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Ndayisaba A, Pitaro AT, Willett AS, Jones KA, de Gusmao CM, Olsen AL, Kim J, Rissanen E, Woods JK, Srinivasan SR, Nagy A, Nagy A, Mesidor M, Cicero S, Patel V, Oakley DH, Tuncali I, Taglieri-Noble K, Clark EC, Paulson J, Krolewski RC, Ho GP, Hung AY, Wills AM, Hayes MT, Macmore JP, Warren L, Bower PG, Langer CB, Kellerman LR, Humphreys CW, Glanz BI, Dielubanza EJ, Frosch MP, Freeman RL, Gibbons CH, Stefanova N, Chitnis T, Weiner HL, Scherzer CR, Scholz SW, Vuzman D, Cox LM, Wenning G, Schmahmann JD, Gupta AS, Novak P, Young GS, Feany MB, Singhal T, Khurana V. Clinical Trial-Ready Patient Cohorts for Multiple System Atrophy: Coupling Biospecimen and iPSC Banking to Longitudinal Deep-Phenotyping. CEREBELLUM (LONDON, ENGLAND) 2024; 23:31-51. [PMID: 36190676 PMCID: PMC9527378 DOI: 10.1007/s12311-022-01471-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 08/26/2022] [Indexed: 11/30/2022]
Abstract
Multiple system atrophy (MSA) is a fatal neurodegenerative disease of unknown etiology characterized by widespread aggregation of the protein alpha-synuclein in neurons and glia. Its orphan status, biological relationship to Parkinson's disease (PD), and rapid progression have sparked interest in drug development. One significant obstacle to therapeutics is disease heterogeneity. Here, we share our process of developing a clinical trial-ready cohort of MSA patients (69 patients in 2 years) within an outpatient clinical setting, and recruiting 20 of these patients into a longitudinal "n-of-few" clinical trial paradigm. First, we deeply phenotype our patients with clinical scales (UMSARS, BARS, MoCA, NMSS, and UPSIT) and tests designed to establish early differential diagnosis (including volumetric MRI, FDG-PET, MIBG scan, polysomnography, genetic testing, autonomic function tests, skin biopsy) or disease activity (PBR06-TSPO). Second, we longitudinally collect biospecimens (blood, CSF, stool) and clinical, biometric, and imaging data to generate antecedent disease-progression scores. Third, in our Mass General Brigham SCiN study (stem cells in neurodegeneration), we generate induced pluripotent stem cell (iPSC) models from our patients, matched to biospecimens, including postmortem brain. We present 38 iPSC lines derived from MSA patients and relevant disease controls (spinocerebellar ataxia and PD, including alpha-synuclein triplication cases), 22 matched to whole-genome sequenced postmortem brain. iPSC models may facilitate matching patients to appropriate therapies, particularly in heterogeneous diseases for which patient-specific biology may elude animal models. We anticipate that deeply phenotyped and genotyped patient cohorts matched to cellular models will increase the likelihood of success in clinical trials for MSA.
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Affiliation(s)
- Alain Ndayisaba
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
- Division of Clinical Neurobiology, Department of Neurology, Medical University of Innsbruck, Anichstraße 35, 6020, Innsbruck, Austria
| | - Ariana T Pitaro
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Andrew S Willett
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Kristie A Jones
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Claudio Melo de Gusmao
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Abby L Olsen
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Jisoo Kim
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Eero Rissanen
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Jared K Woods
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Sharan R Srinivasan
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
- Department of Neurology, University of Michigan, Ann Arbor, MI , 48103, USA
| | - Anna Nagy
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Amanda Nagy
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Merlyne Mesidor
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Steven Cicero
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Viharkumar Patel
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Derek H Oakley
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Idil Tuncali
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Katherine Taglieri-Noble
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Emily C Clark
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Jordan Paulson
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Richard C Krolewski
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Gary P Ho
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Albert Y Hung
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Anne-Marie Wills
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Michael T Hayes
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Jason P Macmore
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | | | - Pamela G Bower
- The Multiple System Atrophy Coalition, Inc., 7918 Jones Branch Drive, Suite 300, McLean, VA, 22102, USA
| | - Carol B Langer
- The Multiple System Atrophy Coalition, Inc., 7918 Jones Branch Drive, Suite 300, McLean, VA, 22102, USA
| | - Lawrence R Kellerman
- The Multiple System Atrophy Coalition, Inc., 7918 Jones Branch Drive, Suite 300, McLean, VA, 22102, USA
| | - Christopher W Humphreys
- Department of Pulmonary, Sleep and Critical Care Medicine, Salem Hospital, MassGeneral Brigham, Salem, MA, 01970, USA
| | - Bonnie I Glanz
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Elodi J Dielubanza
- Department of Urology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Matthew P Frosch
- Department of Pathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Roy L Freeman
- Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, 02115, USA
