential applicability to enriching for PD subjects in a population screening-type approach.
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Kent DM, Leung LY, Puttock EJ, Wang AY, Luetmer PH, Kallmes DF, Nelson J, Fu S, Zheng C, Vickery EM, Liu H, Noyce AJ, Chen W. Development of Parkinson Disease and Its Relationship with Incidentally Discovered White Matter Disease and Covert Brain Infarction in a Real-World Cohort. Ann Neurol 2022; 92:620-630. [PMID: 35866711 PMCID: PMC9489676 DOI: 10.1002/ana.26458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Revised: 07/18/2022] [Accepted: 07/20/2022] [Indexed: 11/08/2022]
Abstract
OBJECTIVE This study aimed to examine the relationship between covert cerebrovascular disease, comprised of covert brain infarction and white matter disease, discovered incidentally in routine care, and subsequent Parkinson disease. METHODS Patients were ≥50 years and received neuroimaging for non-stroke indications in the Kaiser Permanente Southern California system from 2009 to 2019. Natural language processing identified incidentally discovered covert brain infarction and white matter disease and classified white matter disease severity. The Parkinson disease outcome was defined as 2 ICD diagnosis codes. RESULTS 230,062 patients were included (median follow-up 3.72 years). A total of 1,941 Parkinson disease cases were identified (median time-to-event 2.35 years). Natural language processing identified covert cerebrovascular disease in 70,592 (30.7%) patients, 10,622 (4.6%) with covert brain infarction and 65,814 (28.6%) with white matter disease. After adjustment for known risk factors, white matter disease was associated with Parkinson disease (hazard ratio 1.67 [95%CI, 1.44, 1.93] for patients <70 years and 1.33 [1.18, 1.50] for those ≥70 years). Greater severity of white matter disease was associated with increased incidence of Parkinson disease(/1,000 person-years), from 1.52 (1.43, 1.61) in patients without white matter disease to 4.90 (3.86, 6.13) in those with severe disease. Findings were robust when more specific definitions of Parkinson disease were used. Covert brain infarction was not associated with Parkinson disease (adjusted hazard ratio = 1.05 [0.88, 1.24]). INTERPRETATION Incidentally discovered white matter disease was associated with subsequent Parkinson disease, an association strengthened with younger age and increased white matter disease severity. Incidentally discovered covert brain infarction did not appear to be associated with subsequent Parkinson disease. ANN NEUROL 2022;92:620-630.
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Affiliation(s)
- David M. Kent
- Predictive Analytics and Comparative Effectiveness Center,
Tufts Medical Center, Boston, MA, USA
| | - Lester Y. Leung
- Department of Neurology, Tufts Medical Center, Boston, MA,
USA
| | - Eric J. Puttock
- Department of Research and Evaluation, Kaiser Permanente
Southern California, Pasadena, CA, USA
| | - Andy Y. Wang
- Predictive Analytics and Comparative Effectiveness Center,
Tufts Medical Center, Boston, MA, USA
| | | | | | - Jason Nelson
- Predictive Analytics and Comparative Effectiveness Center,
Tufts Medical Center, Boston, MA, USA
| | - Sunyang Fu
- Department of AI and Informatics, Mayo Clinic, Rochester,
MN, USA
| | - Chengyi Zheng
- Department of Research and Evaluation, Kaiser Permanente
Southern California, Pasadena, CA, USA
| | - Ellen M. Vickery
- Predictive Analytics and Comparative Effectiveness Center,
Tufts Medical Center, Boston, MA, USA
| | - Hongfang Liu
- Department of AI and Informatics, Mayo Clinic, Rochester,
MN, USA
| | - Alastair J. Noyce
- Preventive Neurology Unit, Wolfson Institute of Population
Health, Queen Mary University of London, UK
- Department of Clinical and Movement Neuroscience, UCL
Institute of Neurology, London, UK
| | - Wansu Chen
- Department of Research and Evaluation, Kaiser Permanente
Southern California, Pasadena, CA, USA
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9
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Macklin EA, Coffey CS, Brumm MC, Seibyl JP. Statistical Considerations in the Design of Clinical Trials Targeting Prodromal Parkinson Disease. Neurology 2022; 99:68-75. [PMID: 35970588 DOI: 10.1212/wnl.0000000000200897] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Accepted: 05/13/2022] [Indexed: 11/15/2022] Open
Abstract
