1
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Liu S, Liu Z, Wu G, Ye H, Wu Z, Yang Z, Jiang S. Assessment of sepsis-associated encephalopathy by quantitative magnetic resonance spectroscopy in a rat model of cecal ligation and puncture. Heliyon 2024; 10:e26836. [PMID: 38434271 PMCID: PMC10906417 DOI: 10.1016/j.heliyon.2024.e26836] [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: 07/23/2023] [Revised: 02/06/2024] [Accepted: 02/20/2024] [Indexed: 03/05/2024] Open
Abstract
Proton magnetic resonance spectroscopy (1H-MRS) is the only non-invasive technique to quantify neurometabolic compounds in the living brain. We used 1H-MRS to evaluate the brain metabolites in a rat model of Sepsis-associated encephalopathy (SAE) established by cecal ligation and puncture (CLP). 36 male Sprague-Dawley rats were randomly divided into sham and CLP groups. Each group was further divided into three subgroups: subgroup O, subgroup M, and subgroup N. Neurological function assessments were performed on the animals in the subgroup O and subgroup N at 24 h, 48 h, and 72 h. The animals in the subgroup M were examined by magnetic resonance imaging (MRI) at 12 h after CLP. Compared with the sham group, the ratio of N-acetylaspartate (NAA) to creatine (Cr) in the hippocampus was significantly lower in the CLP group. The respective ratios of lactate (Lac), myo-inositol (mIns), glutamate and glutamine (Glx), lipid (Lip), and choline (Cho) to Cr in the CLP group were clearly higher than those in the sham group. Cytochrome c, intimately related to oxidative stress, was elevated in the CLP group. Neurofilament light (NfL) chain and glial fibrillary acidic protein (GFAP) scores in the CLP group were significantly higher than those in the sham group, while zonula occludens-1 (ZO-1) was downregulated. Compared with the sham group, the CLP group displayed higher values of oxygen extraction fraction (OEF), central venous-arterial partial pressure of carbon dioxide (P (cv-a) CO2), and central venous lactate (VLac). In contrast, jugular venous oxygen saturation (SjvO2) declined. In the present study, 1H-MRS could be used to quantitatively assess brain injury in terms of microcirculation disorder, oxidative stress, blood-brain barrier disruption, and glial cell activation through changes in metabolites within brain tissue.
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Affiliation(s)
- Siqi Liu
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Zhifeng Liu
- The Fourth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 511300, China
| | - Gongfa Wu
- The Fourth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 511300, China
| | - Haoyi Ye
- The Fourth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 511300, China
| | - Zhihua Wu
- The Fourth Affiliated Hospital of Guangzhou Medical University, Guangzhou, 511300, China
| | - Zhengfei Yang
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
| | - Shanping Jiang
- Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, 510120, China
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Jayasinghe D, Momin MM, Beckmann K, Hyppönen E, Benyamin B, Lee SH. Mitigating type 1 error inflation and power loss in GxE PRS: Genotype-environment interaction in polygenic risk score models. Genet Epidemiol 2024; 48:85-100. [PMID: 38303123 DOI: 10.1002/gepi.22546] [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: 07/25/2023] [Revised: 01/03/2024] [Accepted: 01/08/2024] [Indexed: 02/03/2024]
Abstract
The use of polygenic risk score (PRS) models has transformed the field of genetics by enabling the prediction of complex traits and diseases based on an individual's genetic profile. However, the impact of genotype-environment interaction (GxE) on the performance and applicability of PRS models remains a crucial aspect to be explored. Currently, existing genotype-environment interaction polygenic risk score (GxE PRS) models are often inappropriately used, which can result in inflated type 1 error rates and compromised results. In this study, we propose novel GxE PRS models that jointly incorporate additive and interaction genetic effects although also including an additional quadratic term for nongenetic covariates, enhancing their robustness against model misspecification. Through extensive simulations, we demonstrate that our proposed models outperform existing models in terms of controlling type 1 error rates and enhancing statistical power. Furthermore, we apply the proposed models to real data, and report significant GxE effects. Specifically, we highlight the impact of our models on both quantitative and binary traits. For quantitative traits, we uncover the GxE modulation of genetic effects on body mass index by alcohol intake frequency. In the case of binary traits, we identify the GxE modulation of genetic effects on hypertension by waist-to-hip ratio. These findings underscore the importance of employing a robust model that effectively controls type 1 error rates, thus preventing the occurrence of spurious GxE signals. To facilitate the implementation of our approach, we have developed an innovative R software package called GxEprs, specifically designed to detect and estimate GxE effects. Overall, our study highlights the importance of accurate GxE modeling and its implications for genetic risk prediction, although providing a practical tool to support further research in this area.
