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Majhi V, Paul S, Saha G, Kunwar AJ, Saikia MJ. Importance of health history analysis in Parkinson's disease. Heliyon 2024; 10:e34858. [PMID: 39144964 PMCID: PMC11320301 DOI: 10.1016/j.heliyon.2024.e34858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Revised: 07/04/2024] [Accepted: 07/17/2024] [Indexed: 08/16/2024] Open
Abstract
The objective of this research article is to investigate the impact of various health history factors on the risk of developing Parkinson's disease (PD). From the medical history we can identify PD Symptoms and this also help to detect the progression of PD symptoms. By conducting statistical analyses, the study seeks to identify independent risk and protective factors for Parkinson's disease (PD), considering variations in impact across genders and BMI categories. Introduction In the diagnosis of PD the analysis of previous health history is very rare in practice while the main diagnosis have been done through the different motor and non-motor symptoms taking in consideration besides the cardinal symptoms of PD for identification and determination the stages of PD. Here we have analyzed the impact of 56 different diseases, symptoms, and surgeries which a subject may have experienced in their life before PD, considered as a health history. Methods The behavioral impact for each types of health history have been analyzed statistically with 31,265 subjects including PD, and Control. In this analysis we have calculated the variation of impact for both the Male, and Female, as well as subjects BMI. Results 98.12 % PD patients, where 97.63 % Male PD, and 98.71 % Female PD were found with at least one health history record. Coronary heart disease odds ratio (OR) 2.15 (1.85-2.51), Colon Cancer OR 2.11 (1.45-3.05), Cranial brain surgery OR 6.21 (5.11-7.56) have the higher risks to PD. Having the history of Asthma OR 0.66 (0.6-0.72), Anemia OR 0.56 (0.51-0.63), Cirrhosis in Liver OR 0.7 (0.57-0.86), Cosmetic surgery OR 0.7 (0.64-0.77), and Gastritis OR 0.78 (0.71-0.87) have been found to be protective to PD. The risk of developing PD varies between male, and female including subjects BMI for each individual health history types. The diseases which reduce the oxygen saturation in blood like, anemia, asthma, and thalassemia act as protective to PD. Conclusions In this study we have analyzed fifty six diseases which include surgeries as a health history of PD patients. Study suggests that a thorough health history could greatly aid in understanding the onset and progression of Parkinson's disease (PD).
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Affiliation(s)
- Vinayak Majhi
- Amity Innovation and Design Centre, Amity University Uttar Pradesh, Noida, 301213, Uttar Pradesh, India
| | - Sudip Paul
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong, 793022, Meghalaya, India
| | - Goutam Saha
- Department of Information Technology, North-Eastern Hill University, Shillong, 793022, Meghalaya, India
| | - Ajaya Jang Kunwar
- Kathmandu Center for Genomics and Research Laboratory, Lalitpur, Nepal
| | - Manob Jyoti Saikia
- Department of Electrical Engineering, University of North Florida, Jacksonville, FL, 32224, USA
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2
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Reddy A, Reddy RP, Roghani AK, Garcia RI, Khemka S, Pattoor V, Jacob M, Reddy PH, Sehar U. Artificial intelligence in Parkinson's disease: Early detection and diagnostic advancements. Ageing Res Rev 2024; 99:102410. [PMID: 38972602 DOI: 10.1016/j.arr.2024.102410] [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: 10/02/2023] [Accepted: 07/04/2024] [Indexed: 07/09/2024]
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disorder, globally affecting men and women at an exponentially growing rate, with currently no cure. Disease progression starts when dopaminergic neurons begin to die. In PD, the loss of neurotransmitter, dopamine is responsible for the overall communication of neural cells throughout the body. Clinical symptoms of PD are slowness of movement, involuntary muscular contractions, speech & writing changes, lessened automatic movement, and chronic tremors in the body. PD occurs in both familial and sporadic forms and modifiable and non-modifiable risk factors and socioeconomic conditions cause PD. Early detectable diagnostics and treatments have been developed in the last several decades. However, we still do not have precise early detectable biomarkers and therapeutic agents/drugs that prevent and/or delay the disease process. Recently, artificial intelligence (AI) science and machine learning tools have been promising in identifying early detectable markers with a greater rate of accuracy compared to past forms of treatment and diagnostic processes. Artificial intelligence refers to the intelligence exhibited by machines or software, distinct from the intelligence observed in humans that is based on neural networks in a form and can be used to diagnose the longevity and disease severity of disease. The term Machine Learning or Neural Networks is a blanket term used to identify an emerging technology that is created to work in the way of a "human brain" using many intertwined neurons to achieve the same level of raw intelligence as that of a brain. These processes have been used for neurodegenerative diseases such as Parkinson's disease and Alzheimer's disease, to assess the severity of the patient's condition. In the current article, we discuss the prevalence and incidence of PD, and currently available diagnostic biomarkers and therapeutic strategies. We also highlighted currently available artificial intelligence science and machine learning tools and their applications to detect disease and develop therapeutic interventions.
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Affiliation(s)
- Aananya Reddy
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Lubbock High School, Lubbock, TX 79401, USA.
| | - Ruhananhad P Reddy
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Lubbock High School, Lubbock, TX 79401, USA.
| | - Aryan Kia Roghani
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Frenship High School, Lubbock, TX 79382, USA.
| | - Ricardo Isaiah Garcia
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA.
| | - Sachi Khemka
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA.
| | - Vasanthkumar Pattoor
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; University of South Florida, Tampa, FL 33620, USA.
| | - Michael Jacob
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Department of Biology, The University of Texas at San Antonio, San Antonio, TX 78249, USA.
| | - P Hemachandra Reddy
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Nutritional Sciences Department, College of Human Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Public Health Department of Graduate School of Biomedical Sciences, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Department pf Speech, Language and Hearing Services, School Health Professions, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA; Department of Pharmacology and Neuroscience, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA.
| | - Ujala Sehar
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79430, USA.
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Zhang S, Yuan J, Sun Y, Wu F, Liu Z, Zhai F, Zhang Y, Somekh J, Peleg M, Zhu YC, Huang Z. Machine learning on longitudinal multi-modal data enables the understanding and prognosis of Alzheimer's disease progression. iScience 2024; 27:110263. [PMID: 39040055 PMCID: PMC11261013 DOI: 10.1016/j.isci.2024.110263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 03/01/2024] [Accepted: 06/11/2024] [Indexed: 07/24/2024] Open
Abstract
Alzheimer's disease (AD) is a complex pathophysiological disease. Allowing for heterogeneity, not only in disease manifestations but also in different progression patterns, is critical for developing effective disease models that can be used in clinical and research settings. We introduce a machine learning model for identifying underlying patterns in Alzheimer's disease (AD) trajectory using longitudinal multi-modal data from the ADNI cohort and the AIBL cohort. Ten biologically and clinically meaningful disease-related states were identified from data, which constitute three non-overlapping stages (i.e., neocortical atrophy [NCA], medial temporal atrophy [MTA], and whole brain atrophy [WBA]) and two distinct disease progression patterns (i.e., NCA → WBA and MTA → WBA). The index of disease-related states provided a remarkable performance in predicting the time to conversion to AD dementia (C-Index: 0.923 ± 0.007). Our model shows potential for promoting the understanding of heterogeneous disease progression and early predicting the conversion time to AD dementia.