Clinical trials testing interventions for prodromal Parkinson disease (PD) hold particular promise for preserving neuronal function and thereby slowing or even forestalling progression to overt PD. Selection of the appropriate target population and outcome measures presents challenges unique to prodromal PD. We propose 3 clinical trial designs, spanning phase 2a, phase 2b, and phase 3 development, that might serve as templates for prodromal PD trials. The proposed phase 2a trial is of a 3-arm design of short duration and focuses on proof of concept with respect to target engagement and change in a motor outcome in a subset of prodromal participants who already manifest asymptomatic but measurable motor dysfunction as an exploratory aim. The proposed phase 2b trial suggests progression of dopamine transporter imaging specific binding ratio as a primary outcome evaluated annually over 2 years with phenoconversion to PD as a key secondary outcome. The proposed phase 3 trial is a large, simple design of a nutraceutical or behavioral intervention with remote administration and phenoconversion as the primary outcome. We then consider what additional data are needed in the short term to better design prodromal PD trials and examine what longer-term goals would accelerate discovery of safe and effective therapies for individuals at risk of PD. Clear and potentially context-specific definitions of phenoconversion and validation of intermediate endpoints are needed in the short term. The use of adaptive trial designs, master protocols, and research registries would help accelerate therapy development in the long term.
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Affiliation(s)
- Eric A Macklin
- From the Biostatistics Center (E.A.M.), Massachusetts General Hospital and Harvard Medical School, Boston; Department of Biostatistics (C.S.C., M.C.B.), College of Public Health, University of Iowa, Iowa City; and Institute for Neurodegenerative Disorders (J.P.S.), New Haven, CT.
| | - Christopher S Coffey
- From the Biostatistics Center (E.A.M.), Massachusetts General Hospital and Harvard Medical School, Boston; Department of Biostatistics (C.S.C., M.C.B.), College of Public Health, University of Iowa, Iowa City; and Institute for Neurodegenerative Disorders (J.P.S.), New Haven, CT
| | - Michael C Brumm
- From the Biostatistics Center (E.A.M.), Massachusetts General Hospital and Harvard Medical School, Boston; Department of Biostatistics (C.S.C., M.C.B.), College of Public Health, University of Iowa, Iowa City; and Institute for Neurodegenerative Disorders (J.P.S.), New Haven, CT
| | - John Peter Seibyl
- From the Biostatistics Center (E.A.M.), Massachusetts General Hospital and Harvard Medical School, Boston; Department of Biostatistics (C.S.C., M.C.B.), College of Public Health, University of Iowa, Iowa City; and Institute for Neurodegenerative Disorders (J.P.S.), New Haven, CT
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10
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Liepelt-Scarfone I, Ophey A, Kalbe E. Cognition in prodromal Parkinson's disease. PROGRESS IN BRAIN RESEARCH 2022; 269:93-111. [PMID: 35248208 DOI: 10.1016/bs.pbr.2022.01.003] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
One characteristic of Parkinson's disease (PD) is a prodromal phase, lasting many years during which both pre-clinical motor and non-motor symptoms occur. Around one-fifth of patients with PD manifest mild cognitive impairment at time of clinical diagnosis. Thus, important challenges are to define the time of onset of cognitive dysfunction in the prodromal phase of PD, and to define its co-occurrence with other specific characteristics. Evidence for cognitive change in prodromal PD comes from various study designs, including both longitudinal and cross-sectional approaches with different target groups. These studies support the concept that changes in global cognitive function and alterations in executive functions occur, and that these changes may be present up to 6 years before clinical PD diagnosis. Notably, this evidence led to including global cognitive impairment as an independent prodromal marker in the recently updated research criteria of the Movement Disorder Society for prodromal PD. Knowledge in this field, however, is still at its beginning, and evidence is sparse about many aspects of this topic. Further longitudinal studies including standardized assessments of global and domain-specific cognitive functions are needed to gain further knowledge about the first appearance, the course, and the interaction of cognitive deficits with other non-motor symptoms in prodromal stage PD. Treatment approaches, including non-pharmacological interventions, in individuals with prodromal PD might help to prevent or delay cognitive dysfunction in early PD.
