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Janssen Daalen JM, Gerritsen A, Gerritse G, Gouman J, Meijerink H, Rietdijk LE, Darweesh SKL. How Lifetime Evolution of Parkinson's Disease Could Shape Clinical Trial Design: A Shared Patient-Clinician Viewpoint. Brain Sci 2024; 14:358. [PMID: 38672010 PMCID: PMC11048137 DOI: 10.3390/brainsci14040358] [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: 02/12/2024] [Revised: 03/22/2024] [Accepted: 03/27/2024] [Indexed: 04/28/2024] Open
Abstract
Parkinson's disease (PD) has a long, heterogeneous, pre-diagnostic phase, during which pathology insidiously accumulates. Increasing evidence suggests that environmental and lifestyle factors in early life contribute to disease risk and progression. Thanks to the extensive study of this pre-diagnostic phase, the first prevention trials of PD are being designed. However, the highly heterogenous evolution of the disease across the life course is not yet sufficiently taken into account. This could hamper clinical trial success in the advent of biological disease definitions. In an interdisciplinary patient-clinician study group, we discussed how an approach that incorporates the lifetime evolution of PD may benefit the design of disease-modifying trials by impacting population, target and outcome selection. We argue that the timepoint of exposure to risk and protective factors plays a critical role in PD subtypes, influencing population selection. In addition, recent developments in differential disease mechanisms, aided by biological disease definitions, could impact optimal treatment targets. Finally, multimodal biomarker panels using this lifetime approach will likely be most sensitive as progression markers for more personalized trials. We believe that the lifetime evolution of PD should be considered in the design of clinical trials, and that such initiatives could benefit from more patient-clinician partnerships.
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Affiliation(s)
- Jules M. Janssen Daalen
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, 6525 GA Nijmegen, The Netherlands; (J.M.J.D.); (A.G.)
| | - Aranka Gerritsen
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, 6525 GA Nijmegen, The Netherlands; (J.M.J.D.); (A.G.)
| | - Gijs Gerritse
- Dutch Parkinson’s Patient Association, P.O. Box 46, 3980 CA Bunnik, The Netherlands; (G.G.); (J.G.); (H.M.); (L.E.R.)
| | - Jan Gouman
- Dutch Parkinson’s Patient Association, P.O. Box 46, 3980 CA Bunnik, The Netherlands; (G.G.); (J.G.); (H.M.); (L.E.R.)
| | - Hannie Meijerink
- Dutch Parkinson’s Patient Association, P.O. Box 46, 3980 CA Bunnik, The Netherlands; (G.G.); (J.G.); (H.M.); (L.E.R.)
| | - Leny E. Rietdijk
- Dutch Parkinson’s Patient Association, P.O. Box 46, 3980 CA Bunnik, The Netherlands; (G.G.); (J.G.); (H.M.); (L.E.R.)
| | - Sirwan K. L. Darweesh
- Radboud University Medical Center, Donders Institute for Brain, Cognition and Behavior, Department of Neurology, Center of Expertise for Parkinson & Movement Disorders, 6525 GA Nijmegen, The Netherlands; (J.M.J.D.); (A.G.)
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Ikram MA, Kieboom BCT, Brouwer WP, Brusselle G, Chaker L, Ghanbari M, Goedegebure A, Ikram MK, Kavousi M, de Knegt RJ, Luik AI, van Meurs J, Pardo LM, Rivadeneira F, van Rooij FJA, Vernooij MW, Voortman T, Terzikhan N. The Rotterdam Study. Design update and major findings between 2020 and 2024. Eur J Epidemiol 2024; 39:183-206. [PMID: 38324224 DOI: 10.1007/s10654-023-01094-1] [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: 07/21/2023] [Accepted: 12/14/2023] [Indexed: 02/08/2024]
Abstract
The Rotterdam Study is a population-based cohort study, started in 1990 in the district of Ommoord in the city of Rotterdam, the Netherlands, with the aim to describe the prevalence and incidence, unravel the etiology, and identify targets for prediction, prevention or intervention of multifactorial diseases in mid-life and elderly. The study currently includes 17,931 participants (overall response rate 65%), aged 40 years and over, who are examined in-person every 3 to 5 years in a dedicated research facility, and who are followed-up continuously through automated linkage with health care providers, both regionally and nationally. Research within the Rotterdam Study is carried out along two axes. First, research lines are oriented around diseases and clinical conditions, which are reflective of medical specializations. Second, cross-cutting research lines transverse these clinical demarcations allowing for inter- and multidisciplinary research. These research lines generally reflect subdomains within epidemiology. This paper describes recent methodological updates and main findings from each of these research lines. Also, future perspective for coming years highlighted.
