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O'Connell GC, Smothers CG, Wang J, Ruksakulpiwat S, Armentrout BL. Brain Expression Levels of Commonly Measured Blood Biomarkers of Neurological Damage Differ with Respect to Sex, Race, and Age. Neuroscience 2024; 551:79-93. [PMID: 38762083 DOI: 10.1016/j.neuroscience.2024.05.017] [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: 03/03/2024] [Revised: 04/30/2024] [Accepted: 05/13/2024] [Indexed: 05/20/2024]
Abstract
It is increasingly evident that blood biomarkers have potential to improve the diagnosis and management of both acute and chronic neurological conditions. The most well-studied candidates, and arguably those with the broadest utility, are proteins that are highly enriched in neural tissues and released into circulation upon cellular damage. It is currently unknown how the brain expression levels of these proteins is influenced by demographic factors such as sex, race, and age. Given that source tissue abundance is likely a key determinant of the levels observed in the blood during neurological pathology, understanding such influences is important in terms of identifying potential clinical scenarios that could produce diagnostic bias. In this study, we leveraged existing mRNA sequencing data originating from 2,642 normal brain specimens harvested from 382 human donors to examine potential demographic variability in the expression levels of genes which code for 28 candidate blood biomarkers of neurological damage. Existing mass spectrometry data originating from 26 additional normal brain specimens harvested from 26 separate human donors was subsequently used to tentatively assess whether observed transcriptional variance was likely to produce corresponding variance in terms of protein abundance. Genes associated with several well-studied or emerging candidate biomarkers including neurofilament light chain (NfL), ubiquitin carboxyl-terminal hydrolase isozyme L1 (UCH-L1), neuron-specific enolase (NSE), and synaptosomal-associated protein 25 (SNAP-25) exhibited significant differences in expression with respect to sex, race, and age. In many instances, these differences in brain expression align well with and provide a mechanistic explanation for previously reported differences in blood levels.
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Affiliation(s)
- Grant C O'Connell
- Molecular Biomarker Core, Case Western Reserve University, Cleveland, OH, USA; School of Nursing, Case Western Reserve University, Cleveland, OH, USA.
| | | | - Jing Wang
- Molecular Biomarker Core, Case Western Reserve University, Cleveland, OH, USA; School of Nursing, Case Western Reserve University, Cleveland, OH, USA
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Andrés-Benito P, Vázquez-Costa JF, Ñungo Garzón NC, Colomina MJ, Marco C, González L, Terrafeta C, Domínguez R, Ferrer I, Povedano M. Neurodegeneration Biomarkers in Adult Spinal Muscular Atrophy (SMA) Patients Treated with Nusinersen. Int J Mol Sci 2024; 25:3810. [PMID: 38612621 PMCID: PMC11011665 DOI: 10.3390/ijms25073810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2024] [Revised: 03/26/2024] [Accepted: 03/27/2024] [Indexed: 04/14/2024] Open
Abstract
The objective of this study is to evaluate biomarkers for neurodegenerative disorders in adult SMA patients and their potential for monitoring the response to nusinersen. Biomarkers for neurodegenerative disorders were assessed in plasma and CSF samples obtained from a total of 30 healthy older adult controls and 31 patients with adult SMA type 2 and 3. The samples were collected before and during nusinersen treatment at various time points, approximately at 2, 6, 10, and 22 months. Using ELISA technology, the levels of total tau, pNF-H, NF-L, sAPPβ, Aβ40, Aβ42, and YKL-40 were evaluated in CSF samples. Additionally, plasma samples were used to measure NF-L and total tau levels using SIMOA technology. SMA patients showed improvements in clinical outcomes after nusinersen treatment, which were statistically significant only in walkers, in RULM (p = 0.04) and HFMSE (p = 0.05) at 24 months. A reduction in sAPPβ levels was found after nusinersen treatment, but these levels did not correlate with clinical outcomes. Other neurodegeneration biomarkers (NF-L, pNF-H, total tau, YKL-40, Aβ40, and Aβ42) were not found consistently changed with nusinersen treatment. The slow progression rate and mild treatment response of adult SMA types 2 and 3 may not lead to detectable changes in common markers of axonal degradation, inflammation, or neurodegeneration, since it does not involve large pools of damaged neurons as observed in pediatric forms. However, changes in biomarkers associated with the APP processing pathway might be linked to treatment administration. Further studies are warranted to better understand these findings.
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Affiliation(s)
- Pol Andrés-Benito
- Neurologic Diseases and Neurogenetics Group, Institute of Biomedical Research (IDIBELL), 08907 Barcelona, Spain
- CIBERNED (Network Centre of Biomedical Research of Neurodegenerative Diseases), Institute of Health Carlos III, 08907 Barcelona, Spain
| | - Juan Francisco Vázquez-Costa
- Neuromuscular Unit and ERN-NMD Group, Department of Neurology, Hospital Universitario y Politécnico La Fe and IIS La Fe, Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), 46026 Valencia, Spain
- Department of Medicine, University of Valencia, 46021 Valencia, Spain
| | - Nancy Carolina Ñungo Garzón
- Neuromuscular Unit and ERN-NMD Group, Department of Neurology, Hospital Universitario y Politécnico La Fe and IIS La Fe, Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), 46026 Valencia, Spain
| | - María J. Colomina
- Anesthesia and Critical Care Department, Bellvitge University Hospital-University of Barcelona, 08907 Barcelona, Spain
| | - Carla Marco
- Functional Unit of Amyotrophic Lateral Sclerosis (UFELA), Department of Neurology, Bellvitge University Hospital, 08907 Barcelona, Spain
| | - Laura González
- Functional Unit of Amyotrophic Lateral Sclerosis (UFELA), Department of Neurology, Bellvitge University Hospital, 08907 Barcelona, Spain
| | - Cristina Terrafeta
- Functional Unit of Amyotrophic Lateral Sclerosis (UFELA), Department of Neurology, Bellvitge University Hospital, 08907 Barcelona, Spain
| | - Raúl Domínguez
- Neurologic Diseases and Neurogenetics Group, Institute of Biomedical Research (IDIBELL), 08907 Barcelona, Spain
- CIBERNED (Network Centre of Biomedical Research of Neurodegenerative Diseases), Institute of Health Carlos III, 08907 Barcelona, Spain
- Functional Unit of Amyotrophic Lateral Sclerosis (UFELA), Department of Neurology, Bellvitge University Hospital, 08907 Barcelona, Spain
| | - Isidro Ferrer
- CIBERNED (Network Centre of Biomedical Research of Neurodegenerative Diseases), Institute of Health Carlos III, 08907 Barcelona, Spain
- Neuropathology Group, Institute of Biomedical Research (IDIBELL), 08907 Barcelona, Spain
- Department of Pathology and Experimental Therapeutics, University of Barcelona, 08907 Barcelona, Spain
| | - Mónica Povedano
- Neurologic Diseases and Neurogenetics Group, Institute of Biomedical Research (IDIBELL), 08907 Barcelona, Spain
- CIBERNED (Network Centre of Biomedical Research of Neurodegenerative Diseases), Institute of Health Carlos III, 08907 Barcelona, Spain
- Functional Unit of Amyotrophic Lateral Sclerosis (UFELA), Department of Neurology, Bellvitge University Hospital, 08907 Barcelona, Spain
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Foley KE, Winder Z, Sudduth TL, Martin BJ, Nelson PT, Jicha GA, Harp JP, Weekman EM, Wilcock DM. Alzheimer's disease and inflammatory biomarkers positively correlate in plasma in the UK-ADRC cohort. Alzheimers Dement 2024; 20:1374-1386. [PMID: 38011580 PMCID: PMC10917006 DOI: 10.1002/alz.13485] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 08/17/2023] [Accepted: 08/20/2023] [Indexed: 11/29/2023]
Abstract
INTRODUCTION Protein-based plasma assays provide hope for improving accessibility and specificity of molecular diagnostics to diagnose dementia. METHODS Plasma was obtained from participants (N = 837) in our community-based University of Kentucky Alzheimer's Disease Research Center cohort. We evaluated six Alzheimer's disease (AD)- and neurodegeneration-related (Aβ40, Aβ42, Aβ42/40, p-tau181, total tau, and NfLight) and five inflammatory biomarkers (TNF𝛼, IL6, IL8, IL10, and GFAP) using the SIMOA-based protein assay platform. Statistics were performed to assess correlations. RESULTS Our large cohort reflects previous plasma biomarker findings. Relationships between biomarkers to understand AD-inflammatory biomarker correlations showed significant associations between AD and inflammatory biomarkers suggesting peripheral inflammatory interactions with increasing AD pathology. Biomarker associations parsed out by clinical diagnosis (normal, MCI, and dementia) reveal changes in strength of the correlations across the cognitive continuum. DISCUSSION Unique AD-inflammatory biomarker correlations in a community-based cohort reveal a new avenue for utilizing plasma-based biomarkers in the assessment of AD and related dementias. HIGHLIGHTS Large community cohorts studying sex, age, and APOE genotype effects on biomarkers are few. It is unknown how biomarker-biomarker associations vary through aging and dementia. Six AD (Aβ40, Aβ42, Aβ42/40, p-tau181, total tau, and NfLight) and five inflammatory biomarkers (TNFα, IL6, IL8, IL10, and GFAP) were used to examine associations between biomarkers. Plasma biomarkers suggesting increasing cerebral AD pathology corresponded to increases in peripheral inflammatory markers, both pro-inflammatory and anti-inflammatory. Strength of correlations, between pairs of classic AD and inflammatory plasma biomarker, changes throughout cognitive progression to dementia.
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Affiliation(s)
- Kate E. Foley
- Sanders Brown Center on AgingUniversity of KentuckyLexingtonKentuckyUSA
- Department of PhysiologyUniversity of KentuckyLexingtonKentuckyUSA
| | - Zachary Winder
- Department of PhysiologyUniversity of KentuckyLexingtonKentuckyUSA
- College of MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Tiffany L. Sudduth
- Sanders Brown Center on AgingUniversity of KentuckyLexingtonKentuckyUSA
- Department of PhysiologyUniversity of KentuckyLexingtonKentuckyUSA
| | - Barbara J. Martin
- Sanders Brown Center on AgingUniversity of KentuckyLexingtonKentuckyUSA
| | - Peter T. Nelson
- Sanders Brown Center on AgingUniversity of KentuckyLexingtonKentuckyUSA
- Pathology and Laboratory MedicineUniversity of KentuckyLexingtonKentuckyUSA
| | - Gregory A. Jicha
- Sanders Brown Center on AgingUniversity of KentuckyLexingtonKentuckyUSA
- Neurology, College of Public HealthUniversity of KentuckyLexingtonKentuckyUSA
| | - Jordan P. Harp
- Sanders Brown Center on AgingUniversity of KentuckyLexingtonKentuckyUSA
- Neurology, College of Public HealthUniversity of KentuckyLexingtonKentuckyUSA
| | - Erica M. Weekman
- Sanders Brown Center on AgingUniversity of KentuckyLexingtonKentuckyUSA
- Department of PhysiologyUniversity of KentuckyLexingtonKentuckyUSA
| | - Donna M. Wilcock
- Sanders Brown Center on AgingUniversity of KentuckyLexingtonKentuckyUSA
- Department of PhysiologyUniversity of KentuckyLexingtonKentuckyUSA
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Cooper JG, Stukas S, Ghodsi M, Ahmed N, Diaz-Arrastia R, Holmes DT, Wellington CL. Age specific reference intervals for plasma biomarkers of neurodegeneration and neurotrauma in a Canadian population. Clin Biochem 2023; 121-122:110680. [PMID: 37884086 DOI: 10.1016/j.clinbiochem.2023.110680] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2023] [Revised: 10/13/2023] [Accepted: 10/23/2023] [Indexed: 10/28/2023]
Abstract
INTRODUCTION In this study, we aimed to create reference intervals (RI) using a large Canadian population-based cohort, for plasma protein biomarkers with potential utility to screen, diagnosis, prognosticate and manage a variety of neurological diseases and disorders. RIs were generated for: the ratio of amyloid beta 42 over 40 (Aβ42/40), phosphorylated tau-181 (p-tau-181), neurofilament light (NfL), and glial fibrillary acidic protein (GFAP). METHODS 900 plasma specimens from male and female participants aged 3-79 years old were obtained from the Statistics Canada Biobank, which holds specimens from the Canadian Health Measures Survey. Analysis of Aβ42/40, p-tau-181, NfL and GFAP was performed on the Quanterix Simoa HD-X analyzer using the Neurology 4-plex E and p-tau-181 assays. Discrete RIs were produced according to Clinical Laboratory Standards Institute guidelines (EP28-A3c). Continuous RIs were created using quantile regression. RESULTS For discrete RIs, significant age partitions were determined for each biomarker. No significant sex partitions were found. The following ranges and age partitions were determined: Aβ42/40: 3-<55y = 0.053-0.098, 55-<80y = 0.040-0.090; p-tau-181: 3-<12y = 1.4-5.6 pg/ml, 12-<60y = 0.8-3.1 pg/ml, 60-<80y = 0.9-4.0 pg/ml; NfL: 3-<40y = 2.6-11.3 pg/ml, 40-<60y = 4.6-17.7 pg/ml, 60-<80y = 8.1-47.1 pg/ml; GFAP; 3-<10y = 47.0-226 pg/ml, 10-<60y = 21.2-91.9 pg/ml, 60-<80y = 40.7-228 pg/ml. Continuous RIs produced smooth centile curves across the age range, from which point estimates for each year of age were calculated. CONCLUSIONS Discrete and continuous RIs for neurological plasma biomarkers will help refine normative cut-offs across the lifespan and improve the precision of interpretating biomarker levels. Continuous RIs are recommended for use in age groups, such as pediatrics and older adults, that experience rapid concentration changes by age.
