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Hähnel T, Raschka T, Sapienza S, Klucken J, Glaab E, Corvol JC, Falkenburger BH, Fröhlich H. Progression subtypes in Parkinson's disease identified by a data-driven multi cohort analysis. NPJ Parkinsons Dis 2024; 10:95. [PMID: 38698004 PMCID: PMC11066039 DOI: 10.1038/s41531-024-00712-3] [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: 11/15/2023] [Accepted: 04/16/2024] [Indexed: 05/05/2024] Open
Abstract
The progression of Parkinson's disease (PD) is heterogeneous across patients, affecting counseling and inflating the number of patients needed to test potential neuroprotective treatments. Moreover, disease subtypes might require different therapies. This work uses a data-driven approach to investigate how observed heterogeneity in PD can be explained by the existence of distinct PD progression subtypes. To derive stable PD progression subtypes in an unbiased manner, we analyzed multimodal longitudinal data from three large PD cohorts and performed extensive cross-cohort validation. A latent time joint mixed-effects model (LTJMM) was used to align patients on a common disease timescale. Progression subtypes were identified by variational deep embedding with recurrence (VaDER). In each cohort, we identified a fast-progressing and a slow-progressing subtype, reflected by different patterns of motor and non-motor symptoms progression, survival rates, treatment response, features extracted from DaTSCAN imaging and digital gait assessments, education, and Alzheimer's disease pathology. Progression subtypes could be predicted with ROC-AUC up to 0.79 for individual patients when a one-year observation period was used for model training. Simulations demonstrated that enriching clinical trials with fast-progressing patients based on these predictions can reduce the required cohort size by 43%. Our results show that heterogeneity in PD can be explained by two distinct subtypes of PD progression that are stable across cohorts. These subtypes align with the brain-first vs. body-first concept, which potentially provides a biological explanation for subtype differences. Our predictive models will enable clinical trials with significantly lower sample sizes by enriching fast-progressing patients.
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Affiliation(s)
- Tom Hähnel
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.
- Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
| | - Tamara Raschka
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, University of Bonn, Bonn, Germany
| | - Stefano Sapienza
- Biomedical Data Science, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
| | - Jochen Klucken
- Biomedical Data Science, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
- Luxembourg Institute of Health (LIH), Strassen, Luxembourg
- Centre Hospitalier de Luxembourg (CHL), Strassen, Luxembourg
| | - Enrico Glaab
- Biomedical Data Science, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Jean-Christophe Corvol
- Sorbonne Université, Paris Brain Institute - ICM, Inserm, CNRS, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Department of Neurology, Paris, France
| | - Björn H Falkenburger
- Department of Neurology, Medical Faculty and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
- German Center for Neurodegenerative Diseases (DZNE), Dresden, Germany
| | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
- Bonn-Aachen International Center for IT, University of Bonn, Bonn, Germany
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2
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Hernández-Lorenzo L, García-Gutiérrez F, Solbas-Casajús A, Corrochano S, Matías-Guiu JA, Ayala JL. Genetic-based patient stratification in Alzheimer's disease. Sci Rep 2024; 14:9970. [PMID: 38693203 PMCID: PMC11063050 DOI: 10.1038/s41598-024-60707-1] [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: 12/19/2023] [Accepted: 04/26/2024] [Indexed: 05/03/2024] Open
Abstract
Alzheimer's disease (AD) shows a high pathological and symptomatological heterogeneity. To study this heterogeneity, we have developed a patient stratification technique based on one of the most significant risk factors for the development of AD: genetics. We addressed this challenge by including network biology concepts, mapping genetic variants data into a brain-specific protein-protein interaction (PPI) network, and obtaining individualized PPI scores that we then used as input for a clustering technique. We then phenotyped each obtained cluster regarding genetics, sociodemographics, biomarkers, fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging, and neurocognitive assessments. We found three clusters defined mainly by genetic variants found in MAPT, APP, and APOE, considering known variants associated with AD and other neurodegenerative disease genetic architectures. Profiling of these clusters revealed minimal variation in AD symptoms and pathology, suggesting different biological mechanisms may activate the neurodegeneration and pathobiological patterns behind AD and result in similar clinical and pathological presentations, even a shared disease diagnosis. Lastly, our research highlighted MAPT, APP, and APOE as key genes where these genetic distinctions manifest, suggesting them as potential targets for personalized drug development strategies to address each AD subgroup individually.
