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Sekimitsu S, Shweikh Y, Shareef S, Zhao Y, Elze T, Segrè A, Wiggs J, Zebardast N. Association of retinal optical coherence tomography metrics and polygenic risk scores with cognitive function and future cognitive decline. Br J Ophthalmol 2024; 108:599-606. [PMID: 36990674 DOI: 10.1136/bjo-2022-322762] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 03/15/2023] [Indexed: 03/31/2023]
Abstract
PURPOSE To evaluate the potential of retinal optical coherence tomography (OCT) measurements and polygenic risk scores (PRS) to identify people at risk of cognitive impairment. METHODS Using OCT images from 50 342 UK Biobank participants, we examined associations between retinal layer thickness and genetic risk for neurodegenerative disease and combined these metrics with PRS to predict baseline cognitive function and future cognitive deterioration. Multivariate Cox proportional hazard models were used to predict cognitive performance. P values for retinal thickness analyses are false-discovery-rate-adjusted. RESULTS Higher Alzheimer's disease PRS was associated with a thicker inner nuclear layer (INL), chorio-scleral interface (CSI) and inner plexiform layer (IPL) (all p<0.05). Higher Parkinson's disease PRS was associated with thinner outer plexiform layer (p<0.001). Worse baseline cognitive performance was associated with thinner retinal nerve fibre layer (RNFL) (aOR=1.038, 95% CI (1.029 to 1.047), p<0.001) and photoreceptor (PR) segment (aOR=1.035, 95% CI (1.019 to 1.051), p<0.001), ganglion cell complex (aOR=1.007, 95% CI (1.002 to 1.013), p=0.004) and thicker ganglion cell layer (aOR=0.981, 95% CI (0.967 to 0.995), p=0.009), IPL (aOR=0.976, 95% CI (0.961 to 0.992), p=0.003), INL (aOR=0.923, 95% CI (0.905 to 0.941), p<0.001) and CSI (aOR=0.998, 95% CI (0.997 to 0.999), p<0.001). Worse future cognitive performance was associated with thicker IPL (aOR=0.945, 95% CI (0.915 to 0.999), p=0.045) and CSI (aOR=0.996, 95% CI (0.993 to 0.999) 95% CI, p=0.014). Prediction of cognitive decline was significantly improved with the addition of PRS and retinal measurements. CONCLUSIONS AND RELEVANCE Retinal OCT measurements are significantly associated with genetic risk of neurodegenerative disease and may serve as biomarkers predictive of future cognitive impairment.
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Affiliation(s)
| | - Yusrah Shweikh
- Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
- Sussex Eye Hospital, University Hospitals Sussex NHS Foundation Trust, Sussex, UK
| | - Sarah Shareef
- Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
| | - Yan Zhao
- Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Tobias Elze
- Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts, USA
| | - Ayellet Segrè
- Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Janey Wiggs
- Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
- Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Nazlee Zebardast
- Department of Ophthalmology, Massachusetts Eye and Ear Infirmary, Boston, Massachusetts, USA
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Poveda S, Arellano X, Bernal-Pacheco O, Valencia López A. Structural changes in the retina as a potential biomarker in Parkinson's disease: an approach from optical coherence tomography. FRONTIERS IN NEUROIMAGING 2024; 3:1340754. [PMID: 38496013 PMCID: PMC10940411 DOI: 10.3389/fnimg.2024.1340754] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2023] [Accepted: 02/09/2024] [Indexed: 03/19/2024]
Abstract
Introduction Parkinson's disease (PD) presents challenges in early diagnosis and follow-up due to the lack of characteristic findings. Recent studies suggest retinal changes in PD are possibly indicative of neurodegeneration. We explored these changes using optical coherence tomography (OCT) to assess retinal nerve fiber layer (RNFL) and ganglion cell complex (GCC) thickness. Methods Thirty PD and non-PD patients were matched according to demographic characteristics and OCT and clinical evaluations to rule out other neurodegenerative and visual diseases. Results We observed a significant thinning of the RNFL in patients diagnosed with PD compared to non-PD patients (p = 0.015). Additionally, this reduction in RNFL thickness was found to correlate with the severity of the disease (p = 0.04). Conclusion The OCT serves as a tool for quantifying neurodegeneration in PD, showing a significant correlation with disease severity. These findings suggest that OCT could play a crucial role as a potential biomarker in the diagnosis and monitoring of PD.