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Affiliation(s)
- Jennifer G Cooper
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, 2215 Wesbrook Mall, Vancouver, British Columbia V6T 1Z3, Canada; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
| | - Sophie Stukas
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, 2215 Wesbrook Mall, Vancouver, British Columbia V6T 1Z3, Canada; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
| | - Mohammad Ghodsi
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, 2215 Wesbrook Mall, Vancouver, British Columbia V6T 1Z3, Canada; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
| | - Nyra Ahmed
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, 2215 Wesbrook Mall, Vancouver, British Columbia V6T 1Z3, Canada; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
| | - Ramon Diaz-Arrastia
- Clinical TBI Research Center, Penn Presbyterian Medical Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Daniel T Holmes
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada; Department of Pathology and Laboratory Medicine, Providence Health, 1081 Burrard St, Vancouver, British Columbia V6Z 1Y6, Canada
| | - Cheryl L Wellington
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, 2215 Wesbrook Mall, Vancouver, British Columbia V6T 1Z3, Canada; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada; School of Biomedical Engineering, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada; International Collaboration on Repair Discoveries, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada.
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5
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Bao H, Cao J, Chen M, Chen M, Chen W, Chen X, Chen Y, Chen Y, Chen Y, Chen Z, Chhetri JK, Ding Y, Feng J, Guo J, Guo M, He C, Jia Y, Jiang H, Jing Y, Li D, Li J, Li J, Liang Q, Liang R, Liu F, Liu X, Liu Z, Luo OJ, Lv J, Ma J, Mao K, Nie J, Qiao X, Sun X, Tang X, Wang J, Wang Q, Wang S, Wang X, Wang Y, Wang Y, Wu R, Xia K, Xiao FH, Xu L, Xu Y, Yan H, Yang L, Yang R, Yang Y, Ying Y, Zhang L, Zhang W, Zhang W, Zhang X, Zhang Z, Zhou M, Zhou R, Zhu Q, Zhu Z, Cao F, Cao Z, Chan P, Chen C, Chen G, Chen HZ, Chen J, Ci W, Ding BS, Ding Q, Gao F, Han JDJ, Huang K, Ju Z, Kong QP, Li J, Li J, Li X, Liu B, Liu F, Liu L, Liu Q, Liu Q, Liu X, Liu Y, Luo X, Ma S, Ma X, Mao Z, Nie J, Peng Y, Qu J, Ren J, Ren R, Song M, Songyang Z, Sun YE, Sun Y, Tian M, Wang S, Wang S, Wang X, Wang X, Wang YJ, Wang Y, Wong CCL, Xiang AP, Xiao Y, Xie Z, Xu D, Ye J, Yue R, Zhang C, Zhang H, Zhang L, Zhang W, Zhang Y, Zhang YW, Zhang Z, Zhao T, Zhao Y, Zhu D, Zou W, Pei G, Liu GH. Biomarkers of aging. SCIENCE CHINA. LIFE SCIENCES 2023; 66:893-1066. [PMID: 37076725 PMCID: PMC10115486 DOI: 10.1007/s11427-023-2305-0] [Citation(s) in RCA: 77] [Impact Index Per Article: 77.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 02/27/2023] [Indexed: 04/21/2023]
Abstract
Aging biomarkers are a combination of biological parameters to (i) assess age-related changes, (ii) track the physiological aging process, and (iii) predict the transition into a pathological status. Although a broad spectrum of aging biomarkers has been developed, their potential uses and limitations remain poorly characterized. An immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research: How old are we? Why do we get old? And how can we age slower? This review aims to address this need. Here, we summarize our current knowledge of biomarkers developed for cellular, organ, and organismal levels of aging, comprising six pillars: physiological characteristics, medical imaging, histological features, cellular alterations, molecular changes, and secretory factors. To fulfill all these requisites, we propose that aging biomarkers should qualify for being specific, systemic, and clinically relevant.
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Affiliation(s)
- Hainan Bao
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Jiani Cao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
| | - Mengting Chen
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China
| | - Min Chen
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China
| | - Wei Chen
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China
| | - Xiao Chen
- Department of Nuclear Medicine, Daping Hospital, Third Military Medical University, Chongqing, 400042, China
| | - Yanhao Chen
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yu Chen
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Yutian Chen
- The Department of Endovascular Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
| | - Zhiyang Chen
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China
| | - Jagadish K Chhetri
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
| | - Yingjie Ding
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Junlin Feng
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Jun Guo
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China
| | - Mengmeng Guo
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China
| | - Chuting He
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Yujuan Jia
- Department of Neurology, First Affiliated Hospital, Shanxi Medical University, Taiyuan, 030001, China
| | - Haiping Jiang
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Ying Jing
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Dingfeng Li
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China
| | - Jiaming Li
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Jingyi Li
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Qinhao Liang
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China
| | - Rui Liang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China
| | - Feng Liu
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China
| | - Xiaoqian Liu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Zuojun Liu
- School of Life Sciences, Hainan University, Haikou, 570228, China
| | - Oscar Junhong Luo
- Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, 510632, China
| | - Jianwei Lv
- School of Life Sciences, Xiamen University, Xiamen, 361102, China
| | - Jingyi Ma
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China
| | - Kehang Mao
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China
| | - Jiawei Nie
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Xinhua Qiao
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China
| | - Xinpei Sun
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China
| | - Xiaoqiang Tang
- Key Laboratory of Birth Defects and Related Diseases of Women and Children of MOE, State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China
| | - Jianfang Wang
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China
| | - Qiaoran Wang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Siyuan Wang
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China
| | - Xuan Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China
| | - Yaning Wang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Yuhan Wang
- University of Chinese Academy of Sciences, Beijing, 100049, China
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China
| | - Rimo Wu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China
| | - Kai Xia
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China
| | - Fu-Hui Xiao
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China
| | - Lingyan Xu
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China
| | - Yingying Xu
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
| | - Haoteng Yan
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China
| | - Liang Yang
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China
| | - Ruici Yang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China
| | - Yuanxin Yang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China
| | - Yilin Ying
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China
| | - Le Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China
| | - Weiwei Zhang
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China
| | - Wenwan Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Xing Zhang
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China
| | - Zhuo Zhang
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Min Zhou
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China
| | - Rui Zhou
- Department of Nuclear Medicine and PET Center, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, 310009, China
| | - Qingchen Zhu
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China
| | - Zhengmao Zhu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China
| | - Feng Cao
- Department of Cardiology, The Second Medical Centre, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, 100853, China.
| | - Zhongwei Cao
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Piu Chan
- National Clinical Research Center for Geriatric Diseases, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
| | - Chang Chen
- National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Guobing Chen
- Department of Microbiology and Immunology, School of Medicine, Jinan University, Guangzhou, 510632, China.
- Guangdong-Hong Kong-Macau Great Bay Area Geroscience Joint Laboratory, Guangzhou, 510000, China.
| | - Hou-Zao Chen
- Department of Biochemistryand Molecular Biology, State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100005, China.
| | - Jun Chen
- Peking University Research Center on Aging, Beijing Key Laboratory of Protein Posttranslational Modifications and Cell Function, Department of Biochemistry and Molecular Biology, Department of Integration of Chinese and Western Medicine, School of Basic Medical Science, Peking University, Beijing, 100191, China.
| | - Weimin Ci
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
| | - Bi-Sen Ding
- State Key Laboratory of Biotherapy, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
| | - Qiurong Ding
- CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Feng Gao
- Key Laboratory of Ministry of Education, School of Aerospace Medicine, Fourth Military Medical University, Xi'an, 710032, China.
| | - Jing-Dong J Han
- Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Center for Quantitative Biology (CQB), Peking University, Beijing, 100871, China.
| | - Kai Huang
- Clinic Center of Human Gene Research, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Clinical Research Center of Metabolic and Cardiovascular Disease, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Hubei Key Laboratory of Metabolic Abnormalities and Vascular Aging, Huazhong University of Science and Technology, Wuhan, 430022, China.
- Department of Cardiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022, China.
| | - Zhenyu Ju
- Key Laboratory of Regenerative Medicine of Ministry of Education, Institute of Ageing and Regenerative Medicine, Jinan University, Guangzhou, 510632, China.
| | - Qing-Peng Kong
- CAS Center for Excellence in Animal Evolution and Genetics, Chinese Academy of Sciences, Kunming, 650223, China.
- State Key Laboratory of Genetic Resources and Evolution, Key Laboratory of Healthy Aging Research of Yunnan Province, Kunming Key Laboratory of Healthy Aging Study, KIZ/CUHK Joint Laboratory of Bioresources and Molecular Research in Common Diseases, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, China.
| | - Ji Li
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- Hunan Key Laboratory of Aging Biology, Xiangya Hospital, Central South University, Changsha, 410008, China.
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, 410008, China.
| | - Jian Li
- The Key Laboratory of Geriatrics, Beijing Institute of Geriatrics, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing Hospital/National Center of Gerontology of National Health Commission, Beijing, 100730, China.
| | - Xin Li
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Baohua Liu
- School of Basic Medical Sciences, Shenzhen University Medical School, Shenzhen, 518060, China.
| | - Feng Liu
- Metabolic Syndrome Research Center, The Second Xiangya Hospital, Central South Unversity, Changsha, 410011, China.
| | - Lin Liu
- Department of Genetics and Cell Biology, College of Life Science, Nankai University, Tianjin, 300071, China.
- Haihe Laboratory of Cell Ecosystem, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, 300020, China.
- Institute of Translational Medicine, Tianjin Union Medical Center, Nankai University, Tianjin, 300000, China.
- State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, 300350, China.
| | - Qiang Liu
- Department of Neurology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230036, China.
| | - Qiang Liu
- Department of Neurology, Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, 300052, China.
- Tianjin Institute of Immunology, Tianjin Medical University, Tianjin, 300070, China.
| | - Xingguo Liu
- CAS Key Laboratory of Regenerative Biology, Joint School of Life Sciences, Guangzhou Institutes of Biomedicine and Health, Chinese Academy of Sciences, Guangzhou Medical University, Guangzhou, 510530, China.
| | - Yong Liu
- College of Life Sciences, TaiKang Center for Life and Medical Sciences, Wuhan University, Wuhan, 430072, China.
| | - Xianghang Luo
- Department of Endocrinology, Endocrinology Research Center, Xiangya Hospital of Central South University, Changsha, 410008, China.
| | - Shuai Ma
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Xinran Ma
- Shanghai Key Laboratory of Regulatory Biology, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, 200241, China.
| | - Zhiyong Mao
- Shanghai Key Laboratory of Maternal Fetal Medicine, Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Jing Nie
- The State Key Laboratory of Organ Failure Research, National Clinical Research Center of Kidney Disease, Division of Nephrology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
| | - Yaojin Peng
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jing Qu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Jie Ren
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Ruibao Ren
- Shanghai Institute of Hematology, State Key Laboratory for Medical Genomics, National Research Center for Translational Medicine (Shanghai), International Center for Aging and Cancer, Collaborative Innovation Center of Hematology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Center for Aging and Cancer, Hainan Medical University, Haikou, 571199, China.
| | - Moshi Song
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Zhou Songyang
- MOE Key Laboratory of Gene Function and Regulation, Guangzhou Key Laboratory of Healthy Aging Research, School of Life Sciences, Institute of Healthy Aging Research, Sun Yat-sen University, Guangzhou, 510275, China.
