scholarly journals Association between BrainAgeR scores and Alzhemier’s disease brain and plasma biomarkers

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Yousaf Abughofah ◽  
Shannon L. Risacher
Keyword(s):  
Positive Association ◽  
Structural Mri ◽  
Neurofilament Light Chain ◽  
Significant Positive Association ◽  
Neurofilament Light ◽  
Difference Scores ◽  
Correlation Models ◽  
Aging Study ◽  
Brain Age ◽  
The Brain

Background: The goal of this project was to study the association between the age scores assigned by the BrainAgeR computer algorithm and AD biomarker levels in the blood and brain.  Methods: 123 individuals from the Indiana Memory and Aging Study underwent amyloid ([18F]florbetapir or [18F]florbetaben) and Tau [18F]flortaucipir PET. Another set of 156 individuals from the Indiana Memory and Aging Study underwent plasma testing for Amyloid B 40/42, Tau and neurofilament light chain (NFL). Additionally, all participants underwent structural MRI and were processed using BrainAgeR to receive a “brain age” score. Partial Person correlation models were used to evaluate the relationship between BrainAge difference scores (Chronological age-BrainAge) and levels of Amyloid and Tau in the brain and plasma. Age and diagnosis were evaluated as covariates but did not change the observed pattern of results.  Results: Significant negative association between BrainAge difference scores and Tau uptake was observed across the neuroimaging group. This correlation persisted when analysis was limited to MCI/AD subjects but was lost when analysis was only limited to CN/SCD subjects. Across all participants in the neuroimaging group, significant negative associations were found between BrainAge differences and the levels of Amyloid deposition in the global cortex. Significant positive association was found between AB42/40 ratio and BrainAge difference scores across the entire plasma group. Significant negative relationships found between NFL and AB40 and BrainAge difference scores when analyzed in the CN/SCD group, but no statistically significant relationship was found when only the MCI/AD group was analyzed.  Conclusion: BrainAge difference scores had a statistically significant association with various biomarkers of AD depending on the level of diagnosis, with cognitively normal and less impaired subjects showing an association with plasma amyloid and more impaired subjects showing an association with Tau deposition in the brain. Future studies in larger samples are warranted. 

scholarly journals Characterization of Fluid Biomarkers Reveals Lysosome Dysfunction and Neurodegeneration in Neuronopathic MPS II Patients

2020 ◽  
Vol 21 (15) ◽  
pp. 5188 ◽  
Author(s):  
Akhil Bhalla ◽  
Ritesh Ravi ◽  
Meng Fang ◽  
Annie Arguello ◽  
Sonnet S. Davis ◽  
...  
Keyword(s):  
Neuronal Damage ◽  
Lysosomal Storage ◽  
Mucopolysaccharidosis Type Ii ◽  
Neurofilament Light Chain ◽  
Neurofilament Light ◽  
Mps Ii ◽  
Cellular Dysfunction ◽  
The Relationship ◽  
The Brain

Mucopolysaccharidosis type II is a lysosomal storage disorder caused by a deficiency of iduronate-2-sulfatase (IDS) and characterized by the accumulation of the primary storage substrate, glycosaminoglycans (GAGs). Understanding central nervous system (CNS) pathophysiology in neuronopathic MPS II (nMPS II) has been hindered by the lack of CNS biomarkers. Characterization of fluid biomarkers has been largely focused on evaluating GAGs in cerebrospinal fluid (CSF) and the periphery; however, GAG levels alone do not accurately reflect the broad cellular dysfunction in the brains of MPS II patients. We utilized a preclinical mouse model of MPS II, treated with a brain penetrant form of IDS (ETV:IDS) to establish the relationship between markers of primary storage and downstream pathway biomarkers in the brain and CSF. We extended the characterization of pathway and neurodegeneration biomarkers to nMPS II patient samples. In addition to the accumulation of CSF GAGs, nMPS II patients show elevated levels of lysosomal lipids, neurofilament light chain, and other biomarkers of neuronal damage and degeneration. Furthermore, we find that these biomarkers of downstream pathology are tightly correlated with heparan sulfate. Exploration of the responsiveness of not only CSF GAGs but also pathway and disease-relevant biomarkers during drug development will be crucial for monitoring disease progression, and the development of effective therapies for nMPS II.

scholarly journals Haemoglobin causes neuronal damage in vivo which is preventable by haptoglobin

2020 ◽  
Vol 2 (1) ◽  
Author(s):  
Patrick Garland ◽  
Matthew J Morton ◽  
William Haskins ◽  
Ardalan Zolnourian ◽  
Andrew Durnford ◽  
...  
Keyword(s):  
Subarachnoid Haemorrhage ◽  
Prolonged Exposure ◽  
Neuronal Damage ◽  
Clinical Importance ◽  
Large Vessel ◽  
Therapeutic Window ◽  
Neurofilament Light Chain ◽  
Neurofilament Light ◽  
The Brain