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Affiliation(s)
- Santiago Poveda
- Department of Neurology, Central Military Hospital, Bogotá, Colombia
| | - Ximena Arellano
- Department of Ophthalmology, Central Military Hospital, Bogotá, Colombia
| | - Oscar Bernal-Pacheco
- Department of Neurology, Central Military Hospital, Bogotá, Colombia
- Roosevelt Orthopedic Institute, Bogotá, Colombia
- Fundación Santa Fe de Bogotá University Hospital, Bogotá, Colombia
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Murueta-Goyena A, Romero-Bascones D, Teijeira-Portas S, Urcola JA, Ruiz-Martínez J, Del Pino R, Acera M, Petzold A, Wagner SK, Keane PA, Ayala U, Barrenechea M, Tijero B, Gómez Esteban JC, Gabilondo I. Association of retinal neurodegeneration with the progression of cognitive decline in Parkinson's disease. NPJ Parkinsons Dis 2024; 10:26. [PMID: 38263165 PMCID: PMC10805713 DOI: 10.1038/s41531-024-00637-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Accepted: 01/08/2024] [Indexed: 01/25/2024] Open
Abstract
Retinal thickness may serve as a biomarker in Parkinson's disease (PD). In this prospective longitudinal study, we aimed to determine if PD patients present accelerated thinning rate in the parafoveal ganglion cell-inner plexiform layer (pfGCIPL) and peripapillary retinal nerve fiber layer (pRNFL) compared to controls. Additionally, we evaluated the relationship between retinal neurodegeneration and clinical progression in PD. A cohort of 156 PD patients and 72 controls underwent retinal optical coherence tomography, visual, and cognitive assessments between February 2015 and December 2021 in two Spanish tertiary hospitals. The pfGCIPL thinning rate was twice as high in PD (β [SE] = -0.58 [0.06]) than in controls (β [SE] = -0.29 [0.06], p < 0.001). In PD, the progression pattern of pfGCIPL atrophy depended on baseline thickness, with slower thinning rates observed in PD patients with pfGCIPL below 89.8 µm. This result was validated with an external dataset from Moorfields Eye Hospital NHS Foundation Trust (AlzEye study). Slow pfGCIPL progressors, characterized by older at baseline, longer disease duration, and worse cognitive and disease stage scores, showed a threefold increase in the rate of cognitive decline (β [SE] = -0.45 [0.19] points/year, p = 0.021) compared to faster progressors. Furthermore, temporal sector pRNFL thinning was accelerated in PD (βtime x group [SE] = -0.67 [0.26] μm/year, p = 0.009), demonstrating a close association with cognitive score changes (β [SE] = 0.11 [0.05], p = 0.052). This study suggests that a slower pattern of pfGCIPL tissue loss in PD is linked to more rapid cognitive decline, whereas changes in temporal pRNFL could track cognitive deterioration.
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Affiliation(s)
- Ane Murueta-Goyena
- Neurodegenerative Diseases Group, Biobizkaia Health Research Institute, Barakaldo, Spain.
- Department of Neurosciences, Faculty of Medicine and Nursery, University of the Basque Country (UPV/EHU), Leioa, Spain.