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China.
| | - Yi Eve Sun
- Stem Cell Translational Research Center, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200065, China.
| | - Yu Sun
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Department of Medicine and VAPSHCS, University of Washington, Seattle, WA, 98195, USA.
| | - Mei Tian
- Human Phenome Institute, Fudan University, Shanghai, 201203, China.
| | - Shusen Wang
- Research Institute of Transplant Medicine, Organ Transplant Center, NHC Key Laboratory for Critical Care Medicine, Tianjin First Central Hospital, Nankai University, Tianjin, 300384, China.
| | - Si Wang
- Beijing Municipal Geriatric Medical Research Center, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
| | - Xia Wang
- School of Pharmaceutical Sciences, Tsinghua University, Beijing, 100084, China.
| | - Xiaoning Wang
- Institute of Geriatrics, The second Medical Center, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, 100853, China.
| | - Yan-Jiang Wang
- Department of Neurology and Center for Clinical Neuroscience, Daping Hospital, Third Military Medical University, Chongqing, 400042, China.
| | - Yunfang Wang
- Hepatobiliary and Pancreatic Center, Medical Research Center, Beijing Tsinghua Changgung Hospital, Beijing, 102218, China.
| | - Catherine C L Wong
- Clinical Research Institute, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Beijing, 100730, China.
| | - Andy Peng Xiang
- Center for Stem Cell Biologyand Tissue Engineering, Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Sun Yat-sen University, Guangzhou, 510080, China.
- National-Local Joint Engineering Research Center for Stem Cells and Regenerative Medicine, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Yichuan Xiao
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Zhengwei Xie
- Peking University International Cancer Institute, Health Science Center, Peking University, Beijing, 100101, China.
- Beijing & Qingdao Langu Pharmaceutical R&D Platform, Beijing Gigaceuticals Tech. Co. Ltd., Beijing, 100101, China.
| | - Daichao Xu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai, 201210, China.
| | - Jing Ye
- Department of Geriatrics, Medical Center on Aging of Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China.
- International Laboratory in Hematology and Cancer, Shanghai Jiao Tong University School of Medicine/Ruijin Hospital, Shanghai, 200025, China.
| | - Rui Yue
- Institute for Regenerative Medicine, Shanghai East Hospital, Frontier Science Center for Stem Cell Research, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, 200092, China.
| | - Cuntai Zhang
- Gerontology Center of Hubei Province, Wuhan, 430000, China.
- Institute of Gerontology, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
| | - Hongbo Zhang
- Key Laboratory for Stem Cells and Tissue Engineering, Ministry of Education, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
- Advanced Medical Technology Center, The First Affiliated Hospital, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, China.
| | - Liang Zhang
- CAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Weiqi Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
| | - Yong Zhang
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Yun-Wu Zhang
- Fujian Provincial Key Laboratory of Neurodegenerative Disease and Aging Research, Institute of Neuroscience, School of Medicine, Xiamen University, Xiamen, 361102, China.
| | - Zhuohua Zhang
- Key Laboratory of Molecular Precision Medicine of Hunan Province and Center for Medical Genetics, Institute of Molecular Precision Medicine, Xiangya Hospital, Central South University, Changsha, 410078, China.
- Department of Neurosciences, Hengyang Medical School, University of South China, Hengyang, 421001, China.
| | - Tongbiao Zhao
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
| | - Yuzheng Zhao
- Optogenetics & Synthetic Biology Interdisciplinary Research Center, State Key Laboratory of Bioreactor Engineering, Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai, 200237, China.
- Research Unit of New Techniques for Live-cell Metabolic Imaging, Chinese Academy of Medical Sciences, Beijing, 100730, China.
| | - Dahai Zhu
- Bioland Laboratory (Guangzhou Regenerative Medicine and Health Guangdong Laboratory), Guangzhou, 510005, China.
- The State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
| | - Weiguo Zou
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 200031, China.
| | - Gang Pei
- Shanghai Key Laboratory of Signaling and Disease Research, Laboratory of Receptor-Based Biomedicine, The Collaborative Innovation Center for Brain Science, School of Life Sciences and Technology, Tongji University, Shanghai, 200070, China.
| | - Guang-Hui Liu
- University of Chinese Academy of Sciences, Beijing, 100049, China.
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China.
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101, China.
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101, China.
- Advanced Innovation Center for Human Brain Protection, and National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053, China.
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6
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Zabala-Findlay A, Penny LK, Lofthouse RA, Porter AJ, Palliyil S, Harrington CR, Wischik CM, Arastoo M. Utility of Blood-Based Tau Biomarkers for Mild Cognitive Impairment and Alzheimer's Disease: Systematic Review and Meta-Analysis. Cells 2023; 12:cells12081184. [PMID: 37190093 DOI: 10.3390/cells12081184] [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: 02/06/2023] [Revised: 04/04/2023] [Accepted: 04/10/2023] [Indexed: 05/17/2023] Open
Abstract
OBJECTIVES With the development of new technologies capable of detecting low concentrations of Alzheimer's disease (AD) relevant biomarkers, the idea of a blood-based diagnosis of AD is nearing reality. This study aims to consider the evidence of total and phosphorylated tau as blood-based biomarkers for mild cognitive impairment (MCI) and AD when compared to healthy controls. METHODS Studies published between 1 January 2012 and 1 May 2021 (Embase and MEDLINE databases) measuring plasma/serum levels of tau in AD, MCI, and control cohorts were screened for eligibility, including quality and bias assessment via a modified QUADAS. The meta-analyses comprised 48 studies assessing total tau (t-tau), tau phosphorylated at threonine 181 (p-tau181), and tau phosphorylated at threonine 217 (p-tau217), comparing the ratio of biomarker concentrations in MCI, AD, and cognitively unimpaired (CU) controls. RESULTS Plasma/serum p-tau181 (mean effect size, 95% CI, 2.02 (1.76-2.27)) and t-tau (mean effect size, 95% CI, 1.77 (1.49-2.04)) were elevated in AD study participants compared to controls. Plasma/serum p-tau181 (mean effect size, 95% CI, 1.34 (1.20-1.49)) and t-tau (mean effect size, 95% CI, 1.47 (1.26-1.67)) were also elevated with moderate effect size in MCI study participants compared to controls. p-tau217 was also assessed, albeit in a small number of eligible studies, for AD vs. CU (mean effect size, 95% CI, 1.89 (1.86-1.92)) and for MCI vs. CU groups (mean effect size, 95% CI, 4.16 (3.61-4.71)). CONCLUSIONS This paper highlights the growing evidence that blood-based tau biomarkers have early diagnostic utility for Alzheimer's disease. REGISTRATION PROSPERO No. CRD42020209482.
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Affiliation(s)
- Alex Zabala-Findlay
- Institute of Medical Sciences, University of Aberdeen, Aberdeen AB25 2ZD, UK
- Scottish Biologics Facility, University of Aberdeen, Aberdeen AB25 2ZP, UK
| | - Lewis K Penny
- Institute of Medical Sciences, University of Aberdeen, Aberdeen AB25 2ZD, UK
- Scottish Biologics Facility, University of Aberdeen, Aberdeen AB25 2ZP, UK
| | - Richard A Lofthouse
- Institute of Medical Sciences, University of Aberdeen, Aberdeen AB25 2ZD, UK
- Scottish Biologics Facility, University of Aberdeen, Aberdeen AB25 2ZP, UK
| | - Andrew J Porter
- Institute of Medical Sciences, University of Aberdeen, Aberdeen AB25 2ZD, UK
- Scottish Biologics Facility, University of Aberdeen, Aberdeen AB25 2ZP, UK
| | - Soumya Palliyil
- Institute of Medical Sciences, University of Aberdeen, Aberdeen AB25 2ZD, UK
- Scottish Biologics Facility, University of Aberdeen, Aberdeen AB25 2ZP, UK
| | - Charles R Harrington
- Institute of Medical Sciences, University of Aberdeen, Aberdeen AB25 2ZD, UK
- GT Diagnostics, Aberdeen AB24 5RP, UK
- TauRx Therapeutics Ltd., Aberdeen AB24 5RP, UK
| | - Claude M Wischik
- Institute of Medical Sciences, University of Aberdeen, Aberdeen AB25 2ZD, UK
- GT Diagnostics, Aberdeen AB24 5RP, UK
- TauRx Therapeutics Ltd., Aberdeen AB24 5RP, UK
| | - Mohammad Arastoo
- Institute of Medical Sciences, University of Aberdeen, Aberdeen AB25 2ZD, UK
- Scottish Biologics Facility, University of Aberdeen, Aberdeen AB25 2ZP, UK
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7
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Saunders TS, Pozzolo FE, Heslegrave A, King D, McGeachan RI, Spires-Jones MP, Harris SE, Ritchie C, Muniz-Terrera G, Deary IJ, Cox SR, Zetterberg H, Spires-Jones TL. Predictive blood biomarkers and brain changes associated with age-related cognitive decline. Brain Commun 2023; 5:fcad113. [PMID: 37180996 PMCID: PMC10167767 DOI: 10.1093/braincomms/fcad113] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 12/28/2022] [Accepted: 04/05/2023] [Indexed: 04/08/2023] Open
Abstract
Growing evidence supports the use of plasma levels of tau phosphorylated at threonine 181, amyloid-β, neurofilament light and glial fibrillary acidic protein as promising biomarkers for Alzheimer's disease. While these blood biomarkers are promising for distinguishing people with Alzheimer's disease from healthy controls, their predictive validity for age-related cognitive decline without dementia remains unclear. Further, while tau phosphorylated at threonine 181 is a promising biomarker, the distribution of this phospho-epitope of tau in the brain is unknown. Here, we tested whether plasma levels of tau phosphorylated at threonine 181, amyloid-β, neurofilament light and fibrillary acidic protein predict cognitive decline between ages 72 and 82 in 195 participants in the Lothian birth cohorts 1936 study of cognitive ageing. We further examined post-mortem brain samples from temporal cortex to determine the distribution of tau phosphorylated at threonine 181 in the brain. Several forms of tau phosphorylated at threonine 181 have been shown to contribute to synapse degeneration in Alzheimer's disease, which correlates closely with cognitive decline in this form of dementia, but to date, there have not been investigations of whether tau phosphorylated at threonine 181 is found in synapses in Alzheimer's disease or healthy ageing brain. It was also previously unclear whether tau phosphorylated at threonine 181 accumulated in dystrophic neurites around plaques, which could contribute to tau leakage to the periphery due to impaired membrane integrity in dystrophies. Brain homogenate and biochemically enriched synaptic fractions were examined with western blot to examine tau phosphorylated at threonine 181 levels between groups (n = 10-12 per group), and synaptic and astrocytic localization of tau phosphorylated at threonine 181 were examined using array tomography (n = 6-15 per group), and localization of tau phosphorylated at threonine 181 in plaque-associated dystrophic neurites with associated gliosis were examined with standard immunofluorescence (n = 8-9 per group). Elevated baseline plasma tau phosphorylated at threonine 181, neurofilament light and fibrillary acidic protein predicted steeper general cognitive decline during ageing. Further, increasing tau phosphorylated at threonine 181 over time predicted general cognitive decline in females only. Change in plasma tau phosphorylated at threonine 181 remained a significant predictor of g factor decline when taking into account Alzheimer's disease polygenic risk score, indicating that the increase of blood tau phosphorylated at threonine 181 in this cohort was not only due to incipient Alzheimer's disease. Tau phosphorylated at threonine 181 was observed in synapses and astrocytes in both healthy ageing and Alzheimer's disease brain. We observed that a significantly higher proportion of synapses contain tau phosphorylated at threonine 181 in Alzheimer's disease relative to aged controls. Aged controls with pre-morbid lifetime cognitive resilience had significantly more tau phosphorylated at threonine 181 in fibrillary acidic protein-positive astrocytes than those with pre-morbid lifetime cognitive decline. Further, tau phosphorylated at threonine 181 was found in dystrophic neurites around plaques and in some neurofibrillary tangles. The presence of tau phosphorylated at threonine 181 in plaque-associated dystrophies may be a source of leakage of tau out of neurons that eventually enters the blood. Together, these data indicate that plasma tau phosphorylated at threonine 181, neurofilament light and fibrillary acidic protein may be useful biomarkers of age-related cognitive decline, and that efficient clearance of tau phosphorylated at threonine 181 by astrocytes may promote cognitive resilience.