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Affiliation(s)
- Dovini Jayasinghe
- Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, South Australia, Australia
| | - Md Moksedul Momin
- Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, South Australia, Australia
- Department of Genetics and Animal Breeding, Faculty of Veterinary Medicine, Chattogram Veterinary and Animal Sciences University (CVASU), Khulshi, Chattogram, Bangladesh
| | - Kerri Beckmann
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia
| | - Elina Hyppönen
- Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, South Australia, Australia
- UniSA Clinical and Health Sciences, University of South Australia, Adelaide, South Australia, Australia
| | - Beben Benyamin
- Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, South Australia, Australia
| | - S Hong Lee
- Australian Centre for Precision Health, University of South Australia, Adelaide, South Australia, Australia
- UniSA Allied Health and Human Performance, University of South Australia, Adelaide, South Australia, Australia
- South Australian Health and Medical Research Institute (SAHMRI), University of South Australia, Adelaide, South Australia, Australia
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Gillespie NA, Elman JA, McKenzie RE, Tu XM, Xian H, Reynolds CA, Panizzon MS, Lyons MJ, Eglit GML, Neale MC, Rissman RA, Franz C, Kremen WS. The heritability of blood-based biomarkers related to risk of Alzheimer's disease in a population-based sample of early old-age men. Alzheimers Dement 2024; 20:356-365. [PMID: 37622539 PMCID: PMC10843753 DOI: 10.1002/alz.13407] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 06/23/2023] [Accepted: 06/27/2023] [Indexed: 08/26/2023]
Abstract
INTRODUCTION Despite their increased application, the heritability of Alzheimer's disease (AD)-related blood-based biomarkers remains unexplored. METHODS Plasma amyloid beta 40 (Aβ40), Aβ42, the Aβ42/40 ratio, total tau (t-tau), and neurofilament light (NfL) data came from 1035 men 60 to 73 years of age (μ = 67.0, SD = 2.6). Twin models were used to calculate heritability and the genetic and environmental correlations between them. RESULTS Additive genetics explained 44% to 52% of Aβ42, Aβ40, t-tau, and NfL. The Aβ42/40 ratio was not heritable. Aβ40 and Aβ42 were genetically near identical (rg = 0.94). Both Aβ40 and Aβ42 were genetically correlated with NfL (rg = 0.35 to 0.38), but genetically unrelated to t-tau. DISCUSSION Except for Aβ42/40, plasma biomarkers are heritable. Aβ40 and Aβ42 share mostly the same genetic influences, whereas genetic influences on plasma t-tau and NfL are largely unique in early old-age men. The absence of genetic associations between the Aβs and t-tau is not consistent with the amyloid cascade hypothesis.