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Affiliation(s)
- Laura Hernández-Lorenzo
- Department of Computer Architecture and Automation, Computer Science Faculty, Complutense University of Madrid, 28040, Madrid, Spain.
| | - Fernando García-Gutiérrez
- Department of Computer Architecture and Automation, Computer Science Faculty, Complutense University of Madrid, 28040, Madrid, Spain
| | - Ana Solbas-Casajús
- Department of Computer Architecture and Automation, Computer Science Faculty, Complutense University of Madrid, 28040, Madrid, Spain
| | - Silvia Corrochano
- Department of Neurology, San Carlos Research Institute (IdSSC), Hospital Clínico San Carlos, 28040, Madrid, Spain
| | - Jordi A Matías-Guiu
- Department of Neurology, San Carlos Research Institute (IdSSC), Hospital Clínico San Carlos, 28040, Madrid, Spain
| | - Jose L Ayala
- Department of Computer Architecture and Automation, Computer Science Faculty, Complutense University of Madrid, 28040, Madrid, Spain
- Instituto de Tecnología del Conocimiento, Universidad Complutense de Madrid, 28040, Madrid, Spain
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3
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Machado Reyes D, Chao H, Hahn J, Shen L, Yan P. Identifying Progression-Specific Alzheimer's Subtypes Using Multimodal Transformer. J Pers Med 2024; 14:421. [PMID: 38673048 PMCID: PMC11051083 DOI: 10.3390/jpm14040421] [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: 03/15/2024] [Revised: 04/01/2024] [Accepted: 04/08/2024] [Indexed: 04/28/2024] Open
Abstract
Alzheimer's disease (AD) is the most prevalent neurodegenerative disease, yet its current treatments are limited to stopping disease progression. Moreover, the effectiveness of these treatments remains uncertain due to the heterogeneity of the disease. Therefore, it is essential to identify disease subtypes at a very early stage. Current data-driven approaches can be used to classify subtypes during later stages of AD or related disorders, but making predictions in the asymptomatic or prodromal stage is challenging. Furthermore, the classifications of most existing models lack explainability, and these models rely solely on a single modality for assessment, limiting the scope of their analysis. Thus, we propose a multimodal framework that utilizes early-stage indicators, including imaging, genetics, and clinical assessments, to classify AD patients into progression-specific subtypes at an early stage. In our framework, we introduce a tri-modal co-attention mechanism (Tri-COAT) to explicitly capture cross-modal feature associations. Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (slow progressing = 177, intermediate = 302, and fast = 15) were used to train and evaluate Tri-COAT using a 10-fold stratified cross-testing approach. Our proposed model outperforms baseline models and sheds light on essential associations across multimodal features supported by known biological mechanisms. The multimodal design behind Tri-COAT allows it to achieve the highest classification area under the receiver operating characteristic curve while simultaneously providing interpretability to the model predictions through the co-attention mechanism.
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Affiliation(s)
- Diego Machado Reyes
- Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; (D.M.R.); (H.C.); (J.H.)
| | - Hanqing Chao
- Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; (D.M.R.); (H.C.); (J.H.)
| | - Juergen Hahn
- Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; (D.M.R.); (H.C.); (J.H.)
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA;
| | - Pingkun Yan
- Department of Biomedical Engineering, Center for Biotechnology and Interdisciplinary Studies, Rensselaer Polytechnic Institute, Troy, NY 12180, USA; (D.M.R.); (H.C.); (J.H.)