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Affiliation(s)
- Suixia Zhang
- Zhejiang University, 866 Yuhangtang Rd, Hangzhou 310058, P.R. China
- Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830017, China
| | - Jing Yuan
- Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China
| | - Yu Sun
- Zhejiang University, 866 Yuhangtang Rd, Hangzhou 310058, P.R. China
| | - Fei Wu
- Zhejiang University, 866 Yuhangtang Rd, Hangzhou 310058, P.R. China
| | - Ziyue Liu
- Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China
| | - Feifei Zhai
- Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China
| | - Yaoyun Zhang
- DAMO Academy, Alibaba Group, 969 Wenyixi Rd, Hangzhou 310058, P.R. China
| | - Judith Somekh
- Department of Information Systems, University of Haifa, Haifa 3303220, Israel
| | - Mor Peleg
- Department of Information Systems, University of Haifa, Haifa 3303220, Israel
| | - Yi-Cheng Zhu
- Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China
| | - Zhengxing Huang
- Zhejiang University, 866 Yuhangtang Rd, Hangzhou 310058, P.R. China
| | - for the Alzheimer’s Disease Neuroimaging Initiative and the Australian Imaging Biomarkers and Lifestyle Study of Aging
- Zhejiang University, 866 Yuhangtang Rd, Hangzhou 310058, P.R. China
- Department of Neurology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing 100730, P.R. China
- DAMO Academy, Alibaba Group, 969 Wenyixi Rd, Hangzhou 310058, P.R. China
- Department of Information Systems, University of Haifa, Haifa 3303220, Israel
- Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830017, China
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Ramanarayanan V. Multimodal Technologies for Remote Assessment of Neurological and Mental Health. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2024:1-13. [PMID: 38984943 DOI: 10.1044/2024_jslhr-24-00142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2024]
Abstract
PURPOSE Automated remote assessment and monitoring of patients' neurological and mental health is increasingly becoming an essential component of the digital clinic and telehealth ecosystem, especially after the COVID-19 pandemic. This review article reviews various modalities of health information that are useful for developing such remote clinical assessments in the real world at scale. APPROACH We first present an overview of the various modalities of health information-speech acoustics, natural language, conversational dynamics, orofacial or full body movement, eye gaze, respiration, cardiopulmonary, and neural-which can each be extracted from various signal sources-audio, video, text, or sensors. We further motivate their clinical utility with examples of how information from each modality can help us characterize how different disorders affect different aspects of patients' spoken communication. We then elucidate the advantages of combining one or more of these modalities toward a more holistic, informative, and robust assessment. FINDINGS We find that combining multiple modalities of health information allows for improved scientific interpretability, improved performance on downstream health applications such as early detection and progress monitoring, improved technological robustness, and improved user experience. We illustrate how these principles can be leveraged for remote clinical assessment at scale using a real-world case study of the Modality assessment platform. CONCLUSION This review article motivates the combination of human-centric information from multiple modalities to measure various aspects of patients' health, arguing that remote clinical assessment that integrates this complementary information can be more effective and lead to better clinical outcomes than using any one data stream in isolation.
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Affiliation(s)
- Vikram Ramanarayanan
- Modality.AI, Inc., San Francisco, CA
- Department of Otolaryngology-Head and Neck Surgery, University of California, San Francisco
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5
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Loo RTJ, Tsurkalenko O, Klucken J, Mangone G, Khoury F, Vidailhet M, Corvol JC, Krüger R, Glaab E. Levodopa-induced dyskinesia in Parkinson's disease: Insights from cross-cohort prognostic analysis using machine learning. Parkinsonism Relat Disord 2024; 126:107054. [PMID: 38991633 DOI: 10.1016/j.parkreldis.2024.107054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Revised: 06/29/2024] [Accepted: 07/02/2024] [Indexed: 07/13/2024]
Abstract
BACKGROUND Prolonged levodopa treatment in Parkinson's disease (PD) often leads to motor complications, including levodopa-induced dyskinesia (LID). Despite continuous levodopa treatment, some patients do not develop LID symptoms, even in later stages of the disease. OBJECTIVE This study explores machine learning (ML) methods using baseline clinical characteristics to predict the development of LID in PD patients over four years, across multiple cohorts. METHODS Using interpretable ML approaches, we analyzed clinical data from three independent longitudinal PD cohorts (LuxPARK, n = 356; PPMI, n = 484; ICEBERG, n = 113) to develop cross-cohort prognostic models and identify potential predictors for the development of LID. We examined cohort-specific and shared predictive factors, assessing model performance and stability through cross-validation analyses. RESULTS Consistent cross-validation results for single and multiple cohort analyses highlighted the effectiveness of the ML models and identified baseline clinical characteristics with significant predictive value for the LID prognosis in PD. Predictors positively correlated with LID include axial symptoms, freezing of gait, and rigidity in the lower extremities. Conversely, the risk of developing LID was inversely associated with the occurrence of resting tremors, higher body weight, later onset of PD, and visuospatial abilities. CONCLUSIONS This study presents interpretable ML models for dyskinesia prognosis with significant predictive power in cross-cohort analyses. The models may pave the way for proactive interventions against dyskinesia in PD by optimizing levodopa dosing regimens and adjunct treatments with dopamine agonists or MAO-B inhibitors, and by employing non-pharmacological interventions such as dietary adjustments affecting levodopa absorption for high-risk LID patients.
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Affiliation(s)
- Rebecca Ting Jiin Loo
- Biomedical Data Science, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Olena Tsurkalenko
- Translational Neuroscience, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg; Transversal Translational Medicine, Luxembourg Institute of Health (LIH), Strassen, Luxembourg; Digital Medicine Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg; Digital Medicine Group, Department of Precision Health, Luxembourg Institute of Health (LIH), Strassen, Luxembourg; Digital Medicine Group, Centre Hospitalier de Luxembourg (CHL), Luxembourg
| | - Jochen Klucken
- Digital Medicine Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg; Digital Medicine Group, Department of Precision Health, Luxembourg Institute of Health (LIH), Strassen, Luxembourg; Digital Medicine Group, Centre Hospitalier de Luxembourg (CHL), Luxembourg
| | - Graziella Mangone
- Sorbonne Université, Paris Brain Institute - ICM, Inserm, CNRS, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Department of Neurology, Paris, 75013, France
| | - Fouad Khoury
- Sorbonne Université, Paris Brain Institute - ICM, Inserm, CNRS, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Department of Neurology, Paris, 75013, France
| | - Marie Vidailhet
- Sorbonne Université, Paris Brain Institute - ICM, Inserm, CNRS, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Department of Neurology, Paris, 75013, France
| | - Jean-Christophe Corvol
- Sorbonne Université, Paris Brain Institute - ICM, Inserm, CNRS, Assistance Publique Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Department of Neurology, Paris, 75013, France
| | - Rejko Krüger
- Translational Neuroscience, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg; Transversal Translational Medicine, Luxembourg Institute of Health (LIH), Strassen, Luxembourg; Department of Neurology, Centre Hospitalier de Luxembourg (CHL), Luxembourg
| | - Enrico Glaab
- Biomedical Data Science, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg.
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Yoshioka H, Jin R, Hisaka A, Suzuki H. Disease progression modeling with temporal realignment: An emerging approach to deepen knowledge on chronic diseases. Pharmacol Ther 2024; 259:108655. [PMID: 38710372 DOI: 10.1016/j.pharmthera.2024.108655] [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: 01/31/2024] [Revised: 04/22/2024] [Accepted: 05/01/2024] [Indexed: 05/08/2024]
Abstract
The recent development of the first disease-modifying drug for Alzheimer's disease represents a major advancement in dementia treatment. Behind this breakthrough is a quarter century of research efforts to understand the disease not by a particular symptom at a given moment, but by long-term sequential changes in multiple biomarkers. Disease progression modeling with temporal realignment (DPM-TR) is an emerging computational approach proposed with this biomarker-based disease concept. By integrating short-term clinical observations of multiple disease biomarkers in a data-driven manner, DPM-TR provides a way to understand the progression of chronic diseases over decades and predict individual disease stages more accurately. DPM-TR has been developed primarily in the area of neurodegenerative diseases but has recently been extended to non-neurodegenerative diseases, including chronic obstructive pulmonary, autoimmune, and ophthalmologic diseases. This review focuses on opportunities for DPM-TR in clinical practice and drug development and discusses its current status and challenges.