| | - David Romero-Bascones
- Biomedical Engineering Department, Faculty of Engineering (MU-ENG), Mondragon Unibertsitatea, Mondragón, Spain
- NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, EC1V 2PD, London, UK
| | - Sara Teijeira-Portas
- Neurodegenerative Diseases Group, Biobizkaia Health Research Institute, Barakaldo, Spain
| | - J Aritz Urcola
- Department of Ophthalmology, Araba University Hospital, Vitoria-Gasteiz, Spain
| | - Javier Ruiz-Martínez
- Department of Neurology, Donostia University Hospital, Donostia, Spain
- Biogipuzkoa Health Research Institute, Donostia, Spain
- CIBERNED, Institute of Health Carlos III, Madrid, Spain
| | - Rocío Del Pino
- Neurodegenerative Diseases Group, Biobizkaia Health Research Institute, Barakaldo, Spain
| | - Marian Acera
- Neurodegenerative Diseases Group, Biobizkaia Health Research Institute, Barakaldo, Spain
| | - Axel Petzold
- NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, EC1V 2PD, London, UK
- Queen Square Institute of Neurology, University College London, London, UK
- The National Hospital for Neurology and Neurosurgery, London, UK
- Departments of Neurology and Ophthalmology, Amsterdam UMC, Amsterdam, Netherlands
| | - Siegfried Karl Wagner
- NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, EC1V 2PD, London, UK
- Institute of Ophthalmology, University College London, London, UK
| | - Pearse Andrew Keane
- NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, EC1V 2PD, London, UK
- Institute of Ophthalmology, University College London, London, UK
| | - Unai Ayala
- Biomedical Engineering Department, Faculty of Engineering (MU-ENG), Mondragon Unibertsitatea, Mondragón, Spain
| | - Maitane Barrenechea
- Biomedical Engineering Department, Faculty of Engineering (MU-ENG), Mondragon Unibertsitatea, Mondragón, Spain
| | - Beatriz Tijero
- Neurodegenerative Diseases Group, Biobizkaia Health Research Institute, Barakaldo, Spain
- Neurology Department, Cruces University Hospital, Barakaldo, Spain
| | - Juan Carlos Gómez Esteban
- Neurodegenerative Diseases Group, Biobizkaia Health Research Institute, Barakaldo, Spain
- Department of Neurosciences, Faculty of Medicine and Nursery, University of the Basque Country (UPV/EHU), Leioa, Spain
- Neurology Department, Cruces University Hospital, Barakaldo, Spain
| | - Iñigo Gabilondo
- Neurodegenerative Diseases Group, Biobizkaia Health Research Institute, Barakaldo, Spain
- Neurology Department, Cruces University Hospital, Barakaldo, Spain
- IKERBASQUE, The Basque Foundation for Science, Bilbao, Spain
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Hannaway N, Zarkali A, Leyland LA, Bremner F, Nicholas JM, Wagner SK, Roig M, Keane PA, Toosy A, Chataway J, Weil RS. Visual dysfunction is a better predictor than retinal thickness for dementia in Parkinson's disease. J Neurol Neurosurg Psychiatry 2023; 94:742-750. [PMID: 37080759 PMCID: PMC10447370 DOI: 10.1136/jnnp-2023-331083] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 03/30/2023] [Indexed: 04/22/2023]
Abstract
BACKGROUND Dementia is a common and devastating symptom of Parkinson's disease (PD). Visual function and retinal structure are both emerging as potentially predictive for dementia in Parkinson's but lack longitudinal evidence. METHODS We prospectively examined higher order vision (skew tolerance and biological motion) and retinal thickness (spectral domain optical coherence tomography) in 100 people with PD and 29 controls, with longitudinal cognitive assessments at baseline, 18 months and 36 months. We examined whether visual and retinal baseline measures predicted longitudinal cognitive scores using linear mixed effects models and whether they predicted onset of dementia, death and frailty using time-to-outcome methods. RESULTS Patients with PD with poorer baseline visual performance scored lower on a composite cognitive score (β=0.178, SE=0.05, p=0.0005) and showed greater decreases in cognition over time (β=0.024, SE=0.001, p=0.013). Poorer visual performance also predicted greater probability of dementia (χ² (1)=5.2, p=0.022) and poor outcomes (χ² (1) =10.0, p=0.002). Baseline retinal thickness of the ganglion cell-inner plexiform layer did not predict cognitive scores or change in cognition with time in PD (β=-0.013, SE=0.080, p=0.87; β=0.024, SE=0.001, p=0.12). CONCLUSIONS In our deeply phenotyped longitudinal cohort, visual dysfunction predicted dementia and poor outcomes in PD. Conversely, retinal thickness had less power to predict dementia. This supports mechanistic models for Parkinson's dementia progression with onset in cortical structures and shows potential for visual tests to enable stratification for clinical trials.
