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Morgan AE, Mc Auley MT. Vascular dementia: From pathobiology to emerging perspectives. Ageing Res Rev 2024; 96:102278. [PMID: 38513772 DOI: 10.1016/j.arr.2024.102278] [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: 01/26/2024] [Revised: 03/16/2024] [Accepted: 03/17/2024] [Indexed: 03/23/2024]
Abstract
Vascular dementia (VaD) is the second most common type of dementia. VaD is synonymous with ageing, and its symptoms place a significant burden on the health and wellbeing of older people. Despite the identification of a substantial number of risk factors for VaD, the pathological mechanisms underpinning this disease remain to be fully elucidated. Consequently, a biogerontological imperative exists to highlight the modifiable lifestyle factors which can mitigate against the risk of developing VaD. This review will critically examine some of the factors which have been revealed to modulate VaD risk. The survey commences by providing an overview of the putative mechanisms which are associated with the pathobiology of VaD. Next, the factors which influence the risk of developing VaD are examined. Finally, emerging treatment avenues including epigenetics, the gut microbiome, and pro-longevity pharmaceuticals are discussed. By drawing this key evidence together, it is our hope that it can be used to inform future experimental investigations in this field.
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Affiliation(s)
- Amy Elizabeth Morgan
- School of Health and Sports Sciences, Hope Park, Liverpool Hope University, Liverpool L16 9JD, United Kingdom.
| | - Mark Tomás Mc Auley
- School of Science, Engineering and Environment, University of Salford Manchester, Salford M5 4NT, United Kingdom
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2
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Sakkaki E, Jafari B, Gharesouran J, Rezazadeh M. Gene expression patterns of CRYM and SIGLEC10 in Alzheimer's disease: potential early diagnostic indicators. Mol Biol Rep 2024; 51:349. [PMID: 38401023 DOI: 10.1007/s11033-023-09113-w] [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: 09/09/2023] [Accepted: 12/06/2023] [Indexed: 02/26/2024]
Abstract
BACKGROUND Alzheimer's disease (AD) is a neurological condition that may lead to dementia as well as a slow and steady decline in cognitive ability. Finding early signs that may be used in the diagnosis of AD is still a difficult aim to achieve in the field of medical practice. METHODS AND RESULTS The purpose of this research was to investigate to determine any differences in the gene expression patterns of crystallin mu (CRYM) and sialic acid-binding immunoglobulin-like lectin 10 (SIGLEC10) in whole blood samples obtained from fifty individuals who were diagnosed with AD and fifty individuals as a control group. When compared with controls, it was discovered that the expression of the CRYM gene was substantially decreased in AD patients, but the expression of the SIGLEC10 gene was significantly higher. A positive correlation between CRYM and SIGLEC10 was noticed solely in patients with AD. Furthermore, assessing the diagnostic value of these genes, CRYM and SIGLEC10 transcript levels displayed an area under the curve (AUC) of 0.74 and 0.81, respectively. CONCLUSIONS These results suggest that alterations in CRYM and SIGLEC10 expression may be implicated in AD pathology and that these genes expression levels can potentially serve as biomarkers for early detection and diagnosis of AD. Nevertheless, further validation of these findings requires the inclusion of more extensive and heterogeneous cohorts. The findings derived from this study possess the capability to offer a significant contribution towards the progression of innovative diagnostic and therapeutic strategies for AD.
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Affiliation(s)
- Ehsan Sakkaki
- Department of Genetics, Ahar Branch, Islamic Azad University, Ahar, Iran
- Clinical Research Development Unit of Tabriz Valiasr Hospital, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Behboud Jafari
- Department of Microbiology, Ahar Branch, Islamic Azad University, Ahar, Iran.
| | - Jalal Gharesouran
- Department of Medical Genetics, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran
| | - Maryam Rezazadeh
- Clinical Research Development Unit of Tabriz Valiasr Hospital, Tabriz University of Medical Sciences, Tabriz, Iran.
- Department of Medical Genetics, Faculty of Medicine, Tabriz University of Medical Sciences, Tabriz, Iran.
