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Background: Epstein-Barr virus (EBV) infection is believed to be a critical prerequisite for the development of multiple sclerosis (MS). This study aims to investigate whether anti-EBV titres are elevated before the onset of MS symptoms in people with radiologically isolated syndrome (pwRIS) and to evaluate their association with markers of adverse clinical outcomes. Methods: Epstein-Barr nuclear antigen 1 (EBNA1) and viral capsid antigen (VCA) titres were quantified in a cohort of 47 pwRIS and 24 healthy controls using Enzyme-Linked Immuno-Sorbent Assay. Plasma glial fibrillary acidic protein (GFAP) and neurofilament light protein (NfL) were measured using single-molecule array. MRI lesion metrics and the development of MS symptoms over time were also evaluated. Results: EBNA1 titres were higher pwRIS compared to healthy controls (p=0.038), while VCA titres were not (p=0.237). A positive correlation was observed between EBNA1 titres and plasma GFAP in pwRIS (p=0.005). Neither EBNA1 nor VCA titres correlated with NfL. MRI lesion measures and the development of MS symptoms did not show any significant relationship with EBNA1 or VCA titres. Conclusions: Eelevated EBNA1 titres are detectable prior to MS symptom onset and correlate with GFAP, a biomarker associated with worse clinical outcomes. However, their role in disease progression and clinical outcomes requires further investigation.
Background: Radiologically isolated syndrome (RIS) is characterized by incidental MRI findings suggestive of multiple sclerosis in asymptomatic individuals. Emerging blood biomarkers, including neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), and chitinase 3-like 1 protein (CHI3L1) are promising tools for evaluating neuroinflammation and neurodegeneration. Methods: This cross-sectional analysis included 47 individuals with RIS who underwent MRI and plasma biomarker assessments. Plasma levels of CHI3L1, NfL, and GFAP were measured using highly sensitive assays. Correlations between biomarkers and MRI markers, including T1-black holes (BHs), central vein sign (CVS) positive lesions, paramagnetic rim lesions (PRLs), choroid plexus volume (CPV), and thalamic and hippocampal volumes, were analyzed using linear regression. Results: Plasma CHI3L1 levels correlated with increased CPV (β = 0.347, p = 0.017) and reduced thalamic (β = -0.309, p = 0.035) and hippocampal (β = -0.535, p < 0.001) volumes. Plasma GFAP levels were associated with BHs, CVS, and PRLs, whereas plasma NfL showed no correlations with MRI measures. Conclusions: Plasma CHI3L1 correlates with subcortical grey matter atrophy and CPV increase in RIS, distinct from correlations observed with GFAP or NfL. This suggests that plasma CHI3L1 may reflect neurodegeneration and inflammation in RIS and provide insights into disease activity not captured by other biomarkers.
Background: In multiple sclerosis (MS), soluble mediators of neuroinflammation are released by activated lymphocytes and resident immune cells, leading to demyelination and neurodegeneration. Radiologically isolated syndrome (RIS) is an entity in which white matter lesions fulfilling criteria for MS occur in individuals without any suggestive symptoms. The exact nature of pro- and anti-inflammatory cytokines in blood, and their association with disease activity in RIS/MS requires further clarification. Methods: Plasma was collected and cryopreserved from healthy controls (HCs), people with RIS and relapsing-remitting MS (RRMS) at the Barlo MS Centre. All samples were analyzed with OLink Target 96 Inflammation Multiplex Immunoassay Panel. Results: Individuals with RIS (p=0.0001; p= 0.0007; p= 0.0012) and RRMS (p<0.0001; p= 0.0003; p= 0.00112) had significantly higher concentrations of hepatocyte growth factor (HGF), interleukin-6 (IL-6), and chemokine ligand 23 (CCL23) in plasma compared to HCs, and patients with RRMS (p=0.0087) had significantly higher concentrations of HGF compared to individuals with RIS. Conclusions: Our study demonstrates that HGF, IL-6 and CCL23 are significantly increased in the plasma of patients with RIS and RRMS compared to HCs. Our observations suggest that the biology of MS is present in those with RIS, and these neuroinflammatory mediators may serve as a biomarker of disease activity.