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Affiliation(s)
- Tyler S Saunders
- UK Dementia Research Institute and Centre for Discovery Brain Sciences at the University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Edinburgh Dementia Prevention & Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Francesca E Pozzolo
- UK Dementia Research Institute and Centre for Discovery Brain Sciences at the University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Amanda Heslegrave
- United Kingdom UK Dementia Research Institute at University College London, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
| | - Declan King
- UK Dementia Research Institute and Centre for Discovery Brain Sciences at the University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Robert I McGeachan
- UK Dementia Research Institute and Centre for Discovery Brain Sciences at the University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Maxwell P Spires-Jones
- UK Dementia Research Institute and Centre for Discovery Brain Sciences at the University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Sarah E Harris
- Lothian Birth Cohort studies, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9AD, UK
| | - Craig Ritchie
- Edinburgh Dementia Prevention & Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Graciela Muniz-Terrera
- Edinburgh Dementia Prevention & Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, EH4 2XU, UK
- Department of Social Medicine, Ohio University, Athens, Ohio 45701, USA
- Latin American Institute for Brain Health (BrainLat), Universidad Adolfo Ibanez, Santiago 3485, Chile
| | - Ian J Deary
- Lothian Birth Cohort studies, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9AD, UK
| | - Simon R Cox
- Lothian Birth Cohort studies, Department of Psychology, University of Edinburgh, Edinburgh, EH8 9AD, UK
| | - Henrik Zetterberg
- United Kingdom UK Dementia Research Institute at University College London, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
- Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
- Department of Psychiatry and Neurochemistry, Sahlgrenska Academy at the University of Gothenburg, S-431 80 Molndal, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, S-431 80 Molndal, Sweden
- Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China
| | - Tara L Spires-Jones
- UK Dementia Research Institute and Centre for Discovery Brain Sciences at the University of Edinburgh, Edinburgh, EH8 9JZ, UK
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8
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Chiu PY, Yang FC, Chiu MJ, Lin WC, Lu CH, Yang SY. Relevance of plasma biomarkers to pathologies in Alzheimer's disease, Parkinson's disease and frontotemporal dementia. Sci Rep 2022; 12:17919. [PMID: 36289355 PMCID: PMC9605966 DOI: 10.1038/s41598-022-22647-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 10/18/2022] [Indexed: 01/20/2023] Open
Abstract
Amyloid plaques and tau tangles are pathological hallmarks of Alzheimer's disease (AD). Parkinson's disease (PD) results from the accumulation of α-synuclein. TAR DNA-binding protein (TDP-43) and total tau protein (T-Tau) play roles in FTD pathology. All of the pathological evidence was found in the biopsy. However, it is impossible to perform stein examinations in clinical practice. Assays of biomarkers in plasma would be convenient. It would be better to investigate the combinations of various biomarkers in AD, PD and FTD. Ninety-one subjects without neurodegenerative diseases, 76 patients with amnesic mild cognitive impairment (aMCI) or AD dementia, combined as AD family, were enrolled. One hundred and nine PD patients with normal cognition (PD-NC) or dementia (PDD), combined as PD family, were enrolled. Twenty-five FTD patients were enrolled for assays of plasma amyloid β 1-40 (Aβ1-40), Aβ1-42, T-Tau, α-synuclein and TDP-43 using immunomagnetic reduction (IMR). The results show that Aβs and T-Tau are major domains in AD family. α-synuclein is highly dominant in PD family. FTD is closely associated with TDP-43 and T-Tau. The dominant plasma biomarkers in AD family, PD family and FTD are consistent with pathology. This implies that plasma biomarkers are promising for precise and differential assessments of AD, PD and FTD in clinical practice.
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Affiliation(s)
- Pai-Yi Chiu
- grid.452796.b0000 0004 0634 3637Department of Neurology, Show Chwan Memorial Hospital, Chunghwa, 500 Taiwan ,MR-Guided Focus Ultrasound Center, Chang Bin Shaw Chwan Memorial Hospital, Changhwa, 505 Taiwan
| | - Fu-Chi Yang
- grid.278244.f0000 0004 0638 9360Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei, 114 Taiwan
| | - Ming-Jang Chiu
- grid.19188.390000 0004 0546 0241Department of Neurology, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, 100 Taiwan ,grid.19188.390000 0004 0546 0241Graduate Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University, Taipei, 100 Taiwan ,grid.19188.390000 0004 0546 0241Department of Psychology, National Taiwan University, Taipei, 106 Taiwan ,grid.19188.390000 0004 0546 0241Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, 106 Taiwan
| | - Wei-Che Lin
- grid.145695.a0000 0004 1798 0922Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Kaohsiung, 833 Taiwan
| | - Cheng-Hsien Lu
- grid.145695.a0000 0004 1798 0922Department of Neurology, Kaohsiung Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Kaohsiung, 833 Taiwan
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9
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Traub J, Otto M, Sell R, Göpfert D, Homola G, Steinacker P, Oeckl P, Morbach C, Frantz S, Pham M, Störk S, Stoll G, Frey A. Serum phosphorylated tau protein 181 and neurofilament light chain in cognitively impaired heart failure patients. Alzheimers Res Ther 2022; 14:149. [PMID: 36217177 PMCID: PMC9549648 DOI: 10.1186/s13195-022-01087-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 09/22/2022] [Indexed: 11/05/2022]
Abstract
BACKGROUND Chronic heart failure (HF) is known to increase the risk of developing Alzheimer's dementia significantly. Thus, detecting and preventing mild cognitive impairment, which is common in patients with HF, is of great importance. Serum biomarkers are increasingly used in neurological disorders for diagnostics, monitoring, and prognostication of disease course. It remains unclear if neuronal biomarkers may help detect cognitive impairment in this high-risk population. Also, the influence of chronic HF and concomitant renal dysfunction on these biomarkers is not well understood. METHODS Within the monocentric Cognition.Matters-HF study, we quantified the serum levels of phosphorylated tau protein 181 (pTau) and neurofilament light chain (NfL) of 146 extensively phenotyped chronic heart failure patients (aged 32 to 85 years; 15.1% women) using ultrasensitive bead-based single-molecule immunoassays. The clinical work-up included advanced cognitive testing and cerebral magnetic resonance imaging (MRI). RESULTS Serum concentrations of NfL ranged from 5.4 to 215.0 pg/ml (median 26.4 pg/ml) and of pTau from 0.51 to 9.22 pg/ml (median 1.57 pg/ml). We detected mild cognitive impairment (i.e., T-score < 40 in at least one cognitive domain) in 60% of heart failure patients. pTau (p = 0.014), but not NfL, was elevated in this group. Both NfL (ρ = - 0.21; p = 0.013) and pTau (ρ = - 0.25; p = 0.002) related to the cognitive domain visual/verbal memory, as well as white matter hyperintensity volume and cerebral and hippocampal atrophy. In multivariable analysis, both biomarkers were independently influenced by age (T = 4.6 for pTau; T = 5.9 for NfL) and glomerular filtration rate (T = - 2.4 for pTau; T = - 3.4 for NfL). Markers of chronic heart failure, left atrial volume index (T = 4.6) and NT-proBNP (T = 2.8), were further cardiological determinants of pTau and NfL, respectively. In addition, pTau was also strongly affected by serum creatine kinase levels (T = 6.5) and ferritin (T = - 3.1). CONCLUSIONS pTau and NfL serum levels are strongly influenced by age-dependent renal and cardiac dysfunction. These findings point towards the need for longitudinal examinations and consideration of frequent comorbidities when using neuronal serum biomarkers.
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Affiliation(s)
- Jan Traub
- grid.411760.50000 0001 1378 7891Department of Internal Medicine I, University Hospital Würzburg, Oberdürrbacherstraße 6, 97080 Würzburg, Germany ,grid.411760.50000 0001 1378 7891Comprehensive Heart Failure Center, University and University Hospital Würzburg, Am Schwarzenberg 15, Würzburg, 97078 Germany
| | - Markus Otto
- grid.410712.10000 0004 0473 882XDepartment of Neurology, University Hospital Ulm, Ulm, Germany ,grid.461820.90000 0004 0390 1701Department of Neurology, University Hospital Halle-Wittenberg, Halle, Germany
| | - Roxane Sell
- grid.411760.50000 0001 1378 7891Department of Psychiatry, Psychosomatics and Psychotherapy, University Hospital Würzburg, Würzburg, Germany
| | - Dennis Göpfert
- grid.411760.50000 0001 1378 7891Department of Internal Medicine I, University Hospital Würzburg, Oberdürrbacherstraße 6, 97080 Würzburg, Germany
| | - György Homola
- grid.411760.50000 0001 1378 7891Comprehensive Heart Failure Center, University and University Hospital Würzburg, Am Schwarzenberg 15, Würzburg, 97078 Germany ,grid.411760.50000 0001 1378 7891Department of Neuroradiology, University Hospital Würzburg, Würzburg, Germany
| | - Petra Steinacker
- grid.410712.10000 0004 0473 882XDepartment of Neurology, University Hospital Ulm, Ulm, Germany ,grid.461820.90000 0004 0390 1701Department of Neurology, University Hospital Halle-Wittenberg, Halle, Germany
| | - Patrick Oeckl
- grid.410712.10000 0004 0473 882XDepartment of Neurology, University Hospital Ulm, Ulm, Germany ,grid.424247.30000 0004 0438 0426German Center for Neurodegenerative Diseases (DZNE e.V.), Ulm, Germany
| | - Caroline Morbach
- grid.411760.50000 0001 1378 7891Department of Internal Medicine I, University Hospital Würzburg, Oberdürrbacherstraße 6, 97080 Würzburg, Germany ,grid.411760.50000 0001 1378 7891Comprehensive Heart Failure Center, University and University Hospital Würzburg, Am Schwarzenberg 15, Würzburg, 97078 Germany
| | - Stefan Frantz
- grid.411760.50000 0001 1378 7891Department of Internal Medicine I, University Hospital Würzburg, Oberdürrbacherstraße 6, 97080 Würzburg, Germany ,grid.411760.50000 0001 1378 7891Comprehensive Heart Failure Center, University and University Hospital Würzburg, Am Schwarzenberg 15, Würzburg, 97078 Germany
| | - Mirko Pham
- grid.411760.50000 0001 1378 7891Comprehensive Heart Failure Center, University and University Hospital Würzburg, Am Schwarzenberg 15, Würzburg, 97078 Germany ,grid.411760.50000 0001 1378 7891Department of Neuroradiology, University Hospital Würzburg, Würzburg, Germany
| | - Stefan Störk
- grid.411760.50000 0001 1378 7891Department of Internal Medicine I, University Hospital Würzburg, Oberdürrbacherstraße 6, 97080 Würzburg, Germany ,grid.411760.50000 0001 1378 7891Comprehensive Heart Failure Center, University and University Hospital Würzburg, Am Schwarzenberg 15, Würzburg, 97078 Germany
| | - Guido Stoll
- grid.411760.50000 0001 1378 7891Comprehensive Heart Failure Center, University and University Hospital Würzburg, Am Schwarzenberg 15, Würzburg, 97078 Germany ,grid.411760.50000 0001 1378 7891Department of Neurology, University Hospital Würzburg, Würzburg, Germany
| | - Anna Frey
- grid.411760.50000 0001 1378 7891Department of Internal Medicine I, University Hospital Würzburg, Oberdürrbacherstraße 6, 97080 Würzburg, Germany ,grid.411760.50000 0001 1378 7891Comprehensive Heart Failure Center, University and University Hospital Würzburg, Am Schwarzenberg 15, Würzburg, 97078 Germany
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Israel LL, Galstyan A, Cox A, Shatalova ES, Sun T, Rashid MH, Grodzinski Z, Chiechi A, Fuchs DT, Patil R, Koronyo-Hamaoui M, Black KL, Ljubimova JY, Holler E. Signature Effects of Vector-Guided Systemic Nano Bioconjugate Delivery Across Blood-Brain Barrier of Normal, Alzheimer's, and Tumor Mouse Models. ACS NANO 2022; 16:11815-11832. [PMID: 35961653 PMCID: PMC9413444 DOI: 10.1021/acsnano.1c10034] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
The ability to cross the blood-brain barrier (BBB) is critical for targeted therapy of the central nerve system (CNS). Six peptide vectors were covalently attached to a 50 kDa poly(β-l-malic acid)-trileucine polymer forming P/LLL(40%)/vector conjugates. The vectors were Angiopep-2 (AP2), B6, Miniap-4 (M4), and d-configurated peptides D1, D3, and ACI-89, with specificity for transcytosis receptors low-density lipoprotein receptor-related protein-1 (LRP-1), transferrin receptor (TfR), bee venom-derived ion channel, and Aβ/LRP-1 related transcytosis complex, respectively. The BBB-permeation efficacies were substantially increased ("boosted") in vector conjugates of P/LLL(40%). We have found that the copolymer group binds at the endothelial membrane and, by an allosterically membrane rearrangement, exposes the sites for vector-receptor complex formation. The specificity of vectors is indicated by competition experiments with nonconjugated vectors. P/LLL(40%) does not function as an inhibitor, suggesting that the copolymer binding site is eliminated after binding of the vector-nanoconjugate. The two-step mechanism, binding to endothelial membrane and allosteric exposure of transcytosis receptors, is supposed to be an integral feature of nanoconjugate-transcytosis pathways. In vivo brain delivery signatures of the nanoconjugates were recapitulated in mouse brains of normal, tumor (glioblastoma), and Alzheimer's disease (AD) models. BBB permeation of the tumor was most efficient, followed by normal and then AD-like brain. In tumor-bearing and normal brains, AP2 was the top performing vector; however, in AD models, D3 and D1 peptides were superior ones. The TfR vector B6 was equally efficient in normal and AD-model brains. Cross-permeation efficacies are manifested through modulated vector coligation and dosage escalation such as supra-linear dose dependence and crossover transcytosis activities.