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Higginbotham L, Carter EK, Dammer EB, Haque RU, Johnson ECB, Duong DM, Yin L, De Jager PL, Bennett DA, Felsky D, Tio ES, Lah JJ, Levey AI, Seyfried NT. Unbiased classification of the elderly human brain proteome resolves distinct clinical and pathophysiological subtypes of cognitive impairment. Neurobiol Dis 2023; 186:106286. [PMID: 37689213 PMCID: PMC10750427 DOI: 10.1016/j.nbd.2023.106286] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/24/2023] [Accepted: 09/06/2023] [Indexed: 09/11/2023] Open
Abstract
Cognitive impairment in the elderly features complex molecular pathophysiology extending beyond the hallmark pathologies of traditional disease classification. Molecular subtyping using large-scale -omic strategies can help resolve this biological heterogeneity. Using quantitative mass spectrometry, we measured ∼8000 proteins across >600 dorsolateral prefrontal cortex tissues with clinical diagnoses of no cognitive impairment (NCI), mild cognitive impairment (MCI), and Alzheimer's disease (AD) dementia. Unbiased classification of MCI and AD cases based on individual proteomic profiles resolved three classes with expression differences across numerous cell types and biological ontologies. Two classes displayed molecular signatures atypical of AD neurodegeneration, such as elevated synaptic and decreased inflammatory markers. In one class, these atypical proteomic features were associated with clinical and pathological hallmarks of cognitive resilience. We were able to replicate these classes and their clinicopathological phenotypes across two additional tissue cohorts. These results promise to better define the molecular heterogeneity of cognitive impairment and meaningfully impact its diagnostic and therapeutic precision.
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Affiliation(s)
- Lenora Higginbotham
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA; Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA.
| | - E Kathleen Carter
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA; Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA; Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - Eric B Dammer
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA; Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - Rafi U Haque
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA; Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Erik C B Johnson
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA; Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Duc M Duong
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA; Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - Luming Yin
- Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA
| | - Philip L De Jager
- Center for Translational & Computational Neuroimmunology, Department of Neurology, Taub Institute, Columbia University Irving Medical Center, New York Presbyterian Hospital, New York, NY, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Daniel Felsky
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - Earvin S Tio
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada; Institute of Medical Science, University of Toronto, Toronto, ON, Canada
| | - James J Lah
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA; Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Allan I Levey
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA; Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA
| | - Nicholas T Seyfried
- Goizueta Alzheimer's Disease Research Center, Emory University School of Medicine, Atlanta, GA, USA; Department of Neurology, Emory University School of Medicine, Atlanta, GA, USA; Department of Biochemistry, Emory University School of Medicine, Atlanta, GA, USA.
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5
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Yang M, Matan-Lithwick S, Wang Y, De Jager PL, Bennett DA, Felsky D. Multi-omic integration via similarity network fusion to detect molecular subtypes of ageing. Brain Commun 2023; 5:fcad110. [PMID: 37082508 PMCID: PMC10110975 DOI: 10.1093/braincomms/fcad110] [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: 01/23/2023] [Revised: 02/17/2023] [Accepted: 04/03/2023] [Indexed: 04/07/2023] Open
Abstract