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Hansra GK, Jayasena T, Hosoki S, Poljak A, Lam BCP, Rust R, Sagare A, Zlokovic B, Thalamuthu A, Sachdev PS. Fluid biomarkers of the neurovascular unit in cerebrovascular disease and vascular cognitive disorders: A systematic review and meta-analysis. CEREBRAL CIRCULATION - COGNITION AND BEHAVIOR 2024; 6:100216. [PMID: 38510579 PMCID: PMC10951911 DOI: 10.1016/j.cccb.2024.100216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/10/2023] [Revised: 01/30/2024] [Accepted: 02/16/2024] [Indexed: 03/22/2024]
Abstract
Background The disruption of the neurovascular unit (NVU), which maintains the integrity of the blood brain barrier (BBB), has been identified as a critical mechanism in the development of cerebrovascular and neurodegenerative disorders. However, the understanding of the pathophysiological mechanisms linking NVU dysfunction to the disorders is incomplete, and reliable blood biomarkers to measure NVU dysfunction are yet to be established. This systematic review and meta-analysis aimed to identify biomarkers associated with BBB dysfunction in large vessel disease, small vessel disease (SVD) and vascular cognitive disorders (VCD). Methods A literature search was conducted in PubMed, EMBASE, Scopus and PsychINFO to identify blood biomarkers related to dysfunction of the NVU in disorders with vascular pathologies published until 20 November 2023. Studies that assayed one or more specific markers in human serum or plasma were included. Quality of studies was assessed using the Newcastle-Ottawa Quality Assessment Scale. Effects were pooled and methodological heterogeneity examined using the random effects model. Results A total of 112 studies were included in this review. Where study numbers allowed, biomarkers were analysed using random effect meta-analysis for VCD (1 biomarker; 5 studies) and cerebrovascular disorders, including stroke and SVD (9 biomarkers; 29 studies) while all remaining biomarkers (n = 17 biomarkers; 78 studies) were examined through qualitative analysis. Results of the meta-analysis revealed that cerebrospinal fluid/serum albumin quotient (Q-Alb) reliably differentiates VCD patients from healthy controls (MD = 2.77; 95 % CI = 1.97-3.57; p < 0.0001) while commonly measured biomarkers of endothelial dysfunction (VEGF, VCAM-1, ICAM-1, vWF and E-selectin) and neuronal injury (NfL) were significantly elevated in vascular pathologies. A qualitative assessment of non-meta-analysed biomarkers revealed NSE, NfL, vWF, ICAM-1, VCAM-1, lipocalin-2, MMP-2 and MMP-9 levels to be upregulated in VCD, although these findings were not consistently replicated. Conclusions This review identifies several promising biomarkers of NVU dysfunction which require further validation. A panel of biomarkers representing multiple pathophysiological pathways may offer greater discriminative power in distinguishing possible disease mechanisms of VCD.
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Affiliation(s)
- Gurpreet Kaur Hansra
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney, Australia
| | - Tharusha Jayasena
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney, Australia
| | - Satoshi Hosoki
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney, Australia
- Department of Neurology, National Cerebral and Cardiovascular Centre, Suita, Japan
| | - Anne Poljak
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney, Australia
- Bioanalytical Mass Spectrometry Facility, Mark Wainwright Analytical Centre, University of New South Wales, NSW, Australia
| | - Ben Chun Pan Lam
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney, Australia
- School of Psychology and Public Health, La Trobe University, Melbourne, Australia
| | - Ruslan Rust
- Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Abhay Sagare
- Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Berislav Zlokovic
- Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Anbupalam Thalamuthu
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney, Australia
| | - Perminder S. Sachdev
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, University of New South Wales, Sydney, Australia
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Ni YC, Lin ZK, Cheng CH, Pai MC, Chiu PY, Chang CC, Chang YT, Hung GU, Lin KJ, Hsiao IT, Lin CY, Yang HC. Classification Prediction of Alzheimer's Disease and Vascular Dementia Using Physiological Data and ECD SPECT Images. Diagnostics (Basel) 2024; 14:365. [PMID: 38396404 PMCID: PMC10888136 DOI: 10.3390/diagnostics14040365] [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: 12/06/2023] [Revised: 01/18/2024] [Accepted: 01/29/2024] [Indexed: 02/25/2024] Open
Abstract
Alzheimer's disease (AD) and vascular dementia (VaD) are the two most common forms of dementia. However, their neuropsychological and pathological features often overlap, making it difficult to distinguish between AD and VaD. In addition to clinical consultation and laboratory examinations, clinical dementia diagnosis in Taiwan will also include Tc-99m-ECD SPECT imaging examination. Through machine learning and deep learning technology, we explored the feasibility of using the above clinical practice data to distinguish AD and VaD. We used the physiological data (33 features) and Tc-99m-ECD SPECT images of 112 AD patients and 85 VaD patients in the Taiwanese Nuclear Medicine Brain Image Database to train the classification model. The results, after filtering by the number of SVM RFE 5-fold features, show that the average accuracy of physiological data in distinguishing AD/VaD is 81.22% and the AUC is 0.836; the average accuracy of training images using the Inception V3 model is 85% and the AUC is 0.95. Finally, Grad-CAM heatmap was used to visualize the areas of concern of the model and compared with the SPM analysis method to further understand the differences. This research method can quickly use machine learning and deep learning models to automatically extract image features based on a small amount of general clinical data to objectively distinguish AD and VaD.