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Affiliation(s)
- Liron L. Israel
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Anna Galstyan
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Alysia Cox
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Ekaterina S. Shatalova
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Tao Sun
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Mohammad-Harun Rashid
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Zachary Grodzinski
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Antonella Chiechi
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Dieu-Trang Fuchs
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Rameshwar Patil
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Maya Koronyo-Hamaoui
- Department of Neurosurgery and Department of Biomedical Sciences,
Division of Applied Cell Biology and Physiology, Cedars-Sinai Medical Center, Los
Angeles, California 90048, United States
| | - Keith L. Black
- Department of Neurosurgery, Cedars-Sinai Medical Center, Los Angeles, California 90048, United States
| | - Julia Y. Ljubimova
- Terasaki Institute for Biomedical Innovation
(TIBI), 1018 Westwood
Boulevard, Los Angeles, California 90024, United States
| | - Eggehard Holler
- Terasaki Institute for Biomedical Innovation
(TIBI), 1018 Westwood
Boulevard, Los Angeles, California 90024, United States
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11
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Morsiani C, Bacalini MG, Collura S, Moreno-Villanueva M, Breusing N, Bürkle A, Grune T, Franceschi C, De Eguileor M, Capri M. Blood circulating miR-28-5p and let-7d-5p associate with premature ageing in Down Syndrome. Mech Ageing Dev 2022; 206:111691. [PMID: 35780970 DOI: 10.1016/j.mad.2022.111691] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 06/01/2022] [Accepted: 06/22/2022] [Indexed: 12/27/2022]
Abstract
Persons with Down Syndrome (DS) undergo a premature ageing with early onset of age-related diseases. The main endpoint of this study was the identification of blood circulating microRNAs (c-miRs) signatures characterizing DS ageing process. A discovery phase based on array was performed in plasma samples obtained from 3 young (31±2 years-old) and 3 elderly DS persons (66±2 years-old). Then, a validation phase was carried out for relevant miRs by RT-qPCR in an enlarged cohort of 43 DS individuals (from 19 up to 68 years-old). A group of 30 non-trisomic subjects, as representative of physiological ageing, was compared. In particular miR-628-5p, miR-152-3p, miR-28-5p, and let-7d-5p showed a lower level in younger DS persons (age ≤ 50 years) respect to the age-matched controls. Among those, miR-28-5p and let-7d-5p were found significantly decreased in physiological ageing (control group with age threshold of 50 years), thus they emerged as possible biomarkers of premature ageing in DS. Moreover, measuring blood levels of beta amyloid peptides, Aβ-42 was assessed at the lowest levels in physiological ageing and correlated with miR-28-5p and let-7d-5p in DS, while Aβ-40 correlated with miR-628-5p in the same cohort. New perspectives in terms of biomarkers are discussed.
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Affiliation(s)
- Cristina Morsiani
- DIMES-Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Italy.
| | | | - Salvatore Collura
- DIMES-Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Italy
| | - María Moreno-Villanueva
- Molecular Toxicology Group, Department of Biology, University of Konstanz, Konstanz, Germany
| | - Nicolle Breusing
- Department of Applied Nutritional Science/Dietetics, Institute of Nutritional Medicine, University of Hohenheim, Germany
| | - Alexander Bürkle
- Molecular Toxicology Group, Department of Biology, University of Konstanz, Konstanz, Germany
| | - Tilman Grune
- Department of Molecular Toxicology, German Institute of Human Nutrition Potsdam-Rehbruecke (DIfE), Nuthetal, Germany; German Centre for Cardiovascular Research (DZHK), partner site Berlin, Germany
| | - Claudio Franceschi
- Laboratory of Systems Medicine of Healthy Aging and Department of Applied Mathematics, Lobachevsky University, Nizhny Novgorod, Russia
| | - Magda De Eguileor
- Department of Biotechnology and Life Sciences, University of Insubria, Varese, Italy
| | - Miriam Capri
- DIMES-Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, Italy; Interdepartmental Center "Alma Mater Research Institute on Global Challenges and Climate Change (Alma Climate)", University of Bologna, Italy
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12
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Pien LC, Tsai HT, Cheng WJ, Rias YA, Chou KR, Chen SR. Sex-Influenced Risk Factors for Cognitive Impairment Among Community-Dwelling Older Adults. J Gerontol Nurs 2022; 48:19-25. [PMID: 35648583 DOI: 10.3928/00989134-20220505-02] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
The current study aimed to explore sex-influenced risk factors for cognitive impairment among community-dwelling older adults in Taiwan. This cross-sectional study was a secondary analysis using a population-based design. We accessed and analyzed data from the Taiwan Longitudinal Study on Aging survey of 2011. Participants were older adults aged ≥55 years living in non-indigenous townships. A total of 3,392 community-dwelling older adults were included. Results showed that the prevalence of cognitive impairment in females and males was 15.3% and 5.7%, respectively. Having a low educational level and being single (i.e., single, widowed, or divorced) were risk factors for cognitive impairment in both sexes. Males who had more than two chronic diseases had a higher risk of cognitive impairment. Self-reported hearing loss and depression increased risk of cognitive impairment in older females. Older age, lower educational level, and single marital status were associated with cognitive impairment among community-dwelling older adults in Taiwan. The effects of self-reported hearing loss, depression, and chronic disease on cognitive impairment were influenced by sex. [Journal of Gerontological Nursing, 48(6), 19-25.].
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13
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Evidence of plasma biomarkers indicating high risk of dementia in cognitively normal subjects. Sci Rep 2022; 12:1192. [PMID: 35075194 PMCID: PMC8786959 DOI: 10.1038/s41598-022-05177-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Accepted: 01/07/2022] [Indexed: 11/08/2022] Open
Abstract
Subjects with comorbidities are at risk for neurodegeneration. There is a lack of a direct relationship between comorbidities and neurodegeneration. In this study, immunomagnetic reduction (IMR) assays were utilized to assay plasma Aβ1-42 and total tau protein (T-Tau) levels in poststroke (PS, n = 27), family history of Alzheimer's disease (ADFH, n = 35), diabetes (n = 21), end-stage renal disease (ESRD, n = 41), obstructive sleep apnea (OSA, n = 20), Alzheimer's disease (AD, n = 65). Thirty-seven healthy controls (HCs) were enrolled. The measured concentrations of plasma Aβ1-42 were 14.26 ± 1.42, 15.43 ± 1.76, 15.52 ± 1.60, 16.15 ± 1.05, 16.52 ± 0.59, 15.97 ± 0.54 and 20.06 ± 3.09 pg/mL in HC, PS, ADFH, diabetes, ESRD, OSA and AD groups, respectively. The corresponding concentrations of plasma T-Tau were 15.13 ± 3.62, 19.29 ± 8.01, 17.93 ± 6.26, 19.74 ± 2.92, 21.54 ± 2.72, 20.17 ± 2.77 and 41.24 ± 14.64 pg/mL. The plasma levels of Aβ1-42 and T-Tau in were significantly higher in the PS, ADFH, diabetes, ESRD and OSA groups than controls (Aβ1-42 in PS: 15.43 ± 1.76 pg/mL vs. 14.26 ± 1.42 pg/mL, p < 0.005; T-Tau in PS: 19.29 ± 8.01 vs. 15.13 ± 3.62 pg/mL, p < 0.005, Aβ1-42 in ADFH: 15.52 ± 1.60 pg/mL vs. 14.26 ± 1.42 pg/mL, p < 0.001; T-Tau in ADFH: 17.93 ± 6.26 vs. 15.13 ± 3.62 pg/mL, p < 0.005, Aβ1-42 in diabetes: 16.15 ± 1.05 pg/mL vs. 14.26 ± 1.42 pg/mL, p < 0.001; T-Tau in diabetes: 19.74 ± 2.92 vs. 15.13 ± 3.62 pg/mL, p < 0.001, Aβ1-42 in ESRD: 16.52 ± 0.59 pg/mL vs. 14.26 ± 1.42 pg/mL, p < 0.001; T-Tau in ESRD: 21.54 ± 2.72 vs. 15.13 ± 3.62 pg/mL, p < 0.001, Aβ1-42 in OSA: 15.97 ± 0.54 pg/mL vs. 14.26 ± 1.42 pg/mL, p < 0.001; T-Tau in OSA: 20.17 ± 2.77 vs. 15.13 ± 3.62 pg/mL, p < 0.001). This evidence indicates the high risk for dementia in these groups from the perspective of plasma biomarkers.
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14
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Wu Y, Wang Z, Yin J, Yang B, Fan J, Cheng Z. Association Plasma Aβ42 Levels with Alzheimer's Disease and Its Influencing Factors in Chinese Elderly Population. Neuropsychiatr Dis Treat 2022; 18:1831-1841. [PMID: 36043117 PMCID: PMC9420413 DOI: 10.2147/ndt.s374722] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 08/17/2022] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND AND PURPOSE Intracerebral Aβ protein deposition is an important pathological mechanism of Alzheimer's disease (AD) and is one of the indicators of early diagnosis of AD. However, invasive lumbar puncture and Aβ PET are difficult to perform in primary units, resulting delays in early diagnosis of AD. In recent years, it has been found that plasma Aβ can reflect the pathological state of AD in early stage, but the results are not consistent. The objective of this study was to explore the association between plasma Aβ42 levels and AD cognitive impairment and its influencing factors in Chinese elderly population, so as to provide guidance for the clinical application of plasma Aβ42 as a blood biomarker of AD. METHODS This is a cross-sectional study based on the community population. Plasma samples were collected from 604 healthy controls (HC), 508 mild cognitive impairment (MCI) and 202 dementia with Alzheimer's type (DAT) patients from three cities. We analyzed the correlation between plasma Aβ42 levels and cognitive function and the influence of confounding factors on the relationship between plasma Aβ42 levels and AD. The independent influencing factors of plasma Aβ42 levels were determined by covariance and linear regression analysis. RESULTS Our results suggest that there is a special linear relationship between plasma Aβ42 and cognitive impairment of AD in Chinese elderly population, with Aβ42 levels slightly decreased in early AD and significantly increased in moderate-to-severe AD (P<0.01). There are many factors influencing the association between plasma Aβ42 levels and AD cognitive impairment, and sample source, gender and BMI are independent influencing factors of plasma Aβ42. CONCLUSION This indentifies that plasma Aβ42 may be a peripheral biomarker for AD screening in Chinese elderly population, but it is necessary to establish standardized detection methods and establish different demarcation criteria for various influencing factors.
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Affiliation(s)
- Yue Wu
- Department of Geriatric Psychiatry, The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi, People's Republic of China
| | - Zhiqiang Wang
- Department of Clinical Psychology, The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi, People's Republic of China
| | - Jiajun Yin
- Brain Science Basic Laboratory, The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi, People's Republic of China
| | - Bixiu Yang
- Department of Clinical Psychology, The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi, People's Republic of China
| | - Jie Fan
- Department of Geriatric Psychiatry, The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi, People's Republic of China
| | - Zaohuo Cheng
- Department of Geriatric Psychiatry, The Affiliated Wuxi Mental Health Center of Jiangnan University, Wuxi, People's Republic of China
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15
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Ren B, Wu Y, Huang L, Zhang Z, Huang B, Zhang H, Ma J, Li B, Liu X, Wu G, Zhang J, Shen L, Liu Q, Ni J. Deep transfer learning of structural magnetic resonance imaging fused with blood parameters improves brain age prediction. Hum Brain Mapp 2021; 43:1640-1656. [PMID: 34913545 PMCID: PMC8886664 DOI: 10.1002/hbm.25748] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Revised: 11/14/2021] [Accepted: 12/01/2021] [Indexed: 12/27/2022] Open
Abstract
Machine learning has been applied to neuroimaging data for estimating brain age and capturing early cognitive impairment in neurodegenerative diseases. Blood parameters like neurofilament light chain are associated with aging. In order to improve brain age predictive accuracy, we constructed a model based on both brain structural magnetic resonance imaging (sMRI) and blood parameters. Healthy subjects (n = 93; 37 males; aged 50–85 years) were recruited. A deep learning network was firstly pretrained on a large set of MRI scans (n = 1,481; 659 males; aged 50–85 years) downloaded from multiple open‐source datasets, to provide weights on our recruited dataset. Evaluating the network on the recruited dataset resulted in mean absolute error (MAE) of 4.91 years and a high correlation (r = .67, p <.001) against chronological age. The sMRI data were then combined with five blood biochemical indicators including GLU, TG, TC, ApoA1 and ApoB, and 9 dementia‐associated biomarkers including ApoE genotype, HCY, NFL, TREM2, Aβ40, Aβ42, T‐tau, TIMP1, and VLDLR to construct a bilinear fusion model, which achieved a more accurate prediction of brain age (MAE, 3.96 years; r = .76, p <.001). Notably, the fusion model achieved better improvement in the group of older subjects (70–85 years). Extracted attention maps of the network showed that amygdala, pallidum, and olfactory were effective for age estimation. Mediation analysis further showed that brain structural features and blood parameters provided independent and significant impact. The constructed age prediction model may have promising potential in evaluation of brain health based on MRI and blood parameters.
