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Ameli A, Peña-Castillo L, Usefi H. Assessing the reproducibility of machine-learning-based biomarker discovery in Parkinson's disease. Comput Biol Med 2024; 174:108407. [PMID: 38603902 DOI: 10.1016/j.compbiomed.2024.108407] [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: 03/21/2024] [Accepted: 04/01/2024] [Indexed: 04/13/2024]
Abstract
Feature selection and machine learning algorithms can be used to analyze Single Nucleotide Polymorphisms (SNPs) data and identify potential disease biomarkers. Reproducibility of identified biomarkers is critical for them to be useful for clinical research; however, genotyping platforms and selection criteria for individuals to be genotyped affect the reproducibility of identified biomarkers. To assess biomarkers reproducibility, we collected five SNPs datasets from the database of Genotypes and Phenotypes (dbGaP) and explored several data integration strategies. While combining datasets can lead to a reduction in classification accuracy, it has the potential to improve the reproducibility of potential biomarkers. We evaluated the agreement among different strategies in terms of the SNPs that were identified as potential Parkinson's disease (PD) biomarkers. Our findings indicate that, on average, 93% of the SNPs identified in a single dataset fail to be identified in other datasets. However, through dataset integration, this lack of replication is reduced to 62%. We discovered fifty SNPs that were identified at least twice, which could potentially serve as novel PD biomarkers. These SNPs are indirectly linked to PD in the literature but have not been directly associated with PD before. These findings open up new potential avenues of investigation.
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Affiliation(s)
- Ali Ameli
- Department of Computer Science, Memorial University of Newfoundland, 230 Elizabeth Ave, St. John's, A1C5S7, NL, Canada
| | - Lourdes Peña-Castillo
- Department of Computer Science, Memorial University of Newfoundland, 230 Elizabeth Ave, St. John's, A1C5S7, NL, Canada; Department of Biology, Memorial University of Newfoundland, 230 Elizabeth Ave, St. John's, A1C5S7, NL, Canada.
| | - Hamid Usefi
- Department of Computer Science, Memorial University of Newfoundland, 230 Elizabeth Ave, St. John's, A1C5S7, NL, Canada; Department of Mathematics and Statistics, Memorial University of Newfoundland, 230 Elizabeth Ave, St. John's, A1C5S7, NL, Canada.
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2
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Wang X, Dong T, Li X, Yu W, Jia Z, Liu Y, Yang J. Global biomarker trends in Parkinson's disease research: A bibliometric analysis. Heliyon 2024; 10:e27437. [PMID: 38501016 PMCID: PMC10945172 DOI: 10.1016/j.heliyon.2024.e27437] [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: 09/14/2023] [Revised: 12/11/2023] [Accepted: 02/28/2024] [Indexed: 03/20/2024] Open
Abstract
As the second most common neurodegenerative disease globally, Parkinson's disease (PD) affects millions of people worldwide. In recent years, the scientific publications related to PD biomarker research have exploded, reflecting the growing interest in unraveling the complex pathophysiology of PD. In this study, we aim to use various bibliometric tools to identify key scientific concepts, detect emerging trends, and analyze the global trends and development of PD biomarker research.The research encompasses various stages of biomarker development, including exploration, identification, and multi-modal research. MOVEMENT DISORDERS emerged as the leading journal in terms of publications and citations. Key authors such as Mollenhauer and Salem were identified, while the University of Pennsylvania and USA stood out in collaboration and research output. NEUROSCIENCES emerged as the most important research direction. Key biomarker categories include α-synuclein-related markers, neurotransmitter-related markers, inflammation and immune system-related markers, oxidative stress and mitochondrial function-related markers, and brain imaging-related markers. Furthermore, future trends in PD biomarker research focus on exosomes and plasma biomarkers, miRNA, cerebrospinal fluid biomarkers, machine learning applications, and animal models of PD. These trends contribute to early diagnosis, disease progression monitoring, and understanding the pathological mechanisms of PD.
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Affiliation(s)
- Xingxin Wang
- School of Acupuncture-Moxibustion and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Tiantian Dong
- School of Acupuncture-Moxibustion and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Xuhao Li
- School of Acupuncture-Moxibustion and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Wenyan Yu
- School of Acupuncture-Moxibustion and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Zhixia Jia
- The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Yuanxiang Liu
- The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Jiguo Yang
- School of Acupuncture-Moxibustion and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
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3
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Wang X, Dong T, Li X, Yu W, Jia Z, Liu Y, Yang J. Global biomarker trends in Parkinson's disease research: A bibliometric analysis. Heliyon 2024; 10:e27437. [PMID: 38501016 DOI: 10.1016/j.heliyon.2024.e27437if:] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 12/11/2023] [Accepted: 02/28/2024] [Indexed: 07/25/2024] Open
Abstract
As the second most common neurodegenerative disease globally, Parkinson's disease (PD) affects millions of people worldwide. In recent years, the scientific publications related to PD biomarker research have exploded, reflecting the growing interest in unraveling the complex pathophysiology of PD. In this study, we aim to use various bibliometric tools to identify key scientific concepts, detect emerging trends, and analyze the global trends and development of PD biomarker research.The research encompasses various stages of biomarker development, including exploration, identification, and multi-modal research. MOVEMENT DISORDERS emerged as the leading journal in terms of publications and citations. Key authors such as Mollenhauer and Salem were identified, while the University of Pennsylvania and USA stood out in collaboration and research output. NEUROSCIENCES emerged as the most important research direction. Key biomarker categories include α-synuclein-related markers, neurotransmitter-related markers, inflammation and immune system-related markers, oxidative stress and mitochondrial function-related markers, and brain imaging-related markers. Furthermore, future trends in PD biomarker research focus on exosomes and plasma biomarkers, miRNA, cerebrospinal fluid biomarkers, machine learning applications, and animal models of PD. These trends contribute to early diagnosis, disease progression monitoring, and understanding the pathological mechanisms of PD.