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Affiliation(s)
- Hideki Yoshioka
- Office of Regulatory Science Research, Pharmaceuticals and Medical Devices Agency, Tokyo, Japan; Laboratory of Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, Japan
| | - Ryota Jin
- Laboratory of Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, Japan
| | - Akihiro Hisaka
- Laboratory of Clinical Pharmacology and Pharmacometrics, Graduate School of Pharmaceutical Sciences, Chiba University, Chiba, Japan.
| | - Hiroshi Suzuki
- Executive Director, Pharmaceuticals and Medical Devices Agency, Tokyo, Japan; Department of Pharmacy, The University of Tokyo Hospital, Faculty of Medicine, The University of Tokyo, Tokyo, Japan
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Hu R, Wang R, Yuan J, Lin Z, Hutchins E, Landin B, Liao Z, Liu G, Scherzer CR, Dong X. Transcriptional pathobiology and multi-omics predictors for Parkinson's disease. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.18.599639. [PMID: 38948706 PMCID: PMC11212969 DOI: 10.1101/2024.06.18.599639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Early diagnosis and biomarker discovery to bolster the therapeutic pipeline for Parkinson's disease (PD) are urgently needed. In this study, we leverage the large-scale whole-blood total RNA-seq dataset from the Accelerating Medicine Partnership in Parkinson's Disease (AMP PD) program to identify PD-associated RNAs, including both known genes and novel circular RNAs (circRNA) and enhancer RNAs (eRNAs). There were 1,111 significant marker RNAs, including 491 genes, 599 eRNAs, and 21 circRNAs, that were first discovered in the PPMI cohort (FDR < 0.05) and confirmed in the PDBP/BioFIND cohorts (nominal p < 0.05). Functional enrichment analysis showed that the PD-associated genes are involved in neutrophil activation and degranulation, as well as the TNF-alpha signaling pathway. We further compare the PD-associated genes in blood with those in post-mortem brain dopamine neurons in our BRAINcode cohort. 44 genes show significant changes with the same direction in both PD brain neurons and PD blood, including neuroinflammation-associated genes IKBIP, CXCR2, and NFKBIB. Finally, we built a novel multi-omics machine learning model to predict PD diagnosis with high performance (AUC = 0.89), which was superior to previous studies and might aid the decision-making for PD diagnosis in clinical practice. In summary, this study delineates a wide spectrum of the known and novel RNAs linked to PD and are detectable in circulating blood cells in a harmonized, large-scale dataset. It provides a generally useful computational framework for further biomarker development and early disease prediction.
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Affiliation(s)
- Ruifeng Hu
- APDA Center for Advanced Parkinson Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Precision Neurology Program, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Genomics and Bioinformatics Hub, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Ruoxuan Wang
- APDA Center for Advanced Parkinson Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Precision Neurology Program, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Genomics and Bioinformatics Hub, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Jie Yuan
- APDA Center for Advanced Parkinson Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Precision Neurology Program, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Genomics and Bioinformatics Hub, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Zechuan Lin
- APDA Center for Advanced Parkinson Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Precision Neurology Program, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Elizabeth Hutchins
- Neurogenomics Division, Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
| | | | - Zhixiang Liao
- APDA Center for Advanced Parkinson Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Precision Neurology Program, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Ganqiang Liu
- APDA Center for Advanced Parkinson Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Shenzhen Key Laboratory of Systems Medicine in Inflammatory Diseases, School of Medicine, Shenzhen Campus of Sun Yat-sen University, Shenzhen, Guangdong, China
| | - Clemens R. Scherzer
- APDA Center for Advanced Parkinson Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Precision Neurology Program, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
| | - Xianjun Dong
- APDA Center for Advanced Parkinson Research, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Precision Neurology Program, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Genomics and Bioinformatics Hub, Department of Neurology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Aligning Science Across Parkinson’s (ASAP) Collaborative Research Network, Chevy Chase, MD, USA
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Su MC, Lee AM, Zhang W, Maeser D, Gruener RF, Deng Y, Huang RS. Computational Modeling to Identify Drugs Targeting Metastatic Castration-Resistant Prostate Cancer Characterized by Heightened Glycolysis. Pharmaceuticals (Basel) 2024; 17:569. [PMID: 38794139 PMCID: PMC11124089 DOI: 10.3390/ph17050569] [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: 03/29/2024] [Revised: 04/22/2024] [Accepted: 04/26/2024] [Indexed: 05/26/2024] Open
Abstract
Metastatic castration-resistant prostate cancer (mCRPC) remains a deadly disease due to a lack of efficacious treatments. The reprogramming of cancer metabolism toward elevated glycolysis is a hallmark of mCRPC. Our goal is to identify therapeutics specifically associated with high glycolysis. Here, we established a computational framework to identify new pharmacological agents for mCRPC with heightened glycolysis activity under a tumor microenvironment, followed by in vitro validation. First, using our established computational tool, OncoPredict, we imputed the likelihood of drug responses to approximately 1900 agents in each mCRPC tumor from two large clinical patient cohorts. We selected drugs with predicted sensitivity highly correlated with glycolysis scores. In total, 77 drugs predicted to be more sensitive in high glycolysis mCRPC tumors were identified. These drugs represent diverse mechanisms of action. Three of the candidates, ivermectin, CNF2024, and P276-00, were selected for subsequent vitro validation based on the highest measured drug responses associated with glycolysis/OXPHOS in pan-cancer cell lines. By decreasing the input glucose level in culture media to mimic the mCRPC tumor microenvironments, we induced a high-glycolysis condition in PC3 cells and validated the projected higher sensitivity of all three drugs under this condition (p < 0.0001 for all drugs). For biomarker discovery, ivermectin and P276-00 were predicted to be more sensitive to mCRPC tumors with low androgen receptor activities and high glycolysis activities (AR(low)Gly(high)). In addition, we integrated a protein-protein interaction network and topological methods to identify biomarkers for these drug candidates. EEF1B2 and CCNA2 were identified as key biomarkers for ivermectin and CNF2024, respectively, through multiple independent biomarker nomination pipelines. In conclusion, this study offers new efficacious therapeutics beyond traditional androgen-deprivation therapies by precisely targeting mCRPC with high glycolysis.
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Affiliation(s)
- Mei-Chi Su
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455, USA; (M.-C.S.); (A.M.L.); (R.F.G.)
| | - Adam M. Lee
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455, USA; (M.-C.S.); (A.M.L.); (R.F.G.)
| | - Weijie Zhang
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455, USA; (W.Z.); (D.M.)
| | - Danielle Maeser
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455, USA; (W.Z.); (D.M.)
| | - Robert F. Gruener
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455, USA; (M.-C.S.); (A.M.L.); (R.F.G.)
| | - Yibin Deng
- Department of Urology, Masonic Cancer Center, University of Minnesota Medical School, Minneapolis, MN 55455, USA;
| | - R. Stephanie Huang
- Department of Experimental and Clinical Pharmacology, College of Pharmacy, University of Minnesota, Minneapolis, MN 55455, USA; (M.-C.S.); (A.M.L.); (R.F.G.)
- Bioinformatics and Computational Biology, University of Minnesota, Minneapolis, MN 55455, USA; (W.Z.); (D.M.)