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Affiliation(s)
- Naomi Hannaway
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Angeliki Zarkali
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Louise-Ann Leyland
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
| | - Fion Bremner
- National Hospital for Neurology and Neurosurgery, University College London Hospitals, London, UK
| | - Jennifer M Nicholas
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- Department of Medical Statistics, London School of Hygiene and Tropical Medicine, London, UK
| | | | - Matthew Roig
- UCL Queen Square Institute of Neurology, London, UK
| | - Pearse A Keane
- UCL Queen Square Institute of Neurology, London, UK
- Institute of Ophthalmology, University College London, London, UK
| | - Ahmed Toosy
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Jeremy Chataway
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, UK
- National Institute for Health Research, University College London Hospitals Biomedical Research Centre, London, UK
- MRC CTU at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK
- Movement Disorders Centre, University College London, London, UK
| | - Rimona Sharon Weil
- Dementia Research Centre, Institute of Neurology, University College London, London, UK
- National Hospital for Neurology and Neurosurgery, University College London Hospitals, London, UK
- Movement Disorders Centre, University College London, London, UK
- The Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London, London, UK
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Maran JJ, Adesina MM, Green CR, Kwakowsky A, Mugisho OO. Retinal inner nuclear layer thickness in the diagnosis of cognitive impairment explored using a C57BL/6J mouse model. Sci Rep 2023; 13:8150. [PMID: 37208533 DOI: 10.1038/s41598-023-35229-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 05/15/2023] [Indexed: 05/21/2023] Open
Abstract
Major neurocognitive disorder (NCD) affects over 55 million people worldwide and is characterized by cognitive impairment (CI). This study aimed to develop a non-invasive diagnostic test for CI based upon retinal thickness measurements explored in a mouse model. Discrimination indices and retinal layer thickness of healthy C57BL/6J mice were quantified through a novel object recognition test (NORT) and ocular coherence tomography (OCT), respectively. Based on criteria from the Diagnostic and statistical manual of mental disorders 5th ed. (DSM-V), a diagnostic test was generated by transforming data into rolling monthly averages and categorizing mice into those with and without CI and those with a high or low decline in retinal layer thickness. Only inner nuclear layer thickness had a statistically significant relationship with discrimination indices. Furthermore, our diagnostic test was 85.71% sensitive and 100% specific for diagnosing CI, with a positive predictive value of 100%. These findings have potential clinical implications for the early diagnosis of CI in NCD. However, further investigation in comorbid mice and humans is warranted.
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Affiliation(s)
- Jack J Maran
- Buchanan Ocular Therapeutics Unit, Department of Ophthalmology and The New Zealand National Eye Centre, University of Auckland, Auckland, New Zealand
| | - Moradeke M Adesina
- Department of Ophthalmology and The New Zealand National Eye Centre, Faculty of Medical and Health Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
| | - Colin R Green
- Department of Ophthalmology and The New Zealand National Eye Centre, Faculty of Medical and Health Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
| | - Andrea Kwakowsky
- Centre for Brain Research, University of Auckland, Auckland, New Zealand
- Department of Anatomy and Medical Imaging, University of Auckland, Auckland, New Zealand
- Pharmacology and Therapeutics, School of Medicine, Galway Neuroscience Centre, University of Galway, Galway, Ireland
| | - Odunayo O Mugisho
- Buchanan Ocular Therapeutics Unit, Department of Ophthalmology and The New Zealand National Eye Centre, University of Auckland, Auckland, New Zealand.
- Department of Ophthalmology and The New Zealand National Eye Centre, Faculty of Medical and Health Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand.
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Objective Diagnosis of Fibromyalgia Using Neuroretinal Evaluation and Artificial Intelligence. Int J Clin Health Psychol 2022; 22:100294. [PMID: 35281771 PMCID: PMC8873600 DOI: 10.1016/j.ijchp.2022.100294] [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: 10/02/2021] [Accepted: 01/03/2022] [Indexed: 11/23/2022] Open
Abstract
Background/Objective This study aims to identify objective biomarkers of fibromyalgia (FM) by applying artificial intelligence algorithms to structural data on the neuroretina obtained using swept-source optical coherence tomography (SS-OCT). Method The study cohort comprised 29 FM patients and 32 control subjects. The thicknesses of complete retina, 3 retinal layers [ganglion cell layer (GCL+), GCL++ (between the inner limiting membrane and the inner nuclear layer boundaries) and retinal nerve fiber layer (RNFL)] and choroid in 9 areas around the macula were obtained using SS-OCT. Discriminant capacity was evaluated using the area under the curve (AUC) and the Relief algorithm. A diagnostic aid system with an automatic classifier was implemented. Results No significant difference (p ≥ .660) was found anywhere in the choroid. In the RNFL, a significant difference was found in the inner inferior region (p = .010). In the GCL+, GCL++ layers and complete retina, a significant difference was found in the 4 regions defining the inner ring: temporal, superior, nasal and inferior. Applying an ensemble RUSBoosted tree classifier to the features with greatest discriminant capacity achieved accuracy = .82 and AUC = .82. Conclusions This study identifies a potential novel objective and non-invasive biomarker of FM based on retina analysis using SS-OCT.