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Affiliation(s)
- Yu-Ching Ni
- Department of Radiation Protection, National Atomic Research Institute, Taoyuan 325, Taiwan
| | - Zhi-Kun Lin
- Department of Radiation Protection, National Atomic Research Institute, Taoyuan 325, Taiwan
| | - Chen-Han Cheng
- Department of Radiation Protection, National Atomic Research Institute, Taoyuan 325, Taiwan
| | - Ming-Chyi Pai
- Division of Behavioral Neurology, Department of Neurology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan
- Institute of Gerontology, National Cheng Kung University, Tainan 701, Taiwan
- Alzheimer’s Disease Research Center, National Cheng Kung University Hospital, Tainan 704, Taiwan
| | - Pai-Yi Chiu
- Department of Neurology, Show Chwan Memorial Hospital, Changhua 500, Taiwan
| | - Chiung-Chih Chang
- Department of Neurology, Institute of Translational Research in Biomedicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 833, Taiwan
| | - Ya-Ting Chang
- Department of Neurology, Institute of Translational Research in Biomedicine, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung 833, Taiwan
| | - Guang-Uei Hung
- Department of Nuclear Medicine, Chang Bing Show Chwan Memorial Hospital, Changhua 505, Taiwan
| | - Kun-Ju Lin
- Healthy Aging Research Center and Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan
- Molecular Imaging Center and Department of Nuclear Medicine, Chang Gung University and Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
| | - Ing-Tsung Hsiao
- Healthy Aging Research Center and Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan 333, Taiwan
- Molecular Imaging Center and Department of Nuclear Medicine, Chang Gung University and Chang Gung Memorial Hospital, Taoyuan 333, Taiwan
| | - Chia-Yu Lin
- Department of Radiation Protection, National Atomic Research Institute, Taoyuan 325, Taiwan
| | - Hui-Chieh Yang
- Department of Radiation Protection, National Atomic Research Institute, Taoyuan 325, Taiwan
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Hosoki S, Hansra GK, Jayasena T, Poljak A, Mather KA, Catts VS, Rust R, Sagare A, Kovacic JC, Brodtmann A, Wallin A, Zlokovic BV, Ihara M, Sachdev PS. Molecular biomarkers for vascular cognitive impairment and dementia. Nat Rev Neurol 2023; 19:737-753. [PMID: 37957261 DOI: 10.1038/s41582-023-00884-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/29/2023] [Indexed: 11/15/2023]
Abstract
As disease-specific interventions for dementia are being developed, the ability to identify the underlying pathology and dementia subtypes is increasingly important. Vascular cognitive impairment and dementia (VCID) is the second most common cause of dementia after Alzheimer disease, but progress in identifying molecular biomarkers for accurate diagnosis of VCID has been relatively limited. In this Review, we examine the roles of large and small vessel disease in VCID, considering the underlying pathophysiological processes that lead to vascular brain injury, including atherosclerosis, arteriolosclerosis, ischaemic injury, haemorrhage, hypoperfusion, endothelial dysfunction, blood-brain barrier breakdown, inflammation, oxidative stress, hypoxia, and neuronal and glial degeneration. We consider the key molecules in these processes, including proteins and peptides, metabolites, lipids and circulating RNA, and consider their potential as molecular biomarkers alone and in combination. We also discuss the challenges in translating the promise of these biomarkers into clinical application.