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Marras C, Fereshtehnejad SM, Berg D, Bohnen NI, Dujardin K, Erro R, Espay AJ, Halliday G, Van Hilten JJ, Hu MT, Jeon B, Klein C, Leentjens AFG, Mollenhauer B, Postuma RB, Rodríguez-Violante M, Simuni T, Weintraub D, Lawton M, Mestre TA. Transitioning from Subtyping to Precision Medicine in Parkinson's Disease: A Purpose-Driven Approach. Mov Disord 2024; 39:462-471. [PMID: 38243775 DOI: 10.1002/mds.29708] [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: 09/21/2023] [Revised: 11/29/2023] [Accepted: 12/18/2023] [Indexed: 01/21/2024] Open
Abstract
The International Parkinson and Movement Disorder Society (MDS) created a task force (TF) to provide a critical overview of the Parkinson's disease (PD) subtyping field and develop a guidance on future research in PD subtypes. Based on a literature review, we previously concluded that PD subtyping requires an ultimate alignment with principles of precision medicine, and consequently novel approaches were needed to describe heterogeneity at the individual patient level. In this manuscript, we present a novel purpose-driven framework for subtype research as a guidance to clinicians and researchers when proposing to develop, evaluate, or use PD subtypes. Using a formal consensus methodology, we determined that the key purposes of PD subtyping are: (1) to predict disease progression, for both the development of therapies (use in clinical trials) and prognosis counseling, (2) to predict response to treatments, and (3) to identify therapeutic targets for disease modification. For each purpose, we describe the desired product and the research required for its development. Given the current state of knowledge and data resources, we see purpose-driven subtyping as a pragmatic and necessary step on the way to precision medicine. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Connie Marras
- Edmond J. Safra Program in Parkinson's Disease and the Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada
| | | | - Daniela Berg
- Department of Neurology, Christian-Albrechts-University, Kiel, Germany
| | - Nicolaas I Bohnen
- Departments of Radiology & Neurology, University of Michigan, University of Michigan Udall Center, Ann Arbor, Michigan, USA
| | - Kathy Dujardin
- Center of Excellence for Parkinson's Disease, CHU Lille, Univ Lille, Inserm, Lille Neuroscience & Cognition, Lille, France
| | - Roberto Erro
- Department of Medicine, Surgery and Dentistry "Scuola Medica Salernitana", Neuroscience Section, University of Salerno, Baronissi, Italy
| | - Alberto J Espay
- James J. and Joan A. Gardner Family Center for Parkinson's Disease and Movement Disorders, Department of Neurology, University of Cincinnati, Cincinnati, Ohio, USA
| | - Glenda Halliday
- Brain and Mind Centre and Faculty of Medicine and Health School of Medical Sciences, University of Sydney, Sydney, New South Wales, Australia
| | - Jacobus J Van Hilten
- Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands
| | - Michele T Hu
- Nuffield Department of Clinical Neurosciences, Oxford University and John Radcliffe Hospital, West Wing, Neurology Department, Level 3, Oxford, United Kingdom
| | - Beomseok Jeon
- Department of Neurology, Seoul National University Hospital, Seoul, South Korea
| | - Christine Klein
- Institute of Neurogenetics, University of Luebeck, Luebeck, Germany
| | - Albert F G Leentjens
- Department of Psychiatry, Maastricht University Medical Center, Maastricht, The Netherlands
| | - Brit Mollenhauer
- Paracelsus-Elena-Klinik, Kassel, Department of Neurology, University Medical Center Goettingen, Kassel, Germany
| | - Ronald B Postuma
- Department of Neurology, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | | | - Tanya Simuni
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Daniel Weintraub
- Departments of Psychiatry and Neurology, Perelman School of Medicine at the University of Pennsylvania; Parkinson's Disease Research, Education and Clinical Center (PADRECC), Philadelphia Veterans Affairs Medical Center, Philadelphia, Pennsylvania, USA
| | - Michael Lawton
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Tiago A Mestre
- Edmond J. Safra Program in Parkinson's Disease and the Morton and Gloria Shulman Movement Disorders Clinic, Toronto Western Hospital, University Health Network, Toronto, Ontario, Canada
- Parkinson's Disease and Movement Disorders Center, Division of Neurology, Department of Medicine, The Ottawa Hospital Research Institute, The University of Ottawa Brain and Research Institute, Ottawa, Ontario, Canada
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10
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Cheng F, Wang F, Tang J, Zhou Y, Fu Z, Zhang P, Haines JL, Leverenz JB, Gan L, Hu J, Rosen-Zvi M, Pieper AA, Cummings J. Artificial intelligence and open science in discovery of disease-modifying medicines for Alzheimer's disease. Cell Rep Med 2024; 5:101379. [PMID: 38382465 PMCID: PMC10897520 DOI: 10.1016/j.xcrm.2023.101379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2022] [Revised: 08/15/2023] [Accepted: 12/19/2023] [Indexed: 02/23/2024]
Abstract
The high failure rate of clinical trials in Alzheimer's disease (AD) and AD-related dementia (ADRD) is due to a lack of understanding of the pathophysiology of disease, and this deficit may be addressed by applying artificial intelligence (AI) to "big data" to rapidly and effectively expand therapeutic development efforts. Recent accelerations in computing power and availability of big data, including electronic health records and multi-omics profiles, have converged to provide opportunities for scientific discovery and treatment development. Here, we review the potential utility of applying AI approaches to big data for discovery of disease-modifying medicines for AD/ADRD. We illustrate how AI tools can be applied to the AD/ADRD drug development pipeline through collaborative efforts among neurologists, gerontologists, geneticists, pharmacologists, medicinal chemists, and computational scientists. AI and open data science expedite drug discovery and development of disease-modifying therapeutics for AD/ADRD and other neurodegenerative diseases.
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Affiliation(s)
- Feixiong Cheng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Cleveland Clinic Genome Center, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH 44195, USA.
| | - Fei Wang
- Department of Population Health Sciences, Weill Cornell Medical College, Cornell University, New York, NY 10065, USA
| | - Jian Tang
- Mila-Quebec Institute for Learning Algorithms and CIFAR AI Research Chair, HEC Montreal, Montréal, QC H3T 2A7, Canada
| | - Yadi Zhou
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Zhimin Fu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA; College of Pharmacy, Northeast Ohio Medical University, Rootstown, OH 44272, USA
| | - Pengyue Zhang
- Department of Biostatistics and Health Data Science, Indiana University, Indianapolis, IN 46037, USA
| | - Jonathan L Haines
- Cleveland Institute for Computational Biology, and Department of Population & Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH 44106, USA
| | - James B Leverenz
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, OH 44195, USA
| | - Li Gan
- Helen and Robert Appel Alzheimer's Disease Research Institute, Brain and Mind Research Institute, Weill Cornell Medicine, New York, NY 10021, USA
| | - Jianying Hu
- IBM Research, Yorktown Heights, New York, NY 10598, USA
| | - Michal Rosen-Zvi
- AI for Accelerated Healthcare and Life Sciences Discovery, IBM Research Labs, Haifa 3498825, Israel; Faculty of Medicine, The Hebrew University of Jerusalem, Jerusalem 9190500, Israel
| | - Andrew A Pieper
- Brain Health Medicines Center, Harrington Discovery Institute, University Hospitals Cleveland Medical Center, Cleveland, OH, 44106, USA; Department of Psychiatry, Case Western Reserve University, Cleveland, OH 44106, USA; Geriatric Psychiatry, GRECC, Louis Stokes Cleveland VA Medical Center, Cleveland, OH 44106, USA; Institute for Transformative Molecular Medicine, School of Medicine, Case Western Reserve University, Cleveland OH 44106, USA; Department of Pathology, Case Western Reserve University, School of Medicine, Cleveland, OH, 44106, USA; Department of Neurosciences, Case Western Reserve University, School of Medicine, Cleveland, OH 44106, USA
| | - Jeffrey Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, UNLV, Las Vegas, NV 89154, USA
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11
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Young AL, Oxtoby NP, Garbarino S, Fox NC, Barkhof F, Schott JM, Alexander DC. Data-driven modelling of neurodegenerative disease progression: thinking outside the black box. Nat Rev Neurosci 2024; 25:111-130. [PMID: 38191721 DOI: 10.1038/s41583-023-00779-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/30/2023] [Indexed: 01/10/2024]
Abstract
Data-driven disease progression models are an emerging set of computational tools that reconstruct disease timelines for long-term chronic diseases, providing unique insights into disease processes and their underlying mechanisms. Such methods combine a priori human knowledge and assumptions with large-scale data processing and parameter estimation to infer long-term disease trajectories from short-term data. In contrast to 'black box' machine learning tools, data-driven disease progression models typically require fewer data and are inherently interpretable, thereby aiding disease understanding in addition to enabling classification, prediction and stratification. In this Review, we place the current landscape of data-driven disease progression models in a general framework and discuss their enhanced utility for constructing a disease timeline compared with wider machine learning tools that construct static disease profiles. We review the insights they have enabled across multiple neurodegenerative diseases, notably Alzheimer disease, for applications such as determining temporal trajectories of disease biomarkers, testing hypotheses about disease mechanisms and uncovering disease subtypes. We outline key areas for technological development and translation to a broader range of neuroscience and non-neuroscience applications. Finally, we discuss potential pathways and barriers to integrating disease progression models into clinical practice and trial settings.
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Affiliation(s)
- Alexandra L Young
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.
| | - Neil P Oxtoby
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK.