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Affiliation(s)
- Xingxin Wang
- School of Acupuncture-Moxibustion and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Tiantian Dong
- School of Acupuncture-Moxibustion and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Xuhao Li
- School of Acupuncture-Moxibustion and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Wenyan Yu
- School of Acupuncture-Moxibustion and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Zhixia Jia
- The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Yuanxiang Liu
- The First Clinical Medical College, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Jiguo Yang
- School of Acupuncture-Moxibustion and Tuina, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
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4
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Lai H, Li XY, Xu F, Zhu J, Li X, Song Y, Wang X, Wang Z, Wang C. Applications of Machine Learning to Diagnosis of Parkinson's Disease. Brain Sci 2023; 13:1546. [PMID: 38002506 PMCID: PMC10670005 DOI: 10.3390/brainsci13111546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 10/28/2023] [Accepted: 10/31/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Accurate diagnosis of Parkinson's disease (PD) is challenging due to its diverse manifestations. Machine learning (ML) algorithms can improve diagnostic precision, but their generalizability across medical centers in China is underexplored. OBJECTIVE To assess the accuracy of an ML algorithm for PD diagnosis, trained and tested on data from different medical centers in China. METHODS A total of 1656 participants were included, with 1028 from Beijing (training set) and 628 from Fuzhou (external validation set). Models were trained using the least absolute shrinkage and selection operator-logistic regression (LASSO-LR), decision tree (DT), random forest (RF), eXtreme gradient boosting (XGboost), support vector machine (SVM), and k-nearest neighbor (KNN) techniques. Hyperparameters were optimized using five-fold cross-validation and grid search techniques. Model performance was evaluated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curve, accuracy, sensitivity (recall), specificity, precision, and F1 score. Variable importance was assessed for all models. RESULTS SVM demonstrated the best differentiation between healthy controls (HCs) and PD patients (AUC: 0.928, 95% CI: 0.908-0.947; accuracy: 0.844, 95% CI: 0.814-0.871; sensitivity: 0.826, 95% CI: 0.786-0.866; specificity: 0.861, 95% CI: 0.820-0.898; precision: 0.849, 95% CI: 0.807-0.891; F1 score: 0.837, 95% CI: 0.803-0.868) in the validation set. Constipation, olfactory decline, and daytime somnolence significantly influenced predictability. CONCLUSION We identified multiple pivotal variables and SVM as a precise and clinician-friendly ML algorithm for prediction of PD in Chinese patients.
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Affiliation(s)
- Hong Lai
- Department of Neurology, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; (H.L.); (X.-Y.L.); (F.X.); (J.Z.); (X.L.); (Y.S.); (X.W.); (Z.W.)
- Department of Neurology, The First Affiliated Hospital of Gannan Medical University, Ganzhou 341000, China
| | - Xu-Ying Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; (H.L.); (X.-Y.L.); (F.X.); (J.Z.); (X.L.); (Y.S.); (X.W.); (Z.W.)
| | - Fanxi Xu
- Department of Neurology, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; (H.L.); (X.-Y.L.); (F.X.); (J.Z.); (X.L.); (Y.S.); (X.W.); (Z.W.)
| | - Junge Zhu
- Department of Neurology, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; (H.L.); (X.-Y.L.); (F.X.); (J.Z.); (X.L.); (Y.S.); (X.W.); (Z.W.)
| | - Xian Li
- Department of Neurology, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; (H.L.); (X.-Y.L.); (F.X.); (J.Z.); (X.L.); (Y.S.); (X.W.); (Z.W.)
| | - Yang Song
- Department of Neurology, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; (H.L.); (X.-Y.L.); (F.X.); (J.Z.); (X.L.); (Y.S.); (X.W.); (Z.W.)
| | - Xianlin Wang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; (H.L.); (X.-Y.L.); (F.X.); (J.Z.); (X.L.); (Y.S.); (X.W.); (Z.W.)
| | - Zhanjun Wang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; (H.L.); (X.-Y.L.); (F.X.); (J.Z.); (X.L.); (Y.S.); (X.W.); (Z.W.)
| | - Chaodong Wang
- Department of Neurology, Xuanwu Hospital of Capital Medical University, National Clinical Research Center for Geriatric Diseases, Beijing 100053, China; (H.L.); (X.-Y.L.); (F.X.); (J.Z.); (X.L.); (Y.S.); (X.W.); (Z.W.)