Previous studies have shown that repetitive transcranial magnetic stimulation (rTMS) can treat suicidal symptoms; however, the effects of rTMS on suicidal ideation (SI) in late-life depression (LLD) have not been well-characterized, particularly with theta burst stimulation (TBS).
Methods
Data were analyzed from 84 older adults with depression from the FOUR-D trial (ClinicalTrials.gov identifier: NCT02998580), who received either bilateral standard rTMS or bilateral TBS targeting the dorsolateral prefrontal cortex. The primary outcome was change in the Beck Scale for Suicide Ideation (SSI). The secondary outcome was remission of SI. Demographic, cognitive, and clinical characteristics that may moderate the effects of rTMS or TBS on SI were explored.
Results
There was a statistically significant change in the total SSI score over time [χ2(7) = 136.018, p < 0.001], with no difference between the two treatment groups. Remission of SI was 55.8% in the standard rTMS group and 53.7% in the TBS group. In the standard rTMS group, there was no difference in remission of SI between males and females, whereas remission was higher in females in the TBS group (χ2(1) =6.87, p = 0.009). There was a significant correlation between time to remission of SI and RCI z-score for D-KEFS inhibition/switching [rs = −0.389, p = 0.012].
Conclusions
Both bilateral rTMS and bilateral TBS were effective in reducing SI in LLD. There may be sex differences in response to TBS, with females having more favorable response in reducing SI. There may be an association between improvement in cognitive flexibility and inhibition and reduction of SI.
Recent changes to US research funding are having far-reaching consequences that imperil the integrity of science and the provision of care to vulnerable populations. Resisting these changes, the BJPsych Portfolio reaffirms its commitment to publishing mental science and advancing psychiatric knowledge that improves the mental health of one and all.
The First Large Absorption Survey in H i (FLASH) is a large-area radio survey for neutral hydrogen in and around galaxies in the intermediate redshift range $0.4\lt z\lt1.0$, using the 21-cm H i absorption line as a probe of cold neutral gas. The survey uses the ASKAP radio telescope and will cover 24,000 deg$^2$ of sky over the next five years. FLASH breaks new ground in two ways – it is the first large H i absorption survey to be carried out without any optical preselection of targets, and we use an automated Bayesian line-finding tool to search through large datasets and assign a statistical significance to potential line detections. Two Pilot Surveys, covering around 3000 deg$^2$ of sky, were carried out in 2019-22 to test and verify the strategy for the full FLASH survey. The processed data products from these Pilot Surveys (spectral-line cubes, continuum images, and catalogues) are public and available online. In this paper, we describe the FLASH spectral-line and continuum data products and discuss the quality of the H i spectra and the completeness of our automated line search. Finally, we present a set of 30 new H i absorption lines that were robustly detected in the Pilot Surveys, almost doubling the number of known H i absorption systems at $0.4\lt z\lt1$. The detected lines span a wide range in H i optical depth, including three lines with a peak optical depth $\tau\gt1$, and appear to be a mixture of intervening and associated systems. Interestingly, around two-thirds of the lines found in this untargeted sample are detected against sources with a peaked-spectrum radio continuum, which are only a minor (5–20%) fraction of the overall radio-source population. The detection rate for H i absorption lines in the Pilot Surveys (0.3 to 0.5 lines per 40 deg$^2$ ASKAP field) is a factor of two below the expected value. One possible reason for this is the presence of a range of spectral-line artefacts in the Pilot Survey data that have now been mitigated and are not expected to recur in the full FLASH survey. A future paper in this series will discuss the host galaxies of the H i absorption systems identified here.