| | - Sara Garbarino
- Life Science Computational Laboratory, IRCCS Ospedale Policlinico San Martino, Genova, Italy
| | - Nick C Fox
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Frederik Barkhof
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Department of Radiology & Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Jonathan M Schott
- Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Daniel C Alexander
- UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
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12
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Di Folco C, Couronné R, Arnulf I, Mangone G, Leu-Semenescu S, Dodet P, Vidailhet M, Corvol JC, Lehéricy S, Durrleman S. Charting Disease Trajectories from Isolated REM Sleep Behavior Disorder to Parkinson's Disease. Mov Disord 2024; 39:64-75. [PMID: 38006282 DOI: 10.1002/mds.29662] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 10/03/2023] [Accepted: 10/30/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Clinical presentation and progression dynamics are variable in patients with Parkinson's disease (PD). Disease course mapping is an innovative disease modelling technique that summarizes the range of possible disease trajectories and estimates dimensions related to onset, sequence, and speed of progression of disease markers. OBJECTIVE To propose a disease course map for PD and investigate progression profiles in patients with or without rapid eye movement sleep behavioral disorders (RBD). METHODS Data of 919 PD patients and 88 isolated RBD patients from three independent longitudinal cohorts were analyzed (follow-up duration = 5.1; 95% confidence interval, 1.1-8.1] years). Disease course map was estimated by using eight clinical markers (motor and non-motor symptoms) and four imaging markers (dopaminergic denervation). RESULTS PD course map showed that the first changes occurred in the contralateral putamen 13 years before diagnosis, followed by changes in motor symptoms, dysautonomia, sleep-all before diagnosis-and finally cognitive decline at the time of diagnosis. The model showed earlier disease onset, earlier non-motor and later motor symptoms, more rapid progression of cognitive decline in PD patients with RBD than PD patients without RBD. This pattern was even more pronounced in patients with isolated RBD with early changes in sleep, followed by cognition and non-motor symptoms and later changes in motor symptoms. CONCLUSIONS Our findings are consistent with the presence of distinct patterns of progression between patients with and without RBD. Understanding heterogeneity of PD progression is key to decipher the underlying pathophysiology and select homogeneous subgroups of patients for precision medicine. © 2023 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Cécile Di Folco
- Inria, Centre de Paris, Paris, France
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Raphaël Couronné
- Inria, Centre de Paris, Paris, France
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Isabelle Arnulf
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Graziella Mangone
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Smaranda Leu-Semenescu
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Pauline Dodet
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Marie Vidailhet
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Jean-Christophe Corvol
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Stéphane Lehéricy
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
| | - Stanley Durrleman
- Inria, Centre de Paris, Paris, France
- Paris Brain Institute-ICM, Paris, France
- Inserm, Paris, France
- CNRS, Paris, France
- Sorbonne Université, Paris, France
- AP-HP, Hôpital de la Pitié Salpêtrière, Paris, France
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13
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Hossain MA, Amenta F. Machine Learning-Based Classification of Parkinson's Disease Patients Using Speech Biomarkers. JOURNAL OF PARKINSON'S DISEASE 2024; 14:95-109. [PMID: 38160364 PMCID: PMC10836572 DOI: 10.3233/jpd-230002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 11/09/2023] [Indexed: 01/03/2024]
Abstract
BACKGROUND Parkinson's disease (PD) is the most prevalent neurodegenerative movement disorder and a growing health concern in demographically aging societies. The prevalence of PD among individuals over the age of 60 and 80 years has been reported to range between 1% and 4%. A timely diagnosis of PD is desirable, even though it poses challenges to medical systems. OBJECTIVE This study aimed to classify PD and healthy controls based on the analysis of voice records at different frequencies using machine learning (ML) algorithms. METHODS The voices of 252 individuals aged 33 to 87 years were recorded. Based on the voice record data, ML algorithms can distinguish PD patients and healthy controls. One binary decision variable was associated with 756 instances and 754 attributes. Voice records data were analyzed through supervised ML algorithms and pipelines. A 10-fold cross-validation method was used to validate models. RESULTS In the classification of PD patients, ML models were performed with 84.21 accuracy, 93 precision, 89 Sensitivity, 89 F1-scores, and 87 AUC. The pipeline performance improved to accuracy: 85.09, precision: 92, Sensitivity:91, F1-score: 89, and AUC: 90. The Pipeline methods improved the performance of classifying PD from voice record. CONCLUSIONS Our study demonstrated that ML classifiers and pipelines can classify PD patients based on speech biomarkers. It was found that pipelines were more effective at selecting the most relevant features from high-dimensional data and at accurately classifying PD patients and healthy controls. This approach can therefore be used for early diagnosis of initial forms of PD.
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Affiliation(s)
- Mohammad Amran Hossain
- Telemedicine and Telepharmacy Centre, School of Medicinal and Health Products Sciences, University of Camerino, Camerino, Italy
| | - Francesco Amenta
- Telemedicine and Telepharmacy Centre, School of Medicinal and Health Products Sciences, University of Camerino, Camerino, Italy
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14
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Georgiev D, Torkmani A, Song R, Limousin P, Jahanshahi M. Daily Habits in Parkinson's Disease: Validation of the Daily Habit Scale. Mov Disord Clin Pract 2023; 10:1485-1495. [PMID: 37868920 PMCID: PMC10585975 DOI: 10.1002/mdc3.13863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 06/26/2023] [Accepted: 07/13/2023] [Indexed: 10/24/2023] Open
Abstract
Objective The objective of the study was to validate a new scale for assessing habitual behavior-the Daily Habit Scale in patients with Parkinson's disease. Background Parkinson's disease patients are impaired in habit learning and skill acquisition. Despite repeated practice, they have difficulty developing habitual responses. Methods One hundred seventy-nine patients (Median (Mdn) = 69 [64-76], 65 females) participated in the study. Corrected item-to-total correlations were calculated to assess the item-convergent and item discriminant validity. Confirmatory factor analysis and assessment of internal consistency were also carried out. Concurrent validity in respect to measures of anxiety and depression, apathy, impulsivity, personality, multidimensional health locus of control, and health-related quality of life was also calculated. To determine the test-retest reliability of the scale, 30 patients (Mdn = 69 [66-73], 9 females) completed a second copy of the scale 6 months after the first. Results Twenty-nine items (76%) and 9 items (24%) of the 38-item scale, respectively, showed a very good and good convergent validity. All the items discriminated between their own factor and the other factors. The comparative fit index of 0.932 indicated an acceptable model fit of the data, whereas the root mean square error of approximation of 0.06 moderate model fit. The scale had a good internal consistency (Cronbach α = 0.792), and a moderate test-retest reliability (0.57). Females had higher scores on two factors compared to men (Factor 3: household activities and Factor 8: sleep-related activities). Conclusions The Daily Habit Scale is a reliable and valid tool to measure daily habits in Parkinson's disease.
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Affiliation(s)
- Dejan Georgiev
- Department Clinical and Motor Neurosciences, Institute of NeurologyUniversity College LondonLondonUnited Kingdom
- Department of NeurologyUniversity Medical Centre LjubljanaLjubljanaSlovenia
- Artificial Intelligence Lab, Faculty of Computer and Information SciencesUniversity of LjubljanaLjubljanaSlovenia
| | - Asma Torkmani
- Department Clinical and Motor Neurosciences, Institute of NeurologyUniversity College LondonLondonUnited Kingdom
| | - Ruifeng Song
- Department Clinical and Motor Neurosciences, Institute of NeurologyUniversity College LondonLondonUnited Kingdom
| | - Patricia Limousin
- Department Clinical and Motor Neurosciences, Institute of NeurologyUniversity College LondonLondonUnited Kingdom
| | - Marjan Jahanshahi
- Department Clinical and Motor Neurosciences, Institute of NeurologyUniversity College LondonLondonUnited Kingdom
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15
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Eskofier BM, Klucken J. Predictive Models for Health Deterioration: Understanding Disease Pathways for Personalized Medicine. Annu Rev Biomed Eng 2023; 25:131-156. [PMID: 36854259 DOI: 10.1146/annurev-bioeng-110220-030247] [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] [Indexed: 03/02/2023]
Abstract
Artificial intelligence (AI) and machine learning (ML) methods are currently widely employed in medicine and healthcare. A PubMed search returns more than 100,000 articles on these topics published between 2018 and 2022 alone. Notwithstanding several recent reviews in various subfields of AI and ML in medicine, we have yet to see a comprehensive review around the methods' use in longitudinal analysis and prediction of an individual patient's health status within a personalized disease pathway. This review seeks to fill that gap. After an overview of the AI and ML methods employed in this field and of specific medical applications of models of this type, the review discusses the strengths and limitations of current studies and looks ahead to future strands of research in this field. We aim to enable interested readers to gain a detailed impression of the research currently available and accordingly plan future work around predictive models for deterioration in health status.
