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Luo X, Liu Y, Balck A, Klein C, Fleming RMT. Identification of metabolites reproducibly associated with Parkinson's Disease via meta-analysis and computational modelling. NPJ Parkinsons Dis 2024; 10:126. [PMID: 38951523 PMCID: PMC11217404 DOI: 10.1038/s41531-024-00732-z] [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: 07/27/2023] [Accepted: 05/30/2024] [Indexed: 07/03/2024] Open
Abstract
Many studies have reported metabolomic analysis of different bio-specimens from Parkinson's disease (PD) patients. However, inconsistencies in reported metabolite concentration changes make it difficult to draw conclusions as to the role of metabolism in the occurrence or development of Parkinson's disease. We reviewed the literature on metabolomic analysis of PD patients. From 74 studies that passed quality control metrics, 928 metabolites were identified with significant changes in PD patients, but only 190 were replicated with the same changes in more than one study. Of these metabolites, 60 exclusively increased, such as 3-methoxytyrosine and glycine, 54 exclusively decreased, such as pantothenic acid and caffeine, and 76 inconsistently changed in concentration in PD versus control subjects, such as ornithine and tyrosine. A genome-scale metabolic model of PD and corresponding metabolic map linking most of the replicated metabolites enabled a better understanding of the dysfunctional pathways of PD and the prediction of additional potential metabolic markers from pathways with consistent metabolite changes to target in future studies.
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Affiliation(s)
- Xi Luo
- School of Medicine, University of Galway, University Rd, Galway, Ireland
| | - Yanjun Liu
- School of Medicine, University of Galway, University Rd, Galway, Ireland
| | - Alexander Balck
- Institute of Neurogenetics and Department of Neurology, University of Luebeck and University Hospital Schleswig-Holstein, Luebeck, Germany
| | - Christine Klein
- Institute of Neurogenetics and Department of Neurology, University of Luebeck and University Hospital Schleswig-Holstein, Luebeck, Germany
| | - Ronan M T Fleming
- School of Medicine, University of Galway, University Rd, Galway, Ireland.
- Division of Systems Biomedicine and Pharmacology, Leiden Academic Centre for Drug Research, Leiden University, Leiden, Netherlands.
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2
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Abdik E, Çakır T. Transcriptome-based biomarker prediction for Parkinson's disease using genome-scale metabolic modeling. Sci Rep 2024; 14:585. [PMID: 38182712 PMCID: PMC10770157 DOI: 10.1038/s41598-023-51034-y] [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: 09/15/2023] [Accepted: 12/29/2023] [Indexed: 01/07/2024] Open
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disease in the world. Identification of PD biomarkers is crucial for early diagnosis and to develop target-based therapeutic agents. Integrative analysis of genome-scale metabolic models (GEMs) and omics data provides a computational approach for the prediction of metabolite biomarkers. Here, we applied the TIMBR (Transcriptionally Inferred Metabolic Biomarker Response) algorithm and two modified versions of TIMBR to investigate potential metabolite biomarkers for PD. To this end, we mapped thirteen post-mortem PD transcriptome datasets from the substantia nigra region onto Human-GEM. We considered a metabolite as a candidate biomarker if its production was predicted to be more efficient by a TIMBR-family algorithm in control or PD case for the majority of the datasets. Different metrics based on well-known PD-related metabolite alterations, PD-associated pathways, and a list of 25 high-confidence PD metabolite biomarkers compiled from the literature were used to compare the prediction performance of the three algorithms tested. The modified algorithm with the highest prediction power based on the metrics was called TAMBOOR, TrAnscriptome-based Metabolite Biomarkers by On-Off Reactions, which was introduced for the first time in this study. TAMBOOR performed better in terms of capturing well-known pathway alterations and metabolite secretion changes in PD. Therefore, our tool has a strong potential to be used for the prediction of novel diagnostic biomarkers for human diseases.
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Affiliation(s)
- Ecehan Abdik
- Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey
| | - Tunahan Çakır
- Department of Bioengineering, Gebze Technical University, Kocaeli, Turkey.