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Affiliation(s)
- Satoshi Hosoki
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- Department of Neurology, National Cerebral and Cardiovascular Center, Osaka, Japan
| | - Gurpreet K Hansra
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - Tharusha Jayasena
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - Anne Poljak
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- Bioanalytical Mass Spectrometry Facility, Mark Wainwright Analytical Centre, University of New South Wales, Sydney, NSW, Australia
| | - Karen A Mather
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - Vibeke S Catts
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - Ruslan Rust
- Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Abhay Sagare
- Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jason C Kovacic
- Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, New York, NY, USA
- Victor Chang Cardiac Research Institute, Darlinghurst, NSW, Australia
- St Vincent's Clinical School, University of New South Wales, Sydney, NSW, Australia
| | - Amy Brodtmann
- Department of Neurology, Royal Melbourne Hospital, Parkville, VIC, Australia
| | - Anders Wallin
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Berislav V Zlokovic
- Department of Physiology and Neuroscience, Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Masafumi Ihara
- Department of Neurology, National Cerebral and Cardiovascular Center, Osaka, Japan
| | - Perminder S Sachdev
- Centre for Healthy Brain Ageing, Discipline of Psychiatry and Mental Health, School of Clinical Medicine, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia.
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Jiang Y, Zhao Y, Li ZY, Chen S, Fang F, Cai JH. Potential roles of microRNAs and long noncoding RNAs as diagnostic, prognostic and therapeutic biomarkers in coronary artery disease. Int J Cardiol 2023:S0167-5273(23)00478-3. [PMID: 37019219 DOI: 10.1016/j.ijcard.2023.03.067] [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: 11/24/2022] [Revised: 02/27/2023] [Accepted: 03/31/2023] [Indexed: 04/07/2023]
Abstract
Coronary artery disease (CAD), which is mainly caused by atherosclerotic processes in coronary arteries, became a significant health issue. MicroRNAs (miRNAs), and long noncoding RNAs (lncRNAs), have been shown to be stable in plasma and could thereby be adopted as biomarkers for CAD diagnosis and treatment. MiRNAs can regulate CAD development through different pathways and mechanisms, including modulation of vascular smooth muscle cell (VSMC) activity, inflammatory responses, myocardial injury, angiogenesis, and leukocyte adhesion. Similarly, previously studies have indicated that the causal effects of lncRNAs in CAD pathogenesis and their utility in CAD diagnosis and treatment, has been found to lead to cell cycle transition, proliferation dysregulation, and migration in favour of CAD development. Differential expression of miRNAs and lncRNAs in CAD patients has been identified and served as diagnostic, prognostic and therapeutic biomarkers for the assessment of CAD patients. Thus, in the current review, we summarize the functions of miRNAs and lncRNAs, which aimed to identify novel targets for the CAD diagnosis, prognosis, and treatment.
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Affiliation(s)
- Yong Jiang
- Department of Laboratory Medicine, Jilin Medical University, No. 5 Jilin Street, Jilin 132013, China.
| | - Ying Zhao
- Department of Cardiology, Jilin Central Hospital, Jilin 132011, China
| | - Zheng-Yi Li
- Department of Laboratory Medicine, Jilin Medical University, No. 5 Jilin Street, Jilin 132013, China
| | - Shuang Chen
- Department of Laboratory Medicine, Jilin Medical University, No. 5 Jilin Street, Jilin 132013, China
| | - Fang Fang
- Department of Laboratory Medicine, Jilin Medical University, No. 5 Jilin Street, Jilin 132013, China.
| | - Jian-Hui Cai
- Department of Clinical Medicine, Jilin Medical University, Jilin 132013, China; Jilin Collaborative Innovation Center for Antibody Engineering, Jilin Medical University, Jilin 132013, China.
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Martínez-Iglesias O, Naidoo V, Carril JC, Seoane S, Cacabelos N, Cacabelos R. Gene Expression Profiling as a Novel Diagnostic Tool for Neurodegenerative Disorders. Int J Mol Sci 2023; 24:ijms24065746. [PMID: 36982820 PMCID: PMC10057696 DOI: 10.3390/ijms24065746] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/02/2023] [Accepted: 03/13/2023] [Indexed: 03/19/2023] Open
Abstract
There is a lack of effective diagnostic biomarkers for neurodegenerative disorders (NDDs). Here, we established gene expression profiles for diagnosing Alzheimer’s disease (AD), Parkinson’s disease (PD), and vascular (VaD)/mixed dementia. Patients with AD had decreased APOE, PSEN1, and ABCA7 mRNA expression. Subjects with VaD/mixed dementia had 98% higher PICALM mRNA levels, but 75% lower ABCA7 mRNA expression than healthy individuals. Patients with PD and PD-related disorders showed increased SNCA mRNA levels. There were no differences in mRNA expression for OPRK1, NTRK2, and LRRK2 between healthy subjects and NDD patients. APOE mRNA expression had high diagnostic accuracy for AD, and moderate accuracy for PD and VaD/mixed dementia. PSEN1 mRNA expression showed promising accuracy for AD. PICALM mRNA expression was less accurate as a biomarker for AD. ABCA7 and SNCA mRNA expression showed high-to-excellent diagnostic accuracy for AD and PD, and moderate-to-high accuracy for VaD/mixed dementia. The APOE E4 allele reduced APOE expression in patients with different APOE genotypes. There was no association between PSEN1, PICALM, ABCA7, and SNCA gene polymorphisms and expression. Our study suggests that gene expression analysis has diagnostic value for NDDs and provides a liquid biopsy alternative to current diagnostic methods.