We present the first results from a new backend on the Australian Square Kilometre Array Pathfinder, the Commensal Realtime ASKAP Fast Transient COherent (CRACO) upgrade. CRACO records millisecond time resolution visibility data, and searches for dispersed fast transient signals including fast radio bursts (FRB), pulsars, and ultra-long period objects (ULPO). With the visibility data, CRACO can localise the transient events to arcsecond-level precision after the detection. Here, we describe the CRACO system and report the result from a sky survey carried out by CRACO at 110-ms resolution during its commissioning phase. During the survey, CRACO detected two FRBs (including one discovered solely with CRACO, FRB 20231027A), reported more precise localisations for four pulsars, discovered two new RRATs, and detected one known ULPO, GPM J1839 $-$10, through its sub-pulse structure. We present a sensitivity calibration of CRACO, finding that it achieves the expected sensitivity of 11.6 Jy ms to bursts of 110 ms duration or less. CRACO is currently running at a 13.8 ms time resolution and aims at a 1.7 ms time resolution before the end of 2024. The planned CRACO has an expected sensitivity of 1.5 Jy ms to bursts of 1.7 ms duration or less and can detect $10\times$ more FRBs than the current CRAFT incoherent sum system (i.e. 0.5 $-$2 localised FRBs per day), enabling us to better constrain the models for FRBs and use them as cosmological probes.
Transcranial direct current stimulation (tDCS) is a promising treatment for major depressive disorder (MDD). This study evaluated its antidepressant and cognitive effects as a safe, effective, home-based therapy for MDD.
Methods
This double-blind, sham-controlled, randomized trial divided participants into low-intensity (1 mA, n = 47), high-intensity (2 mA, n = 49), and sham (n = 45) groups, receiving 42 daily tDCS sessions, including weekends and holidays, targeting the dorsolateral prefrontal cortex for 30 minutes. Assessments were conducted at baseline and weeks 2, 4, and 6. The primary outcome was cognitive improvement assessed by changes in total accuracy on the 2-back test from baseline to week 6. Secondary outcomes included changes in depressive symptoms (HAM-D), anxiety (HAM-A), and quality of life (QLES). Adverse events were monitored. This trial was registered with ClinicalTrials.gov (NCT04709952).
Results
In the tDCS study, of 141 participants (102 [72.3%] women; mean age 35.7 years, standard deviation 12.7), 95 completed the trial. Mean changes in the total accuracy scores from baseline to week 6 were compared across the three groups using an F-test. Linear mixed-effects models examined the interaction of group and time. Results showed no significant differences among groups in cognitive or depressive outcomes at week 6. Active groups experienced more mild adverse events compared to sham but had similar rates of severe adverse events and dropout.
Conclusions
Home-based tDCS for MDD demonstrated no evidence of effectiveness but was safe and well-tolerated. Further research is needed to address the technical limitations, evaluate broader cognitive functions, and extend durations to evaluate its therapeutic potential.
Education can be viewed as a control theory problem in which students seek ongoing exogenous input—either through traditional classroom teaching or other alternative training resources—to minimize the discrepancies between their actual and target (reference) performance levels. Using illustrative data from \documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$n=784$$\end{document} Dutch elementary school students as measured using the Math Garden, a web-based computer adaptive practice and monitoring system, we simulate and evaluate the outcomes of using off-line and finite memory linear quadratic controllers with constraintsto forecast students’ optimal training durations. By integrating population standards with each student’s own latent change information, we demonstrate that adoption of the control theory-guided, person- and time-specific training dosages could yield increased training benefits at reduced costs compared to students’ actual observed training durations, and a fixed-duration training scheme. The control theory approach also outperforms a linear scheme that provides training recommendations based on observed scores under noisy and the presence of missing data. Design-related issues such as ways to determine the penalty cost of input administration and the size of the control horizon window are addressed through a series of illustrative and empirically (Math Garden) motivated simulations.