Molecular subtyping of brain tissue provides insights into the heterogeneity of common neurodegenerative conditions, such as Alzheimer's disease. However, existing subtyping studies have mostly focused on single data modalities and only those individuals with severe cognitive impairment. To address these gaps, we applied similarity network fusion, a method capable of integrating multiple high-dimensional multi-omic data modalities simultaneously, to an elderly sample spanning the full spectrum of cognitive ageing trajectories. We analyzed human frontal cortex brain samples characterized by five omic modalities: bulk RNA sequencing (18 629 genes), DNA methylation (53 932 CpG sites), histone acetylation (26 384 peaks), proteomics (7737 proteins) and metabolomics (654 metabolites). Similarity network fusion followed by spectral clustering was used for subtype detection, and subtype numbers were determined by Eigen-gap and rotation cost statistics. Normalized mutual information determined the relative contribution of each modality to the fused network. Subtypes were characterized by associations with 13 age-related neuropathologies and cognitive decline. Fusion of all five data modalities (n = 111) yielded two subtypes (n S1 = 53, n S2 = 58), which were nominally associated with diffuse amyloid plaques; however, this effect was not significant after correction for multiple testing. Histone acetylation (normalized mutual information = 0.38), DNA methylation (normalized mutual information = 0.18) and RNA abundance (normalized mutual information = 0.15) contributed most strongly to this network. Secondary analysis integrating only these three modalities in a larger subsample (n = 513) indicated support for both three- and five-subtype solutions, which had significant overlap, but showed varying degrees of internal stability and external validity. One subtype showed marked cognitive decline, which remained significant even after correcting for tests across both three- and five-subtype solutions (p Bonf = 5.9 × 10-3). Comparison to single-modality subtypes demonstrated that the three-modal subtypes were able to uniquely capture cognitive variability. Comprehensive sensitivity analyses explored influences of sample size and cluster number parameters. We identified highly integrative molecular subtypes of ageing derived from multiple high dimensional, multi-omic data modalities simultaneously. Fusing RNA abundance, DNA methylation, and histone acetylation measures generated subtypes that were associated with cognitive decline. This work highlights the potential value and challenges of multi-omic integration in unsupervised subtyping of post-mortem brain.
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Affiliation(s)
- Mu Yang
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
| | - Stuart Matan-Lithwick
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
| | - Yanling Wang
- Rush Alzheimer’s Disease Center, Rush University, Chicago, IL 60612, USA
| | - Philip L De Jager
- The Center for Translational and Computational Neuroimmunology, Columbia University Medical Center, New York, NY 10033, USA
| | - David A Bennett
- Rush Alzheimer’s Disease Center, Rush University, Chicago, IL 60612, USA
| | - Daniel Felsky
- Division of Biostatistics, Dalla Lana School of Public Health, University of Toronto, Toronto, ON M5T 3M7, Canada
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON M5T 1R8, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON M5T 1R8, Canada
- Institute of Medical Science, University of Toronto, Toronto, ON M5S 1A8, Canada
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6
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Aborageh M, Krawitz P, Fröhlich H. Genetics in parkinson's disease: From better disease understanding to machine learning based precision medicine. FRONTIERS IN MOLECULAR MEDICINE 2022; 2:933383. [PMID: 39086979 PMCID: PMC11285583 DOI: 10.3389/fmmed.2022.933383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 08/30/2022] [Indexed: 08/02/2024]
Abstract
Parkinson's Disease (PD) is a neurodegenerative disorder with highly heterogeneous phenotypes. Accordingly, it has been challenging to robustly identify genetic factors associated with disease risk, prognosis and therapy response via genome-wide association studies (GWAS). In this review we first provide an overview of existing statistical methods to detect associations between genetic variants and the disease phenotypes in existing PD GWAS. Secondly, we discuss the potential of machine learning approaches to better quantify disease phenotypes and to move beyond disease understanding towards a better-personalized treatment of the disease.