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3
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Lee HJ, Han K, Kim YW, Yang SN, Yoon SY. Association between lipid levels and the risk of Parkinson's disease in individuals with diabetes mellitus: A nationwide population-based cohort study. Parkinsonism Relat Disord 2023; 117:105881. [PMID: 37951145 DOI: 10.1016/j.parkreldis.2023.105881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 09/27/2023] [Accepted: 09/30/2023] [Indexed: 11/13/2023]
Abstract
INTRODUCTION Many studies have examined the positive association between diabetes mellitus (DM) and the risk of Parkinson's disease (PD). Dyslipidemia has been reported to be prevalent in patients with diabetes; thus, lipid levels and the drugs for dyslipidemia could influence the development of PD in patients with DM. This study aimed to examine the association between lipid levels and the risk of PD in individuals with DM and evaluate whether the association changes with the use of statins. METHODS This nationwide population-based retrospective cohort study included individuals with DM according to the International Classification of Diseases between 2009 and 2012. Among the 2,361,633 patients with DM followed up for up to 9 years, 17,046 were newly diagnosed with PD. Patients with DM were categorized into quartile groups of total cholesterol, low-density lipoprotein cholesterol, and triglyceride levels. RESULTS There was an inverse association between lipid levels and PD development in the unadjusted model; however, this relationship became less significant after adjusting the use of statins in triglyceride and total cholesterol. In the analysis stratified by statin use, total cholesterol level was associated with decreased PD risk in non-statin users with DM; however, there was no significant association between total cholesterol level and PD risk in statin users. CONCLUSION We found an inverse relationship between lipid levels and PD risk in patients with DM, which was influenced by statin use. Future studies about optimal target lipid levels relevant to PD risk considering statin dose in DM patients are needed.
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Affiliation(s)
- Hyo Jeong Lee
- Department of Rehabilitation Medicine, Bundang Jesaeng General Hospital, Seongnam-si, Republic of Korea
| | - Kyungdo Han
- Statistics and Actuarial Science, Soongsil University, Seoul, Republic of Korea
| | - Yong Wook Kim
- Department and Research Institute of Rehabilitation Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea
| | - Seung Nam Yang
- Department of Physical Medicine & Rehabilitation, Korea University Guro Hospital, Seoul, Republic of Korea
| | - Seo Yeon Yoon
- Department and Research Institute of Rehabilitation Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea.
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Ghini V, Meoni G, Vignoli A, Di Cesare F, Tenori L, Turano P, Luchinat C. Fingerprinting and profiling in metabolomics of biosamples. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2023; 138-139:105-135. [PMID: 38065666 DOI: 10.1016/j.pnmrs.2023.10.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 10/13/2023] [Accepted: 10/15/2023] [Indexed: 12/18/2023]
Abstract
This review focuses on metabolomics from an NMR point of view. It attempts to cover the broad scope of metabolomics and describes the NMR experiments that are most suitable for each sample type. It is addressed not only to NMR specialists, but to all researchers who wish to approach metabolomics with a clear idea of what they wish to achieve but not necessarily with a deep knowledge of NMR. For this reason, some technical parts may seem a bit naïve to the experts. The review starts by describing standard metabolomics procedures, which imply the use of a dedicated 600 MHz instrument and of four properly standardized 1D experiments. Standardization is a must if one wants to directly compare NMR results obtained in different labs. A brief mention is also made of standardized pre-analytical procedures, which are even more essential. Attention is paid to the distinction between fingerprinting and profiling, and the advantages and disadvantages of fingerprinting are clarified. This aspect is often not fully appreciated. Then profiling, and the associated problems of signal assignment and quantitation, are discussed. We also describe less conventional approaches, such as the use of different magnetic fields, the use of signal enhancement techniques to increase sensitivity, and the potential of field-shuttling NMR. A few examples of biomedical applications are also given, again with the focus on NMR techniques that are most suitable to achieve each particular goal, including a description of the most common heteronuclear experiments. Finally, the growing applications of metabolomics to foodstuffs are described.