We present the Pilot Survey Phase 2 data release for the Wide-field ASKAP L-band Legacy All-sky Blind surveY (WALLABY), carried-out using the Australian SKA Pathfinder (ASKAP). We present 1760 H i detections (with a default spatial resolution of 30′′) from three pilot fields including the NGC 5044 and NGC 4808 groups as well as the Vela field, covering a total of $\sim 180$ deg$^2$ of the sky and spanning a redshift up to $z \simeq 0.09$. This release also includes kinematic models for over 126 spatially resolved galaxies. The observed median rms noise in the image cubes is 1.7 mJy per 30′′ beam and 18.5 kHz channel. This corresponds to a 5$\sigma$ H i column density sensitivity of $\sim 9.1\times10^{19}(1 + z)^4$ cm$^{-2}$ per 30′′ beam and $\sim 20$ km s$^{-1}$ channel and a 5$\sigma$ H i mass sensitivity of $\sim 5.5\times10^8 (D/100$ Mpc)$^{2}$ M$_{\odot}$ for point sources. Furthermore, we also present for the first time 12′′ high-resolution images (“cut-outs”) and catalogues for a sub-sample of 80 sources from the Pilot Survey Phase 2 fields. While we are able to recover sources with lower signal-to-noise ratio compared to sources in the Public Data Release 1, we do note that some data quality issues still persist, notably, flux discrepancies that are linked to the impact of side lobes associated with the dirty beams due to inadequate deconvolution. However, in spite of these limitations, the WALLABY Pilot Survey Phase 2 has already produced roughly a third of the number of HIPASS sources, making this the largest spatially resolved H i sample from a single survey to date.
The increased severity and frequency of bushfires accompanying human-induced global warming have dire implications for biodiversity conservation. Here we investigate the response of a cryptic, cool-climate elapid, the mustard-bellied snake Drysdalia rhodogaster, to the extensive Black Summer fires of 2019/2020 in south-eastern Australia. The species is categorized as Least Concern on the IUCN Red List (last assessed in 2017), but because a large part of its range was burnt during the Black Summer and little was known about its ecology, D. rhodogaster was identified as a priority species for post-fire impact assessment. We evaluated three lines of evidence to assess the impact of the Black Summer fires on D. rhodogaster. Habitat suitability modelling indicated that c. 46% of the predicted range of the species was affected by bushfire. Field surveys conducted 9–36 months post-fire and collation of records from public databases submitted 0–24 months post-fire indicated that D. rhodogaster persisted in burnt landscapes. Fire severity and proportion of the landscape that was burnt within a 1,000-m radius of survey sites were poor predictors of site occupancy by D. rhodogaster. Although conclusions regarding the effects of fire on D. rhodogaster are limited because of the lack of baseline data, it is evident that the species has persisted across the landscape in the wake of extensive bushfires. Our work highlights the need for baseline knowledge on cryptic species even when they are categorized as Least Concern, as otherwise assessments of the impacts of catastrophic events will be constrained.
Major Depressive Disorder (MDD) stands as a prevalent psychiatric condition within the general population. Despite extensive research efforts, the identification of definitive diagnostic biomarkers for depressive disorders remains elusive. Currently, machine learning methods are gaining prominence in the diagnosis of medical illnesses.
Objectives
This study aims to construct a machine learning-based prediction model for Major Depressive Disorder (MDD) by harnessing diffusion tensor imaging (DTI) data.
Methods
The DTI datasets comprising MDD (N=83) and Healthy Control (N=70) groups were procured from the cohort study of Anxiety and Depression conducted at the National Center for Mental Health in South Korea. A machine learning method using a decision tree algorithm was employed to select relevant brain regions and establish a robust diagnostic model. Features associated with white matter (WM) tracts were chosen through recursive feature elimination.
Results
Demographic characteristics, including age, sex, and handedness, displayed no significant differences between the MDD and Healthy Control groups. However, the total score of the Beck Depression Inventory was notably higher in individuals with MDD compared to Healthy Controls. A diagnostic model was crafted using the decision tree algorithms to distinguish between the two groups. The model demonstrated the following classification performance metrics: accuracy (65.6% ± 8.5), sensitivity (66.6% ± 12.5), and specificity (64.7% ± 13.6). Furthermore, through recursive feature elimination, specific neuroanatomical features tied to brain structures such as the inferior cerebellar peduncle, posterior thalamic radiation, cingulum (hippocampus), uncinate fasciculus, and tapetum were identified.
Conclusions
Despite of limited performance of classification, a machine learning-based approach could provide insights into the development of a diagnostic model for MDD using neuroimaging data. Furthermore, these features, derived from DTI-derived data, may have implications for understanding the neural underpinnings of major depressive disorder.