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Redhya M, Sathesh Kumar K. Refining PD classification through ensemble bionic machine learning architecture with adaptive threshold based image denoising. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104870] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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6
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Xu Y, Huang X, Geng X, Wang F. Meta-analysis of iron metabolism markers levels of Parkinson's disease patients determined by fluid and MRI measurements. J Trace Elem Med Biol 2023; 78:127190. [PMID: 37224790 DOI: 10.1016/j.jtemb.2023.127190] [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: 11/08/2022] [Revised: 03/17/2023] [Accepted: 04/26/2023] [Indexed: 05/26/2023]
Abstract
BACKGROUND Parkinson' s disease (PD) is a progressive neurodegenerative disease featured neuropathologically by the loss of dopaminergic neurons of the substantia nigra (SN). Iron overload in the SN is mainly relative to the pathology and pathogenesis of PD. Postmortem samples of PD has indicated the increased levels of brain iron. However, there is no consensus on iron content through iron-sensitive magnetic resonance imaging (MRI) techniques and the alteration of iron and iron related metabolism markers levels in blood and cerebrospinal fluids (CSF) are still unclear based on the current studies. In this study, we performed a meta-analysis to explore the iron concentration and iron metabolism markers levels through iron-sensitive MRI quantification and body fluid. METHODS A comprehensive literature search was performed in PubMed, EMBASE and Cochrane Library databases for relevant published studies that analyzed iron load in the SN of PD patients using quantitative susceptibility mapping (QSM) or susceptibility weighting imaging (SWI), and iron metabolism markers, iron, ferritin, transferrin, total iron-binding capacity(TIBC)in CSF sample or serum/plasma sample (from Jan 2010 to Sep 2022 to filter these inaccurate researches attributed to unadvanced equipment, inaccurate analytical methods). Standardized mean differences (SMD) or mean differences (MD) and 95% confidence intervals (CI) with random or fixed effect model was used to estimate the results. RESULTS Forty-two articles fulfilled the inclusion criteria including 19 for QSM, 6 for SWI, and 17 for serum/plasma/CSF sample including 2874 PD patients and 2821 healthy controls (HCs). Our meta-analysis results founded a notable difference for QSM values increase (19.67, 95% CI=18.69-20.64) and for SWI measurements (-1.99, 95% CI= -3.52 to -0.46) in the SN in PD patients. However, the serum/plasma/CSF iron levels and serum/plasma ferritin, transferrin, total iron-binding capacity (TIBC) did not differ significantly between PD patients and HCs. CONCLUSIONS Our meta-analysis showed the consistent increase in the SN in PD patients using QSM and SWI techniques of iron-sensitive MRI measures while no significant differences were observed in other iron metabolism markers levels.
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Affiliation(s)
- Yiyuan Xu
- Department of Neurology, General Hospital, Tianjin Medical University, Tianjin 300052, China
| | - Xinyu Huang
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Xin Geng
- Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Tianjin Medical University, Tianjin, 300070, China
| | - Fei Wang
- Department of Neurology, General Hospital, Tianjin Medical University, Tianjin 300052, China.
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7
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Chan YN, Wang P, Chun KH, Lum JTS, Wang H, Zhang Y, Leung KSY. A machine learning approach for early prediction of gestational diabetes mellitus using elemental contents in fingernails. Sci Rep 2023; 13:4184. [PMID: 36918683 PMCID: PMC10015050 DOI: 10.1038/s41598-023-31270-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Accepted: 03/09/2023] [Indexed: 03/16/2023] Open
Abstract
The aim of this pilot study was to predict the risk of gestational diabetes mellitus (GDM) by the elemental content in fingernails and urine with machine learning analysis. Sixty seven pregnant women (34 control and 33 GDM patient) were included. Fingernails and urine were collected in the first and second trimesters, respectively. The concentrations of elements were determined by inductively coupled plasma-mass spectrometry. Logistic regression model was applied to estimate the adjusted odd ratios and 95% confidence intervals. The predictive performances of multiple machine learning algorithms were evaluated, and an ensemble model was built to predict the risk for GDM based on the elemental contents in the fingernails. Beryllium, selenium, tin and copper were positively associated with the risk of GDM while nickel and mercury showed opposite result. The trained ensemble model showed larger area under curve (AUC) of receiver operating characteristic curve (0.81) using fingernail Ni, Cu and Se concentrations. The model was validated by external data set with AUC = 0.71. In summary, the results of the present study highlight the potential of fingernails, as an alternative sample, together with machine learning in human biomonitoring studies.
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Affiliation(s)
- Yun-Nam Chan
- Department of Chemistry, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR
| | - Pengpeng Wang
- Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China
- Key Lab of Health Technology Assessment, National Health Commission of the People's Republic of China (Fudan University), Shanghai, China
| | - Ka-Him Chun
- Department of Chemistry, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR
| | - Judy Tsz-Shan Lum
- Department of Chemistry, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR
| | - Hang Wang
- Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China
- Key Lab of Health Technology Assessment, National Health Commission of the People's Republic of China (Fudan University), Shanghai, China
| | - Yunhui Zhang
- Key Laboratory of Public Health Safety, Ministry of Education, School of Public Health, Fudan University, Shanghai, 200032, China
- Key Lab of Health Technology Assessment, National Health Commission of the People's Republic of China (Fudan University), Shanghai, China
| | - Kelvin Sze-Yin Leung
- Department of Chemistry, Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR.
- HKBU Institute of Research and Continuing Education, Shenzhen Virtual University Park, Shenzhen, China.
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Shusharina N, Yukhnenko D, Botman S, Sapunov V, Savinov V, Kamyshov G, Sayapin D, Voznyuk I. Modern Methods of Diagnostics and Treatment of Neurodegenerative Diseases and Depression. Diagnostics (Basel) 2023; 13:diagnostics13030573. [PMID: 36766678 PMCID: PMC9914271 DOI: 10.3390/diagnostics13030573] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/27/2023] [Accepted: 01/29/2023] [Indexed: 02/09/2023] Open
Abstract
This paper discusses the promising areas of research into machine learning applications for the prevention and correction of neurodegenerative and depressive disorders. These two groups of disorders are among the leading causes of decline in the quality of life in the world when estimated using disability-adjusted years. Despite decades of research, the development of new approaches for the assessment (especially pre-clinical) and correction of neurodegenerative diseases and depressive disorders remains among the priority areas of research in neurophysiology, psychology, genetics, and interdisciplinary medicine. Contemporary machine learning technologies and medical data infrastructure create new research opportunities. However, reaching a consensus on the application of new machine learning methods and their integration with the existing standards of care and assessment is still a challenge to overcome before the innovations could be widely introduced to clinics. The research on the development of clinical predictions and classification algorithms contributes towards creating a unified approach to the use of growing clinical data. This unified approach should integrate the requirements of medical professionals, researchers, and governmental regulators. In the current paper, the current state of research into neurodegenerative and depressive disorders is presented.