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Affiliation(s)
- Veronica Ghini
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Gaia Meoni
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Alessia Vignoli
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Francesca Di Cesare
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy
| | - Leonardo Tenori
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy; Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy
| | - Paola Turano
- Magnetic Resonance Center (CERM), University of Florence, Sesto Fiorentino, Italy; Department of Chemistry "Ugo Schiff", University of Florence, Sesto Fiorentino, Italy; Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy.
| | - Claudio Luchinat
- Consorzio Interuniversitario Risonanze Magnetiche Metallo Proteine (CIRMMP), Sesto Fiorentino, Italy; Giotto Biotech S.r.l., Sesto Fiorentino, Italy.
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Lauritsen J, Romero-Ramos M. The systemic immune response in Parkinson's disease: focus on the peripheral immune component. Trends Neurosci 2023; 46:863-878. [PMID: 37598092 DOI: 10.1016/j.tins.2023.07.005] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 05/19/2023] [Accepted: 07/24/2023] [Indexed: 08/21/2023]
Abstract
During Parkinson's disease (PD), both the central nervous system (CNS) and peripheral nervous system (PNS) are affected. In parallel, innate immune cells respond early to neuronal changes and alpha-synuclein (α-syn) pathology. Moreover, some of the affected neuronal groups innervate organs with a relevant role in immunity. Consequently, not only microglia, but also peripheral immune cells are altered, resulting in a systemic immune response. Innate and adaptive immune cells may participate in the neurodegenerative process by acting peripherally, infiltrating the brain, or releasing mediators that can protect or harm neurons. However, the sequence of the changes and the significance of each immune compartment in the disease remain to be clarified. In this review, we describe current understanding of the peripheral immune response in PD and discuss the road ahead.
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Affiliation(s)
- Johanne Lauritsen
- Department of Biomedicine, Health Faculty & Danish Research Institute of Translational Neuroscience - DANDRITE, Aarhus University, Aarhus, Denmark
| | - Marina Romero-Ramos
- Department of Biomedicine, Health Faculty & Danish Research Institute of Translational Neuroscience - DANDRITE, Aarhus University, Aarhus, Denmark.
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Raheel K, Deegan G, Di Giulio I, Cash D, Ilic K, Gnoni V, Chaudhuri KR, Drakatos P, Moran R, Rosenzweig I. Sex differences in alpha-synucleinopathies: a systematic review. Front Neurol 2023; 14:1204104. [PMID: 37545736 PMCID: PMC10398394 DOI: 10.3389/fneur.2023.1204104] [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: 04/11/2023] [Accepted: 06/13/2023] [Indexed: 08/08/2023] Open
Abstract
Background Past research indicates a higher prevalence, incidence, and severe clinical manifestations of alpha-synucleinopathies in men, leading to a suggestion of neuroprotective properties of female sex hormones (especially estrogen). The potential pathomechanisms of any such effect on alpha-synucleinopathies, however, are far from understood. With that aim, we undertook to systematically review, and to critically assess, contemporary evidence on sex and gender differences in alpha-synucleinopathies using a bench-to-bedside approach. Methods In this systematic review, studies investigating sex and gender differences in alpha-synucleinopathies (Rapid Eye Movement (REM) Behavior Disorder (RBD), Parkinson's Disease (PD), Dementia with Lewy Bodies (DLB), Multiple System Atrophy (MSA)) from 2012 to 2022 were identified using electronic database searches of PubMed, Embase and Ovid. Results One hundred sixty-two studies were included; 5 RBD, 6 MSA, 20 DLB and 131 PD studies. Overall, there is conclusive evidence to suggest sex-and gender-specific manifestation in demographics, biomarkers, genetics, clinical features, interventions, and quality of life in alpha-synucleinopathies. Only limited data exists on the effects of distinct sex hormones, with majority of studies concentrating on estrogen and its speculated neuroprotective effects. Conclusion Future studies disentangling the underlying sex-specific mechanisms of alpha-synucleinopathies are urgently needed in order to enable novel sex-specific therapeutics.