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Multimodal brain and retinal imaging of dopaminergic degeneration in Parkinson disease. Nat Rev Neurol 2022; 18:203-220. [PMID: 35177849 DOI: 10.1038/s41582-022-00618-9] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/13/2022] [Indexed: 12/12/2022]
Abstract
Parkinson disease (PD) is a progressive disorder characterized by dopaminergic neurodegeneration in the brain. The development of parkinsonism is preceded by a long prodromal phase, and >50% of dopaminergic neurons can be lost from the substantia nigra by the time of the initial diagnosis. Therefore, validation of in vivo imaging biomarkers for early diagnosis and monitoring of disease progression is essential for future therapeutic developments. PET and single-photon emission CT targeting the presynaptic terminals of dopaminergic neurons can be used for early diagnosis by detecting axonal degeneration in the striatum. However, these techniques poorly differentiate atypical parkinsonian syndromes from PD, and their availability is limited in clinical settings. Advanced MRI in which pathological changes in the substantia nigra are visualized with diffusion, iron-sensitive susceptibility and neuromelanin-sensitive sequences potentially represents a more accessible imaging tool. Although these techniques can visualize the classic degenerative changes in PD, they might be insufficient for phenotyping or prognostication of heterogeneous aspects of PD resulting from extranigral pathologies. The retina is an emerging imaging target owing to its pathological involvement early in PD, which correlates with brain pathology. Retinal optical coherence tomography (OCT) is a non-invasive technique to visualize structural changes in the retina. Progressive parafoveal thinning and fovea avascular zone remodelling, as revealed by OCT, provide potential biomarkers for early diagnosis and prognostication in PD. As we discuss in this Review, multimodal imaging of the substantia nigra and retina is a promising tool to aid diagnosis and management of PD.
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Ksinan Jiskrova G, Pikhart H, Bobák M, Klanova J, Stepanikova I. Prenatal psychosocial stress and children's sleep problems: Evidence from the ELSPAC-CZ study. J Sleep Res 2021; 31:e13531. [PMID: 34879444 DOI: 10.1111/jsr.13531] [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: 07/13/2021] [Revised: 10/24/2021] [Accepted: 11/23/2021] [Indexed: 11/28/2022]
Abstract
Prenatal exposure to maternal stress may increase the risk of developing sleep problems in childhood. This study examined the association between prenatal stressful life events (PSLE) and children's sleep problems, taking into consideration their trajectory over time. Data were obtained from the Czech portion of the European Longitudinal Cohort Study of Pregnancy and Childhood (ELSPAC-CZ; N = 4,371 children). Mothers reported PSLE using an inventory of 42 life events and child sleep problems at five time-points (child age of 1.5, 3, 5, 7, and 11 years). The association was tested by a Poisson latent growth model, controlling for maternal and family demographics, birth characteristics, maternal depression, and alcohol use in pregnancy. The average rate of sleep problems was 2.06 (p < 0.001) at the age of 1.5 years and the rate of sleep problems decreased in a linear fashion over time (estimate = -0.118; p < 0.001). A higher number of PSLE was associated with a higher rate of sleep problems at the age of 1.5 years (incidence rate ratio [IRR] per interquartile range = 1.08, 95% confidence interval [CI] 1.05-1.12, p < 0.001) and with a reduced rate of decrease in sleep problems between the ages of 1.5 and 11 years (p < 0.001). Thus, PSLE were associated with chronicity of sleep problems in addition to their amount during early childhood. Prenatal exposure to stress may predispose individuals to the development of sleep problems in later life.