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Affiliation(s)
- Bjoern M Eskofier
- Machine Learning and Data Analytics Lab, Department of Artificial Intelligence in Biomedical Engineering, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany;
| | - Jochen Klucken
- Digital Medicine Group, Luxembourg Centre for Systems Biomedicine, Université du Luxembourg, Belvaux, Luxembourg
- Digital Medicine Group, Department of Precision Health, Luxembourg Institute of Health, Strassen, Luxembourg
- Centre Hospitalier de Luxembourg, Luxembourg City, Luxembourg
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16
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Junaid M, Ali S, Eid F, El-Sappagh S, Abuhmed T. Explainable machine learning models based on multimodal time-series data for the early detection of Parkinson's disease. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 234:107495. [PMID: 37003039 DOI: 10.1016/j.cmpb.2023.107495] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 02/23/2023] [Accepted: 03/17/2023] [Indexed: 06/19/2023]
Abstract
BACKGROUND AND OBJECTIVES Parkinson's Disease (PD) is a devastating chronic neurological condition. Machine learning (ML) techniques have been used in the early prediction of PD progression. Fusion of heterogeneous data modalities proved its capability to improve the performance of ML models. Time series data fusion supports the tracking of the disease over time. In addition, the trustworthiness of the resulting models is improved by adding model explainability features. The literature on PD has not sufficiently explored these three points. METHODS In this work, we proposed an ML pipeline for predicting the progression of PD that is both accurate and explainable. We explore the fusion of different combinations of five time series modalities from the Parkinson's Progression Markers Initiative (PPMI) real-world dataset, including patient characteristics, biosamples, medication history, motor, and non-motor function data. Each patient has six visits. The problem has been formulated in two ways: ❶ a three-class based progression prediction with 953 patients in each time series modality, and ❷ a four-class based progression prediction with 1,060 patients in each time series modality. The statistical features of these six visits were calculated from each modality and diverse feature selection methods were applied to select the most informative feature sets. The extracted features were used to train a set of well-known ML models including Support vector machines (SVM), random forests (RF), extra tree classifier (ETC), light gradient boosting machines (LGBM), and stochastic gradient descent (SGD). We examined a number of data-balancing strategies in the pipeline with different combinations of modalities. ML models have been optimized using the Bayesian optimizer. A comprehensive evaluation of various ML methods has been conducted, and the best models have been extended to provide different explainability features. RESULTS We compare the performance of ML models before and after optimization and using and without using feature selection. In the three-class experiment and with various modality fusions, the LGBM model produced the most accurate results with a 10-fold cross-validation (10-CV) accuracy of 90.73% using non-motor function modality. RF produced the best results in the four-class experiment with various modality fusions with a 10-CV accuracy of 94.57% using non-motor modality. With the fused dataset of non-motor and motor function modalities, the LGBM model outperformed the other ML models in both the 3-class and 4-class experiments (i.e., 10-CV accuracy of 94.89% and 93.73%, respectively). Using the Shapely Additive Explanations (SHAP) framework, we employed global and instance-based explanations to explain the behavior of each ML classifier. Moreover, we extended the explainability by implementing the LIME and SHAPASH local explainers. The consistency of these explainers has been explored. The resultant classifiers were accurate, explainable, and thus medically more relevant and applicable. CONCLUSIONS The select modalities and feature sets were confirmed by the literature and medical experts. The various explainers suggest that the bradykinesia (NP3BRADY) feature was the most dominant and consistent. By providing thorough insights into the influence of multiple modalities on the disease risk, the suggested approach is expected to help improve the clinical knowledge of PD progression processes.
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Affiliation(s)
- Muhammad Junaid
- Information Laboratory (InfoLab), Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, South Korea.
| | - Sajid Ali
- Information Laboratory (InfoLab), Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, South Korea.
| | - Fatma Eid
- Technology Management, Stony Brook University, New York 11794, USA.
| | - Shaker El-Sappagh
- Information Laboratory (InfoLab), College of Computing and Informatics, Sungkyunkwan University, Suwon 16419, South Korea; Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt; Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha, 13518, Egypt.
| | - Tamer Abuhmed
- Information Laboratory (InfoLab), College of Computing and Informatics, Sungkyunkwan University, Suwon 16419, South Korea.
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17
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Gerraty RT, Provost A, Li L, Wagner E, Haas M, Lancashire L. Machine learning within the Parkinson's progression markers initiative: Review of the current state of affairs. Front Aging Neurosci 2023; 15:1076657. [PMID: 36861121 PMCID: PMC9968811 DOI: 10.3389/fnagi.2023.1076657] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2022] [Accepted: 01/16/2023] [Indexed: 02/17/2023] Open
Abstract
The Parkinson's Progression Markers Initiative (PPMI) has collected more than a decade's worth of longitudinal and multi-modal data from patients, healthy controls, and at-risk individuals, including imaging, clinical, cognitive, and 'omics' biospecimens. Such a rich dataset presents unprecedented opportunities for biomarker discovery, patient subtyping, and prognostic prediction, but it also poses challenges that may require the development of novel methodological approaches to solve. In this review, we provide an overview of the application of machine learning methods to analyzing data from the PPMI cohort. We find that there is significant variability in the types of data, models, and validation procedures used across studies, and that much of what makes the PPMI data set unique (multi-modal and longitudinal observations) remains underutilized in most machine learning studies. We review each of these dimensions in detail and provide recommendations for future machine learning work using data from the PPMI cohort.
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Affiliation(s)
| | | | - Lin Li
- PharmaLex, Frederick, MD, United States
| | | | - Magali Haas
- Cohen Veterans Bioscience, New York, NY, United States
| | - Lee Lancashire
- Cohen Veterans Bioscience, New York, NY, United States,*Correspondence: Lee Lancashire, ✉
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18
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Ma LY, Feng T, He C, Li M, Ren K, Tu J. A progression analysis of motor features in Parkinson's disease based on the mapper algorithm. Front Aging Neurosci 2023; 15:1047017. [PMID: 36896420 PMCID: PMC9989279 DOI: 10.3389/fnagi.2023.1047017] [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: 09/17/2022] [Accepted: 02/02/2023] [Indexed: 02/25/2023] Open
Abstract
Background Parkinson's disease (PD) is a neurodegenerative disease with a broad spectrum of motor and non-motor symptoms. The great heterogeneity of clinical symptoms, biomarkers, and neuroimaging and lack of reliable progression markers present a significant challenge in predicting disease progression and prognoses. Methods We propose a new approach to disease progression analysis based on the mapper algorithm, a tool from topological data analysis. In this paper, we apply this method to the data from the Parkinson's Progression Markers Initiative (PPMI). We then construct a Markov chain on the mapper output graphs. Results The resulting progression model yields a quantitative comparison of patients' disease progression under different usage of medications. We also obtain an algorithm to predict patients' UPDRS III scores. Conclusions By using mapper algorithm and routinely gathered clinical assessments, we developed a new dynamic models to predict the following year's motor progression in the early stage of PD. The use of this model can predict motor evaluations at the individual level, assisting clinicians to adjust intervention strategy for each patient and identifying at-risk patients for future disease-modifying therapy clinical trials.
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Affiliation(s)
- Ling-Yan Ma
- Department of Neurology, Center for Movement Disorders, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurology, China National Clinical Research Center for Neurological Disease, Beijing, China
| | - Tao Feng
- Department of Neurology, Center for Movement Disorders, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Department of Neurology, China National Clinical Research Center for Neurological Disease, Beijing, China.,Parkinson's Disease Center, Beijing Institute for Brain Disorders, Beijing, China
| | - Chengzhang He
- Institute of Mathematical Sciences, ShanghaiTech University, Shanghai, China
| | - Mujing Li
- Institute of Mathematical Sciences, ShanghaiTech University, Shanghai, China
| | - Kang Ren
- GYENNO Science Co., LTD., Shenzhen, China.,Department of Neurology, HUST-GYENNO Central Neural System Intelligent Digital Medicine Technology Center, Wuhan, China
| | - Junwu Tu
- Institute of Mathematical Sciences, ShanghaiTech University, Shanghai, China
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19
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Herrero-Zazo M, Fitzgerald T, Taylor V, Street H, Chaudhry AN, Bradley JR, Birney E, Keevil VL. Using machine learning to model older adult inpatient trajectories from electronic health records data. iScience 2022; 26:105876. [PMID: 36691609 PMCID: PMC9860485 DOI: 10.1016/j.isci.2022.105876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2022] [Revised: 10/25/2022] [Accepted: 12/20/2022] [Indexed: 12/26/2022] Open
Abstract
Electronic Health Records (EHR) data can provide novel insights into inpatient trajectories. Blood tests and vital signs from de-identified patients' hospital admission episodes (AE) were represented as multivariate time-series (MVTS) to train unsupervised Hidden Markov Models (HMM) and represent each AE day as one of 17 states. All HMM states were clinically interpreted based on their patterns of MVTS variables and relationships with clinical information. Visualization differentiated patients progressing toward stable 'discharge-like' states versus those remaining at risk of inpatient mortality (IM). Chi-square tests confirmed these relationships (two states associated with IM; 12 states with ≥1 diagnosis). Logistic Regression and Random Forest (RF) models trained with MVTS data rather than states had higher prediction performances of IM, but results were comparable (best RF model AUC-ROC: MVTS data = 0.85; HMM states = 0.79). ML models extracted clinically interpretable signals from hospital data. The potential of ML to develop decision-support tools for EHR systems warrants investigation.