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Affiliation(s)
- Kausar Raheel
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
| | - Gemma Deegan
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
- BRAIN, Imaging Centre, CNS, King’s College London, London, United Kingdom
| | - Irene Di Giulio
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
- School of Basic and Medical Biosciences, Faculty of Life Science and Medicine, King’s College London, London, United Kingdom
| | - Diana Cash
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
- BRAIN, Imaging Centre, CNS, King’s College London, London, United Kingdom
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
| | - Katarina Ilic
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
- BRAIN, Imaging Centre, CNS, King’s College London, London, United Kingdom
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
| | - Valentina Gnoni
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
- Center for Neurodegenerative Diseases and the Aging Brain, University of Bari Aldo Moro, Lecce, Italy
| | - K. Ray Chaudhuri
- Movement Disorders Unit, King’s College Hospital and Department of Clinical and Basic Neurosciences, Institute of Psychiatry, Psychology and Neuroscience and Parkinson Foundation Centre of Excellence, King’s College London, London, United Kingdom
| | - Panagis Drakatos
- School of Basic and Medical Biosciences, Faculty of Life Science and Medicine, King’s College London, London, United Kingdom
- Sleep Disorders Centre, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
| | - Rosalyn Moran
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
| | - Ivana Rosenzweig
- Sleep and Brain Plasticity Centre, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King’s College London, London, United Kingdom
- Sleep Disorders Centre, Guy’s and St Thomas’ NHS Foundation Trust, London, United Kingdom
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Xu W, Chen L, Cai G, Gao M, Chen Y, Pu J, Chen X, Liu N, Ye Q, Qian K. Diagnosis of Parkinson's Disease via the Metabolic Fingerprint in Saliva by Deep Learning. SMALL METHODS 2023:e2300285. [PMID: 37236160 DOI: 10.1002/smtd.202300285] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/17/2023] [Indexed: 05/28/2023]
Abstract
Parkinson's disease (PD) is the second cause of the neurodegenerative disorder, affecting over 6 million people worldwide. The World Health Organization estimated that population aging will cause global PD prevalence to double in the coming 30 years. Optimal management of PD shall start at diagnosis and requires both a timely and accurate method. Conventional PD diagnosis needs observations and clinical signs assessment, which are time-consuming and low-throughput. A lack of body fluid diagnostic biomarkers for PD has been a significant challenge, although substantial progress has been made in genetic and imaging marker development. Herein, a platform that noninvasively collects saliva metabolic fingerprinting (SMF) by nanoparticle-enhanced laser desorption-ionization mass spectrometry with high-reproducibility and high-throughput, using ultra-small sample volume (down to 10 nL), is developed. Further, excellent diagnostic performance is achieved with an area-under-the-curve of 0.8496 (95% CI: 0.7393-0.8625) by constructing deep learning model from 312 participants. In conclusion, an alternative solution is provided for the molecular diagnostics of PD with SMF and metabolic biomarker screening for therapeutic intervention.
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Affiliation(s)
- Wei Xu
- State Key Laboratory of Systems Medicine for Cancer, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Lina Chen
- Department of Neurology, Fujian Medical University Union Hospital, Fujian Key Laboratory of Molecular Neurology and Institute of Neuroscience, Fujian Medical University, Fuzhou, 350001, P. R. China
| | - Guoen Cai
- Department of Neurology, Fujian Medical University Union Hospital, Fujian Key Laboratory of Molecular Neurology and Institute of Neuroscience, Fujian Medical University, Fuzhou, 350001, P. R. China
| | - Ming Gao
- School of Management Science and Engineering, Key Laboratory of Big Data Management Optimization and Decision of Liaoning Province, Dongbei University of Finance of Economics, Dongbei, 116025, P. R. China
| | - Yifan Chen
- State Key Laboratory of Systems Medicine for Cancer, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Jun Pu
- State Key Laboratory of Systems Medicine for Cancer, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
| | - Xiaochun Chen
- Department of Neurology, Fujian Medical University Union Hospital, Fujian Key Laboratory of Molecular Neurology and Institute of Neuroscience, Fujian Medical University, Fuzhou, 350001, P. R. China
| | - Ning Liu
- School of Electronics Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, P. R. China
| | - Qinyong Ye
- Department of Neurology, Fujian Medical University Union Hospital, Fujian Key Laboratory of Molecular Neurology and Institute of Neuroscience, Fujian Medical University, Fuzhou, 350001, P. R. China