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Salai KHT, Wu LY, Chong JR, Chai YL, Gyanwali B, Robert C, Hilal S, Venketasubramanian N, Dawe GS, Chen CP, Lai MKP. Elevated Soluble TNF-Receptor 1 in the Serum of Predementia Subjects with Cerebral Small Vessel Disease. Biomolecules 2023; 13:biom13030525. [PMID: 36979460 PMCID: PMC10046240 DOI: 10.3390/biom13030525] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 03/07/2023] [Accepted: 03/10/2023] [Indexed: 03/15/2023] Open
Abstract
Tumor necrosis factor-receptor 1 (TNF-R1)-mediated signaling is critical to the regulation of inflammatory responses. TNF-R1 can be proteolytically released into systemic blood circulation in a soluble form (sTNF-R1), where it binds to circulating TNF and functions to attenuate TNF-mediated inflammation. Increases of peripheral sTNF-R1 have been reported in both Alzheimer’s disease (AD) dementia and vascular dementia (VaD). However, the status of sTNF-R1 in predementia subjects (cognitive impairment, no dementia, CIND) is unknown, and putative associations with cerebral small vessel disease (CSVD), as well as with longitudinal changes in cognitive functions are unclear. We measured baseline serum sTNF-R1 in a longitudinally assessed cohort of 93 controls and 103 CIND, along with neuropsychological evaluations and neuroimaging assessments. Serum sTNF-R1 levels were increased in CIND compared with controls (p < 0.001). Higher baseline sTNF-R1 levels were specifically associated with lacunar infarcts (rate ratio = 6.91, 95% CI 3.19–14.96, p < 0.001), as well as lower rates of cognitive decline in the CIND subgroup. Our data suggest that sTNF-R1 interacts with vascular cognitive impairment in a complex manner at predementia stages, with elevated levels associated with more severe CSVD at baseline, but which may subsequently be protective against cognitive decline.
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Affiliation(s)
- Kaung H. T. Salai
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
| | - Liu-Yun Wu
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore
- Memory Aging and Cognition Centre, National University Health System, Singapore 117600, Singapore
| | - Joyce R. Chong
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore
- Memory Aging and Cognition Centre, National University Health System, Singapore 117600, Singapore
| | - Yuek Ling Chai
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore
- Memory Aging and Cognition Centre, National University Health System, Singapore 117600, Singapore
| | - Bibek Gyanwali
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore
- Memory Aging and Cognition Centre, National University Health System, Singapore 117600, Singapore
| | - Caroline Robert
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore
| | - Saima Hilal
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore
- Memory Aging and Cognition Centre, National University Health System, Singapore 117600, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore 117597, Singapore
- Departments of Epidemiology and Radiology & Nuclear Medicine, Erasmus University Medical Center, 3015 GD Rotterdam, The Netherlands
| | | | - Gavin S. Dawe
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
- Precision Medicine Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117596, Singapore
- Neurobiology Programme, Life Sciences Institute, Centre for Life Sciences, National University of Singapore, Singapore 117456, Singapore
| | - Christopher P. Chen
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
- Memory Aging and Cognition Centre, National University Health System, Singapore 117600, Singapore
| | - Mitchell K. P. Lai
- Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117600, Singapore
- Healthy Longevity Translational Research Programme, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 117456, Singapore
- Memory Aging and Cognition Centre, National University Health System, Singapore 117600, Singapore
- Correspondence:
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Gorelick PB. Blood and Cerebrospinal Fluid Biomarkers in Vascular Dementia and Alzheimer's Disease: A Brief Review. Clin Geriatr Med 2023; 39:67-76. [PMID: 36404033 DOI: 10.1016/j.cger.2022.08.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
The Maintenance of brain health is a lifelong process whereby potentially deleterious exposures such as cardiovascular risks, amyloid beta, and phosphorylated tau may adversely affect the brain decades before there are clinical manifestations. Thus, the early structural and neuropathological foundation for the development of cognitive impairment and its allied features later in life may provide precursor targets such that interventions may be applied to prevent or slow cognitively impairing processes if the underlying mechanism(s) can be addressed in time.
