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Kwak IY, Kim KS, Min HJ. Gustatory dysfunction is related to Parkinson's disease: A systematic review and meta-analysis. Int Forum Allergy Rhinol 2023; 13:1949-1957. [PMID: 36934313 DOI: 10.1002/alr.23158] [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: 01/15/2023] [Revised: 03/14/2023] [Accepted: 03/15/2023] [Indexed: 03/20/2023]
Abstract
BACKGROUND Olfactory dysfunction has been reported to be involved in Parkinson's disease (PD) pathogenesis. However, gustatory dysfunction in PD has not been evaluated as in-depth as olfactory dysfunction. We reviewed the previously published studies regarding gustatory function in PD patients and suggested the possibility that gustatory dysfunction may also be associated with PD. METHODS MEDLINE, Cochrane Library, Embase, and PubMed databases were searched for studies evaluating gustatory function in PD patients. We used the standardized mean difference and a 95% confidence interval (CI) as the effect analysis index regarding the taste strip test. The relative risk and 95% CI were used as the effect analysis index for the questionnaires and propylthiouracil (PTU)/phenylthiocarbamide (PTC) perception test. Statistical heterogeneity was assessed using forest plots, Cochran's Q, and the I2 statistic; heterogeneity was considered high when I2 was over 75%. Publication bias was assessed by funnel plots and the Egger bias test. RESULTS We identified 19 articles that reported the results of gustatory function tests in PD patients and healthy controls. Most of these studies used various gustatory tests, including taste strips, questionnaires, taste solutions, PTU/PTC perception tests, and electrogustometry, and reported significantly lower gustatory function in PD patients than in the controls. However, several articles reported contradictory results. CONCLUSIONS Based on these studies, gustatory dysfunction is closely related to PD. However, the number of studies and enrolled subjects was small, and a unified gustatory function test was lacking. Therefore, further studies with larger populations and normalized gustatory function tests are needed.
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Affiliation(s)
- Il-Youp Kwak
- Department of Applied Statistics, Chung-Ang University, Dongjak-gu, Seoul, South Korea
| | - Kyung Soo Kim
- Department of Otorhinolaryngology-Head and Neck Surgery, Chung-Ang University College of Medicine, Dongjak-gu, Seoul, South Korea
| | - Hyun Jin Min
- Department of Otorhinolaryngology-Head and Neck Surgery, Chung-Ang University College of Medicine, Dongjak-gu, Seoul, South Korea
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Ghaheri P, Nasiri H, Shateri A, Homafar A. Diagnosis of Parkinson's disease based on voice signals using SHAP and hard voting ensemble method. Comput Methods Biomech Biomed Engin 2023:1-17. [PMID: 37771234 DOI: 10.1080/10255842.2023.2263125] [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: 05/15/2023] [Accepted: 09/17/2023] [Indexed: 09/30/2023]
Abstract
Parkinson's disease (PD) is the second most common progressive neurological condition after Alzheimer's. The significant number of individuals afflicted with this illness makes it essential to develop a method to diagnose the conditions in their early phases. PD is typically identified from motor symptoms or via other Neuroimaging techniques. Expensive, time-consuming, and unavailable to the general public, these methods are not very accurate. Another issue to be addressed is the black-box nature of machine learning methods that needs interpretation. These issues encourage us to develop a novel technique using Shapley additive explanations (SHAP) and Hard Voting Ensemble Method based on voice signals to diagnose PD more accurately. Another purpose of this study is to interpret the output of the model and determine the most important features in diagnosing PD. The present article uses Pearson Correlation Coefficients to understand the relationship between input features and the output. Input features with high correlation are selected and then classified by the Extreme Gradient Boosting, Light Gradient Boosting Machine, Gradient Boosting, and Bagging. Moreover, the weights in Hard Voting Ensemble Method are determined based on the performance of the mentioned classifiers. At the final stage, it uses SHAP to determine the most important features in PD diagnosis. The effectiveness of the proposed method is validated using 'Parkinson Dataset with Replicated Acoustic Features' from the UCI machine learning repository. It has achieved an accuracy of 85.42%. The findings demonstrate that the proposed method outperformed state-of-the-art approaches and can assist physicians in diagnosing Parkinson's cases.