| | - Kun Qian
- State Key Laboratory of Systems Medicine for Cancer, Division of Cardiology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- School of Biomedical Engineering, Institute of Medical Robotics and Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
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LeWitt PA, Li J, Wu KH, Lu M. Diagnostic metabolomic profiling of Parkinson's disease biospecimens. Neurobiol Dis 2023; 177:105962. [PMID: 36563791 DOI: 10.1016/j.nbd.2022.105962] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2022] [Revised: 12/02/2022] [Accepted: 12/07/2022] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Reliable and sensitive biomarkers are needed for enhancing and predicting Parkinson's disease (PD) diagnosis. OBJECTIVE To investigate comprehensive metabolomic profiling of biochemicals in CSF and serum for determining diagnostic biomarkers of PD. METHODS Fifty subjects, symptomatic with PD for ≥5 years, were matched to 50 healthy controls (HCs). We used ultrahigh-performance liquid chromatography linked to tandem mass spectrometry (UHPLC-MS/MS) for measuring relative concentrations of ≤1.5 kDalton biochemicals. A reference library created from authentic standards facilitated chemical identifications. Analytes underwent univariate analysis for PD association, with false discovery rate-adjusted p-value (≤0.05) determinations. Multivariate analysis (for identifying a panel of biochemicals discriminating PD from HCs) used several biostatistical methods, including logistic LASSO regression. RESULTS Comparing PD and HCs, strong differentiation was achieved from CSF but not serum specimens. With univariate analysis, 21 CSF compounds exhibited significant differential concentrations. Logistic LASSO regression led to selection of 23 biochemicals (11 shared with those determined by the univariate analysis). The selected compounds, as a group, distinguished PD from HCs, with Area-Under-the-Receiver-Operating-Characteristic (ROC) curve of 0.897. With optimal cutoff, logistic LASSO achieved 100% sensitivity and 96% specificity (and positive and negative predictive values of 96% and 100%). Ten-fold cross-validation gave 84% sensitivity and 82% specificity (and 82% positive and 84% negative predictive values). From the logistic LASSO-chosen regression model, 2 polyamine metabolites (N-acetylcadaverine and N-acetylputrescine) were chosen and had the highest fold-changes in comparing PD to HCs. Another chosen biochemical, acisoga (N-(3-acetamidopropyl)pyrrolidine-2-one), also is a polyamine metabolism derivative. CONCLUSIONS UHPLC-MS/MS assays provided a metabolomic signature highly predictive of PD. These findings provide further evidence for involvement of polyamine pathways in the neurodegeneration of PD.
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Affiliation(s)
- Peter A LeWitt
- Departments of Neurology, Henry Ford Hospital, West Bloomfield, MI, USA; Wayne State University School of Medicine, West Bloomfield, MI, USA.
| | - Jia Li
- The Department of Public Health Science, Henry Ford Health System, Detroit, MI, USA
| | - Kuan-Han Wu
- The Department of Public Health Science, Henry Ford Health System, Detroit, MI, USA
| | - Mei Lu
- The Department of Public Health Science, Henry Ford Health System, Detroit, MI, USA
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Narrative Review Concerning the Clinical Spectrum of Ophthalmological Impairments in Parkinson's Disease. Neurol Int 2023; 15:140-161. [PMID: 36810467 PMCID: PMC9944508 DOI: 10.3390/neurolint15010012] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Revised: 01/20/2023] [Accepted: 01/23/2023] [Indexed: 01/30/2023] Open
Abstract
Ophthalmic non-motor impairments are common in Parkinson's disease patients, from the onset of the neurodegenerative disease and even prior to the development of motor symptoms. This is a very crucial component of the potential for early detection of this disease, even in its earliest stages. Since the ophthalmological disease is extensive and impacts all extraocular and intraocular components of the optical analyzer, a competent assessment of it would be beneficial for the patients. Because the retina is an extension of the nervous system and has the same embryonic genesis as the central nervous system, it is helpful to investigate the retinal changes in Parkinson's disease in order to hypothesize insights that may also be applicable to the brain. As a consequence, the detection of these symptoms and signs may improve the medical evaluation of PD and predict the illness' prognosis. Another valuable aspect of this pathology is the fact that the ophthalmological damage contributes significantly to the decrease in the quality of life of patients with Parkinson's disease. We provide an overview of the most significant ophthalmologic impairments associated with Parkinson's disease. These results certainly constitute a large number of the prevalent visual impairments experienced by PD patients.