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Affiliation(s)
- Mohamed Aborageh
- Bonn-Aachen International Center for Information Technology (B-IT), Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
| | - Peter Krawitz
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Bonn, Germany
| | - Holger Fröhlich
- Bonn-Aachen International Center for Information Technology (B-IT), Rheinische Friedrich-Wilhelms-Universität Bonn, Bonn, Germany
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany
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7
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Fröhlich H, Bontridder N, Petrovska-Delacréta D, Glaab E, Kluge F, Yacoubi ME, Marín Valero M, Corvol JC, Eskofier B, Van Gyseghem JM, Lehericy S, Winkler J, Klucken J. Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease. Front Neurol 2022; 13:788427. [PMID: 35295840 PMCID: PMC8918525 DOI: 10.3389/fneur.2022.788427] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2021] [Accepted: 01/31/2022] [Indexed: 12/18/2022] Open
Abstract
Recent years have witnessed a strongly increasing interest in digital technology within medicine (sensor devices, specific smartphone apps) and specifically also neurology. Quantitative measures derived from digital technology could provide Digital Biomarkers (DMs) enabling a quantitative and continuous monitoring of disease symptoms, also outside clinics. This includes the possibility to continuously and sensitively monitor the response to treatment, hence opening the opportunity to adapt medication pathways quickly. In addition, DMs may in the future allow early diagnosis, stratification of patient subgroups and prediction of clinical outcomes. Thus, DMs could complement or in certain cases even replace classical examiner-based outcome measures and molecular biomarkers measured in cerebral spinal fluid, blood, urine, saliva, or other body liquids. Altogether, DMs could play a prominent role in the emerging field of precision medicine. However, realizing this vision requires dedicated research. First, advanced data analytical methods need to be developed and applied, which extract candidate DMs from raw signals. Second, these candidate DMs need to be validated by (a) showing their correlation to established clinical outcome measures, and (b) demonstrating their diagnostic and/or prognostic value compared to established biomarkers. These points again require the use of advanced data analytical methods, including machine learning. In addition, the arising ethical, legal and social questions associated with the collection and processing of sensitive patient data and the use of machine learning methods to analyze these data for better individualized treatment of the disease, must be considered thoroughly. Using Parkinson's Disease (PD) as a prime example of a complex multifactorial disorder, the purpose of this article is to critically review the current state of research regarding the use of DMs, discuss open challenges and highlight emerging new directions.
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Affiliation(s)
- Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing, Sankt Augustin, Germany
- Bonn-Aachen International Center for IT (b-it), University of Bonn, Bonn, Germany
| | - Noémi Bontridder
- Centre de Recherches Information, Droit et Societe, University of Namur, Namur, Belgium
| | | | - Enrico Glaab
- Luxembourg Center for Systems Medicine, University of Luxembourg, Esch, Luxembourg
| | - Felix Kluge
- Department of Artificial Intelligence in Biomedical Engineering, University of Erlangen Nuremberg, Erlangen, Germany
| | | | | | | | - Bjoern Eskofier
- Department of Artificial Intelligence in Biomedical Engineering, University of Erlangen Nuremberg, Erlangen, Germany
| | | | | | - Jürgen Winkler
- Department of Neurology, University Hospital Erlangen, Erlangen, Germany
| | - Jochen Klucken
- Luxembourg Center for Systems Medicine, University of Luxembourg, Esch, Luxembourg
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8
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Banerjee R, Rai A, Iyer SM, Narwal S, Tare M. Animal models in the study of Alzheimer's disease and Parkinson's disease: A historical perspective. Animal Model Exp Med 2022; 5:27-37. [PMID: 35229999 PMCID: PMC8879627 DOI: 10.1002/ame2.12209] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 12/20/2021] [Accepted: 01/10/2022] [Indexed: 12/12/2022] Open
Abstract
Alzheimer's disease and Parkinson's disease are two of the most prevalent and disabling neurodegenerative diseases globally. Both are proteinopathic conditions and while occasionally inherited, are largely sporadic in nature. Although the advances in our understanding of the two have been significant, they are far from complete and neither diagnosis nor the current practices in treatment and rehabilitation is adequately helpful. Animal models have historically found application as testing beds for novel therapeutics and continue to be valuable aids in pharmacological research. This review chronicles the development of those models in the context of Alzheimer's and Parkinson's disease, and highlights the shifting paradigms in studying two human-specific conditions in non-human organisms.