Abstract After subarachnoid haemorrhage, prolonged exposure to toxic extracellular haemoglobin occurs in the brain. Here, we investigate the role of haemoglobin neurotoxicity in vivo and its prevention. In humans after subarachnoid haemorrhage, haemoglobin in cerebrospinal fluid was associated with neurofilament light chain, a marker of neuronal damage. Most haemoglobin was not complexed with haptoglobin, an endogenous haemoglobin scavenger present at very low concentration in the brain. Exogenously added haptoglobin bound most uncomplexed haemoglobin, in the first 2 weeks after human subarachnoid haemorrhage, indicating a wide therapeutic window. In mice, the behavioural, vascular, cellular and molecular changes seen after human subarachnoid haemorrhage were recapitulated by modelling a single aspect of subarachnoid haemorrhage: prolonged intrathecal exposure to haemoglobin. Haemoglobin-induced behavioural deficits and astrocytic, microglial and synaptic changes were attenuated by haptoglobin. Haptoglobin treatment did not attenuate large-vessel vasospasm, yet improved clinical outcome by restricting diffusion of haemoglobin into the parenchyma and reducing small-vessel vasospasm. In summary, haemoglobin toxicity is of clinical importance and preventable by haptoglobin, independent of large-vessel vasospasm.

scholarly journals Rajyoga meditation induces grey matter volume changes in regions that process reward and happiness

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
M. G. Ramesh Babu ◽  
Rajagopal Kadavigere ◽  
Prakashini Koteshwara ◽  
Brijesh Sathian ◽  
Kiranmai S. Rai
Keyword(s):  
Neural Plasticity ◽  
Grey Matter ◽  
Positive Association ◽  
Structural Mri ◽  
Reward Processing ◽  
Anterior Cingulate ◽  
Grey Matter Volume ◽  
Significant Positive Association ◽  
Superior Frontal Gyrus ◽  
Matter Volume

Abstract Studies provide evidence that practicing meditation enhances neural plasticity in reward processing areas of brain. No studies till date, provide evidence of such changes in Rajyoga meditation (RM) practitioners. The present study aimed to identify grey matter volume (GMV) changes in reward processing areas of brain and its association with happiness scores in RM practitioners compared to non-meditators. Structural MRI of selected participants matched for age, gender and handedness (n = 40/group) were analyzed using voxel-based morphometric method and Oxford Happiness Questionnaire (OHQ) scores were correlated. Significant increase in OHQ happiness scores were observed in RM practitioners compared to non-meditators. Whereas, a trend towards significance was observed in more experienced RM practitioners, on correlating OHQ scores with hours of meditation experience. Additionally, in RM practitioners, higher GMV were observed in reward processing centers—right superior frontal gyrus, left inferior orbitofrontal cortex (OFC) and bilateral precuneus. Multiple regression analysis showed significant association between OHQ scores of RM practitioners and reward processing regions right superior frontal gyrus, left middle OFC, right insula and left anterior cingulate cortex. Further, with increasing hours of RM practice, a significant positive association was observed in bilateral ventral pallidum. These findings indicate that RM practice enhances GMV in reward processing regions associated with happiness.

scholarly journals Brain Age in Early Stages of Bipolar Disorders or Schizophrenia

2017 ◽  
Vol 45 (1) ◽  
pp. 190-198 ◽  
Author(s):  
Tomas Hajek ◽  
Katja Franke ◽  
Marian Kolenic ◽  
Jana Capkova ◽  
Martin Matejka ◽  
...  
Keyword(s):  
Bipolar Disorders ◽  
Structural Mri ◽  
Chronological Age ◽  
First Episode ◽  
Schizophrenia Spectrum Disorders ◽  
Early Stages ◽  
Age Gap ◽  
Gap Estimate ◽  
Brain Age ◽  
The Brain