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Affiliation(s)
- Natalia Shusharina
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
- Correspondence:
| | - Denis Yukhnenko
- Department of Social Security and Humanitarian Technologies, N. I. Lobachevsky State University of Nizhny Novgorod, 603022 Nizhny Novgorod, Russia
| | - Stepan Botman
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
| | - Viktor Sapunov
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
| | - Vladimir Savinov
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
| | - Gleb Kamyshov
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
| | - Dmitry Sayapin
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
| | - Igor Voznyuk
- Baltic Center for Neurotechnologies and Artificial Intelligence, Immanuel Kant Baltic Federal University, 236041 Kaliningrad, Russia
- Department of Neurology, Pavlov First Saint Petersburg State Medical University, 197022 Saint Petersburg, Russia
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9
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Gopinath N. Artificial intelligence and neuroscience: An update on fascinating relationships. Process Biochem 2023. [DOI: 10.1016/j.procbio.2022.12.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
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10
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Barukab O, Ahmad A, Khan T, Thayyil Kunhumuhammed MR. Analysis of Parkinson's Disease Using an Imbalanced-Speech Dataset by Employing Decision Tree Ensemble Methods. Diagnostics (Basel) 2022; 12:diagnostics12123000. [PMID: 36553007 PMCID: PMC9776735 DOI: 10.3390/diagnostics12123000] [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: 06/10/2022] [Revised: 11/07/2022] [Accepted: 11/24/2022] [Indexed: 12/05/2022] Open
Abstract
Parkinson's disease (PD) currently affects approximately 10 million people worldwide. The detection of PD positive subjects is vital in terms of disease prognostics, diagnostics, management and treatment. Different types of early symptoms, such as speech impairment and changes in writing, are associated with Parkinson disease. To classify potential patients of PD, many researchers used machine learning algorithms in various datasets related to this disease. In our research, we study the dataset of the PD vocal impairment feature, which is an imbalanced dataset. We propose comparative performance evaluation using various decision tree ensemble methods, with or without oversampling techniques. In addition, we compare the performance of classifiers with different sizes of ensembles and various ratios of the minority class and the majority class with oversampling and undersampling. Finally, we combine feature selection with best-performing ensemble classifiers. The result shows that AdaBoost, random forest, and decision tree developed for the RUSBoost imbalanced dataset perform well in performance metrics such as precision, recall, F1-score, area under the receiver operating characteristic curve (AUROC) and the geometric mean. Further, feature selection methods, namely lasso and information gain, were used to screen the 10 best features using the best ensemble classifiers. AdaBoost with information gain feature selection method is the best performing ensemble method with an F1-score of 0.903.
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Affiliation(s)
- Omar Barukab
- Department of Information Technology, Faculty of Computing and Information Technology in Rabigh (FCITR), King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Correspondence:
| | - Amir Ahmad
- College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
| | - Tabrej Khan
- Department of Information Systems, Faculty of Computing and Information Technology in Rabigh (FCITR), King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Mujeeb Rahiman Thayyil Kunhumuhammed
- Department of Computer Science, Faculty of Computing and Information Technology in Rabigh (FCITR), King Abdulaziz University, Jeddah 21589, Saudi Arabia
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Rana A, Dumka A, Singh R, Panda MK, Priyadarshi N. A Computerized Analysis with Machine Learning Techniques for the Diagnosis of Parkinson's Disease: Past Studies and Future Perspectives. Diagnostics (Basel) 2022; 12:2708. [PMID: 36359550 PMCID: PMC9689408 DOI: 10.3390/diagnostics12112708] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Revised: 10/30/2022] [Accepted: 11/02/2022] [Indexed: 08/03/2023] Open
Abstract
According to the World Health Organization (WHO), Parkinson's disease (PD) is a neurodegenerative disease of the brain that causes motor symptoms including slower movement, rigidity, tremor, and imbalance in addition to other problems like Alzheimer's disease (AD), psychiatric problems, insomnia, anxiety, and sensory abnormalities. Techniques including artificial intelligence (AI), machine learning (ML), and deep learning (DL) have been established for the classification of PD and normal controls (NC) with similar therapeutic appearances in order to address these problems and improve the diagnostic procedure for PD. In this article, we examine a literature survey of research articles published up to September 2022 in order to present an in-depth analysis of the use of datasets, various modalities, experimental setups, and architectures that have been applied in the diagnosis of subjective disease. This analysis includes a total of 217 research publications with a list of the various datasets, methodologies, and features. These findings suggest that ML/DL methods and novel biomarkers hold promising results for application in medical decision-making, leading to a more methodical and thorough detection of PD. Finally, we highlight the challenges and provide appropriate recommendations on selecting approaches that might be used for subgrouping and connection analysis with structural magnetic resonance imaging (sMRI), DaTSCAN, and single-photon emission computerized tomography (SPECT) data for future Parkinson's research.