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Affiliation(s)
- Bingyu Ren
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Yingtong Wu
- Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Liumei Huang
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Zhiguo Zhang
- MIND Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Bingsheng Huang
- Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Huajie Zhang
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Jinting Ma
- Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Bing Li
- Medical AI Lab, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Xukun Liu
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Guangyao Wu
- Radiology Department, Shenzhen University General Hospital and Shenzhen University Clinical Medical Academy, Shenzhen University, Shenzhen, China
| | - Jian Zhang
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China.,Health Science Center, Shenzhen University, Shenzhen, China
| | - Liming Shen
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
| | - Qiong Liu
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China.,Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China.,Shenzhen Bay Laboratory, Shenzhen, China
| | - Jiazuan Ni
- Shenzhen Key Laboratory of Marine Biotechnology and Ecology, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, China
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16
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Hu CJ, Chiu MJ, Pai MC, Yan SH, Wang PN, Chiu PY, Lin CH, Chen TF, Yang FC, Huang KL, Hsu YT, Hou YC, Lin WC, Lu CH, Huang LK, Yang SY. Assessment of High Risk for Alzheimer's Disease Using Plasma Biomarkers in Subjects with Normal Cognition in Taiwan: A Preliminary Study. J Alzheimers Dis Rep 2021; 5:761-770. [PMID: 34870102 PMCID: PMC8609520 DOI: 10.3233/adr-210310] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Background In Alzheimer's disease (AD), cognitive impairment begins 10-15 years later than neurodegeneration in the brain. Plasma biomarkers are promising candidates for assessing neurodegeneration in people with normal cognition. It has been reported that subjects with the concentration of plasma amyloid-β 1-42×total tau protein higher than 455 pg2/ml2 are assessed as having a high risk of amnesic mild impairment or AD, denoted as high risk of AD (HRAD). Objective The prevalence of high-risk for dementia in cognitively normal controls is explored by assaying plasma biomarkers. Methods 422 subjects with normal cognition were enrolled around Taiwan. Plasma Aβ1-40, Aβ1-42, and T-Tau levels were assayed using immunomagnetic reduction to assess the risk of dementia. Results The results showed that 4.6% of young adults (age: 20-44 years), 8.5% of middle-aged adults (age: 45-64 years), and 7.3% of elderly adults (age: 65-90 years) had HRAD. The percentage of individuals with HRAD dramatically increased in middle-aged and elderly adults compared to young adults. Conclusion The percentage of HRAD in cognitively normal subjects are approximately 10%, which reveals that the potentially public-health problem of AD in normal population. Although the subject having abnormal levels of Aβ or tau is not definitely going on to develop cognitive declines or AD, the risk of suffering cognitive impairment in future is relatively high. Suitable managements are suggested for these high-risk cognitively normal population. Worth noting, attention should be paid to preventing cognitive impairment due to AD, not only in elderly adults but also middle-aged adults.
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Affiliation(s)
- Chaur-Jong Hu
- Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Department of Neurology, Dementia Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.,Graduate Institute of Neural Regenerative Medicine, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.,Taipei Neuroscience Institute, Taipei Medical University, Taipei, Taiwan
| | - Ming-Jang Chiu
- Department of Neurology, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan.,Graduate Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University, Taipei, Taiwan.,Department of Psychology, National Taiwan University, Taipei, Taiwan.,Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
| | - Ming-Chyi Pai
- Division of Behavioral Neurology, Department of Neurology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Sui-Hing Yan
- Department of Neurology, Far Eastern Memorial Hospital, New Taipei City, Taiwan
| | - Pei-Ning Wang
- Department of Neurology, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan.,Department of Neurology, School of Medicine, National Yang-Ming University, Taipei, Taiwan.,Brain Research Center, National Yang-Ming University, Taipei, Taiwan
| | - Pai-Yi Chiu
- Department of Neurology, Show Chwan Memorial Hospital, Chunghwa, Taiwan.,MR-guided Focus Ultrasound Center, Chang Bin Show Chwan Memorial Hospital, Chunghwa, Taiwan
| | - Chin-Hsien Lin
- Department of Neurology, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Ta-Fu Chen
- Department of Neurology, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Fu-Chi Yang
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei, Taiwan
| | - Kuo-Lun Huang
- Department of Neurology, Linkou Chang Gung Memorial Hospital, and College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Yi-Ting Hsu
- Department of Neurology, China Medical University Hospital, China Medical University, Taichung, Taiwan
| | - Yi-Chou Hou
- Graduate Institute of Clinical Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Department of Internal Medicine, Cardinal Tien Hospital, New Taipei City, Taiwan.,School of Medicine, Fu Jen Catholic University, New Taipei City, Taiwan
| | - Wei-Che Lin
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Cheng-Hsien Lu
- Department of Neurology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Li-Kai Huang
- Department of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Department of Neurology, Dementia Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
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17
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Long CM, Zheng QX, Zhou Y, Liu YT, Gong LP, Zeng YC, Liu S. N-linoleyltyrosine exerts neuroprotective effects in APP/PS1 transgenic mice via cannabinoid receptor-mediated autophagy. J Pharmacol Sci 2021; 147:315-324. [PMID: 34663513 DOI: 10.1016/j.jphs.2021.08.008] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 08/14/2021] [Accepted: 08/17/2021] [Indexed: 11/20/2022] Open
Abstract
Anandamide (AEA) analogs show fair effects in counteracting the deterioration of Alzheimer's disease (AD). Our previous studies demonstrated that AEA analog-N-linoleyltyrosine (NITyr) exerted significant activities. In our current research, the role and mechanisms of NITyr were assessed in APP/PS1 mice mimicking the AD model. NITyr improved motor coordination in the rotarod test (RRT) and ameliorated spatial memory in the Morris water maze (MWM) but did not increase spontaneous locomotor activity in the open field test (OFT). In addition, NITyr protected neurons against β-amyloid (Aβ) injury via hematoxylin-eosin (HE) and Nissl staining. Moreover, the related biochemical indexes showed that NITyr reduced the levels of Aβ40 and Aβ42 in the hippocampus but did not affect the expression of p-APP and β-secretase 1 (BACE1). Furthermore, the autophagy inhibitor 3-methyladenine (3 MA) attenuated the effect of NITyr on animal behaviors and neurons. Meanwhile, NITyr upregulated the expression levels of LC3-II and Beclin-1, which were weakened by AM630 (an antagonist of CB2 receptor and a weak partial agonist of CB1 receptors). AM630 also weakened the role of NITyr in animal behaviors. Thus, NITyr improved behavioral impairment and neural loss by inducing autophagy mainly mediated by the CB2 receptor, and weakly mediated by the CB1 receptor.
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Affiliation(s)
- Chun-Mei Long
- Department of Pharmacy, Study on the Structure-specific Small Molecule Drug in Sichuan Province College Key Laboratory, Chengdu Medical College, Chengdu, Sichuan, 610500, People's Republic of China
| | - Qi-Xue Zheng
- Department of Pharmacy, Study on the Structure-specific Small Molecule Drug in Sichuan Province College Key Laboratory, Chengdu Medical College, Chengdu, Sichuan, 610500, People's Republic of China
| | - Yi Zhou
- Research and Development Center, Sichuan Yuanda Shuyang Pharmaceutical Co.,Ltd, Chengdu, Sichuan, 610214, People's Republic of China
| | - Yuan-Ting Liu
- Department of Pharmacy, Study on the Structure-specific Small Molecule Drug in Sichuan Province College Key Laboratory, Chengdu Medical College, Chengdu, Sichuan, 610500, People's Republic of China
| | - Liu-Ping Gong
- Department of Pharmacy, Study on the Structure-specific Small Molecule Drug in Sichuan Province College Key Laboratory, Chengdu Medical College, Chengdu, Sichuan, 610500, People's Republic of China
| | - Ying-Chun Zeng
- Department of Pharmacy, Study on the Structure-specific Small Molecule Drug in Sichuan Province College Key Laboratory, Chengdu Medical College, Chengdu, Sichuan, 610500, People's Republic of China.
| | - Sha Liu
- Department of Pharmacy, Study on the Structure-specific Small Molecule Drug in Sichuan Province College Key Laboratory, Chengdu Medical College, Chengdu, Sichuan, 610500, People's Republic of China.
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18
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Zecca C, Pasculli G, Tortelli R, Dell'Abate MT, Capozzo R, Barulli MR, Barone R, Accogli M, Arima S, Pollice A, Brescia V, Logroscino G. The Role of Age on Beta-Amyloid 1-42 Plasma Levels in Healthy Subjects. Front Aging Neurosci 2021; 13:698571. [PMID: 34531734 PMCID: PMC8438760 DOI: 10.3389/fnagi.2021.698571] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Accepted: 08/09/2021] [Indexed: 01/02/2023] Open
Abstract
Beta-amyloid (Aβ) plaques have been observed in the brain of healthy elderlies with frequencies strongly influenced by age. The aim of the study is to evaluate the role of age and other biochemical and hematological parameters on Aβ1–42 plasma levels in cognitively and neurologically normal individuals. Two-hundred and seventy-five normal subjects stratified by age groups (<35 years, 35–65 years, and >65 years) were included in the study. Aβ1–42 plasma levels significantly correlated with age (rs = 0.27; p < 0.0001) in the whole sample, inversely correlated with age in the first age group (rs = −0.25, p = 0.01), positively correlated in the second group (rs = 0.22, p = 0.03), while there was no significant correlation in the older group (rs = 0.02, p = 0.86). Both age (β-estimate = 0.08; p < 0.001) and cholesterol (β-estimate = 0.03; p = 0.009) were significantly associated with Aβ1–42 plasma level in multivariable analysis. However, only the association with age survived post hoc adjustment for multiple comparisons. The different effects of age on the Aβ level across age groups should be explored in further studies to better understand the age-dependent variability. This could better define the value of plasma Aβ as a biomarker of the Alzheimer neuropathology.
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Affiliation(s)
- Chiara Zecca
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology of the University of Bari "Aldo Moro" at "Pia Fondazione Card G. Panico" Hospital Tricase, Lecce, Italy
| | - Giuseppe Pasculli
- Department of Computer, Control, and Management Engineering Antonio Ruberti (DIAG), La Sapienza University, Rome, Italy
| | - Rosanna Tortelli
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology of the University of Bari "Aldo Moro" at "Pia Fondazione Card G. Panico" Hospital Tricase, Lecce, Italy
| | - Maria Teresa Dell'Abate
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology of the University of Bari "Aldo Moro" at "Pia Fondazione Card G. Panico" Hospital Tricase, Lecce, Italy
| | - Rosa Capozzo
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology of the University of Bari "Aldo Moro" at "Pia Fondazione Card G. Panico" Hospital Tricase, Lecce, Italy
| | - Maria Rosaria Barulli
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology of the University of Bari "Aldo Moro" at "Pia Fondazione Card G. Panico" Hospital Tricase, Lecce, Italy
| | - Roberta Barone
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology of the University of Bari "Aldo Moro" at "Pia Fondazione Card G. Panico" Hospital Tricase, Lecce, Italy
| | - Miriam Accogli
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology of the University of Bari "Aldo Moro" at "Pia Fondazione Card G. Panico" Hospital Tricase, Lecce, Italy
| | - Serena Arima
- Department of History, Society and Human Studies, University of Salento, Lecce, Italy
| | - Alessio Pollice
- Department of Economics and Finance, University of Bari "Aldo Moro", Bari, Italy
| | - Vincenzo Brescia
- Unit of Laboratory Medicine, "Pia Fondazione Card. G. Panico" Hospital Tricase, Lecce, Italy
| | - Giancarlo Logroscino
- Center for Neurodegenerative Diseases and the Aging Brain, Department of Clinical Research in Neurology of the University of Bari "Aldo Moro" at "Pia Fondazione Card G. Panico" Hospital Tricase, Lecce, Italy.,Department of Basic Medicine Sciences, Neuroscience, and Sense Organs, University of Bari "Aldo Moro", Bari, Italy
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19
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Supraja P, Tripathy S, Singh R, Singh V, Chaudhury G, Singh SG. Towards point-of-care diagnosis of Alzheimer's disease: Multi-analyte based portable chemiresistive platform for simultaneous detection of β-amyloid (1-40) and (1-42) in plasma. Biosens Bioelectron 2021; 186:113294. [PMID: 33971525 DOI: 10.1016/j.bios.2021.113294] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Revised: 04/11/2021] [Accepted: 04/28/2021] [Indexed: 01/05/2023]
Abstract
Label-free simultaneous detection of Alzheimer's disease (AD) specific biomarkers Aβ40 and Aβ42 peptides on a single platform using polypyrrole nanoparticle-based chemiresistive biosensors is reported here. The proposed interdigitated-microelectrode based inexpensive multisensor-platform can concurrently detect Aβ40 and Aβ42 in spiked-plasma in the range of 10-14 - 10-6 g/mL (with LoDs being 5.71 and 9.09 fg/mL, respectively), enabling the estimation of diagnostically significant Aβ42/Aβ40 ratio. A detailed study has been undertaken here to record the individual sensor responses against spiked-plasma samples with varying amounts and proportions of the two target peptides, towards enabling disease-progression monitoring using the Aβ-ratio. As compared to the existing cost-ineffective brain-imaging techniques such as PET and MRI, and the high-risk CSF based invasive AD biomarkers detecting procedures, the proposed approach offers a viable alternative for affordable point-of-care AD diagnostics, with possible usage in performance evaluation of therapeutic drugs. Towards point-of-care applications, the portable readout used in this work was conjugated with an android-based mobile app for data-acquisition and analysis.