| | - Christopher H Gibbons
- Department of Neurology, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, 02115, USA
| | - Nadia Stefanova
- Division of Clinical Neurobiology, Department of Neurology, Medical University of Innsbruck, Anichstraße 35, 6020, Innsbruck, Austria
| | - Tanuja Chitnis
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Howard L Weiner
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Clemens R Scherzer
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Sonja W Scholz
- Laboratory of Neurogenetics, Disorders and Stroke, National Institute of Neurological, National Institute of Neurological Disorders and Stroke, Bethesda, MD, 20892, USA
- Department of Neurology, Johns Hopkins University Medical Center, Baltimore, MD, 21287, USA
| | - Dana Vuzman
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Division of Genetics, Department of Medicine, Brigham and Women's Hospital, Boston, MA, 02115, USA
| | - Laura M Cox
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Gregor Wenning
- Division of Clinical Neurobiology, Department of Neurology, Medical University of Innsbruck, Anichstraße 35, 6020, Innsbruck, Austria
| | - Jeremy D Schmahmann
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Anoopum S Gupta
- Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA
| | - Peter Novak
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Geoffrey S Young
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Mel B Feany
- Department of Pathology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, 02115, USA
| | - Tarun Singhal
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA
| | - Vikram Khurana
- Department of Neurology, Building for Transformative Medicine Room 10016L, Brigham and Women's Hospital and Harvard Medical School, 60 Fenwood Road, Boston, 02115, USA.
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Ni X, Zhao H, Li R, Su H, Jiao J, Yang Z, Lv Y, Pang G, Sun M, Hu C, Yuan H. Development of a model for the prediction of biological age. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107686. [PMID: 37421874 DOI: 10.1016/j.cmpb.2023.107686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 06/04/2023] [Accepted: 06/20/2023] [Indexed: 07/10/2023]
Abstract
BACKGROUND AND OBJECTIVE Rates of aging vary markedly among individuals, and biological age serves as a more reliable predictor of current health status than does chronological age. As such, the ability to predict biological age can support appropriate and timely active interventions aimed at improving coping with the aging process. However, the aging process is highly complex and multifactorial. Therefore, it is more scientific to construct a prediction model for biological age from multiple dimensions systematically. METHODS Physiological and biochemical parameters were evaluated to gage individual health status. Then, age-related indices were screened for inclusion in a model capable of predicting biological age. For subsequent modeling analyses, samples were divided into training and validation sets for subsequent deep learning model-based analyses (e.g. linear regression, lasso model, ridge regression, bayesian ridge regression, elasticity network, k-nearest neighbor, linear support vector machine, support vector machine, and decision tree models, and so on), with the model exhibiting the best ability to predict biological age thereby being identified. RESULTS First, we defined the individual biological age according to the individual health status. Then, after 22 candidate indices (DNA methylation, leukocyte telomere length, and specific physiological and biochemical indicators) were screened for inclusion in a model capable of predicting biological age, 14 age-related indices and gender were used to construct a model via the Bagged Trees method, which was found to be the most reliable qualitative prediction model for biological age (accuracy=75.6%, AUC=0.84) by comparing 30 different classification algorithm models. The most reliable quantitative predictive model for biological age was found to be the model developed using the Rational Quadratic method (R2=0.85, RMSE=8.731 years) by comparing 24 regression algorithm models. CONCLUSIONS Both qualitative model and quantitative model of biological age were successfully constructed from a multi-dimensional and systematic perspective. The predictive performance of our models was similar in both smaller and larger datasets, making it well-suited to predicting a given individual's biological age.
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Affiliation(s)
- Xiaolin Ni
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, PR China
| | - Hanqing Zhao
- College of Traditional Chinese Medicine, Hebei University, Baoding, 071000, PR China
| | - Rongqiao Li
- Jiangbin Hospital, Guangxi Zhuang Autonomous Region, Nanning, 530021, PR China
| | - Huabin Su
- Jiangbin Hospital, Guangxi Zhuang Autonomous Region, Nanning, 530021, PR China
| | - Juan Jiao
- Clinical Lab, The Seventh Medical Center of Chinese People's Liberation Army General Hospital, Beijing 100700, China
| | - Ze Yang
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, PR China
| | - Yuan Lv
- Jiangbin Hospital, Guangxi Zhuang Autonomous Region, Nanning, 530021, PR China
| | - Guofang Pang
- Jiangbin Hospital, Guangxi Zhuang Autonomous Region, Nanning, 530021, PR China
| | - Meiqi Sun
- College of Traditional Chinese Medicine, Hebei University, Baoding, 071000, PR China
| | - Caiyou Hu
- Jiangbin Hospital, Guangxi Zhuang Autonomous Region, Nanning, 530021, PR China.
| | - Huiping Yuan
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, PR China.
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