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Affiliation(s)
- Paria Ghaheri
- Electrical and Computer Engineering Department, Semnan University, Semnan, Iran
| | - Hamid Nasiri
- Department of Computer Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
| | - Ahmadreza Shateri
- Electrical and Computer Engineering Department, Semnan University, Semnan, Iran
| | - Arman Homafar
- Electrical and Computer Engineering Department, Semnan University, Semnan, Iran
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Schlosser RJ, Dubno JR, Eckert MA, Benitez AM, Gregoski M, Ramakrishnan V, Matthews L, Soler ZM. Unsupervised Clustering of Olfactory Phenotypes. Am J Rhinol Allergy 2022; 36:796-803. [PMID: 35837713 PMCID: PMC10031609 DOI: 10.1177/19458924221114255] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Current clinical classifications of olfactory function are based primarily upon a percentage of correct answers in olfactory identification testing. This simple classification provides little insight into etiologies of olfactory loss, associated comorbidities, or impact on the quality of life (QOL). METHODS Community-based subjects underwent olfactory psychophysical testing using Sniffin Sticks to measure threshold (T), discrimination (D), and identification (I). The cognitive screening was performed using Mini-Mental Status Examination (MMSE). Unsupervised clustering was performed based upon T, D, I, and MMSE. Post hoc differences in demographics, comorbidities, and QOL measures were assessed. RESULTS Clustering of 219 subjects, mean age 51 years (range 20-93 years) resulted in 4 unique clusters. Cluster 1 was the largest and predominantly younger normosmics. Cluster 2 had the worst olfaction with impairment in nearly all aspects of olfaction and decreased MMSE scores. This cluster had higher rates of smoking, heart disease, and cancer and had the worst olfactory-specific QOL. Cluster 3 had normal MMSE with relative preservation of D and I, but severely impaired T. This cluster had higher rates of smoking and heart disease with moderately impaired QOL. Cluster 4 was notable for the worst MMSE scores, but general preservation of D and I with moderate loss of T. This cluster had higher rates of Black subjects, diabetes, and viral/traumatic olfactory loss. CONCLUSION Unsupervised clustering based upon detailed olfactory testing and cognitive testing results in clinical phenotypes with unique risk factors and QOL impacts. These clusters may provide additional information regarding etiologies and subsequent therapies to treat olfactory loss.
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Affiliation(s)
- Rodney J Schlosser
- Department of Otolaryngology-Head and Neck Surgery, 2345Medical University of South Carolina, Charleston, South Carolina
| | - Judy R Dubno
- Department of Otolaryngology-Head and Neck Surgery, 2345Medical University of South Carolina, Charleston, South Carolina
| | - Mark A Eckert
- Department of Otolaryngology-Head and Neck Surgery, 2345Medical University of South Carolina, Charleston, South Carolina
| | - Andreana M Benitez
- Department of Neurology, 2345Medical University of South Carolina, Charleston, South Carolina
| | - Matthew Gregoski
- Department of Public Health Sciences, 2345Medical University of South Carolina, Charleston, South Carolina
| | - Viswanathan Ramakrishnan
- Department of Public Health Sciences, 2345Medical University of South Carolina, Charleston, South Carolina
| | - Lois Matthews
- Department of Otolaryngology-Head and Neck Surgery, 2345Medical University of South Carolina, Charleston, South Carolina
| | - Zachary M Soler
- Department of Otolaryngology-Head and Neck Surgery, 2345Medical University of South Carolina, Charleston, South Carolina
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Salari N, Kazeminia M, Sagha H, Daneshkhah A, Ahmadi A, Mohammadi M. The performance of various machine learning methods for Parkinson’s disease recognition: a systematic review. CURRENT PSYCHOLOGY 2022. [DOI: 10.1007/s12144-022-02949-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
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Zhang J, Zhang X, Sh Y, Liu B, Hu Z. Diagnostic AI Modeling and Pseudo Time Series Profiling of AD and PD Based on Individualized Serum Proteome Data. FRONTIERS IN BIOINFORMATICS 2021; 1:764497. [PMID: 36303784 PMCID: PMC9581001 DOI: 10.3389/fbinf.2021.764497] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 10/04/2021] [Indexed: 07/13/2024] Open
Abstract
Background: Parkinson's disease (PD), Alzheimer's disease (AD) are common neurodegenerative disease, while mild cognitive impairment (MCI) may be happened in the early stage of AD or PD. Blood biomarkers are considered to be less invasive, less cost and more convenient, and there is tremendous potential for the diagnosis and prediction of neurodegenerative diseases. As a recently mentioned field, artificial intelligence (AI) is often applied in biology and shows excellent results. In this article, we use AI to model PD, AD, MCI data and analyze the possible connections between them. Method: Human blood protein microarray profiles including 156 CT, 50 MCI, 132 PD, 50 AD samples are collected from Gene Expression Omnibus (GEO). First, we used bioinformatics methods and feature engineering in machine learning to screen important features, constructed artificial