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Affiliation(s)
- Nathan A. Gillespie
- Virginia Institute for Psychiatric and Behaviour GeneticsDepartment of PsychiatryVirginia Commonwealth UniversityRichmondVirginiaUSA
- QIMR Berghofer Medical Research InstituteBrisbaneQueenslandAustralia
| | - Jeremy A. Elman
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
- Center for Behavior Genetics of AgingUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Ruth E. McKenzie
- Department of PsychologyBoston UniversityBostonMassachusettsUSA
- School of Education and Social PolicyMerrimack CollegeNorth AndoverMassachusettsUSA
| | - Xin M. Tu
- Center for Behavior Genetics of AgingUniversity of California, San DiegoLa JollaCaliforniaUSA
- Department of Family Medicine and Public HealthUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Hong Xian
- Department of Epidemiology and BiostatisticsSaint. Louis UniversitySt. LouisMissouriUSA
- Research Service, VA St. Louis Healthcare SystemSt. LouisMissouriUSA
| | | | - Matthew S. Panizzon
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
- Center for Behavior Genetics of AgingUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Michael J. Lyons
- Department of Psychological and Brain SciencesBoston UniversityBostonMassachusettsUSA
| | - Graham M. L. Eglit
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
- Sam and Rose Stein Institute for Research on AgingUniversity of California San DiegoLa JollaCaliforniaUSA
| | - Michael C. Neale
- Virginia Institute for Psychiatric and Behaviour GeneticsDepartment of PsychiatryVirginia Commonwealth UniversityRichmondVirginiaUSA
| | - Robert A. Rissman
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
- Center for Behavior Genetics of AgingUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - Carol Franz
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
- Center for Behavior Genetics of AgingUniversity of California, San DiegoLa JollaCaliforniaUSA
| | - William S. Kremen
- Department of PsychiatryUniversity of California, San DiegoLa JollaCaliforniaUSA
- Center for Behavior Genetics of AgingUniversity of California, San DiegoLa JollaCaliforniaUSA
- Department of NeurosciencesUniversity of California, San DiegoLa JollaCaliforniaUSA
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4
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Liu X, Chen J, Meng C, Zhou L, Liu Y. Serum neurofilament light chain and cognition decline in US elderly: A cross-sectional study. Ann Clin Transl Neurol 2024; 11:17-29. [PMID: 37902309 PMCID: PMC10791034 DOI: 10.1002/acn3.51929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 09/15/2023] [Accepted: 10/10/2023] [Indexed: 10/31/2023] Open
Abstract
OBJECTIVE Early identification of cognitive impairment in neurodegenerative diseases like Alzheimer's disease (AD) is crucial. Neurofilament, a potential biomarker for neurological disorders, has gained attention. Our study aims to investigate the relationship between serum neurofilament light (sNfL) levels and cognitive function in elderly individuals in the United States. METHODS This cross-sectional study analyzed data from participants aged 60 and above in the National Health and Nutrition Examination Survey (2013-2014). We collected sNfL levels, cognitive function tests, sociodemographic characteristics, comorbidities, and other variables. Weighted multiple linear regression models examined the relationship between ln(sNfL) and cognitive scores. Restricted cubic spline (RCS) visualization explored nonlinear relationships. The stratified analysis examined subgroups' ln(sNfL) and cognitive function association. RESULTS The study included 446 participants (47.73% male). Participants with ln(sNfL) levels between 2.58 and 2.81 pg/mL (second quintile) performed relatively well in cognitive tests. After adjusting for multiple factors, ln(sNfL) levels were negatively correlated with cognitive function, with adjusted β (95% CI) as follows: immediate recall test (IRT): -0.763 (-1.301 to -0.224), delayed recall test (DRT): -0.308 (-0.576 to -0.04), animal fluency test (AFT): -1.616 (-2.639 to -0.594), and digit symbol substitution test (DSST): -2.790 (-4.369 to -1.21). RCS curves showed nonlinear relationships between ln(sNfL) and DRT, AFT, with inflection points around 2.7 pg/mL. The stratified analysis revealed a negative correlation between ln(sNfL) and cognition in specific subgroups with distinct features, with an interaction between diabetes and ln(sNfL). INTERPRETATION Higher sNfL levels are associated with poorer cognitive function in the elderly population of the United States. sNfL shows promise as a potential biomarker for early identification of cognitive decline.