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Affiliation(s)
- Patta Supraja
- Department of Electrical Engineering, Indian Institute of Technology Hyderabad, 502285, India.
| | - Suryasnata Tripathy
- Department of Electrical Engineering, Indian Institute of Technology Hyderabad, 502285, India.
| | - Ranjana Singh
- Department of Electrical Engineering, Indian Institute of Technology Hyderabad, 502285, India.
| | - Vikrant Singh
- School of Medicine, University of California Davis, USA.
| | - Gajendranath Chaudhury
- Department of Electrical Engineering, Indian Institute of Technology Hyderabad, 502285, India.
| | - Shiv Govind Singh
- Department of Electrical Engineering, Indian Institute of Technology Hyderabad, 502285, India.
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20
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Serum Amyloid Beta42 Is Not Eliminated by the Cirrhotic Liver: A Pilot Study. J Clin Med 2021; 10:jcm10122669. [PMID: 34204545 PMCID: PMC8235170 DOI: 10.3390/jcm10122669] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 06/15/2021] [Accepted: 06/16/2021] [Indexed: 12/11/2022] Open
Abstract
Amyloid-beta (Aβ) deposition in the brain is the main pathological hallmark of Alzheimer disease. Peripheral clearance of Aβ may possibly also lower brain levels. Recent evidence suggested that hepatic clearance of Aβ42 is impaired in liver cirrhosis. To further test this hypothesis, serum Aβ42 was measured by ELISA in portal venous serum (PVS), systemic venous serum (SVS), and hepatic venous serum (HVS) of 20 patients with liver cirrhosis. Mean Aβ42 level was 24.7 ± 20.4 pg/mL in PVS, 21.2 ± 16.7 pg/mL in HVS, and 19.2 ± 11.7 pg/mL in SVS. Similar levels in the three blood compartments suggested that the cirrhotic liver does not clear Aβ42. Aβ42 was neither associated with the model of end-stage liver disease score nor the Child–Pugh score. Patients with abnormal creatinine or bilirubin levels or prolonged prothrombin time did not display higher Aβ42 levels. Patients with massive ascites and patients with large varices had serum Aβ42 levels similar to patients without these complications. Serum Aβ42 was negatively associated with connective tissue growth factor levels (r = −0.580, p = 0.007) and a protective role of Aβ42 in fibrogenesis was already described. Diabetic patients with liver cirrhosis had higher Aβ42 levels (p = 0.069 for PVS, p = 0.047 for HVS and p = 0.181 for SVS), which is in accordance with previous reports. Present analysis showed that the cirrhotic liver does not eliminate Aβ42. Further studies are needed to explore the association of liver cirrhosis, Aβ42 levels, and cognitive dysfunction.
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21
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Chiu MJ, Yang SY, Chen TF, Lin CH, Yang FC, Chen WP, Zetterberg H, Blennow K. Synergistic Association between Plasma Aβ 1-42 and p-tau in Alzheimer's Disease but Not in Parkinson's Disease or Frontotemporal Dementia. ACS Chem Neurosci 2021; 12:1376-1383. [PMID: 33825443 PMCID: PMC9278807 DOI: 10.1021/acschemneuro.1c00010] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
![]()
Beta-amyloid (Aβ1–42) triggers the phosphorylation
of tau protein in Alzheimer’s disease (AD), but the relationship
between phosphorylated tau (p-tau) and Aβ1–42 in the blood is not elucidated. We investigated the association
in individuals with AD (n = 62, including amnesic
mild cognitive impairment and dementia), Parkinson’s disease
(n = 30), frontotemporal dementia (n = 25), and cognitively unimpaired controls (n =
41) using immunomagnetic reduction assays to measure plasma Aβ1–42 and p-tau181 concentrations. Correlation and regression
analyses were performed to examine the relation between plasma levels,
demographic factors, and clinical severity. Both plasma Aβ1–42 and p-tau concentrations were significantly higher
in AD and frontotemporal dementia than in the controls and Parkinson’s
disease. A significant positive association was found between plasma
p-tau and Aβ1–42 in controls (r = 0.579, P < 0.001) and AD (r = 0.699, P < 0.001) but not in frontotemporal
dementia or Parkinson’s disease. Plasma p-tau was significantly
associated with clinical severity in the AD in terms of scores of
clinical dementia rating (r = 0.288, P = 0.025) and mini-mental state examination (r =
−0.253, P = 0.049). Regression analysis showed
that plasma Aβ1–42 levels explain approximately
47.7% of the plasma p-tau levels in the AD after controlling age,
gender, and clinical severity. While in non-AD participants, the clinical
dementia rating explained about 47.5% of the plasma p-tau levels.
The disease-specific association between plasma Aβ1–42 and p-tau levels in AD implies a possible synergic effect in mechanisms
involving these two pathological proteins’ genesis.
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Affiliation(s)
- Ming-Jang Chiu
- Department of Neurology, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei 100, Taiwan
- Graduate Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University, Taipei 100, Taiwan
- Department of Psychology, National Taiwan University, Taipei 100, Taiwan
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 116, Taiwan
| | - Shieh-Yueh Yang
- MagQu Co., Ltd., New Taipei City 231, Taiwan
- MagQu LLC, Surprise, Arizona 85378, United States
| | - Ta-Fu Chen
- Department of Neurology, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei 100, Taiwan
| | - Chin-Hsien Lin
- Department of Neurology, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei 100, Taiwan
| | - Fu-Chi Yang
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
| | | | - Henrik Zetterberg
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal 405 30, Sweden
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, United Kingdom
- UK Dementia Research Institute at UCL, London WC1E 6BT, United Kingdom
| | - Kaj Blennow
- Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at the University of Gothenburg, Mölndal, Sweden Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal 405 30, Sweden
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London WC1N 3BG, United Kingdom
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22
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Deniz K, Ho CCG, Malphrus KG, Reddy JS, Nguyen T, Carnwath TP, Crook JE, Lucas JA, Graff-Radford NR, Carrasquillo MM, Ertekin-Taner N. Plasma Biomarkers of Alzheimer's Disease in African Americans. J Alzheimers Dis 2021; 79:323-334. [PMID: 33252078 PMCID: PMC7902984 DOI: 10.3233/jad-200828] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/02/2022]
Abstract
Background/Objective: The aim of this study was to determine if plasma concentrations of 5 surrogate markers of Alzheimer’s disease (AD) pathology and neuroinflammation are associated with disease status in African Americans. Methods: We evaluated 321 African Americans (159 AD, 162 controls) from the Florida Consortium for African-American Alzheimer’s Disease Studies (FCA3DS). Five plasma proteins reflecting AD neuropathology or inflammation (Aβ42, tau, IL6, IL10, TNFα) were tested for associations with AD, age, sex, APOE and MAPT genotypes, and for pairwise correlations. Results: Plasma tau levels were higher in AD when adjusted for biological and technical covariates. APOEɛ4 was associated with lower plasma Aβ42 and tau levels. Older age was associated with higher plasma Aβ42, tau, and TNFα. Females had lower IL10 levels. Inflammatory proteins had strong pairwise correlations amongst themselves and with Aβ42. Conclusion: We identified effects of demographic and genetic variants on five potential plasma biomarkers in African Americans. Plasma inflammatory biomarkers and Aβ42 may reflect correlated pathologies and elevated plasma tau may be a biomarker of AD in this population.
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Affiliation(s)
- Kaancan Deniz
- Mayo Clinic, Department of Neuroscience, Jacksonville, FL, USA
| | | | | | - Joseph S Reddy
- Mayo Clinic, Department of Health Science Research, Jacksonville, FL, USA
| | - Thuy Nguyen
- Mayo Clinic, Department of Neuroscience, Jacksonville, FL, USA
| | - Troy P Carnwath
- Mayo Clinic, Department of Neuroscience, Jacksonville, FL, USA
| | - Julia E Crook
- Mayo Clinic, Department of Health Science Research, Jacksonville, FL, USA
| | - John A Lucas
- Mayo Clinic, Department of Psychiatry and Psychology, Jacksonville, FL, USA
| | | | | | - Nilüfer Ertekin-Taner
- Mayo Clinic, Department of Neuroscience, Jacksonville, FL, USA.,Mayo Clinic, Department of Neurology, Jacksonville, FL, USA
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23
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Wu YW, Yeh YT, Wu CC, Huang CL, Chang YY, Wu CC. Clinical Feasibility of Biofunctionalized Magnetic Nanoparticles for Detecting Multiple Cardiac Biomarkers in Emergency Chest Pain Patients. ACTA CARDIOLOGICA SINICA 2020; 36:649-659. [PMID: 33235422 PMCID: PMC7677641 DOI: 10.6515/acs.202011_36(6).20200414a] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
BACKGROUND The rapid diagnosis of acute myocardial infarction (AMI) is a clinical and operational priority in emergency departments. Serial serum levels of cardiac biomarkers play a crucial role in the evaluation of patients presenting with acute chest pain, so that an accurate and rapidly responsive assay of cardiac biomarkers is vital for emergency departments. METHODS Immunomagnetic reduction (IMR) has been developed for rapid and on-site assays with a small sample volume. IMR kits for three biomarkers [myoglobin, creatine kinase-MB (CK-MB), and troponin-I] have been developed by MagQu Co., Ltd., Taiwan (US patent: US20190072563A1). In this study, we examined correlations between IMR signals and biomarker concentrations. The measurement threshold of the IMR kits, dynamic ranges, interference tests in vitro, and reagent stability were tested. Clinical cases were included with serial IMR measurements to determine the time course and peak of IMR-measured cardiac biomarkers after AMI. RESULTS The correlations between IMR signals and biomarker concentrations fitted well to logistic functions. The measurement thresholds of the IMR kits (1.03 × 10-8 ng/mL for myoglobin, 1.46 × 10-6 ng/mL for CK-MB, and 0.08 ng/mL for troponin-I) were much lower than the levels detected in the patients with AMI. There was no significant interference in vitro. The peak times of IMR-detected myoglobin, CK-MB, and troponin-I after AMI were 8.2 hours, 24.4 hours, and 24.7 hours, respectively. CONCLUSIONS IMR is an accurate and sensitive on-site rapid assay for multiple cardiac biomarkers in vitro, and may play a role in the early diagnosis of AMI. Clinical trials are needed.
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Affiliation(s)
- Yen-Wen Wu
- Cardiology Division of Cardiovascular Medical Center, Far Eastern Memorial Hospital, New Taipei City;
,
National Yang-Ming University School of Medicine;
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Department of Internal Medicine, Taoyuan General Hospital; Taoyuan
| | - Yen-Ting Yeh
- Cardiology Division of Cardiovascular Medical Center, Far Eastern Memorial Hospital, New Taipei City
| | - Chih-Cheng Wu
- National Yang-Ming University School of Medicine;
,
National Taiwan University College of Medicine, Taipei;
,
Department of Internal Medicine, National Taiwan University Hospital Hsinchu Branch, Hsinchu
| | - Chi-Lun Huang
- National Taiwan University College of Medicine, Taipei;
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Department of Internal Medicine, Taoyuan General Hospital; Taoyuan
| | - Yi-Yao Chang
- Cardiology Division of Cardiovascular Medical Center, Far Eastern Memorial Hospital, New Taipei City;
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National Taiwan University College of Medicine, Taipei
| | - Chau-Chung Wu
- Department of Internal Medicine, National Taiwan University Hospital;
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Department of Medical Education and Bioethics, College of Medicine, National Taiwan University, Taipei, Taiwan
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24
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Lin CH, Chiu SI, Chen TF, Jang JSR, Chiu MJ. Classifications of Neurodegenerative Disorders Using a Multiplex Blood Biomarkers-Based Machine Learning Model. Int J Mol Sci 2020; 21:ijms21186914. [PMID: 32967146 PMCID: PMC7555120 DOI: 10.3390/ijms21186914] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2020] [Revised: 09/17/2020] [Accepted: 09/18/2020] [Indexed: 12/15/2022] Open
Abstract
Easily accessible biomarkers for Alzheimer's disease (AD), Parkinson's disease (PD), frontotemporal dementia (FTD), and related neurodegenerative disorders are urgently needed in an aging society to assist early-stage diagnoses. In this study, we aimed to develop machine learning algorithms using the multiplex blood-based biomarkers to identify patients with different neurodegenerative diseases. Plasma samples (n = 377) were obtained from healthy controls, patients with AD spectrum (including mild cognitive impairment (MCI)), PD spectrum with variable cognitive severity (including PD with dementia (PDD)), and FTD. We measured plasma levels of amyloid-beta 42 (Aβ42), Aβ40, total Tau, p-Tau181, and α-synuclein using an immunomagnetic reduction-based immunoassay. We observed increased levels of all biomarkers except Aβ40 in the AD group when compared to the MCI and controls. The plasma α-synuclein levels increased in PDD when compared to PD with normal cognition. We applied machine learning-based frameworks, including a linear discriminant analysis (LDA), for feature extraction and several classifiers, using features from these blood-based biomarkers to classify these neurodegenerative disorders. We found that the random forest (RF) was the best classifier to separate different dementia syndromes. Using RF, the established LDA model had an average accuracy of 76% when classifying AD, PD spectrum, and FTD. Moreover, we found 83% and 63% accuracies when differentiating the individual disease severity of subgroups in the AD and PD spectrum, respectively. The developed LDA model with the RF classifier can assist clinicians in distinguishing variable neurodegenerative disorders.