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Affiliation(s)
- Rajashree Banerjee
- Department of Biological SciencesBirla Institute of Technology and SciencePilaniIndia
| | - Arushi Rai
- Department of Biological SciencesBirla Institute of Technology and SciencePilaniIndia
| | - Shreyas M. Iyer
- Department of Biological SciencesBirla Institute of Technology and SciencePilaniIndia
| | - Sonia Narwal
- Department of Biological SciencesBirla Institute of Technology and SciencePilaniIndia
| | - Meghana Tare
- Department of Biological SciencesBirla Institute of Technology and SciencePilaniIndia
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9
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Comprehensive subtyping of Parkinson's disease patients with similarity fusion: a case study with BioFIND data. NPJ PARKINSONS DISEASE 2021; 7:83. [PMID: 34535682 PMCID: PMC8448859 DOI: 10.1038/s41531-021-00228-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Accepted: 08/30/2021] [Indexed: 12/28/2022]
Abstract
Parkinson’s disease (PD) is a complex neurodegenerative disorder with diverse clinical manifestations. To better understand this disease, research has been done to categorize, or subtype, patients, using an array of criteria derived from clinical assessments and biospecimen analyses. In this study, using data from the BioFIND cohort, we aimed at identifying subtypes of moderate-to-advanced PD via comprehensively considering motor and non-motor manifestations. A total of 103 patients were included for analysis. Through the use of a patient-wise similarity matrix fusion technique and hierarchical agglomerative clustering analysis, three unique subtypes emerged from the clustering results. Subtype I, comprised of 60 patients (~58.3%), was characterized by mild symptoms, both motor and non-motor. Subtype II, comprised of 20 (~19.4%) patients, was characterized by an intermediate severity, with a high tremor score and mild non-motor symptoms. Subtype III, comprised of 23 (~22.3%) patients, was characterized by more severe motor and non-motor symptoms. These subtypes show statistically significant differences when looking at motor (on and off medication) clinical features and non-motor clinical features, while there was no clear difference in demographics, biomarker levels, and genetic risk scores.
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10
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Golubnitschaja O, Liskova A, Koklesova L, Samec M, Biringer K, Büsselberg D, Podbielska H, Kunin AA, Evsevyeva ME, Shapira N, Paul F, Erb C, Dietrich DE, Felbel D, Karabatsiakis A, Bubnov R, Polivka J, Polivka J, Birkenbihl C, Fröhlich H, Hofmann-Apitius M, Kubatka P. Caution, "normal" BMI: health risks associated with potentially masked individual underweight-EPMA Position Paper 2021. EPMA J 2021; 12:243-264. [PMID: 34422142 PMCID: PMC8368050 DOI: 10.1007/s13167-021-00251-4] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 07/30/2021] [Indexed: 02/06/2023]
Abstract
An increasing interest in a healthy lifestyle raises questions about optimal body weight. Evidently, it should be clearly discriminated between the standardised "normal" body weight and individually optimal weight. To this end, the basic principle of personalised medicine "one size does not fit all" has to be applied. Contextually, "normal" but e.g. borderline body mass index might be optimal for one person but apparently suboptimal for another one strongly depending on the individual genetic predisposition, geographic origin, cultural and nutritional habits and relevant lifestyle parameters-all included into comprehensive individual patient profile. Even if only slightly deviant, both overweight and underweight are acknowledged risk factors for a shifted metabolism which, if being not optimised, may strongly contribute to the development and progression of severe pathologies. Development of innovative screening programmes is essential to promote population health by application of health risks assessment, individualised patient profiling and multi-parametric analysis, further used for cost-effective targeted prevention and treatments tailored to the person. The following healthcare areas are considered to be potentially strongly benefiting from the above proposed measures: suboptimal health conditions, sports medicine, stress overload and associated complications, planned pregnancies, periodontal health and dentistry, sleep medicine, eye health and disorders, inflammatory disorders, healing and pain management, metabolic disorders, cardiovascular disease, cancers, psychiatric and neurologic disorders, stroke of known and unknown aetiology, improved individual and population outcomes under pandemic conditions such as COVID-19. In a long-term way, a significantly improved healthcare economy is one of benefits of the proposed paradigm shift from reactive to Predictive, Preventive and Personalised Medicine (PPPM/3PM). A tight collaboration between all stakeholders including scientific community, healthcare givers, patient organisations, policy-makers and educators is essential for the smooth implementation of 3PM concepts in daily practice.