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Affiliation(s)
- Xiaodong Liu
- Department of Neurology, Taihe HospitalHubei University of MedicineShiyanChina
| | - Jun Chen
- Department of Neurology, Taihe HospitalHubei University of MedicineShiyanChina
| | - Chen Meng
- Department of Anesthesiology, Taihe HospitalHubei University of MedicineShiyanHubeiChina
| | - Lan Zhou
- Department of Neurology, Taihe HospitalHubei University of MedicineShiyanChina
| | - Yong Liu
- Department of Neurology, Taihe HospitalHubei University of MedicineShiyanChina
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Küçükali F, Neumann A, Van Dongen J, De Pooter T, Joris G, De Rijk P, Ohlei O, Dobricic V, Bos I, Vos SJB, Engelborghs S, De Roeck E, Vandenberghe R, Gabel S, Meersmans K, Tsolaki M, Verhey F, Martinez‐Lage P, Tainta M, Frisoni G, Blin O, Richardson JC, Bordet R, Scheltens P, Popp J, Peyratout G, Johannsen P, Frölich L, Freund‐Levi Y, Streffer J, Lovestone S, Legido‐Quigley C, Kate MT, Barkhof F, Zetterberg H, Bertram L, Strazisar M, Visser PJ, Van Broeckhoven C, Sleegers K. Whole‐exome rare‐variant analysis of Alzheimer's disease and related biomarker traits. Alzheimers Dement 2022. [DOI: 10.1002/alz.12842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 09/16/2022] [Accepted: 09/28/2022] [Indexed: 12/08/2022]
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Zhu J, Lee MJ, Chang HJ, Ju X, Cui J, Lee YL, Go D, Chung W, Oh E, Heo JY. Reactive microglia and mitochondrial unfolded protein response following ventriculomegaly and behavior defects in kaolin-induced hydrocephalus. BMB Rep 2022. [PMID: 34903317 PMCID: PMC9058473 DOI: 10.5483/bmbrep.2022.55.4.126] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Ventriculomegaly induced by the abnormal accumulation of cerebrospinal fluid (CSF) leads to hydrocephalus, which is accompanied by neuroinflammation and mitochondrial oxidative stress. The mitochondrial stress activates mitochondrial unfolded protein response (UPRmt), which is essential for mitochondrial protein homeostasis. However, the association of inflammatory response and UPRmt in the pathogenesis of hydrocephalus is still unclear. To assess their relevance in the pathogenesis of hydrocephalus, we established a kaolin-induced hydrocephalus model in 8-week-old male C57BL/6J mice and evaluated it over time. We found that kaolin-injected mice showed prominent ventricular dilation, motor behavior defects at the 3-day, followed by the activation of microglia and UPRmt in the motor cortex at the 5-day. In addition, PARP-1/NF-κB signaling and apoptotic cell death appeared at the 5-day. Taken together, our findings demonstrate that activation of microglia and UPRmt occurs after hydrocephalic ventricular expansion and behavioral abnormal-ities which could be lead to apoptotic neuronal cell death, providing a new perspective on the pathogenic mechanism of hydrocephalus.
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Affiliation(s)
- Jiebo Zhu
- Department of Medical Science, Chungnam National University School of Medicine, Daejeon 35015, Korea
- Department of Biochemistry, Chungnam National University School of Medicine, Daejeon 35015, Korea
- Infection Control Convergence Research Center, Chungnam National University School of Medicine, Daejeon 35015, Korea
| | - Min Joung Lee
- Department of Medical Science, Chungnam National University School of Medicine, Daejeon 35015, Korea
- Department of Biochemistry, Chungnam National University School of Medicine, Daejeon 35015, Korea
- Infection Control Convergence Research Center, Chungnam National University School of Medicine, Daejeon 35015, Korea
| | - Hee Jin Chang
- Department of Medical Science, Chungnam National University School of Medicine, Daejeon 35015, Korea
- Department of Neurology, Chungnam National University Hospital, Daejeon 35015, Korea
| | - Xianshu Ju
- Department of Medical Science, Chungnam National University School of Medicine, Daejeon 35015, Korea
- Infection Control Convergence Research Center, Chungnam National University School of Medicine, Daejeon 35015, Korea
| | - Jianchen Cui
- Department of Medical Science, Chungnam National University School of Medicine, Daejeon 35015, Korea
- Infection Control Convergence Research Center, Chungnam National University School of Medicine, Daejeon 35015, Korea
| | - Yu Lim Lee
- Department of Medical Science, Chungnam National University School of Medicine, Daejeon 35015, Korea
- Infection Control Convergence Research Center, Chungnam National University School of Medicine, Daejeon 35015, Korea