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Zacharias HU, Kaleta C, Cossais F, Schaeffer E, Berndt H, Best L, Dost T, Glüsing S, Groussin M, Poyet M, Heinzel S, Bang C, Siebert L, Demetrowitsch T, Leypoldt F, Adelung R, Bartsch T, Bosy-Westphal A, Schwarz K, Berg D. Microbiome and Metabolome Insights into the Role of the Gastrointestinal-Brain Axis in Parkinson's and Alzheimer's Disease: Unveiling Potential Therapeutic Targets. Metabolites 2022; 12:metabo12121222. [PMID: 36557259 PMCID: PMC9786685 DOI: 10.3390/metabo12121222] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 11/25/2022] [Accepted: 11/28/2022] [Indexed: 12/12/2022] Open
Abstract
Neurodegenerative diseases such as Parkinson's (PD) and Alzheimer's disease (AD), the prevalence of which is rapidly rising due to an aging world population and westernization of lifestyles, are expected to put a strong socioeconomic burden on health systems worldwide. Clinical trials of therapies against PD and AD have only shown limited success so far. Therefore, research has extended its scope to a systems medicine point of view, with a particular focus on the gastrointestinal-brain axis as a potential main actor in disease development and progression. Microbiome and metabolome studies have already revealed important insights into disease mechanisms. Both the microbiome and metabolome can be easily manipulated by dietary and lifestyle interventions, and might thus offer novel, readily available therapeutic options to prevent the onset as well as the progression of PD and AD. This review summarizes our current knowledge on the interplay between microbiota, metabolites, and neurodegeneration along the gastrointestinal-brain axis. We further illustrate state-of-the art methods of microbiome and metabolome research as well as metabolic modeling that facilitate the identification of disease pathomechanisms. We conclude with therapeutic options to modulate microbiome composition to prevent or delay neurodegeneration and illustrate potential future research directions to fight PD and AD.
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Affiliation(s)
- Helena U. Zacharias
- Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School, 30625 Hannover, Germany
- Department of Internal Medicine I, University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Correspondence: (H.U.Z.); (C.K.)
| | - Christoph Kaleta
- Research Group Medical Systems Biology, Institute for Experimental Medicine, Kiel University, 24105 Kiel, Germany
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Correspondence: (H.U.Z.); (C.K.)
| | | | - Eva Schaeffer
- Department of Neurology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
| | - Henry Berndt
- Research Group Comparative Immunobiology, Zoological Institute, Kiel University, 24118 Kiel, Germany
| | - Lena Best
- Research Group Medical Systems Biology, Institute for Experimental Medicine, Kiel University, 24105 Kiel, Germany
| | - Thomas Dost
- Research Group Medical Systems Biology, Institute for Experimental Medicine, Kiel University, 24105 Kiel, Germany
| | - Svea Glüsing
- Institute of Human Nutrition and Food Science, Food Technology, Kiel University, 24118 Kiel, Germany
| | - Mathieu Groussin
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
| | - Mathilde Poyet
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Sebastian Heinzel
- Department of Neurology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Institute of Medical Informatics and Statistics, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
| | - Corinna Bang
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
| | - Leonard Siebert
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Functional Nanomaterials, Department of Materials Science, Kiel University, 24143 Kiel, Germany
| | - Tobias Demetrowitsch
- Institute of Human Nutrition and Food Science, Food Technology, Kiel University, 24118 Kiel, Germany
- Kiel Network of Analytical Spectroscopy and Mass Spectrometry, Kiel University, 24118 Kiel, Germany
| | - Frank Leypoldt
- Department of Neurology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
- Neuroimmunology, Institute of Clinical Chemistry, University Medical Center Schleswig-Holstein, 24105 Kiel, Germany
| | - Rainer Adelung
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Functional Nanomaterials, Department of Materials Science, Kiel University, 24143 Kiel, Germany