neural network (ANN) classifier models based on these features to distinguish samples, and evaluated the model's performance with classification accuracy and Area Under Curve (AUC). Second, we used Ingenuity Pathway Analysis (IPA) methods to analyse the pathways and functions in early stage and late stage samples of different diseases, and potential targets for drug intervention by predicting upstream regulators. Result: We used different classifier to construct the model and finally found that ANN model would outperform the traditional machine learning model. In summary, three different classifiers were constructed to be used in different application scenarios, First, we incorporated 6 indicators, including EPHA2, MRPL19, SGK2, to build a diagnostic model for AD with a test set accuracy of up to 98.07%. Secondly, incorporated 15 indicators such as ERO1LB, FAM73B, IL1RN to build a diagnostic model for PD, with a test set accuracy of 97.05%. Then, 15 indicators such as XG, FGFR3 and CDC37 were incorporated to establish a four-category diagnostic model for both AD and PD, with a test set accuracy of 98.71%. All classifier models have an auc value greater than 0.95. Then, we verified that the constructed feature engineering filtered out fewer important features but contained more information, which helped to build a better model. In addition, by classifying the disease types more carefully into early and late stages of AD, MCI, and PD, respectively, we found that early PD may occur earlier than early MCI. Finally, there are 24 proteins that are both differentially expressed proteins and upstream regulators in the disease group versus the normal group, and these proteins may serve as potential therapeutic targets and targets for subsequent studies. Conclusion: The feature engineering we build allows better extraction of information while reducing the number of features, which may help in subsequent applications. Building a classifier based on blood protein profiles using deep learning methods can achieve better classification performance, and it can help us to diagnose the disease early. Overall, it is important for us to study neurodegenerative diseases from both diagnostic and interventional aspects.
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Affiliation(s)
- Jianhu Zhang
- Fujian Provincial Key Laboratory of Brain Aging and Neurodegenerative Diseases, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Xiuli Zhang
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing, China
| | - Yuan Sh
- Fujian Provincial Key Laboratory of Brain Aging and Neurodegenerative Diseases, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
| | - Benliang Liu
- China National Center for Bioinformation, Beijing, China
- Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China
| | - Zhiyuan Hu
- Fujian Provincial Key Laboratory of Brain Aging and Neurodegenerative Diseases, School of Basic Medical Sciences, Fujian Medical University, Fuzhou, China
- CAS Key Laboratory of Standardization and Measurement for Nanotechnology, CAS Key Laboratory for Biomedical Effects of Nanomaterials and Nanosafety, CAS Center for Excellence in Nanoscience, National Center for Nanoscience and Technology of China, Beijing, China
- School of Nanoscience and Technology, Sino-Danish College, University of Chinese Academy of Sciences, Beijing, China
- School of Chemical Engineering and Pharmacy, Wuhan Institute of Technology, Wuhan, China
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Tremblay C, Frasnelli J. Olfactory-Trigeminal Interactions in Patients with Parkinson's Disease. Chem Senses 2021; 46:6218692. [PMID: 33835144 DOI: 10.1093/chemse/bjab018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Olfactory dysfunction (OD) is a highly frequent early non-motor symptom of Parkinson's disease (PD). An important step to potentially use OD for the development of early diagnostic tools of PD is to differentiate PD-related OD from other forms of non-parkinsonian OD (NPOD: postviral, sinunasal, post-traumatic, and idiopathic OD). Measuring non-olfactory chemosensory modalities, especially the trigeminal system, may allow to characterize a PD-specific olfactory profile. We here review the literature on PD-specific chemosensory alteration patterns compared with NPOD. Specifically, we focused on the impact of PD on the trigeminal system and particularly on the interaction between olfactory and trigeminal systems. As this interaction is seemingly affected in a disease-specific manner, we propose a model of interaction between both chemosensory systems that is distinct for PD-related OD and NPOD. These patterns of chemosensory impairment still need to be confirmed in prodromal PD; nevertheless, appropriate chemosensory tests may eventually help to develop diagnostic tools to identify individuals at risks for PD.
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Affiliation(s)
- Cécilia Tremblay
- Department of Anatomy, Université du Québec à Trois-Rivières, 3351 Boulevard des Forges, Trois-Rivières, QC, G9A 5H7, Canada
| | - Johannes Frasnelli
- Department of Anatomy, Université du Québec à Trois-Rivières, 3351 Boulevard des Forges, Trois-Rivières, QC, G9A 5H7, Canada.,Research Center, Sacré-Coeur Hospital of Montreal, 5400 Boulevard Gouin Ouest, Montréal, QC, H4J 1C5, Canada
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