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Affiliation(s)
- Philip B Gorelick
- Section of Stroke and Neurocritical Care, Davee Department of Neurology, Northwestern University Feinberg School of Medicine, 625 North Michigan Avenue Suite 1150, Chicago, IL 60611, USA.
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10
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Li D, Huang Z, Dai Y, Guo L, Lin S, Liu X. Bioinformatic identification of potential biomarkers and therapeutic targets in carotid atherosclerosis and vascular dementia. Front Neurol 2023; 13:1091453. [PMID: 36703641 PMCID: PMC9872033 DOI: 10.3389/fneur.2022.1091453] [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: 11/07/2022] [Accepted: 12/22/2022] [Indexed: 01/12/2023] Open
Abstract
Background Vascular disease is the second most common cause of dementia. The prevalence of vascular dementia (VaD) has increased over the past decade. However, there are no licensed treatments for this disease. Carotid atherosclerosis (CAS) is highly prevalent and is the main cause of ischemic stroke and VaD. We studied co-expressed genes to understand the relationships between CAS and VaD and further reveal the potential biomarkers and therapeutic targets of CAS and VaD. Methods CAS and VaD differentially expressed genes (DEGs) were identified through bioinformatic analysis Gene Expression Omnibus (GEO) datasets GSE43292 and GSE122063, respectively. Furthermore, a variety of target prediction methods and network analysis approaches were used to assess the protein-protein interaction (PPI) networks, the Gene Ontology (GO) terms, and the pathway enrichment for DEGs, and the top 7 hub genes, coupled with corresponding predicted miRNAs involved in CAS and VaD, were assessed as well. Result A total of 60 upregulated DEGs and 159 downregulated DEGs were identified, of which the top 7 hub genes with a high degree of connectivity were selected. Overexpression of these hub genes was associated with CAS and VaD. Finally, the top 7 hub genes were coupled with corresponding predicted miRNAs. hsa-miR-567 and hsa-miR-4652-5p may be significantly associated with CAS and VaD.
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Shi Y, Mao H, Gao Q, Xi G, Zeng S, Ma L, Zhang X, Li L, Wang Z, Ji W, He P, You Y, Chen K, Shao J, Mao X, Fang X, Wang F. Potential of brain age in identifying early cognitive impairment in subcortical small-vessel disease patients. Front Aging Neurosci 2022; 14:973054. [PMID: 36118707 PMCID: PMC9475066 DOI: 10.3389/fnagi.2022.973054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2022] [Accepted: 08/16/2022] [Indexed: 12/04/2022] Open
Abstract
Background Reliable and individualized biomarkers are crucial for identifying early cognitive impairment in subcortical small-vessel disease (SSVD) patients. Personalized brain age prediction can effectively reflect cognitive impairment. Thus, the present study aimed to investigate the association of brain age with cognitive function in SSVD patients and assess the potential value of brain age in clinical assessment of SSVD. Materials and methods A prediction model for brain age using the relevance vector regression algorithm was developed using 35 healthy controls. Subsequently, the prediction model was tested using 51 SSVD patients [24 subjective cognitive impairment (SCI) patients and 27 mild cognitive impairment (MCI) patients] to identify brain age-related imaging features. A support vector machine (SVM)-based classification model was constructed to differentiate MCI from SCI patients. The neurobiological basis of brain age-related imaging features was also investigated based on cognitive assessments and oxidative stress biomarkers. Results The gray matter volume (GMV) imaging features accurately predicted brain age in individual patients with SSVD (R2 = 0.535, p < 0.001). The GMV features were primarily distributed across the subcortical system (e.g., thalamus) and dorsal attention network. SSVD patients with age acceleration showed significantly poorer Mini-Mental State Examination and Montreal Cognitive Assessment (MoCA) scores. The classification model based on GMV features could accurately distinguish MCI patients from SCI patients (area under the curve = 0.883). The classification outputs of the classification model exhibited significant associations with MoCA scores, Trail Making Tests A and B scores, Stroop Color and Word Test C scores, information processing speed total scores, and plasma levels of total antioxidant capacity in SSVD patients. Conclusion Brain age can be accurately quantified using GMV imaging data and shows potential clinical value for identifying early cognitive impairment in SSVD patients.