Background: Hyperacute stroke care demands rapid, coordinated care. Traditional metrics like Door-to-Needle time are pivotal but insufficient for capturing the complexity of endovascular stroke interventions. The SMILES collaboration aims to standardize and optimize protocols for door-to-intervention times, incorporating Crew Resource Management (CRM). Methods: The multidisciplinary initiative integrates both hospitals, ED, neurology, and QI teams. We employed a comprehensive approach: stakeholder engagement, simulation-based learning, process mapping, and literature review. Emphasis was placed on enhancing situational awareness, triage and prioritization, cognitive load management, role clarity, effective communication, and debriefing. Results: The collaboration led to PDSA cycles and development of refined stroke protocols. Interventions included: 1) A ’zero point survey’ for team pre-arrival briefings, enhancing situational awareness and role clarity; 2) Streamlined patient registration to reduce cognitive load and improve triage efficiency; 3) Direct transfer of patients to imaging. Additionally, digital tools were implemented to facilitate communication. Simulation sessions reinforced CRM principles, leading to improved team cohesion and operational performance. Conclusions: The SMILES initiative is grounded in CRM principles by standardizing protocols and emphasizing non-technical skills crucial for high-stakes environments. This improves outcomes but also fosters a culture of safety and efficiency. Future directions include an evaluation of these protocols’ impact on patient factors.
Clinical outcomes of repetitive transcranial magnetic stimulation (rTMS) for treatment of treatment-resistant depression (TRD) vary widely and there is no mood rating scale that is standard for assessing rTMS outcome. It remains unclear whether TMS is as efficacious in older adults with late-life depression (LLD) compared to younger adults with major depressive disorder (MDD). This study examined the effect of age on outcomes of rTMS treatment of adults with TRD. Self-report and observer mood ratings were measured weekly in 687 subjects ages 16–100 years undergoing rTMS treatment using the Inventory of Depressive Symptomatology 30-item Self-Report (IDS-SR), Patient Health Questionnaire 9-item (PHQ), Profile of Mood States 30-item, and Hamilton Depression Rating Scale 17-item (HDRS). All rating scales detected significant improvement with treatment; response and remission rates varied by scale but not by age (response/remission ≥ 60: 38%–57%/25%–33%; <60: 32%–49%/18%–25%). Proportional hazards models showed early improvement predicted later improvement across ages, though early improvements in PHQ and HDRS were more predictive of remission in those < 60 years (relative to those ≥ 60) and greater baseline IDS burden was more predictive of non-remission in those ≥ 60 years (relative to those < 60). These results indicate there is no significant effect of age on treatment outcomes in rTMS for TRD, though rating instruments may differ in assessment of symptom burden between younger and older adults during treatment.
Population-wide restrictions during the COVID-19 pandemic may create barriers to mental health diagnosis. This study aims to examine changes in the number of incident cases and the incidence rates of mental health diagnoses during the COVID-19 pandemic.
Methods
By using electronic health records from France, Germany, Italy, South Korea and the UK and claims data from the US, this study conducted interrupted time-series analyses to compare the monthly incident cases and the incidence of depressive disorders, anxiety disorders, alcohol misuse or dependence, substance misuse or dependence, bipolar disorders, personality disorders and psychoses diagnoses before (January 2017 to February 2020) and after (April 2020 to the latest available date of each database [up to November 2021]) the introduction of COVID-related restrictions.
Results
A total of 629,712,954 individuals were enrolled across nine databases. Following the introduction of restrictions, an immediate decline was observed in the number of incident cases of all mental health diagnoses in the US (rate ratios (RRs) ranged from 0.005 to 0.677) and in the incidence of all conditions in France, Germany, Italy and the US (RRs ranged from 0.002 to 0.422). In the UK, significant reductions were only observed in common mental illnesses. The number of incident cases and the incidence began to return to or exceed pre-pandemic levels in most countries from mid-2020 through 2021.
Conclusions
Healthcare providers should be prepared to deliver service adaptations to mitigate burdens directly or indirectly caused by delays in the diagnosis and treatment of mental health conditions.