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Affiliation(s)
- Inga Liepelt-Scarfone
- German Center for Neurodegenerative Diseases (DZNE) and Hertie Institute for Clinical Brain Research, Department of Neurodegenerative Diseases, University of Tübingen, Tübingen, Germany; IB-Hochschule, Stuttgart, Germany.
| | - Anja Ophey
- Medical Psychology, Neuropsychology and Gender Studies, Center for Neuropsychological Diagnostics and Intervention (CeNDI), University Hospital Cologne and Medical Faculty of the University of Cologne, Cologne, Germany
| | - Elke Kalbe
- Medical Psychology, Neuropsychology and Gender Studies, Center for Neuropsychological Diagnostics and Intervention (CeNDI), University Hospital Cologne and Medical Faculty of the University of Cologne, Cologne, Germany
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Mahlknecht P, Marini K, Werkmann M, Poewe W, Seppi K. Prodromal Parkinson's disease: hype or hope for disease-modification trials? Transl Neurodegener 2022; 11:11. [PMID: 35184752 PMCID: PMC8859908 DOI: 10.1186/s40035-022-00286-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 02/01/2022] [Indexed: 12/24/2022] Open
Abstract
The ultimate goal in Parkinson's disease (PD) research remains the identification of treatments that are capable of slowing or even halting the progression of the disease. The failure of numerous past disease-modification trials in PD has been attributed to a variety of factors related not only to choosing wrong interventions, but also to using inadequate trial designs and target populations. In patients with clinically established PD, neuronal pathology may already have advanced too far to be modified by any intervention. Based on such reasoning, individuals in yet prediagnostic or prodromal disease stages, may provide a window of opportunity to test disease-modifying strategies. There is now sufficient evidence from prospective studies to define diagnostic criteria for prodromal PD and several approaches have been studied in observational cohorts. These include the use of PD-risk algorithms derived from multiple established risk factors for disease as well as follow-up of cohorts with single defined prodromal markers like hyposmia, rapid eye movement sleep behavior disorders, or PD gene carriers. In this review, we discuss recruitment strategies for disease-modification trials in various prodromal PD cohorts, as well as potential trial designs, required trial durations, and estimated sample sizes. We offer a concluding outlook on how the goal of implementing disease-modification trials in prodromal cohorts might be achieved in the future.
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Chang Z, Xie F, Li H, Yuan F, Zeng L, Shi L, Zhu S, Lu X, Wei X, Wang Q. Retinal Nerve Fiber Layer Thickness and Associations With Cognitive Impairment in Parkinson’s Disease. Front Aging Neurosci 2022; 14:832768. [PMID: 35222000 PMCID: PMC8867012 DOI: 10.3389/fnagi.2022.832768] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 01/20/2022] [Indexed: 01/18/2023] Open
Abstract
ObjectiveThis study intended to investigate whether retinal nerve fiber layer (RNFL) thickness could become a potential marker in patients with Parkinson’s disease with cognitive impairment (PD-CI).MethodsFifty-seven PD patients and 45 age-matched healthy controls (HCs) were recruited in our cross-sectional study and completed optical coherence tomography (OCT) evaluations. PD with normal cognition (PD-NC) and cognitive impairment (PD-CI) patients were divided following the 2015 Movement Disorder Society criteria. RNFL thickness was quantified in subfields of the 3.0-mm circle surrounding the optic disk; while a battery of neuropsychiatric assessments was conducted to estimate the Parkinsonism severity. General linear models and one-way ANOVA were adopted to assess RNFL thickness between subgroups with different cognitive statuses; logistic regression analyses were applied to determine the relation between RNFL and PD-CI cases.ResultsCompared with HCs, more thinning of the RNFL was observed in the inferior and temporal sectors in PD patients, especially in the PD-CI group. Inferior RNFL thickness was reduced in PD-CI compared with PD-NC patients. Logistic regression analysis found that inferior RNFL thickness was independently associated with PD-CI cases (odds ratio = 0.923, p = 0.014). Receiver operating characteristic analysis showed that the RNFL-involved combined model provided a high accuracy in screening cognitive deficiency in PD cases (area under the curve = 0.85, p < 0.001).ConclusionReduced RNFL thickness especially in the inferior sector is independently associated with PD-CI patients. Our study present new perspectives into verifying possible indicators for neuropathological processes or disease severity in Parkinsonians with cognitive dysfunction.