Abstract Background The greater presence of neurodevelopmental antecedants may differentiate schizophrenia from bipolar disorders (BD). Machine learning/pattern recognition allows us to estimate the biological age of the brain from structural magnetic resonance imaging scans (MRI). The discrepancy between brain and chronological age could contribute to early detection and differentiation of BD and schizophrenia. Methods We estimated brain age in 2 studies focusing on early stages of schizophrenia or BD. In the first study, we recruited 43 participants with first episode of schizophrenia-spectrum disorders (FES) and 43 controls. In the second study, we included 96 offspring of bipolar parents (48 unaffected, 48 affected) and 60 controls. We used relevance vector regression trained on an independent sample of 504 controls to estimate the brain age of study participants from structural MRI. We calculated the brain-age gap estimate (BrainAGE) score by subtracting the chronological age from the brain age. Results Participants with FES had higher BrainAGE scores than controls (F(1, 83) = 8.79, corrected P = .008, Cohen’s d = 0.64). Their brain age was on average 2.64 ± 4.15 years greater than their chronological age (matched t(42) = 4.36, P < .001). In contrast, participants at risk or in the early stages of BD showed comparable BrainAGE scores to controls (F(2,149) = 1.04, corrected P = .70, η2 = 0.01) and comparable brain and chronological age. Conclusions Early stages of schizophrenia, but not early stages of BD, were associated with advanced BrainAGE scores. Participants with FES showed neurostructural alterations, which made their brains appear 2.64 years older than their chronological age. BrainAGE scores could aid in early differential diagnosis between BD and schizophrenia.

scholarly journals High frequency of cerebrospinal fluid autoantibodies in COVID-19 patients with neurological symptoms

2020 ◽  
Author(s):  
Christiana Franke ◽  
Caroline Ferse ◽  
Jakob Kreye ◽  
S Momsen Reincke ◽  
Elisa Sanchez-Sendin ◽  
...  
Keyword(s):  
Cerebrospinal Fluid ◽  
High Frequency ◽  
Clinical Symptoms ◽  
Epileptic Seizures ◽  
Neurological Symptoms ◽  
Neurofilament Light Chain ◽  
Neurofilament Light ◽  
Intensive Care Patients ◽  
Neuronal Autoantibodies ◽  
The Brain

AbstractCOVID-19 intensive care patients occasionally develop neurological symptoms. The absence of SARS-CoV-2 in most cerebrospinal fluid (CSF) samples suggests the involvement of further mechanisms including autoimmunity. We therefore determined whether anti-neuronal or anti-glial autoantibodies are present in eleven consecutive severely ill COVID-19 patients presenting with unexplained neurological symptoms. These included myoclonus, cranial nerve involvement, oculomotor disturbance, delirium, dystonia and epileptic seizures. Most patients showed signs of CSF inflammation and increased levels of neurofilament light chain. All patients had anti-neuronal autoantibodies in serum or CSF when assessing a large panel of autoantibodies against intracellular and surface antigens relevant for central nervous system diseases using cell-based assays and indirect immunofluorescence on murine brain sections. Antigens included proteins well-established in clinical routine, such as Yo or NMDA receptor, but also a variety of specific undetermined epitopes on brain sections. These included vessel endothelium, astrocytic proteins and neuropil of basal ganglia, hippocampus or olfactory bulb. The high frequency of autoantibodies targeting the brain in the absence of other explanations suggests a causal relationship to clinical symptoms, in particular to hyperexcitability (myoclonus, seizures). While several underlying autoantigens still await identification in future studies, presence of autoantibodies may explain some aspects of multi-organ disease in COVID-19 and can guide immunotherapy in selected cases.

scholarly journals Fluid Candidate Biomarkers for Alzheimer’s Disease: A Precision Medicine Approach

2020 ◽  
Vol 10 (4) ◽  
pp. 221 ◽  
Author(s):  
Eleonora Del Prete ◽  
Maria Francesca Beatino ◽  
Nicole Campese ◽  
Linda Giampietri ◽  
Gabriele Siciliano ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Protein Misfolding ◽  
Plasma Biomarker ◽  
Clinical Role ◽  
Neurofilament Light Chain ◽  
Neurofilament Light ◽  
Aβ Peptides ◽  
The Brain

A plethora of dynamic pathophysiological mechanisms underpins highly heterogeneous phenotypes in the field of dementia, particularly in Alzheimer’s disease (AD). In such a faceted scenario, a biomarker-guided approach, through the implementation of specific fluid biomarkers individually reflecting distinct molecular pathways in the brain, may help establish a proper clinical diagnosis, even in its preclinical stages. Recently, ultrasensitive assays may detect different neurodegenerative mechanisms in blood earlier. ß-amyloid (Aß) peptides, phosphorylated-tau (p-tau), and neurofilament light chain (NFL) measured in blood are gaining momentum as candidate biomarkers for AD. P-tau is currently the more convincing plasma biomarker for the diagnostic workup of AD. The clinical role of plasma Aβ peptides should be better elucidated with further studies that also compare the accuracy of the different ultrasensitive techniques. Blood NFL is promising as a proxy of neurodegeneration process tout court. Protein misfolding amplification assays can accurately detect α-synuclein in cerebrospinal fluid (CSF), thus representing advancement in the pathologic stratification of AD. In CSF, neurogranin and YKL-40 are further candidate biomarkers tracking synaptic disruption and neuroinflammation, which are additional key pathophysiological pathways related to AD genesis. Advanced statistical analysis using clinical scores and biomarker data to bring together individuals with AD from large heterogeneous cohorts into consistent clusters may promote the discovery of pathophysiological causes and detection of tailored treatments.