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Affiliation(s)
- M Arfan Ikram
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands.
| | - Brenda C T Kieboom
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Willem Pieter Brouwer
- Department of Hepatology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Guy Brusselle
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
- Department of Pulmonology, University Hospital Ghent, Ghent, Belgium
| | - Layal Chaker
- Department of Epidemiology, and Department of Internal Medicine, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Mohsen Ghanbari
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - André Goedegebure
- Department of Otorhinolaryngology and Head & Neck Surgery, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - M Kamran Ikram
- Department of Epidemiology, and Department of Neurology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Rob J de Knegt
- Department of Hepatology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Annemarie I Luik
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Joyce van Meurs
- Department of Internal Medicine, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Luba M Pardo
- Department of Dermatology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Fernando Rivadeneira
- Department of Medicine, and Department of Oral & Maxillofacial Surgery, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Frank J A van Rooij
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, and Department of Radiology & Nuclear Medicine, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Trudy Voortman
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
| | - Natalie Terzikhan
- Department of Epidemiology, Erasmus MC University Medical Center, Rotterdam, Netherlands
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Vidal-Petiot E, Pathak A, Azulay JP, Pavy-Le Traon A, Hanon O. Orthostatic hypotension: Review and expert position statement. Rev Neurol (Paris) 2024; 180:53-64. [PMID: 38123372 DOI: 10.1016/j.neurol.2023.11.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 11/19/2023] [Accepted: 11/21/2023] [Indexed: 12/23/2023]
Abstract
Orthostatic hypotension is defined as a drop in systolic blood pressure of at least 20mmHg or a drop in diastolic blood pressure of at least 10mmHg within 3minutes of standing. It is a common disorder, especially in high-risk populations such as elderly subjects and patients with neurological diseases, and is associated with markedly increased morbidity and mortality. Its management can be challenging, particularly in cases where supine hypertension is associated with severe orthostatic hypotension. Education of the patient, non-pharmacological measures, and drug adaptation are the cornerstones of treatment. Pharmacological treatment should be individualized according to the severity, underlying cause, 24-hour blood pressure profile, and associated coexisting conditions. First-line therapies are midodrine and fludrocortisone, which may need to be combined for optimal care of severe cases.
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Affiliation(s)
- E Vidal-Petiot
- Service de physiologie, ESH Excellence Center, hôpital Bichat, Assistance Publique-Hôpitaux de Paris, 75018 Paris, France; INSERM U1148, Université Paris-Cité and Université Sorbonne Paris Nord, LVTS, 75018 Paris, France.