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Affiliation(s)
- Zihan Chang
- Department of Neurology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Fen Xie
- Department of Neurology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Hualing Li
- Department of Neurology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Feilan Yuan
- Department of Neurology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Lina Zeng
- Department of Ophthalmology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Lin Shi
- Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Shuzhen Zhu
- Department of Neurology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
| | - Xiaohe Lu
- Department of Ophthalmology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- *Correspondence: Xiaohe Lu,
| | - Xiaobo Wei
- Department of Neurology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Xiaobo Wei,
| | - Qing Wang
- Department of Neurology, Zhujiang Hospital, Southern Medical University, Guangzhou, China
- Qing Wang, ;
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Goodwin GR, Bestwick JP, Noyce AJ. The potential utility of smell testing to screen for neurodegenerative disorders. Expert Rev Mol Diagn 2022; 22:139-148. [PMID: 35129037 DOI: 10.1080/14737159.2022.2037424] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
INTRODUCTION Loss of smell is a common early feature of neurodegenerative diseases including Alzheimer's and Parkinson's disease. Identifying these conditions in their early stages is important to understand more about early pathophysiological events and the development of disease modifying therapies. Smell testing may be an effective future tool for screening large populations for early neurodegeneration. AREAS COVERED : In this review, we appraise the evidence for, and discuss the likelihood of, the use of smell testing in large screening programs to detect early neurodegeneration. We evaluate the predictive power of smell tests for neurodegenerative disease, compare performance to other established screening programs, and discuss ethical and practical considerations and limitations. EXPERT OPINION : Even if disease modifying therapies were available for neurodegenerative disease, smell tests alone are unlikely to have high enough predictive power to be used in a future screening program. However, we believe they could be a valuable component of a short battery of tests or part of a stepwise process that together could more accurately identify early neurodegeneration in large populations.
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Affiliation(s)
- Gregory R Goodwin
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, E1 4NS, UK
| | - Jonathan P Bestwick
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, E1 4NS, UK
| | - Alastair J Noyce
- Preventive Neurology Unit, Wolfson Institute of Preventive Medicine, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, E1 4NS, UK
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Bestwick JP, Auger SD, Schrag AE, Grosset DG, Kanavou S, Giovannoni G, Lees AJ, Cuzick J, Noyce AJ. Optimising classification of Parkinson's disease based on motor, olfactory, neuropsychiatric and sleep features. NPJ PARKINSONS DISEASE 2021; 7:87. [PMID: 34561458 PMCID: PMC8463675 DOI: 10.1038/s41531-021-00226-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 08/19/2021] [Indexed: 12/11/2022]
Abstract
Olfactory loss, motor impairment, anxiety/depression, and REM-sleep behaviour disorder (RBD) are prodromal Parkinson’s disease (PD) features. PD risk prediction models typically dichotomize test results and apply likelihood ratios (LRs) to scores above and below cut-offs. We investigate whether LRs for specific test values could enhance classification between PD and controls. PD patient data on smell (UPSIT), possible RBD (RBD Screening Questionnaire), and anxiety/depression (LADS) were taken from the Tracking Parkinson’s study (n = 1046). For motor impairment (BRAIN test) in PD cases, published data were supplemented (n = 87). Control data (HADS for anxiety/depression) were taken from the PREDICT-PD pilot study (n = 1314). UPSIT, RBDSQ, and anxiety/depression data were analysed using logistic regression to determine which items were associated with PD. Gaussian distributions were fitted to BRAIN test