scholarly journals A comparison of blood and brain-derived ageing and inflammation-related DNA methylation signatures and their association with microglial burdens

2020 ◽  
Author(s):  
Anna J. Stevenson ◽  
Daniel L. McCartney ◽  
Gemma L. Shireby ◽  
Robert F. Hillary ◽  
Declan King ◽  
...  
Keyword(s):  
Dna Methylation ◽  
Positive Association ◽  
Brain Regions ◽  
Preliminary Evidence ◽  
Epigenetic Clock ◽  
Significant Positive Association ◽  
Epigenetic Age ◽  
Lothian Birth Cohort ◽  
Epigenetic Age Acceleration ◽  
The Brain

AbstractInflammation and ageing-related DNA methylation patterns in the blood have been linked to a variety of morbidities, including cognitive decline and neurodegenerative disease. However, it is unclear how these blood-based patterns relate to patterns within the brain, and how each associates with central cellular profiles. In this study, we profiled DNA methylation in both the blood and in five post-mortem brain regions (BA17, BA20/21, BA24, BA46 and hippocampus) in 14 individuals from the Lothian Birth Cohort 1936. Microglial burdens were additionally quantified in the same brain regions. DNA methylation signatures of five epigenetic ageing biomarkers (‘epigenetic clocks’), and two inflammatory biomarkers (DNA methylation proxies for C-reactive protein and interleukin-6) were compared across tissues and regions. Divergent correlations between the inflammation and ageing signatures in the blood and brain were identified, depending on region assessed. Four out of the five assessed epigenetic age acceleration measures were found to be highest in the hippocampus (β range=0.83-1.14, p≤0.02). The inflammation-related DNA methylation signatures showed no clear variation across brain regions. Reactive microglial burdens were found to be highest in the hippocampus (β=1.32, p=5×10-4); however, the only association identified between the blood- and brain-based methylation signatures and microglia was a significant positive association with acceleration of one epigenetic clock (termed DNAm PhenoAge) averaged over all five brain regions (β=0.40, p=0.002). This work highlights a potential vulnerability of the hippocampus to epigenetic ageing and provides preliminary evidence of a relationship between DNA methylation signatures in the brain and differences in microglial burdens.

scholarly journals Neurofilament light chain in psychiatric and neurodegenerative disorders: A ‘c‐reactive protein’ for the brain?

2020 ◽  
Vol 16 (S4) ◽  
Author(s):  
Dhamidhu Eratne ◽  
Samantha M. Loi ◽  
Alexander Santillo ◽  
Qiao‐Xin Li ◽  
Carolyn Chadunow ◽  
...  
Keyword(s):  
Light Chain ◽  
Neurodegenerative Disorders ◽  
C Reactive Protein ◽  
Neurofilament Light Chain ◽  
Neurofilament Light ◽  
Reactive Protein ◽  
The Brain

scholarly journals Clinical Utility of the Pathogenesis-Related Proteins in Alzheimer’s Disease

2020 ◽  
Vol 21 (22) ◽  
pp. 8661
Author(s):  
Bin Zhou ◽  
Masanori Fukushima
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Protein Interactions ◽  
Neuronal Injury ◽  
Synaptic Dysfunction ◽  
Neurofilament Light Chain ◽  
Neurofilament Light ◽  
Time Order ◽  
Neuro Inflammation ◽  
The Brain

Research on the Aβ cascade and alternations of biomarkers in neuro-inflammation, synaptic dysfunction, and neuronal injury followed by Aβ have progressed. But the question is how to use the biomarkers. Here, we examine the evidence and pathogenic implications of protein interactions and the time order of alternation. After the deposition of Aβ, the change of tau, neurofilament light chain (NFL), and neurogranin (Ng) is the main alternation and connection to others. Neuro-inflammation, synaptic dysfunction, and neuronal injury function is exhibited prior to the structural and metabolic changes in the brain following Aβ deposition. The time order of such biomarkers compared to the tau protein is not clear. Despite the close relationship between biomarkers and plaque Aβ deposition, several factors favor one or the other. There is an interaction between some proteins that can predict the brain amyloid burden. The Aβ cascade hypothesis could be the pathway, but not all subjects suffer from Alzheimer’s disease (AD) within a long follow-up, even with very elevated Aβ. The interaction of biomarkers and the time order of change require further research to identify the right subjects and right molecular target for precision medicine therapies.

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