| | - Dahyun Go
- Department of Medical Science, Chungnam National University School of Medicine, Daejeon 35015, Korea
- Department of Biochemistry, Chungnam National University School of Medicine, Daejeon 35015, Korea
- Infection Control Convergence Research Center, Chungnam National University School of Medicine, Daejeon 35015, Korea
| | - Woosuk Chung
- Department of Medical Science, Chungnam National University School of Medicine, Daejeon 35015, Korea
- Department of Anesthesiology and Pain Medicine, Chungnam National University School of Medicine, Daejeon 35015, Korea
- Department of Anesthesiology and Pain Medicine, Chungnam National University Hospital, Daejeon 35015, Korea
| | - Eungseok Oh
- Department of Medical Science, Chungnam National University School of Medicine, Daejeon 35015, Korea
- Department of Neurology, Chungnam National University Hospital, Daejeon 35015, Korea
| | - Jun Young Heo
- Department of Medical Science, Chungnam National University School of Medicine, Daejeon 35015, Korea
- Department of Biochemistry, Chungnam National University School of Medicine, Daejeon 35015, Korea
- Infection Control Convergence Research Center, Chungnam National University School of Medicine, Daejeon 35015, Korea
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Abstract
Alzheimer's disease (AD) is a complex and multifactorial neurodegenerative disease. Due to its long clinical course and lack of an effective treatment, AD has become a major public health problem in the USA and worldwide. Due to variation in age-at-onset, AD is classified into early-onset (< 60 years) and late-onset (≥ 60 years) forms with early-onset accounting for only 5-10% of all cases. With the exception of a small number of early-onset cases that are afflicted because of high penetrant single gene mutations in APP, PSEN1, and PSEN2 genes, AD is genetically heterogeneous, especially the late-onset form having a polygenic or oligogenic risk inheritance. Since the identification of APOE as the most significant risk factor for late-onset AD in 1993, the path to the discovery of additional AD risk genes had been arduous until 2009 when the use of large genome-wide association studies opened up the discovery gateways that led the identification of ~ 95 additional risk loci from 2009 to early 2022. This article reviews the history of AD genetics followed by the potential molecular pathways and recent application of functional genomics methods to identify the causal AD gene(s) among the many genes that reside within a single locus. The ultimate goal of integrating genomics and functional genomics is to discover novel pathways underlying the AD pathobiology in order to identify drug targets for the therapeutic treatment of this heterogeneous disorder.
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Affiliation(s)
- M Ilyas Kamboh
- Department of Human Genetics, Graduate School of Public Health, University of Pittsburgh, Pittsburgh, PA, USA.
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8
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Ahmed MM, Johnson NR, Boyd TD, Coughlan C, Chial HJ, Potter H. Innate Immune System Activation and Neuroinflammation in Down Syndrome and Neurodegeneration: Therapeutic Targets or Partners? Front Aging Neurosci 2021; 13:718426. [PMID: 34603007 PMCID: PMC8481947 DOI: 10.3389/fnagi.2021.718426] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 08/16/2021] [Indexed: 12/24/2022] Open
Abstract
Innate immune system activation and inflammation are associated with and may contribute to clinical outcomes in people with Down syndrome (DS), neurodegenerative diseases such as Alzheimer's disease (AD), and normal aging. In addition to serving as potential diagnostic biomarkers, innate immune system activation and inflammation may play a contributing or causal role in these conditions, leading to the hypothesis that effective therapies should seek to dampen their effects. However, recent intervention studies with the innate immune system activator granulocyte-macrophage colony-stimulating factor (GM-CSF) in animal models of DS, AD, and normal aging, and in an AD clinical trial suggest that activating the innate immune system and inflammation may instead be therapeutic. We consider evidence that DS, AD, and normal aging are accompanied by innate immune system activation and inflammation and discuss whether and when during the disease process it may be therapeutically beneficial to suppress or promote such activation.