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Affiliation(s)
- Olga Golubnitschaja
- Predictive, Preventive and Personalised (3P) Medicine, Department of Radiation Oncology, University Hospital Bonn, Rheinische Friedrich-Wilhelms-Universität Bonn, 53127 Bonn, Germany
| | - Alena Liskova
- Clinic of Obstetrics and Gynaecology, Jessenius Faculty of Medicine, Comenius University, in Bratislava, 03601 Martin, Slovakia
| | - Lenka Koklesova
- Clinic of Obstetrics and Gynaecology, Jessenius Faculty of Medicine, Comenius University, in Bratislava, 03601 Martin, Slovakia
| | - Marek Samec
- Clinic of Obstetrics and Gynaecology, Jessenius Faculty of Medicine, Comenius University, in Bratislava, 03601 Martin, Slovakia
| | - Kamil Biringer
- Clinic of Obstetrics and Gynaecology, Jessenius Faculty of Medicine, Comenius University, in Bratislava, 03601 Martin, Slovakia
| | - Dietrich Büsselberg
- Weill Cornell Medicine-Qatar, Education City, Qatar Foundation, 24144 Doha, Qatar
| | - Halina Podbielska
- Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wrocław University of Science and Technology, 50-370 Wrocław, Poland
| | - Anatolij A. Kunin
- Departments of Maxillofacial Surgery and Hospital Dentistry, Voronezh N.N. Burdenko State Medical University, Voronezh, Russian Federation
| | | | - Niva Shapira
- Nutrition Department, Ashkelon Academic College, Ashkelon, Tel Aviv, Israel
| | - Friedemann Paul
- NeuroCure Clinical Research Centre, Experimental and Clinical Research Centre, Max Delbrueck Centre for Molecular Medicine and Charité Universitaetsmedizin Berlin, Berlin, Germany
| | - Carl Erb
- Private Institute of Applied Ophthalmology, Berlin, Germany
| | - Detlef E. Dietrich
- European Depression Association, Brussels, Belgium
- AMEOS Clinical Centre for Psychiatry and Psychotherapy, 31135 Hildesheim, Germany
| | - Dieter Felbel
- Fachklinik Kinder und Jugendliche Psychiatrie, AMEOS Klinikum Hildesheim, Akademisches Lehrkrankenhaus für Pflege der FOM Hochschule Essen, Hildesheim, Germany
| | - Alexander Karabatsiakis
- Institute of Psychology, Department of Clinical Psychology II, University of Innsbruck, Innsbruck, Austria
| | - Rostyslav Bubnov
- Ultrasound Department, Clinical Hospital “Pheophania”, Kyiv, Ukraine
- Zabolotny Institute of Microbiology and Virology, National Academy of Sciences of Ukraine, Kyiv, Ukraine
| | - Jiri Polivka
- Department of Neurology, Faculty of Medicine in Pilsen, Charles University and University Hospital Pilsen, Pilsen, Czech Republic
| | - Jiri Polivka
- Department of Histology and Embryology, Faculty of Medicine in Pilsen, Charles University, Staré Město, Czech Republic
- Biomedical Centre, Faculty of Medicine in Pilsen, Charles University, Staré Město, Czech Republic
| | - Colin Birkenbihl
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
- Bonn-Aachen International Centre for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115 Bonn, Germany
| | - Holger Fröhlich
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
- Bonn-Aachen International Centre for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115 Bonn, Germany
- UCB Biosciences GmbH, Alfred-Nobel Str. 10, 40789 Monheim am Rhein, Germany
| | - Martin Hofmann-Apitius
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Schloss Birlinghoven, 53757 Sankt Augustin, Germany
- Bonn-Aachen International Centre for IT, Rheinische Friedrich-Wilhelms-Universität Bonn, 53115 Bonn, Germany
| | - Peter Kubatka
- Department of Medical Biology, Jessenius Faculty of Medicine, Comenius University in Bratislava, 03601 Martin, Slovakia
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