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Affiliation(s)
- Maria Herrero-Zazo
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
- Department of Medicine for the Elderly, Addenbrooke’s Hospital, Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge CB2 0QQ, UK
| | - Tomas Fitzgerald
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
| | - Vince Taylor
- Cambridge Clinical Informatics, Addenbrooke’s Hospital, Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge CB2 0QQ, UK
| | - Helen Street
- Research and Development, Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge CB2 0QQ, UK
| | - Afzal N. Chaudhry
- Department of Medicine, University of Cambridge, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 0QQ, UK
- NIHR Cambridge Biomedical Research Centre, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - John R. Bradley
- Department of Medicine, University of Cambridge, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 0QQ, UK
- NIHR Cambridge Biomedical Research Centre, Cambridge Biomedical Campus, Cambridge CB2 0QQ, UK
| | - Ewan Birney
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
- Corresponding author
| | - Victoria L. Keevil
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire CB10 1SD, UK
- Department of Medicine for the Elderly, Addenbrooke’s Hospital, Cambridge University Hospitals NHS Foundation Trust, Hills Road, Cambridge CB2 0QQ, UK
- Department of Medicine, University of Cambridge, Addenbrooke’s Hospital, Hills Road, Cambridge CB2 0QQ, UK
- Corresponding author
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20
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Frasier M, Fiske BK, Sherer TB. Precision medicine for Parkinson's disease: The subtyping challenge. Front Aging Neurosci 2022; 14:1064057. [PMID: 36533178 PMCID: PMC9751632 DOI: 10.3389/fnagi.2022.1064057] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Accepted: 11/08/2022] [Indexed: 10/29/2023] Open
Abstract
Despite many pharmacological and surgical treatments addressing the symptoms of Parkinson's disease, there are no approved treatments that slow disease progression. Genetic discoveries in the last 20 years have increased our understanding of the molecular contributors to Parkinson's pathophysiology, uncovered many druggable targets and pathways, and increased investment in treatments that might slow or stop the disease process. Longitudinal, observational studies are dissecting Parkinson's disease heterogeneity and illuminating the importance of molecularly defined subtypes more likely to respond to targeted interventions. Indeed, clinical and pathological differences seen within and across carriers of PD-associated gene mutations suggest the existence of greater biological complexity than previously appreciated and increase the likelihood that targeted interventions based on molecular characteristics will be beneficial. This article offers our current perspective on the promise and current challenges in subtype identification and precision medicine approaches in Parkinson's disease.
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21
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Sadaei HJ, Cordova-Palomera A, Lee J, Padmanabhan J, Chen SF, Wineinger NE, Dias R, Prilutsky D, Szalma S, Torkamani A. Genetically-informed prediction of short-term Parkinson's disease progression. NPJ Parkinsons Dis 2022; 8:143. [PMID: 36302787 PMCID: PMC9613892 DOI: 10.1038/s41531-022-00412-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Accepted: 10/11/2022] [Indexed: 11/22/2022] Open
Abstract
Parkinson's disease (PD) treatments modify disease symptoms but have not been shown to slow progression, characterized by gradual and varied motor and non-motor changes overtime. Variation in PD progression hampers clinical research, resulting in long and expensive clinical trials prone to failure. Development of models for short-term PD progression prediction could be useful for shortening the time required to detect disease-modifying drug effects in clinical studies. PD progressors were defined by an increase in MDS-UPDRS scores at 12-, 24-, and 36-months post-baseline. Using only baseline features, PD progression was separately predicted across all timepoints and MDS-UPDRS subparts in independent, optimized, XGBoost models. These predictions plus baseline features were combined into a meta-predictor for 12-month MDS UPDRS Total progression. Data from the Parkinson's Progression Markers Initiative (PPMI) were used for training with independent testing on the Parkinson's Disease Biomarkers Program (PDBP) cohort. 12-month PD total progression was predicted with an F-measure 0.77, ROC AUC of 0.77, and PR AUC of 0.76 when tested on a hold-out PPMI set. When tested on PDBP we achieve a F-measure 0.75, ROC AUC of 0.74, and PR AUC of 0.73. Exclusion of genetic predictors led to the greatest loss in predictive accuracy; ROC AUC of 0.66, PR AUC of 0.66-0.68 for both PPMI and PDBP testing. Short-term PD progression can be predicted with a combination of survey-based, neuroimaging, physician examination, and genetic predictors. Dissection of the interplay between genetic risk, motor symptoms, non-motor symptoms, and longer-term expected rates of progression enable generalizable predictions.
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Affiliation(s)
- Hossein J Sadaei
- Scripps Research Translational Institute, La Jolla, CA, 92037, USA
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, 92037, USA
| | | | - Jonghun Lee
- Takeda Development Center Americas, Inc., Cambridge, MA, 02139, USA
| | - Jaya Padmanabhan
- Takeda Development Center Americas, Inc., Cambridge, MA, 02139, USA
| | - Shang-Fu Chen
- Scripps Research Translational Institute, La Jolla, CA, 92037, USA
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, 92037, USA
| | - Nathan E Wineinger
- Scripps Research Translational Institute, La Jolla, CA, 92037, USA
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, 92037, USA
| | - Raquel Dias
- Scripps Research Translational Institute, La Jolla, CA, 92037, USA
| | - Daria Prilutsky
- Takeda Development Center Americas, Inc., Cambridge, MA, 02139, USA
| | - Sandor Szalma
- Takeda Development Center Americas, Inc., San Diego, CA, 92121, USA
| | - Ali Torkamani
- Scripps Research Translational Institute, La Jolla, CA, 92037, USA.
- Department of Integrative Structural and Computational Biology, Scripps Research, La Jolla, CA, 92037, USA.
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22
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Airaksinen M, Gallen A, Kivi A, Vijayakrishnan P, Häyrinen T, Ilén E, Räsänen O, Haataja LM, Vanhatalo S. Intelligent wearable allows out-of-the-lab tracking of developing motor abilities in infants. COMMUNICATIONS MEDICINE 2022; 2:69. [PMID: 35721830 PMCID: PMC9200857 DOI: 10.1038/s43856-022-00131-6] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/23/2022] [Indexed: 11/10/2022] Open
Abstract
Background Early neurodevelopmental care needs better, effective and objective solutions for assessing infants' motor abilities. Novel wearable technology opens possibilities for characterizing spontaneous movement behavior. This work seeks to construct and validate a generalizable, scalable, and effective method to measure infants' spontaneous motor abilities across all motor milestones from lying supine to fluent walking. Methods A multi-sensor infant wearable was constructed, and 59 infants (age 5-19 months) were recorded during their spontaneous play. A novel gross motor description scheme was used for human visual classification of postures and movements at a second-level time resolution. A deep learning -based classifier was then trained to mimic human annotations, and aggregated recording-level outputs were used to provide posture- and movement-specific developmental trajectories, which enabled more holistic assessments of motor maturity. Results Recordings were technically successful in all infants, and the algorithmic analysis showed human-equivalent-level accuracy in quantifying the observed postures and movements. The aggregated recordings were used to train an algorithm for predicting a novel neurodevelopmental measure, Baba Infant Motor Score (BIMS). This index estimates maturity of infants' motor abilities, and it correlates very strongly (Pearson's r = 0.89, p < 1e-20) to the chronological age of the infant. Conclusions The results show that out-of-hospital assessment of infants' motor ability is possible using a multi-sensor wearable. The algorithmic analysis provides metrics of motility that are transparent, objective, intuitively interpretable, and they link strongly to infants' age. Such a solution could be automated and scaled to a global extent, holding promise for functional benchmarking in individualized patient care or early intervention trials.