scores. LRs were calculated from logistic regression models or score distributions. False-positive rates (FPRs) for specified detection rates (DRs) were calculated. Sixteen odours were associated with PD; LRs for this set ranged from 0.005 to 5511. Six RBDSQ and seven anxiety/depression questions were associated with PD; LRs ranged from 0.35 to 69 and from 0.002 to 402, respectively. BRAIN test LRs ranged from 0.16 to 1311. For a 70% DR, the FPR was 2.4% for the 16 odours, 4.6% for anxiety/depression, 16.0% for the BRAIN test, and 20.0% for the RBDSQ. Specific selections of (prodromal) PD marker features rather than dichotomized marker test results optimize PD classification. Such optimized classification models could improve the ability of algorithms to detect prodromal PD; however, prospective studies are needed to investigate their value for PD-prediction models.
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Affiliation(s)
- Jonathan P Bestwick
- Preventive Neurology Unit, Wolfson Institute of Population Health, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
| | - Stephen D Auger
- Preventive Neurology Unit, Wolfson Institute of Population Health, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Anette E Schrag
- Department of Clinical and Movement Neuroscience, UCL Institute of Neurology, University College London, London, UK
| | - Donald G Grosset
- Department of Neurology, Institute of Neurological Sciences, Queen Elizabeth University Hospital, Glasgow, UK
| | - Sofia Kanavou
- Population Health Sciences, University of Bristol, Bristol, UK
| | - Gavin Giovannoni
- Preventive Neurology Unit, Wolfson Institute of Population Health, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.,Centre for Neuroscience, Surgery and Trauma, Blizard Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Andrew J Lees
- Department of Clinical and Movement Neuroscience, UCL Institute of Neurology, University College London, London, UK
| | - Jack Cuzick
- Preventive Neurology Unit, Wolfson Institute of Population Health, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Alastair J Noyce
- Preventive Neurology Unit, Wolfson Institute of Population Health, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.,Department of Clinical and Movement Neuroscience, UCL Institute of Neurology, University College London, London, UK
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Dommershuijsen LJ, Boon AJW, Ikram MK. Probing the Pre-diagnostic Phase of Parkinson's Disease in Population-Based Studies. Front Neurol 2021; 12:702502. [PMID: 34276552 PMCID: PMC8284316 DOI: 10.3389/fneur.2021.702502] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Accepted: 06/03/2021] [Indexed: 11/13/2022] Open
Abstract
Parkinson's disease covers a wide spectrum of symptoms, ranging from early non-motor symptoms to the characteristic bradykinesia, tremor and rigidity. Although differences in the symptomatology of Parkinson's disease are increasingly recognized, there is still a lack of insight into the heterogeneity of the pre-diagnostic phase of Parkinson's disease. In this perspective, we highlight three aspects regarding the role of population-based studies in providing new insights into the heterogeneity of pre-diagnostic Parkinson's disease. First we describe several specific advantages of population-based cohort studies, including the design which overcomes some common biases, the broad data collection and the high external validity. Second, we draw a parallel with the field of Alzheimer's disease to provide future directions to uncover the heterogeneity of pre-diagnostic Parkinson's disease. Finally, we anticipate on the emergence of prevention and disease-modification trials and the potential role of population-based studies herein. In the coming years, bridging gaps between study designs will be essential to make vital advances in elucidating the heterogeneity of pre-diagnostic Parkinson's disease.
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Affiliation(s)
| | - Agnita J. W. Boon
- Department of Neurology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - M. Kamran Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
- Department of Neurology, Erasmus MC University Medical Center, Rotterdam, Netherlands
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