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Affiliation(s)
- Md. Mahiuddin Ahmed
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- University of Colorado Alzheimer’s and Cognition Center, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- Linda Crnic Institute for Down Syndrome, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Noah R. Johnson
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- University of Colorado Alzheimer’s and Cognition Center, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- Linda Crnic Institute for Down Syndrome, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Timothy D. Boyd
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- University of Colorado Alzheimer’s and Cognition Center, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- Partner Therapeutics, Inc., Lexington, MA, United States
| | - Christina Coughlan
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- University of Colorado Alzheimer’s and Cognition Center, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- Linda Crnic Institute for Down Syndrome, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Heidi J. Chial
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- University of Colorado Alzheimer’s and Cognition Center, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- Linda Crnic Institute for Down Syndrome, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
| | - Huntington Potter
- Department of Neurology, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- University of Colorado Alzheimer’s and Cognition Center, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
- Linda Crnic Institute for Down Syndrome, University of Colorado Anschutz Medical Campus, Aurora, CO, United States
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9
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Hong S, Dobricic V, Ohlei O, Bos I, Vos SJB, Prokopenko D, Tijms BM, Andreasson U, Blennow K, Vandenberghe R, Gabel S, Scheltens P, Teunissen CE, Engelborghs S, Frisoni G, Blin O, Richardson JC, Bordet R, Lleó A, Alcolea D, Popp J, Clark C, Peyratout G, Martinez-Lage P, Tainta M, Dobson RJB, Legido-Quigley C, Sleegers K, Van Broeckhoven C, Tanzi RE, Ten Kate M, Wittig M, Franke A, Lill CM, Barkhof F, Lovestone S, Streffer J, Zetterberg H, Visser PJ, Bertram L. TMEM106B and CPOX are genetic determinants of cerebrospinal fluid Alzheimer's disease biomarker levels. Alzheimers Dement 2021; 17:1628-1640. [PMID: 33991015 DOI: 10.1002/alz.12330] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 01/16/2021] [Accepted: 02/13/2021] [Indexed: 12/18/2022]
Abstract
INTRODUCTION Neurofilament light (NfL), chitinase-3-like protein 1 (YKL-40), and neurogranin (Ng) are biomarkers for Alzheimer's disease (AD) to monitor axonal damage, astroglial activation, and synaptic degeneration, respectively. METHODS We performed genome-wide association studies (GWAS) using DNA and cerebrospinal fluid (CSF) samples from the EMIF-AD Multimodal Biomarker Discovery study for discovery, and the Alzheimer's Disease Neuroimaging Initiative study for validation analyses. GWAS were performed for all three CSF biomarkers using linear regression models adjusting for relevant covariates. RESULTS We identify novel genome-wide significant associations between DNA variants in TMEM106B and CSF levels of NfL, and between CPOX and YKL-40. We confirm previous work suggesting that YKL-40 levels are associated with DNA variants in CHI3L1. DISCUSSION Our study provides important new insights into the genetic architecture underlying interindividual variation in three AD-related CSF biomarkers. In particular, our data shed light on the sequence of events regarding the initiation and progression of neuropathological processes relevant in AD.
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Affiliation(s)
- Shengjun Hong
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
| | - Valerija Dobricic
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
| | - Olena Ohlei
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany
| | - Isabelle Bos
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands.,Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands
| | - Stephanie J B Vos
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands.,Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands
| | - Dmitry Prokopenko
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Betty M Tijms
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands
| | - Ulf Andreasson
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Kaj Blennow
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium.,Neurology Service, University Hospital Leuven, Leuven, Belgium
| | - Silvy Gabel
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Philip Scheltens
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands
| | - Charlotte E Teunissen
- Neurochemistry Laboratory, Department of Clinical Chemistry, Amsterdam Neuroscience, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, the Netherlands
| | - Sebastiaan Engelborghs
- Department of Biomedical Sciences, Institute Born-Bunge, University of Antwerp, Antwerp, Belgium.,Department of Neurology and Center for Neurosciences, UZ Brussel and Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Giovanni Frisoni
- University of Geneva, Geneva, Switzerland.,IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Olivier Blin
- AIX Marseille University, INS, Ap-hm, Marseille, France
| | | | - Regis Bordet
- Inserm, CHU Lille, University of Lille, Lille, France
| | | | - Alberto Lleó
- Memory Unit, Neurology Department. Hospital de Sant Pau, Barcelona and Centro de Investigación Biomédica en Red en enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Daniel Alcolea
- Memory Unit, Neurology Department. Hospital de Sant Pau, Barcelona and Centro de Investigación Biomédica en Red en enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Julius Popp