| | - A Pathak
- Service de cardiologie, ESH Excellence Center, centre hospitalier Princesse Grace, 1, avenue Pasteur, 98000 Monaco, France
| | - J-P Azulay
- Service de neurologie et pathologie du mouvement, hôpital de la Timone, 13385 Marseille cedex 05, France
| | - A Pavy-Le Traon
- Service de neurologie, CHU de Toulouse, 31059 Toulouse cedex, France; UMR 1297, institut des maladies métaboliques et cardiovasculaires, Toulouse, France
| | - O Hanon
- Service de gériatrie, université Paris-Cité, EA4468, hôpital Broca, AP-HP, 75013 Paris, France
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Aguayo GA, Zhang L, Vaillant M, Ngari M, Perquin M, Moran V, Huiart L, Krüger R, Azuaje F, Ferdynus C, Fagherazzi G. Machine learning for predicting neurodegenerative diseases in the general older population: a cohort study. BMC Med Res Methodol 2023; 23:8. [PMID: 36631766 PMCID: PMC9832793 DOI: 10.1186/s12874-023-01837-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 01/06/2023] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND In the older general population, neurodegenerative diseases (NDs) are associated with increased disability, decreased physical and cognitive function. Detecting risk factors can help implement prevention measures. Using deep neural networks (DNNs), a machine-learning algorithm could be an alternative to Cox regression in tabular datasets with many predictive features. We aimed to compare the performance of different types of DNNs with regularized Cox proportional hazards models to predict NDs in the older general population. METHODS We performed a longitudinal analysis with participants of the English Longitudinal Study of Ageing. We included men and women with no NDs at baseline, aged 60 years and older, assessed every 2 years from 2004 to 2005 (wave2) to 2016-2017 (wave 8). The features were a set of 91 epidemiological and clinical baseline variables. The outcome was new events of Parkinson's, Alzheimer or dementia. After applying multiple imputations, we trained three DNN algorithms: Feedforward, TabTransformer, and Dense Convolutional (Densenet). In addition, we trained two algorithms based on Cox models: Elastic Net regularization (CoxEn) and selected features (CoxSf). RESULTS 5433 participants were included in wave 2. During follow-up, 12.7% participants developed NDs. Although the five models predicted NDs events, the discriminative ability was superior using TabTransformer (Uno's C-statistic (coefficient (95% confidence intervals)) 0.757 (0.702, 0.805). TabTransformer showed superior time-dependent balanced accuracy (0.834 (0.779, 0.889)) and specificity (0.855 (0.0.773, 0.909)) than the other models. With the CoxSf (hazard ratio (95% confidence intervals)), age (10.0 (6.9, 14.7)), poor hearing (1.3 (1.1, 1.5)) and weight loss 1.3 (1.1, 1.6)) were associated with a higher DNN risk. In contrast, executive function (0.3 (0.2, 0.6)), memory (0, 0, 0.1)), increased gait speed (0.2, (0.1, 0.4)), vigorous physical activity (0.7, 0.6, 0.9)) and higher BMI (0.4 (0.2, 0.8)) were associated with a lower DNN risk. CONCLUSION TabTransformer is promising for prediction of NDs with heterogeneous tabular datasets with numerous features. Moreover, it can handle censored data. However, Cox models perform well and are easier to interpret than DNNs. Therefore, they are still a good choice for NDs.
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Affiliation(s)
- Gloria A Aguayo
- Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg.
| | - Lu Zhang
- Bioinformatics Platform, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Michel Vaillant
- Competence Center for Methodology and Statistics, Translational Medicine Operations Hub, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Moses Ngari
- Competence Center for Methodology and Statistics, Translational Medicine Operations Hub, Luxembourg Institute of Health, Strassen, Luxembourg
- KEMRI/Wellcome Trust Research Programme, Kilifi, Kenya
| | - Magali Perquin
- Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Valerie Moran
- Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg
- Living Conditions Department, Luxembourg Institute of Socio-Economic Research, Esch-Sur-Alzette, Luxembourg
| | - Laetitia Huiart
- Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Rejko Krüger
- LCSB, Luxembourg Centre for System Biomedicine, University of Luxembourg, Esch-Sur-Alzette, Luxembourg
- Parkinson Research Clinic, Centre Hospitalier de Luxembourg, Luxembourg, Luxembourg
- Transversal Translational Medicine, Luxembourg Institute of Health, Strassen, Luxembourg
| | - Francisco Azuaje
- Bioinformatics Platform, Luxembourg Institute of Health, Strassen, Luxembourg
- Genomics England, London, UK
| | - Cyril Ferdynus
- Methodological Support Unit, Félix Guyon University Hospital Center, Saint-Denis, La Réunion, France
| | - Guy Fagherazzi
- Deep Digital Phenotyping Research Unit, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg
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