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Affiliation(s)
- Manu Airaksinen
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, New Children’s Hospital and HUS Imaging, Helsinki University Hospital, Helsinki, Finland
| | - Anastasia Gallen
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, New Children’s Hospital and HUS Imaging, Helsinki University Hospital, Helsinki, Finland
| | - Anna Kivi
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, New Children’s Hospital and HUS Imaging, Helsinki University Hospital, Helsinki, Finland
- Department of Pediatric Neurology, Children’s Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Pavithra Vijayakrishnan
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, New Children’s Hospital and HUS Imaging, Helsinki University Hospital, Helsinki, Finland
| | - Taru Häyrinen
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, New Children’s Hospital and HUS Imaging, Helsinki University Hospital, Helsinki, Finland
- Department of Pediatric Neurology, Children’s Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Elina Ilén
- Department of Design, Aalto University, Otaniementie 14, FI-02150 Espoo, Finland
| | - Okko Räsänen
- Unit of Computing Sciences, Tampere University, P.O. Box 553, FI-33101 Tampere, Finland
| | - Leena M. Haataja
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, New Children’s Hospital and HUS Imaging, Helsinki University Hospital, Helsinki, Finland
- Department of Pediatric Neurology, Children’s Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Sampsa Vanhatalo
- BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, New Children’s Hospital and HUS Imaging, Helsinki University Hospital, Helsinki, Finland
- Department of Physiology, University of Helsinki, Helsinki, Finland
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23
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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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24
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Termine A, Fabrizio C, Strafella C, Caputo V, Petrosini L, Caltagirone C, Cascella R, Giardina E. A Hybrid Machine Learning and Network Analysis Approach Reveals Two Parkinson's Disease Subtypes from 115 RNA-Seq Post-Mortem Brain Samples. Int J Mol Sci 2022; 23:2557. [PMID: 35269707 PMCID: PMC8910747 DOI: 10.3390/ijms23052557] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 02/16/2022] [Accepted: 02/24/2022] [Indexed: 12/26/2022] Open
Abstract
Precision medicine emphasizes fine-grained diagnostics, taking individual variability into account to enhance treatment effectiveness. Parkinson’s disease (PD) heterogeneity among individuals proves the existence of disease subtypes, so subgrouping patients is vital for better understanding disease mechanisms and designing precise treatment. The purpose of this study was to identify PD subtypes using RNA-Seq data in a combined pipeline including unsupervised machine learning, bioinformatics, and network analysis. Two hundred and ten post mortem brain RNA-Seq samples from PD (n = 115) and normal controls (NCs, n = 95) were obtained with systematic data retrieval following PRISMA statements and a fully data-driven clustering pipeline was performed to identify PD subtypes. Bioinformatics and network analyses were performed to characterize the disease mechanisms of the identified PD subtypes and to identify target genes for drug repurposing. Two PD clusters were identified and 42 DEGs were found (p adjusted ≤ 0.01). PD clusters had significantly different gene network structures (p < 0.0001) and phenotype-specific disease mechanisms, highlighting the differential involvement of the Wnt/β-catenin pathway regulating adult neurogenesis. NEUROD1 was identified as a key regulator of gene networks and ISX9 and PD98059 were identified as NEUROD1-interacting compounds with disease-modifying potential, reducing the effects of dopaminergic neurodegeneration. This hybrid data analysis approach could enable precision medicine applications by providing insights for the identification and characterization of pathological subtypes. This workflow has proven useful on PD brain RNA-Seq, but its application to other neurodegenerative diseases is encouraged.
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Affiliation(s)
- Andrea Termine
- Data Science Unit, IRCCS Santa Lucia Foundation c/o CERC, 00143 Rome, Italy; (A.T.); (C.F.)
| | - Carlo Fabrizio
- Data Science Unit, IRCCS Santa Lucia Foundation c/o CERC, 00143 Rome, Italy; (A.T.); (C.F.)
| | - Claudia Strafella
- Genomic Medicine Laboratory UILDM, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; (C.S.); (V.C.)
| | - Valerio Caputo
- Genomic Medicine Laboratory UILDM, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; (C.S.); (V.C.)
- Medical Genetics Laboratory, Department of Biomedicine and Prevention, Tor Vergata University, 00133 Rome, Italy;
| | - Laura Petrosini
- Experimental and Behavioral Neurophysiology, IRCCS Santa Lucia Foundation c/o CERC, 00143 Rome, Italy;
| | - Carlo Caltagirone
- Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, 00179 Rome, Italy;
| | - Raffaella Cascella
- Medical Genetics Laboratory, Department of Biomedicine and Prevention, Tor Vergata University, 00133 Rome, Italy;
- Department of Biomedical Sciences, Catholic University Our Lady of Good Counsel, 1000 Tirana, Albania
| | - Emiliano Giardina
- Genomic Medicine Laboratory UILDM, IRCCS Santa Lucia Foundation, 00179 Rome, Italy; (C.S.); (V.C.)
- UILDM Lazio ONLUS Foundation, Department of Biomedicine and Prevention, Tor Vergata University, 00133 Rome, Italy
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25
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Sharma A, Clemens RA, Garcia O, Taylor DL, Wagner NL, Shepard KA, Gupta A, Malany S, Grodzinsky AJ, Kearns-Jonker M, Mair DB, Kim DH, Roberts MS, Loring JF, Hu J, Warren LE, Eenmaa S, Bozada J, Paljug E, Roth M, Taylor DP, Rodrigue G, Cantini P, Smith AW, Giulianotti MA, Wagner WR. Biomanufacturing in low Earth orbit for regenerative medicine. Stem Cell Reports 2021; 17:1-13. [PMID: 34971562 PMCID: PMC8758939 DOI: 10.1016/j.stemcr.2021.12.001] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 12/01/2021] [Accepted: 12/02/2021] [Indexed: 02/06/2023] Open
Abstract
Research in low Earth orbit (LEO) has become more accessible. The 2020 Biomanufacturing in Space Symposium reviewed space-based regenerative medicine research and discussed leveraging LEO to advance biomanufacturing for regenerative medicine applications. The symposium identified areas where financial investments could stimulate advancements overcoming technical barriers. Opportunities in disease modeling, stem-cell-derived products, and biofabrication were highlighted. The symposium will initiate a roadmap to a sustainable market for regenerative medicine biomanufacturing in space. This perspective summarizes the 2020 Biomanufacturing in Space Symposium, highlights key biomanufacturing opportunities in LEO, and lays the framework for a roadmap to regenerative medicine biomanufacturing in space.
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Affiliation(s)
- Arun Sharma
- Board of Governors Regenerative Medicine Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA; Department of Biomedical Sciences, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
| | | | - Orquidea Garcia
- Johnson & Johnson 3D Printing Innovation & Customer Solutions, Johnson & Johnson Services, Inc., Irvine, CA, USA
| | - D Lansing Taylor
- University of Pittsburgh Drug Discovery Institute and Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Kelly A Shepard
- California Institute for Regenerative Medicine, Oakland, CA, USA
| | | | - Siobhan Malany
- Department of Pharmacodynamics, College of Pharmacy, University of Florida, Gainesville, FL, USA
| | - Alan J Grodzinsky
- Departments of Biological Engineering, Mechanical Engineering and Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Mary Kearns-Jonker
- Department of Pathology and Human Anatomy, Loma Linda University School of Medicine, Loma Linda, CA, USA
| | - Devin B Mair
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Deok-Ho Kim
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Michael S Roberts
- Center for the Advancement of Science in Space, Inc, Melbourne, FL, USA
| | | | - Jianying Hu
- Center for Computational Health IBM Research, Yorktown Heights, New York, NY, USA
| | - Lara E Warren
- Center for the Advancement of Science in Space, Inc, Melbourne, FL, USA
| | - Sven Eenmaa
- Center for the Advancement of Science in Space, Inc, Melbourne, FL, USA
| | - Joe Bozada
- Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA, USA
| | - Eric Paljug
- Joseph M. Katz Graduate School of Business, University of Pittsburgh, Pittsburgh, PA, USA
| | | | | | - Gary Rodrigue
- Center for the Advancement of Science in Space, Inc, Melbourne, FL, USA
| | - Patrick Cantini
- McGowan Institute for Regenerative Medicine, Pittsburgh, PA, USA
| | - Amelia W Smith
- Center for the Advancement of Science in Space, Inc, Melbourne, FL, USA
| | - Marc A Giulianotti
- Center for the Advancement of Science in Space, Inc, Melbourne, FL, USA.
| | - William R Wagner
- McGowan Institute for Regenerative Medicine, Pittsburgh, PA, USA; Departments of Surgery, Bioengineering, Chemical Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
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26
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Gait based Parkinson’s disease diagnosis and severity rating using multi-class support vector machine. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107939] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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