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Affiliation(s)
- Yachen Shi
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Interventional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Functional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- *Correspondence: Yachen Shi,
| | - Haixia Mao
- Department of Radiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Qianqian Gao
- Department of Radiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Guangjun Xi
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Interventional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Siyuan Zeng
- Department of Radiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Lin Ma
- Department of Radiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Xiuping Zhang
- Department of Radiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Lei Li
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Interventional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Zhuoyi Wang
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Interventional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Wei Ji
- Department of Functional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Neurosurgery, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Ping He
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Yiping You
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Functional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Kefei Chen
- Department of Functional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Neurosurgery, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Junfei Shao
- Department of Radiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Xuqiang Mao
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
| | - Xiangming Fang
- Department of Radiology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Xiangming Fang,
| | - Feng Wang
- Department of Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Department of Interventional Neurology, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
- Feng Wang,
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Metabolomics Deciphering the Potential Biomarkers of Hengqing I Prescription against Vascular Dementia. EVIDENCE-BASED COMPLEMENTARY AND ALTERNATIVE MEDICINE 2022; 2022:1636145. [PMID: 35399642 PMCID: PMC8986386 DOI: 10.1155/2022/1636145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 03/08/2022] [Accepted: 03/11/2022] [Indexed: 11/17/2022]
Abstract
With the aging of population, vascular dementia (VaD) seriously threatens people's health and quality of life. It is of great significance to explore biomarkers of VaD from the perspective of metabolomics and traditional Chinese medicine (TCM). Therefore, VaD was divided into kidney deficiency and blood stasis syndrome (KDBS) and non-KDBS according to TCM. Then, some patients received the treatment of Hengqing I (HQI) prescription. The urine of six groups (VaD group, normal group, KDBS group, non-KDBS group, HQI group, and control group) was detected on LC-MS/MS. Multivariate statistical analysis showed that the metabolic profiles of the three comparisons were significantly different. The top analysis-ready molecules of downregulated histamine and upregulated biotin, methionine, pantothenic acid, SAH, histidine, and kaempferol may be the most related metabolites. These putative biomarkers play an important role in the regulation of key metabolic processes linked to VaD. Additionally, pathway analysis showed aminoacyl-tRNA biosynthesis, and amino acids metabolic pathways were highly correlated with the occurrence of VaD. In this present paper, vitamins, amino acids, and their derivatives were selected as the basis for VaD diagnosis and treatment monitoring, and the significance of TCM classification and Hengqing I prescription in the treatment of VaD was discussed.
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Lee EC, Hong DY, Lee DH, Park SW, Lee JY, Jeong JH, Kim EY, Chung HM, Hong KS, Park SP, Lee MR, Oh JS. Inflammation and Rho-Associated Protein Kinase-Induced Brain Changes in Vascular Dementia. Biomedicines 2022; 10:biomedicines10020446. [PMID: 35203655 PMCID: PMC8962349 DOI: 10.3390/biomedicines10020446] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Revised: 02/11/2022] [Accepted: 02/11/2022] [Indexed: 01/27/2023] Open
Abstract
Patients with vascular dementia, caused by cerebral ischemia, experience long-term cognitive impairment due to the lack of effective treatment. The mechanisms of and treatments for vascular dementia have been investigated in various animal models; however, the insufficient information on gene expression changes that define pathological conditions hampers progress. To investigate the underlying mechanism of and facilitate treatment development for vascular dementia, we established a mouse model of chronic cerebral hypoperfusion, including bilateral carotid artery stenosis, by using microcoils, and elucidated the molecular pathway underlying vascular dementia development. Rho-associated protein kinase (ROCK) 1/2, which regulates cellular structure, and inflammatory cytokines (IL-1 and IL-6) were upregulated in the vascular dementia model. However, expression of claudin-5, which maintains the blood–brain barrier, and MAP2 as a nerve cell-specific factor, was decreased in the hippocampal region of the vascular dementia model. Thus, we revealed that ROCK pathway activation loosens the tight junction of the blood–brain barrier and increases the influx of inflammatory cytokines into the hippocampal region, leading to neuronal death and causing cognitive and emotional dysfunction. Our vascular dementia model allows effective study of the vascular dementia mechanism. Moreover, the ROCK pathway may be a target for vascular dementia treatment development in the future.
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Affiliation(s)
- Eun Chae Lee
- Department of Neurosurgery, College of Medicine, Cheonan Hospital, Soonchunhyang University, Cheonan 31151, Korea; (E.C.L.); (D.-Y.H.); (D.-H.L.); (S.-W.P.); (J.Y.L.)