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Affiliation(s)
- Chin-Hsien Lin
- Department of Neurology, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei 100225, Taiwan; (C.-H.L.); (T.-F.C.)
| | - Shu-I Chiu
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan; (S.-I.C.); (J.-S.R.J.)
- Department of Computer Science, National Chengchi University, Taipei 11605, Taiwan
| | - Ta-Fu Chen
- Department of Neurology, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei 100225, Taiwan; (C.-H.L.); (T.-F.C.)
| | - Jyh-Shing Roger Jang
- Department of Computer Science and Information Engineering, National Taiwan University, Taipei 10617, Taiwan; (S.-I.C.); (J.-S.R.J.)
| | - Ming-Jang Chiu
- Department of Neurology, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei 100225, Taiwan; (C.-H.L.); (T.-F.C.)
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei 10617, Taiwan
- Graduate Institute of Brain and Mind Sciences, National Taiwan University, Taipei 100233, Taiwan
- Graduate Institue of Psychology, National Taiwan University, Taipei 10617, Taiwan
- Correspondence: ; Tel.: +886-2-23123456 (ext. 65339)
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25
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Jiao F, Yi F, Wang Y, Zhang S, Guo Y, Du W, Gao Y, Ren J, Zhang H, Liu L, Song H, Wang L. The Validation of Multifactor Model of Plasma Aβ 42 and Total-Tau in Combination With MoCA for Diagnosing Probable Alzheimer Disease. Front Aging Neurosci 2020; 12:212. [PMID: 32792940 PMCID: PMC7385244 DOI: 10.3389/fnagi.2020.00212] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 06/16/2020] [Indexed: 01/21/2023] Open
Abstract
Alzheimer disease (AD) has an insidious onset and heterogeneous clinical symptoms. The well-accepted biomarkers for clinical diagnosis of AD include β-amyloid (Aβ) deposition and pathologic tau level within cerebral spinal fluid (CSF) and imaging AD pathology such as positive emission tomography (PET) imaging of the amyloid-binding agent Pittsburgh compound B (PET-PiB). However, the high expense and invasive nature of these methods highly limit their wide usage in clinic practice. Therefore, it is imperious to develop less expensive and invasive methods, and plasma biomarkers are the premium targets. In the current study, we utilized a single-blind comparison method; all the probable AD cases met the core clinical National Institute on Aging and Alzheimer’s Association (NIA-AA) criteria and validated by PET-PiB. We used ultrasensitive immunomagnetic reduction (IMR) assays to measure plasma Aβ42 and total-tau (t-tau) levels, in combination with different variables including Aβ42 × t-tau value, Montreal Cognitive Assessment (MoCA), and Mini Mental State Examination (MMSE). We used logistic regression to analyze the effect of all these variables in the algorism. Our results showed that (1) plasma Aβ42 and t-tau are efficient biomarkers for AD diagnosis using IMR platform, whereas Aβ42 × t-tau value is more efficient for discriminating control and AD; (2) in the control group, Aβ42 level and age demonstrated strong negative correlation; Aβ42 × t-tau value and age demonstrated significant negative correlation; (3) in the AD group, t-tau level and MMSE score demonstrated strong negative correlation; (4) using the model that Aβ42, Aβ42 × t-tau, and MoCA as the variable to generate receiver operating characteristic (ROC) curve, cutoff value = 0.48, sensitivity = 0.973, specificity = 0.982, area under the curve (AUC) = 0.986, offered better categorical efficacy, sensitivity, specificity, and AUC. The multifactor model of plasma Aβ42 and t-tau in combination with MoCA can be a viable model separate health and AD subjects in clinical practice.
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Affiliation(s)
- Fubin Jiao
- Medical School of Chinese People's Liberation Army, Beijing, China.,Department of Neurology, The 2nd Medical Center, National Clinical Research Center for Geriatric Disease, Chinese People's Liberation Army General Hospital, Beijing, China.,Health Service Department of the Guard Bureau of the Joint Staff Department, Joint Staff of the Central Military Commission of Chinese PLA, Beijing, China
| | - Fang Yi
- Department of Neurology, The 2nd Medical Center, National Clinical Research Center for Geriatric Disease, Chinese People's Liberation Army General Hospital, Beijing, China.,Department of Neurology, Lishilu Outpatient, Jingzhong Medical District, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Yuanyuan Wang
- Department of Neurology, The 2nd Medical Center, National Clinical Research Center for Geriatric Disease, Chinese People's Liberation Army General Hospital, Beijing, China
| | - Shouzi Zhang
- The Psycho Department of Beijing Geriatric Hospital, Beijing, China
| | - Yanjun Guo
- Department of Neurology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Wenjin Du
- Department of Neurology, Air Force Medical Center, Chinese People's Liberation Army, Beijing, China
| | - Ya Gao
- Department of Geriatrics, The Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Jingjing Ren
- National Engineering Research Center for Protein Drugs, Beijing, China
| | - Haifeng Zhang
- Health Service Department of the Guard Bureau of the Joint Staff Department, Joint Staff of the Central Military Commission of Chinese PLA, Beijing, China
| | - Lixin Liu
- The Psycho Department of Beijing Geriatric Hospital, Beijing, China
| | - Haifeng Song
- National Engineering Research Center for Protein Drugs, Beijing, China
| | - Luning Wang
- Medical School of Chinese People's Liberation Army, Beijing, China.,Department of Neurology, The 2nd Medical Center, National Clinical Research Center for Geriatric Disease, Chinese People's Liberation Army General Hospital, Beijing, China
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26
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Houben S, de Fisenne MA, Ando K, Vanden Dries V, Poncelet L, Yilmaz Z, Mansour S, De Decker R, Brion JP, Leroy K. Intravenous Injection of PHF-Tau Proteins From Alzheimer Brain Exacerbates Neuroinflammation, Amyloid Beta, and Tau Pathologies in 5XFAD Transgenic Mice. Front Mol Neurosci 2020; 13:106. [PMID: 32765217 PMCID: PMC7381181 DOI: 10.3389/fnmol.2020.00106] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 05/20/2020] [Indexed: 02/01/2023] Open
Abstract
Alzheimer's disease (AD) is characterized by the accumulation in the brain of intraneuronal aggregates of abnormally and hyperphosphorylated tau proteins and of extracellular deposits of amyloid-β surrounded by dystrophic neurites. Numerous experimental models have shown that tau pathology develops in the brain after intracerebral injection of brain homogenates or pathological tau [paired helical filaments (PHF)-tau)] from AD brains. Further investigations are however necessary to identify or exclude potential extracerebral routes of tau pathology transmission, e.g., through the intravascular route. In this study, we have analyzed the effect of intravenous injection of PHF-tau proteins from AD brains on the formation of tau and amyloid pathologies in the brain of wild-type (WT) mice and of 5XFAD mice (an amyloid model). We observed that 5XFAD mice with a disrupted blood-brain barrier showed increased plaque-associated astrogliosis, microgliosis, and increased deposits of Aβ40 and Aβ42 after intravenous injection of PHF-tau proteins. In addition, an increased phosphotau immunoreactivity was observed in plaque-associated dystrophic neurites. These results suggest that blood products contaminated by PHF-tau proteins could potentially induce an exacerbation of neuroinflammation and AD pathologies.
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Affiliation(s)
- Sarah Houben
- Laboratory of Histology, Neuroanatomy and Neuropathology, Faculty of Medicine, ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
| | - Marie-Ange de Fisenne
- Laboratory of Histology, Neuroanatomy and Neuropathology, Faculty of Medicine, ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
| | - Kunie Ando
- Laboratory of Histology, Neuroanatomy and Neuropathology, Faculty of Medicine, ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
| | - Virginie Vanden Dries
- Laboratory of Histology, Neuroanatomy and Neuropathology, Faculty of Medicine, ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
| | - Luc Poncelet
- Laboratory of Anatomy, Biomechanics and Organogenesis, Faculty of Medicine, ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
| | - Zehra Yilmaz
- Laboratory of Histology, Neuroanatomy and Neuropathology, Faculty of Medicine, ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
| | - Salwa Mansour
- Laboratory of Histology, Neuroanatomy and Neuropathology, Faculty of Medicine, ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
| | - Robert De Decker
- Laboratory of Histology, Neuroanatomy and Neuropathology, Faculty of Medicine, ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
| | - Jean-Pierre Brion
- Laboratory of Histology, Neuroanatomy and Neuropathology, Faculty of Medicine, ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
| | - Karelle Leroy
- Laboratory of Histology, Neuroanatomy and Neuropathology, Faculty of Medicine, ULB Neuroscience Institute, Université Libre de Bruxelles, Brussels, Belgium
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27
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Tsai CL, Liang CS, Lee JT, Su MW, Lin CC, Chu HT, Tsai CK, Lin GY, Lin YK, Yang FC. Associations between Plasma Biomarkers and Cognition in Patients with Alzheimer's Disease and Amnestic Mild Cognitive Impairment: A Cross-Sectional and Longitudinal Study. J Clin Med 2019; 8:jcm8111893. [PMID: 31698867 PMCID: PMC6912664 DOI: 10.3390/jcm8111893] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2019] [Revised: 10/29/2019] [Accepted: 11/04/2019] [Indexed: 01/16/2023] Open
Abstract
Brain degeneration in patients with Alzheimer's disease (AD) results from the accumulation of pathological amyloid- (Aβ) plaques and tau protein tangles, leading to altered plasma levels of biomarkers. However, few studies have investigated the association between plasma biomarkers and cognitive impairment in patients with AD. In this cross-sectional study, we investigated correlations between mini-mental state examination (MMSE) scores and levels of plasma biomarkers in patients with amnestic mild cognitive impairment (aMCI) and AD. Thirteen individuals with normal cognition, 40 patients with aMCI, and 37 patients with AD were enrolled. Immunomagnetic reduction was used to assess the levels of plasma biomarkers, including amyloid A1-40, A1-42, total tau protein (t-Tau), and phosphorylated tau protein (threonine 181, p-Tau181). Our analysis revealed a significant negative correlation between MMSE and both measures of tau, and a trend toward negative correlation between MMSE and A1-42. In a longitudinal study involving three patients with aMCI and two patients with AD, we observed strong negative correlations (r < -0.8) between changes in MMSE scores and plasma levels of t-Tau. Our results suggest that plasma levels of t-Tau and p-Tau181 can be used to assess the severity of cognitive impairment in patients with AD. Furthermore, the results of our preliminary longitudinal study suggest that levels of t-Tau can be used to monitor the progression of cognitive decline in patients with aMCI/AD.
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Affiliation(s)
- Chia-Lin Tsai
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
| | - Chih-Sung Liang
- Department of Psychiatry, Beitou Branch, Tri-Service General Hospital, National Defense Medical Center, Taipei 112, Taiwan
| | - Jiunn-Tay Lee
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
| | - Ming-Wei Su
- Institute of Biomedical Sciences, Academia Sinica, Taipei 115, Taiwan
| | - Chun-Chieh Lin
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
| | - Hsuan-Te Chu
- Department of Psychiatry, Beitou Branch, Tri-Service General Hospital, National Defense Medical Center, Taipei 112, Taiwan
| | - Chia-Kuang Tsai
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
| | - Guan-Yu Lin
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
| | - Yu-Kai Lin
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
| | - Fu-Chi Yang
- Department of Neurology, Tri-Service General Hospital, National Defense Medical Center, Taipei 114, Taiwan
- Correspondence: ; Tel.: +886-2-879-233-11; Fax: +886-2-879-271-74
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