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Affiliation(s)
- Arti Rana
- Computer Science & Engineering, Veer Madho Singh Bhandari Uttarakhand Technical University, Dehradun 248007, Uttarakhand, India
| | - Ankur Dumka
- Department of Computer Science and Engineering, Women Institute of Technology, Dehradun 248007, Uttarakhand, India
- Department of Computer Science & Engineering, Graphic Era Deemed to be University, Dehradun 248001, Uttarakhand, India
| | - Rajesh Singh
- Division of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, Uttarakhand, India
- Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
| | - Manoj Kumar Panda
- Department of Electrical Engineering, G.B. Pant Institute of Engineering and Technology, Pauri 246194, Uttarakhand, India
| | - Neeraj Priyadarshi
- Department of Electrical Engineering, JIS College of Engineering, Kolkata 741235, West Bengal, India
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12
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Rana A, Dumka A, Singh R, Panda MK, Priyadarshi N, Twala B. Imperative Role of Machine Learning Algorithm for Detection of Parkinson’s Disease: Review, Challenges and Recommendations. Diagnostics (Basel) 2022; 12:diagnostics12082003. [PMID: 36010353 PMCID: PMC9407112 DOI: 10.3390/diagnostics12082003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 08/08/2022] [Accepted: 08/11/2022] [Indexed: 11/24/2022] Open
Abstract
Parkinson’s disease (PD) is a neurodegenerative disease that affects the neural, behavioral, and physiological systems of the brain. This disease is also known as tremor. The common symptoms of this disease are a slowness of movement known as ‘bradykinesia’, loss of automatic movements, speech/writing changes, and difficulty with walking at early stages. To solve these issues and to enhance the diagnostic process of PD, machine learning (ML) algorithms have been implemented for the categorization of subjective disease and healthy controls (HC) with comparable medical appearances. To provide a far-reaching outline of data modalities and artificial intelligence techniques that have been utilized in the analysis and diagnosis of PD, we conducted a literature analysis of research papers published up until 2022. A total of 112 research papers were included in this study, with an examination of their targets, data sources and different types of datasets, ML algorithms, and associated outcomes. The results showed that ML approaches and new biomarkers have a lot of promise for being used in clinical decision-making, resulting in a more systematic and informed diagnosis of PD. In this study, some major challenges were addressed along with a future recommendation.
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Affiliation(s)
- Arti Rana
- Computer Science & Engineering, Veer Madho Singh Bhandari Uttarakhand Technical University, Dehradun 248007, Uttarakhand, India
| | - Ankur Dumka
- Department of Computer Science and Engineering, Women Institute of Technology, Uttarakhand Technical University (UTU), Dehradun 248007, Uttarakhand, India
| | - Rajesh Singh
- Division of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, Uttarakhand, India
- Department of Project Management, Universidad Internacional Iberoamericana, Campeche 24560, Mexico
| | - Manoj Kumar Panda
- Department of Electrical Engineering, G.B. Pant Institute of Engineering and Technology, Pauri 246194, Uttarakhand, India
| | - Neeraj Priyadarshi
- Department of Electrical Engineering, JIS College of Engineering, Kolkata 741235, West Bengal, India
| | - Bhekisipho Twala
- Digital Transformation Portfolio, Tshwane University of Technology, Staatsartillerie Rd, Pretoria West, Pretoria 0183, South Africa
- Correspondence:
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Extraction of Reduced Infrared Biomarker Signatures for the Stratification of Patients Affected by Parkinson’s Disease: An Untargeted Metabolomic Approach. CHEMOSENSORS 2022. [DOI: 10.3390/chemosensors10060229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
An untargeted Fourier transform infrared (FTIR) metabolomic approach was employed to study metabolic changes and disarrangements, recorded as infrared signatures, in Parkinson’s disease (PD). Herein, the principal aim was to propose an efficient sequential classification strategy based on SELECT-LDA, which enabled optimal stratification of three main categories: PD patients from subjects with Alzheimer’s disease (AD) and healthy controls (HC). Moreover, sub-categories, such as PD at the early stage (PDI) from PD in the advanced stage (PDD), and PDD vs. AD, were stratified. Every classification step with selected wavenumbers achieved 90.11% to 100% correct assignment rates in classification and internal validation. Therefore, selected metabolic signatures from new patients could be used as input features for screening and diagnostic purposes.
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14
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Diagnostic classification of Parkinson’s disease based on non-motor manifestations and machine learning strategies. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07256-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
Abstract
AbstractNon-motor manifestations of Parkinson’s disease (PD) appear early and have a significant impact on the quality of life of patients, but few studies have evaluated their predictive potential with machine learning algorithms. We evaluated 9 algorithms for discriminating PD patients from controls using a wide collection of non-motor clinical PD features from two databases: Biocruces (96 subjects) and PPMI (687 subjects). In addition, we evaluated whether the combination of both databases could improve the individual results. For each database 2 versions with different granularity were created and a feature selection process was performed. We observed that most of the algorithms were able to detect PD patients with high accuracy (>80%). Support Vector Machine and Multi-Layer Perceptron obtained the best performance, with an accuracy of 86.3% and 84.7%, respectively. Likewise, feature selection led to a significant reduction in the number of variables and to better performance. Besides, the enrichment of Biocruces database with data from PPMI moderately benefited the performance of the classification algorithms, especially the recall and to a lesser extent the accuracy, while the precision worsened slightly. The use of interpretable rules obtained by the RIPPER algorithm showed that simply using two variables (autonomic manifestations and olfactory dysfunction), it was possible to achieve an accuracy of 84.4%. Our study demonstrates that the analysis of non-motor parameters of PD through machine learning techniques can detect PD patients with high accuracy and recall, and allows us to select the most discriminative non-motor variables to create potential tools for PD screening.