| | - Thorsten Bartsch
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Department of Neurology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
| | - Anja Bosy-Westphal
- Institute of Human Nutrition and Food Science, Kiel University, 24107 Kiel, Germany
| | - Karin Schwarz
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Institute of Human Nutrition and Food Science, Food Technology, Kiel University, 24118 Kiel, Germany
- Kiel Network of Analytical Spectroscopy and Mass Spectrometry, Kiel University, 24118 Kiel, Germany
| | - Daniela Berg
- Kiel Nano, Surface and Interface Science—KiNSIS, Kiel University, 24118 Kiel, Germany
- Department of Neurology, Kiel University and University Medical Center Schleswig-Holstein, Campus Kiel, 24105 Kiel, Germany
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Gątarek P, Sekulska-Nalewajko J, Bobrowska-Korczaka B, Pawełczyk M, Jastrzębski K, Głąbiński A, Kałużna-Czaplińska J. Plasma Metabolic Disturbances in Parkinson's Disease Patients. Biomedicines 2022; 10:biomedicines10123005. [PMID: 36551761 PMCID: PMC9775245 DOI: 10.3390/biomedicines10123005] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/15/2022] [Accepted: 11/18/2022] [Indexed: 11/24/2022] Open
Abstract
Plasma from patients with Parkinson's disease (PD) is a valuable source of information indicating altered metabolites associated with the risk or progression of the disease. Neurotoxicity of dopaminergic neurons, which is triggered by aggregation of α-synuclein, is the main pathogenic feature of PD. However, a growing body of scientific reports indicates that metabolic changes may precede and directly contribute to neurodegeneration. Identification and characterization of the abnormal metabolic pattern in patients' plasma are therefore crucial for the search for potential PD biomarkers. The aims of the present study were (1) to identify metabolic alterations in plasma metabolome in subjects with PD as compared with the controls; (2) to find new potential markers, some correlations among them; (3) to identify metabolic pathways relevant to the pathophysiology of PD. Plasma samples from patients with PD (n = 25) and control group (n = 12) were collected and the gas chromatography-time-of-flight-mass spectrometry GC-TOFMS-based metabolomics approach was used to evaluate the metabolic changes based on the identified 14 metabolites with significantly altered levels using univariate and multivariate statistical analysis. The panel, including 6 metabolites (L-3-methoxytyrosine, aconitic acid, L-methionine, 13-docosenamide, hippuric acid, 9,12-octadecadienoic acid), was identified to discriminate PD from controls with the area under the curve (AUC) of 0.975, with an accuracy of 92%. We also used statistical criteria to identify the significantly altered level of metabolites. The metabolic pathways involved were associated with linoleic acid metabolism, mitochondrial electron transport chain, glycerolipid metabolism, and bile acid biosynthesis. These abnormal metabolic changes in the plasma of patients with PD were mainly related to the amino acid metabolism, TCA cycle metabolism, and mitochondrial function.
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Affiliation(s)
- Paulina Gątarek
- Institute of General and Ecological Chemistry, Faculty of Chemistry, Lodz University of Technology, 90-924 Lodz, Poland
- CONEM Poland Chemistry and Nutrition Research Group, Lodz University of Technology, 90-924 Lodz, Poland
- Correspondence: (P.G.); (J.K.-C.); Tel.: +48-426-313-091 (J.K.-C.); Fax: +48-426-313-128 (J.K.-C.)
| | | | | | - Małgorzata Pawełczyk
- Department of Neurology and Stroke, Medical University of Lodz, 90-549 Lodz, Poland
| | - Karol Jastrzębski
- Department of Neurology and Stroke, Medical University of Lodz, 90-549 Lodz, Poland
| | - Andrzej Głąbiński
- Department of Neurology and Stroke, Medical University of Lodz, 90-549 Lodz, Poland
| | - Joanna Kałużna-Czaplińska
- Institute of General and Ecological Chemistry, Faculty of Chemistry, Lodz University of Technology, 90-924 Lodz, Poland
- CONEM Poland Chemistry and Nutrition Research Group, Lodz University of Technology, 90-924 Lodz, Poland
- Correspondence: (P.G.); (J.K.-C.); Tel.: +48-426-313-091 (J.K.-C.); Fax: +48-426-313-128 (J.K.-C.)
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