- Centre for Gerontopsychiatric Medicine, Department of Geriatric Psychiatry, University Hospital of Psychiatry Zurich, Zürich, Switzerland.,Old Age Psychiatry, Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | - Christopher Clark
- Centre for Gerontopsychiatric Medicine, Department of Geriatric Psychiatry, University Hospital of Psychiatry Zurich, Zürich, Switzerland
| | - Gwendoline Peyratout
- Old Age Psychiatry, Department of Psychiatry, University Hospital of Lausanne, Lausanne, Switzerland
| | - Pablo Martinez-Lage
- Department of Neurology, Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, San Sebastian, Spain
| | - Mikel Tainta
- Department of Neurology, Center for Research and Advanced Therapies, CITA-Alzheimer Foundation, San Sebastian, Spain
| | - Richard J B Dobson
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK.,NIHR BioResource Centre Maudsley, NIHR Maudsley Biomedical Research Centre (BRC) at South London and Maudsley NHS Foundation Trust (SLaM) & Institute of Psychiatry, Psychology and Neuroscience (IoPPN), King's College London, London, UK.,Health Data Research UK London, University College London, London, UK.,Institute of Health Informatics, University College London, London, UK.,The National Institute for Health Research University College London Hospitals Biomedical Research Centre, University College London, London, UK
| | - Cristina Legido-Quigley
- Steno Diabetes Center, Copenhagen, Denmark and Institute of Pharmaceutical Sciences, King's College London, London, UK
| | - Kristel Sleegers
- Neurodegenerative Brain Diseases Group, Center for Molecular Neurology, VIB, Antwerp, Belgium.,Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Christine Van Broeckhoven
- Neurodegenerative Brain Diseases Group, Center for Molecular Neurology, VIB, Antwerp, Belgium.,Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Rudolph E Tanzi
- Genetics and Aging Unit and McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Mara Ten Kate
- Alzheimer Center and Department of Neurology, Amsterdam University Medical Centers, Amsterdam Neuroscience, Amsterdam, the Netherlands.,Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Amsterdam Neuroscience, Amsterdam, the Netherlands
| | - Michael Wittig
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Andre Franke
- Institute of Clinical Molecular Biology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Christina M Lill
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany.,Ageing Epidemiology Research Unit, School of Public Health, Imperial College, London, United Kingdom
| | - Frederik Barkhof
- Institutes of Neurology and Healthcare Engineering, University College London, London, UK
| | | | - Johannes Streffer
- Reference Center for Biological Markers of Dementia (BIODEM), Institute Born-Bunge, University of Antwerp, Antwerp, Belgium.,Translational Medicine Neuroscience, UCB Biopharma SPRL, Braine l'Alleud, Belgium
| | - Henrik Zetterberg
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.,Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden.,Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, Queen Square, London, UK.,UK Dementia Research Institute at UCL, London, UK
| | - Pieter Jelle Visser
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Alzheimer Centrum Limburg, Maastricht University, Maastricht, the Netherlands.,Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, the Netherlands.,Department of Neurobiology, Care Sciences and Society, Division of Neurogeriatrics, Karolinska Instutet, Stockholm, Sweden
| | - Lars Bertram
- Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany.,Department of Psychology, University of Oslo, Oslo, Norway
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10
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Xiong YL, Meng T, Luo J, Zhang H. The Potential of Neurofilament Light as a Biomarker in Alzheimer's Disease. Eur Neurol 2021; 84:6-15. [PMID: 33477142 DOI: 10.1159/000513008] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 11/06/2020] [Indexed: 11/19/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) is the most common neurodegenerative disease characterized by progressive memory loss and cognitive impairment. In 2011, the National Institute on Aging and Alzheimer's Association (NIA-AA) Research Framework has proposed to use biomarkers to diagnose AD in living persons. AD core biomarkers show high diagnostic specificity in distinguishing AD from healthy control subjects, but have little additional value for prognosis or stage of disease. SUMMARY With the update of detection methods and techniques, other AD biomarkers have been discovered. Neurofilament light (NFL) is currently recognized as a biomarker of nerve axonal injury and one of the candidate markers in AD neurodegeneration, and the relationship between NFL and AD pathophysiology has attracted widespread attention. More and more studies have shown that NFL plays an important role in predicting the clinical progress and prognosis of AD. Recently, the genome-wide association study also found that multiple single-nucleotide polymorphisms are associated with NFL levels and AD risk. Key Messages: In this review, we discuss the relationship between the genetic characteristics of NFL and AD, the NFL levels in AD, and the relationship between NFL and AD core biomarkers, neuroimaging, and cognitive performance.
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Affiliation(s)
- Yong-Lan Xiong
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Tao Meng
- Department of Neurology, Chongqing University Central Hospital, Chongqing, China
| | - Jing Luo
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Hua Zhang
- Department of Neurology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China,
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