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15
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Selenium Effects on Oxidative Stress-Induced Calcium Signaling Pathways in Parkinson’s Disease. Indian J Clin Biochem 2022; 37:257-266. [DOI: 10.1007/s12291-022-01031-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 03/02/2022] [Indexed: 02/07/2023]
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16
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Mládek A, Gerla V, Skalický P, Vlasák A, Zazay A, Lhotská L, Beneš V, Beneš V, Bradáč O. Prediction of Shunt Responsiveness in Suspected Patients With Normal Pressure Hydrocephalus Using the Lumbar Infusion Test: A Machine Learning Approach. Neurosurgery 2022; 90:407-418. [PMID: 35080523 DOI: 10.1227/neu.0000000000001838] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2021] [Accepted: 10/27/2021] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Machine learning (ML) approaches can significantly improve the classical Rout-based evaluation of the lumbar infusion test (LIT) and the clinical management of the normal pressure hydrocephalus. OBJECTIVE To develop a ML model that accurately identifies patients as candidates for permanent cerebral spinal fluid shunt implantation using only intracranial pressure and electrocardiogram signals recorded throughout LIT. METHODS This was a single-center cohort study of prospectively collected data of 96 patients who underwent LIT and 5-day external lumbar cerebral spinal fluid drainage (external lumbar drainage) as a reference diagnostic method. A set of selected 48 intracranial pressure/electrocardiogram complex signal waveform features describing nonlinear behavior, wavelet transform spectral signatures, or recurrent map patterns were calculated for each patient. After applying a leave-one-out cross-validation training-testing split of the data set, we trained and evaluated the performance of various state-of-the-art ML algorithms. RESULTS The highest performing ML algorithm was the eXtreme Gradient Boosting. This model showed a good calibration and discrimination on the testing data, with an area under the receiver operating characteristic curve of 0.891 (accuracy: 82.3%, sensitivity: 86.1%, and specificity: 73.9%) obtained for 8 selected features. Our ML model clearly outperforms the classical Rout-based manual classification commonly used in clinical practice with an accuracy of 62.5%. CONCLUSION This study successfully used the ML approach to predict the outcome of a 5-day external lumbar drainage and hence which patients are likely to benefit from permanent shunt implantation. Our automated ML model thus enhances the diagnostic utility of LIT in management.
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Affiliation(s)
- Arnošt Mládek
- Department of Neurosurgery and Neurooncology, Military University Hospital, 1st Faculty of Medicine, Charles University in Prague, Prague, Czech Republic.,Department of Neurosurgery, Motol University Hospital, 2nd Faculty of Medicine, Charles University in Prague, Prague, Czech Republic.,Czech Technical University, Prague, Czech Republic
| | - Václav Gerla
- Department of Cognitive Systems and Neurosciences, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University, Prague, Czech Republic
| | - Petr Skalický
- Department of Neurosurgery and Neurooncology, Military University Hospital, 1st Faculty of Medicine, Charles University in Prague, Prague, Czech Republic.,Department of Neurosurgery, Motol University Hospital, 2nd Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
| | - Aleš Vlasák
- Department of Neurosurgery and Neurooncology, Military University Hospital, 1st Faculty of Medicine, Charles University in Prague, Prague, Czech Republic.,Department of Neurosurgery, Motol University Hospital, 2nd Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
| | - Awista Zazay
- Institute of Pathological Physiology, 1st Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
| | - Lenka Lhotská
- Department of Cognitive Systems and Neurosciences, Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University, Prague, Czech Republic.,Department of Natural Sciences, Faculty of Biomedical Engineering, Czech Technical University, Prague, Czech Republic
| | - Vladimír Beneš
- Department of Neurosurgery and Neurooncology, Military University Hospital, 1st Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
| | - Vladimír Beneš
- Department of Neurosurgery, Motol University Hospital, 2nd Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
| | - Ondřej Bradáč
- Department of Neurosurgery and Neurooncology, Military University Hospital, 1st Faculty of Medicine, Charles University in Prague, Prague, Czech Republic.,Department of Neurosurgery, Motol University Hospital, 2nd Faculty of Medicine, Charles University in Prague, Prague, Czech Republic
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17
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Maass F, Michalke B, Willkommen D, Canaslan S, Schmitz M, Bähr M, Zerr I, Lingor P. Cerebrospinal Fluid Iron-Ferritin Ratio as a Potential Progression Marker for Parkinson's Disease. Mov Disord 2021; 36:2967-2969. [PMID: 34553776 DOI: 10.1002/mds.28790] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2021] [Revised: 08/25/2021] [Accepted: 08/26/2021] [Indexed: 11/07/2022] Open
Abstract
Longitudinal PD CSF samples were subjected to ICP-MS and the total amount of iron and other bioelements was quantified. Additionally, ferritin and protein biomarkers of neurodegeneration were measured. Over time, mean iron levels significantly increased while levels of ferritin decreased.
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Affiliation(s)
- Fabian Maass
- Department of Neurology, University Medical Center Göttingen, Goettingen, Germany
| | - Bernhard Michalke
- Research Unit Analytical BioGeoChemistry, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany
| | - Desiree Willkommen
- Research Unit Analytical BioGeoChemistry, German Research Center for Environmental Health, Helmholtz Zentrum München, Neuherberg, Germany
| | - Sezgi Canaslan
- Department of Neurology, University Medical Center Göttingen, Goettingen, Germany
| | - Matthias Schmitz
- Department of Neurology, University Medical Center Göttingen, Goettingen, Germany
- DZNE, German Center for Neurodegenerative Diseases, Goettingen, Germany
| | - Mathias Bähr
- Department of Neurology, University Medical Center Göttingen, Goettingen, Germany
- Center for Biostructural Imaging of Neurodegeneration (BIN), University of Göttingen Medical Center, Göttingen, Germany
| | - Inga Zerr
- Department of Neurology, University Medical Center Göttingen, Goettingen, Germany
- DZNE, German Center for Neurodegenerative Diseases, Goettingen, Germany
| | - Paul Lingor
- Department of Neurology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, München, Germany
- DZNE, German Center for Neurodegenerative Diseases, Munich, Germany
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18
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Mei J, Desrosiers C, Frasnelli J. Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature. Front Aging Neurosci 2021; 13:633752. [PMID: 34025389 PMCID: PMC8134676 DOI: 10.3389/fnagi.2021.633752] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 03/22/2021] [Indexed: 12/26/2022] Open
Abstract
Diagnosis of Parkinson's disease (PD) is commonly based on medical observations and assessment of clinical signs, including the characterization of a variety of motor symptoms. However, traditional diagnostic approaches may suffer from subjectivity as they rely on the evaluation of movements that are sometimes subtle to human eyes and therefore difficult to classify, leading to possible misclassification. In the meantime, early non-motor symptoms of PD may be mild and can be caused by many other conditions. Therefore, these symptoms are often overlooked, making diagnosis of PD at an early stage challenging. To address these difficulties and to refine the diagnosis and assessment procedures of PD, machine learning methods have been implemented for the classification of PD and healthy controls or patients with similar clinical presentations (e.g., movement disorders or other Parkinsonian syndromes). To provide a comprehensive overview of data modalities and machine learning methods that have been used in the diagnosis and differential diagnosis of PD, in this study, we conducted a literature review of studies published until February 14, 2020, using the PubMed and IEEE Xplore databases. A total of 209 studies were included, extracted for relevant information and presented in this review, with an investigation of their aims, sources of data, types of data, machine learning methods and associated outcomes. These studies demonstrate a high potential for adaptation of machine learning methods and novel biomarkers in clinical decision making, leading to increasingly systematic, informed diagnosis of PD.