- Soonchunhyang Institute of Medi-Bio Science (SIMS), Soon Chun Hyang University, Cheonan 31151, Korea;
| | - Dong-Yong Hong
- Department of Neurosurgery, College of Medicine, Cheonan Hospital, Soonchunhyang University, Cheonan 31151, Korea; (E.C.L.); (D.-Y.H.); (D.-H.L.); (S.-W.P.); (J.Y.L.)
- Soonchunhyang Institute of Medi-Bio Science (SIMS), Soon Chun Hyang University, Cheonan 31151, Korea;
| | - Dong-Hun Lee
- Department of Neurosurgery, College of Medicine, Cheonan Hospital, Soonchunhyang University, Cheonan 31151, Korea; (E.C.L.); (D.-Y.H.); (D.-H.L.); (S.-W.P.); (J.Y.L.)
- Soonchunhyang Institute of Medi-Bio Science (SIMS), Soon Chun Hyang University, Cheonan 31151, Korea;
| | - Sang-Won Park
- Department of Neurosurgery, College of Medicine, Cheonan Hospital, Soonchunhyang University, Cheonan 31151, Korea; (E.C.L.); (D.-Y.H.); (D.-H.L.); (S.-W.P.); (J.Y.L.)
- Soonchunhyang Institute of Medi-Bio Science (SIMS), Soon Chun Hyang University, Cheonan 31151, Korea;
| | - Ji Young Lee
- Department of Neurosurgery, College of Medicine, Cheonan Hospital, Soonchunhyang University, Cheonan 31151, Korea; (E.C.L.); (D.-Y.H.); (D.-H.L.); (S.-W.P.); (J.Y.L.)
| | - Ji Hun Jeong
- Soonchunhyang Institute of Medi-Bio Science (SIMS), Soon Chun Hyang University, Cheonan 31151, Korea;
| | - Eun-Young Kim
- Mireacellbio Co., Ltd., Seoul 04795, Korea; (E.-Y.K.); (H.-M.C.); (K.-S.H.); (S.-P.P.)
| | - Hyung-Min Chung
- Mireacellbio Co., Ltd., Seoul 04795, Korea; (E.-Y.K.); (H.-M.C.); (K.-S.H.); (S.-P.P.)
- Department of Stem Cell Biology, School of Medicine, Konkuk University, Seoul 05029, Korea
| | - Ki-Sung Hong
- Mireacellbio Co., Ltd., Seoul 04795, Korea; (E.-Y.K.); (H.-M.C.); (K.-S.H.); (S.-P.P.)
| | - Se-Pill Park
- Mireacellbio Co., Ltd., Seoul 04795, Korea; (E.-Y.K.); (H.-M.C.); (K.-S.H.); (S.-P.P.)
- Faculty of Biotechnology, College of Applied Life Sciences, Jeju National University, Jeju 63243, Korea
| | - Man Ryul Lee
- Soonchunhyang Institute of Medi-Bio Science (SIMS), Soon Chun Hyang University, Cheonan 31151, Korea;
- Correspondence: (M.R.L.); (J.S.O.)
| | - Jae Sang Oh
- Department of Neurosurgery, College of Medicine, Cheonan Hospital, Soonchunhyang University, Cheonan 31151, Korea; (E.C.L.); (D.-Y.H.); (D.-H.L.); (S.-W.P.); (J.Y.L.)
- Soonchunhyang Institute of Medi-Bio Science (SIMS), Soon Chun Hyang University, Cheonan 31151, Korea;
- Correspondence: (M.R.L.); (J.S.O.)
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Direct Rating Estimation of Enlarged Perivascular Spaces (EPVS) in Brain MRI Using Deep Neural Network. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11209398] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
In this article, we propose a deep-learning-based estimation model for rating enlarged perivascular spaces (EPVS) in the brain’s basal ganglia region using T2-weighted magnetic resonance imaging (MRI) images. The proposed method estimates the EPVS rating directly from the T2-weighted MRI without using either the detection or the segmentation of EVPS. The model uses the cropped basal ganglia region on the T2-weighted MRI. We formulated the rating of EPVS as a multi-class classification problem. Model performance was evaluated using 96 subjects’ T2-weighted MRI data that were collected from two hospitals. The results show that the proposed method can automatically rate EPVS—demonstrating great potential to be used as a risk indicator of dementia to aid early diagnosis.
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