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Affiliation(s)
| | - Hynek Pikhart
- RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic.,Department of Epidemiology & Public Health, University College London, London, UK
| | - Martin Bobák
- RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic.,Department of Epidemiology & Public Health, University College London, London, UK
| | - Jana Klanova
- RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic
| | - Irena Stepanikova
- RECETOX, Faculty of Science, Masaryk University, Brno, Czech Republic.,Department of Sociology, University of Alabama at Birmingham, Birmingham, AL, USA
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Koros C, Stefanis L, Scarmeas N. Parkinsonism and dementia. J Neurol Sci 2021; 433:120015. [PMID: 34642023 DOI: 10.1016/j.jns.2021.120015] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2021] [Revised: 09/01/2021] [Accepted: 09/29/2021] [Indexed: 12/13/2022]
Abstract
The aim of the present review is to summarize literature data on dementia in parkinsonian disorders. Cognitive decline and the gradual development of dementia are considered to be key features in the majority of parkinsonian conditions. The burden of dementia in everyday life of parkinsonian patients and their caregivers is vast and can be even more challenging to handle than the motor component of the disease. Common pathogenetic mechanisms involve the aggregation and spreading of abnormal proteins like alpha-synuclein, tau or amyloid in cortical and subcortical regions with subsequent dysregulation of multiple neurotransmitter systems. The degree of cognitive deterioration in these disorders is variable and ranges from mild cognitive impairment to severe cognitive dysfunction. There is also variation in the number and type of affected cognitive domains which can involve either a single domain like executive or visuospatial function or multiple ones. Novel genetic, biological fluid or imaging biomarkers appear promising in facilitating the diagnosis and staging of dementia in parkinsonian conditions. A significant part of current research in Parkinson's disease and other parkinsonian syndromes is targeted towards the cognitive aspects of these disorders. Stabilization or amelioration of cognitive outcomes represents a primary endpoint in many ongoing clinical trials for novel disease modifying treatments in this field. This article is part of the Special Issue "Parkinsonism across the spectrum of movement disorders and beyond" edited by Joseph Jankovic, Daniel D. Truong and Matteo Bologna.
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Affiliation(s)
- Christos Koros
- 1st Department of Neurology, Aeginition University, Hospital, National and Kapodistrian University of Athens, Medical School, Athens, Greece
| | - Leonidas Stefanis
- 1st Department of Neurology, Aeginition University, Hospital, National and Kapodistrian University of Athens, Medical School, Athens, Greece; Center of Clinical Research, Experimental Surgery and Translational Research, Biomedical Research Foundation of the Academy of Athens, Athens, Greece
| | - Nikolaos Scarmeas
- 1st Department of Neurology, Aeginition University, Hospital, National and Kapodistrian University of Athens, Medical School, Athens, Greece; The Gertrude H. Sergievsky Center, Department of Neurology, Taub Institute for Research in Alzheimer's, Disease and the Aging Brain, Columbia University, New York, USA.
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Landers M, Saria S, Espay AJ. Will Artificial Intelligence Replace the Movement Disorders Specialist for Diagnosing and Managing Parkinson's Disease? JOURNAL OF PARKINSONS DISEASE 2021; 11:S117-S122. [PMID: 34219671 PMCID: PMC8385515 DOI: 10.3233/jpd-212545] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
The use of artificial intelligence (AI) to help diagnose and manage disease is of increasing interest to researchers and clinicians. Volumes of health data are generated from smartphones and ubiquitous inexpensive sensors. By using these data, AI can offer otherwise unobtainable insights about disease burden and patient status in a free-living environment. Moreover, from clinical datasets AI can improve patient symptom monitoring and global epidemiologic efforts. While these applications are exciting, it is necessary to examine both the utility and limitations of these novel analytic methods. The most promising uses of AI remain aspirational. For example, defining the molecular subtypes of Parkinson's disease will be assisted by future applications of AI to relevant datasets. This will allow clinicians to match patients to molecular therapies and will thus help launch precision medicine. Until AI proves its potential in pushing the frontier of precision medicine, its utility will primarily remain in individualized monitoring, complementing but not replacing movement disorders specialists.
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Affiliation(s)
- Matt Landers
- Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Suchi Saria
- Departments of Computer Science and Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA.,Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.,Bayesian Health, New York, NY, USA
| | - Alberto J Espay
- Department of Neurology, James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders, University of Cincinnati, Cincinnati, OH, USA
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