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Affiliation(s)
- Jie Mei
- Chemosensory Neuroanatomy Lab, Department of Anatomy, Université du Québec à Trois-Rivières (UQTR), Trois-Rivières, QC, Canada
| | - Christian Desrosiers
- Laboratoire d'Imagerie, de Vision et d'Intelligence Artificielle (LIVIA), Department of Software and IT Engineering, École de Technologie Supérieure, Montreal, QC, Canada
| | - Johannes Frasnelli
- Chemosensory Neuroanatomy Lab, Department of Anatomy, Université du Québec à Trois-Rivières (UQTR), Trois-Rivières, QC, Canada
- Centre de Recherche de l'Hôpital du Sacré-Coeur de Montréal, Centre Intégré Universitaire de Santé et de Services Sociaux du Nord-de-l'Île-de-Montréal (CIUSSS du Nord-de-l'Île-de-Montréal), Montreal, QC, Canada
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Bogdanov V, Kim A, Nodel M, Pavlenko T, Pavlova E, Blokhin V, Chesnokova N, Ugrumov M. A Pilot Study of Changes in the Level of Catecholamines and the Activity of α-2-Macroglobulin in the Tear Fluid of Patients with Parkinson's Disease and Parkinsonian Mice. Int J Mol Sci 2021; 22:ijms22094736. [PMID: 33947010 PMCID: PMC8125625 DOI: 10.3390/ijms22094736] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 04/27/2021] [Accepted: 04/28/2021] [Indexed: 12/19/2022] Open
Abstract
Development of differential and early (preclinical) diagnostics of Parkinson’s disease (PD) is among the priorities in neuroscience. We searched for changes in the level of catecholamines and α-2-macroglobulin activity in the tear fluid (TF) in PD patients at an early clinical stage. It was shown that TF in patients is characterized by an increased level of noradrenaline mainly on the ipsilateral side of pronounced motor symptoms (72%, p = 0.049), a decreased level of adrenaline on both sides (ipsilateral—53%, p = 0.004; contralateral—42%, p = 0.02), and an increased α-2-macroglobulin activity on both sides (ipsilateral—53%, p = 0.03; contralateral—56%, p = 0.037) compared to controls. These changes are considered as potential biomarkers for differential diagnosis. Similar changes in the TF were found in 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-treated mice when modeling clinical and preclinical stages of PD. These data show the adequacy of models to the pathogenesis of PD along the selected metabolic pathways, and also suggest that the found TF changes can be considered as potential biomarkers for preclinical diagnosis of PD. In Parkinsonian mice, the level of catecholamines also changes in the lacrimal glands, which makes it possible to consider them as one of the sources of catecholamines in the TF.
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Affiliation(s)
- Vsevolod Bogdanov
- Koltzov Institute of Developmental Biology of the Russian Academy of Sciences, 26 Vavilova Street, 119334 Moscow, Russia; (V.B.); (A.K.); (E.P.); (V.B.)
| | - Alexander Kim
- Koltzov Institute of Developmental Biology of the Russian Academy of Sciences, 26 Vavilova Street, 119334 Moscow, Russia; (V.B.); (A.K.); (E.P.); (V.B.)
| | - Marina Nodel
- Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation, 8/2 Trubetskaya Street, 119991 Moscow, Russia;
- Russian Clinical and Research Center of Gerontology, 16 1st Leonova Street, 129226 Moscow, Russia
| | - Tatiana Pavlenko
- Helmholtz Moscow Research Institute of Eye Diseases of the Ministry of Health of the Russian Federation, 14/19 Sadovaya-Chernogryazskaya Street, 105062 Moscow, Russia; (T.P.); (N.C.)
| | - Ekaterina Pavlova
- Koltzov Institute of Developmental Biology of the Russian Academy of Sciences, 26 Vavilova Street, 119334 Moscow, Russia; (V.B.); (A.K.); (E.P.); (V.B.)
| | - Victor Blokhin
- Koltzov Institute of Developmental Biology of the Russian Academy of Sciences, 26 Vavilova Street, 119334 Moscow, Russia; (V.B.); (A.K.); (E.P.); (V.B.)
| | - Natalia Chesnokova
- Helmholtz Moscow Research Institute of Eye Diseases of the Ministry of Health of the Russian Federation, 14/19 Sadovaya-Chernogryazskaya Street, 105062 Moscow, Russia; (T.P.); (N.C.)
| | - Michael Ugrumov
- Koltzov Institute of Developmental Biology of the Russian Academy of Sciences, 26 Vavilova Street, 119334 Moscow, Russia; (V.B.); (A.K.); (E.P.); (V.B.)
- Correspondence:
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20
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Cesario A, D’Oria M, Bove F, Privitera G, Boškoski I, Pedicino D, Boldrini L, Erra C, Loreti C, Liuzzo G, Crea F, Armuzzi A, Gasbarrini A, Calabresi P, Padua L, Costamagna G, Antonelli M, Valentini V, Auffray C, Scambia G. Personalized Clinical Phenotyping through Systems Medicine and Artificial Intelligence. J Pers Med 2021; 11:jpm11040265. [PMID: 33918214 PMCID: PMC8065854 DOI: 10.3390/jpm11040265] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 03/29/2021] [Accepted: 03/30/2021] [Indexed: 02/07/2023] Open
Abstract
Personalized Medicine (PM) has shifted the traditional top-down approach to medicine based on the identification of single etiological factors to explain diseases, which was not suitable for explaining complex conditions. The concept of PM assumes several interpretations in the literature, with particular regards to Genetic and Genomic Medicine. Despite the fact that some disease-modifying genes affect disease expression and progression, many complex conditions cannot be understood through only this lens, especially when other lifestyle factors can play a crucial role (such as the environment, emotions, nutrition, etc.). Personalizing clinical phenotyping becomes a challenge when different pathophysiological mechanisms underlie the same manifestation. Brain disorders, cardiovascular and gastroenterological diseases can be paradigmatic examples. Experiences on the field of Fondazione Policlinico Gemelli in Rome (a research hospital recognized by the Italian Ministry of Health as national leader in "Personalized Medicine" and "Innovative Biomedical Technologies") could help understanding which techniques and tools are the most performing to develop potential clinical phenotypes personalization. The connection between practical experiences and scientific literature highlights how this potential can be reached towards Systems Medicine using Artificial Intelligence tools.
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Affiliation(s)
- Alfredo Cesario
- Open Innovation Unit, Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
| | - Marika D’Oria
- Open Innovation Unit, Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
- Correspondence:
| | - Francesco Bove
- Neurology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (F.B.); (P.C.)
- Department of Neurosciences, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Giuseppe Privitera
- CEMAD—IBD Unit—Internal Medicine and Gastroenterology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (G.P.); (A.A.); (A.G.)
- Department of Medicine and Translational Surgery, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Ivo Boškoski
- Surgical Endoscopy Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (I.B.); (G.C.)
| | - Daniela Pedicino
- Cardiology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (D.P.); (G.L.); (F.C.)
| | - Luca Boldrini
- Radiation Oncology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (L.B.); (V.V.)
| | - Carmen Erra
- High Intensity Neurorehabilitation Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (C.E.); (C.L.); (L.P.)
| | - Claudia Loreti
- High Intensity Neurorehabilitation Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (C.E.); (C.L.); (L.P.)
| | - Giovanna Liuzzo
- Cardiology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (D.P.); (G.L.); (F.C.)
| | - Filippo Crea
- Cardiology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (D.P.); (G.L.); (F.C.)
| | - Alessandro Armuzzi
- CEMAD—IBD Unit—Internal Medicine and Gastroenterology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (G.P.); (A.A.); (A.G.)
- Department of Medicine and Translational Surgery, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Antonio Gasbarrini
- CEMAD—IBD Unit—Internal Medicine and Gastroenterology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (G.P.); (A.A.); (A.G.)
- Department of Medicine and Translational Surgery, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Paolo Calabresi
- Neurology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (F.B.); (P.C.)
- Department of Neurosciences, Università Cattolica del Sacro Cuore, 00168 Rome, Italy
| | - Luca Padua
- High Intensity Neurorehabilitation Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (C.E.); (C.L.); (L.P.)
| | - Guido Costamagna
- Surgical Endoscopy Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (I.B.); (G.C.)
| | - Massimo Antonelli
- Anesthesia, Resuscitation, Intensive Care and Clinical Toxicology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
| | - Vincenzo Valentini
- Radiation Oncology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy; (L.B.); (V.V.)
| | - Charles Auffray
- European Institute for Systems Biology and Medicine (EISBM), 69390 Vourles, France;
| | - Giovanni Scambia
- Scientific Directorate, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
- Gynecological Oncology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy
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21
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Jiménez-Jiménez FJ, Alonso-Navarro H, García-Martín E, Agúndez JAG. Biological fluid levels of iron and iron-related proteins in Parkinson's disease: Review and meta-analysis. Eur J Neurol 2020; 28:1041-1055. [PMID: 33098743 DOI: 10.1111/ene.14607] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Accepted: 10/15/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND AND PURPOSE Several studies suggested a role or iron in the pathogenesis or Parkinson's disease (PD), and substantia nigra iron concentrarions have been found increased in PD. However, the results on cerebrospinal (CSF) and serum/plasma iron levels in PD patients have been controversial. The aim of this systematic review and meta-analysis was to establish the CSF and serum/plasma levels of iron and iron-related proteins (ferritin, transferrin, lactoferrin, haptoglobin, and hepcidine) levels, and the urine levels of iron, in patients with PD. METHODS Four databases (PubMed, EMBASE, MedLine, and Web of Science - Core Collection) were reviewed for studies published from 1966 to October 5, 2020. References of interest were identified. A meta-analysis of eligible studies was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) and Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines, using the R software package meta. RESULTS A non-significant trend towards higher CSF iron levels and marginally significantly lower serum/plasma iron levels was observed in patients with PD compared with age- and sex-matched controls. CSF and serum/plasma ferritin and transferrin concentrations, and serum/plasma lactoferrin and haptoglobin concentrations did not differ significantly between PD patients and controls. CONCLUSION The findings of this study suggest an association between decreased serum/plasma iron levels and, possibly, higher CSF iron levels with risk of PD.
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Affiliation(s)
| | | | - Elena García-Martín
- University Institute of Molecular Pathology Biomarkers, UNEx, ARADyAL Instituto de Salud Carlos III, Cáceres, Spain
| | - José A G Agúndez
- University Institute of Molecular Pathology Biomarkers, UNEx, ARADyAL Instituto de Salud Carlos III, Cáceres, Spain
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