People with schizophrenia on average are more socially isolated, lonelier, have more social cognitive impairment, and are less socially motivated than healthy individuals. People with bipolar disorder also have social isolation, though typically less than that seen in schizophrenia. We aimed to disentangle whether the social cognitive and social motivation impairments observed in schizophrenia are a specific feature of the clinical condition v. social isolation generally.
Methods
We compared four groups (clinically stable patients with schizophrenia or bipolar disorder, individuals drawn from the community with self-described social isolation, and a socially connected community control group) on loneliness, social cognition, and approach and avoidance social motivation.
Results
Individuals with schizophrenia (n = 72) showed intermediate levels of social isolation, loneliness, and social approach motivation between the isolated (n = 96) and connected control (n = 55) groups. However, they showed significant deficits in social cognition compared to both community groups. Individuals with bipolar disorder (n = 48) were intermediate between isolated and control groups for loneliness and social approach. They did not show deficits on social cognition tasks. Both clinical groups had higher social avoidance than both community groups
Conclusions
The results suggest that social cognitive deficits in schizophrenia, and high social avoidance motivation in both schizophrenia and bipolar disorder, are distinct features of the clinical conditions and not byproducts of social isolation. In contrast, differences between clinical and control groups on levels of loneliness and social approach motivation were congruent with the groups' degree of social isolation.
Blood-based biomarkers represent a scalable and accessible approach for the detection and monitoring of Alzheimer’s disease (AD). Plasma phosphorylated tau (p-tau) and neurofilament light (NfL) are validated biomarkers for the detection of tau and neurodegenerative brain changes in AD, respectively. There is now emphasis to expand beyond these markers to detect and provide insight into the pathophysiological processes of AD. To this end, a reactive astrocytic marker, namely plasma glial fibrillary acidic protein (GFAP), has been of interest. Yet, little is known about the relationship between plasma GFAP and AD. Here, we examined the association between plasma GFAP, diagnostic status, and neuropsychological test performance. Diagnostic accuracy of plasma GFAP was compared with plasma measures of p-tau181 and NfL.
Participants and Methods:
This sample included 567 participants from the Boston University (BU) Alzheimer’s Disease Research Center (ADRC) Longitudinal Clinical Core Registry, including individuals with normal cognition (n=234), mild cognitive impairment (MCI) (n=180), and AD dementia (n=153). The sample included all participants who had a blood draw. Participants completed a comprehensive neuropsychological battery (sample sizes across tests varied due to missingness). Diagnoses were adjudicated during multidisciplinary diagnostic consensus conferences. Plasma samples were analyzed using the Simoa platform. Binary logistic regression analyses tested the association between GFAP levels and diagnostic status (i.e., cognitively impaired due to AD versus unimpaired), controlling for age, sex, race, education, and APOE e4 status. Area under the curve (AUC) statistics from receiver operating characteristics (ROC) using predicted probabilities from binary logistic regression examined the ability of plasma GFAP to discriminate diagnostic groups compared with plasma p-tau181 and NfL. Linear regression models tested the association between plasma GFAP and neuropsychological test performance, accounting for the above covariates.
Results:
The mean (SD) age of the sample was 74.34 (7.54), 319 (56.3%) were female, 75 (13.2%) were Black, and 223 (39.3%) were APOE e4 carriers. Higher GFAP concentrations were associated with increased odds for having cognitive impairment (GFAP z-score transformed: OR=2.233, 95% CI [1.609, 3.099], p<0.001; non-z-transformed: OR=1.004, 95% CI [1.002, 1.006], p<0.001). ROC analyses, comprising of GFAP and the above covariates, showed plasma GFAP discriminated the cognitively impaired from unimpaired (AUC=0.75) and was similar, but slightly superior, to plasma p-tau181 (AUC=0.74) and plasma NfL (AUC=0.74). A joint panel of the plasma markers had greatest discrimination accuracy (AUC=0.76). Linear regression analyses showed that higher GFAP levels were associated with worse performance on neuropsychological tests assessing global cognition, attention, executive functioning, episodic memory, and language abilities (ps<0.001) as well as higher CDR Sum of Boxes (p<0.001).
Conclusions:
Higher plasma GFAP levels differentiated participants with cognitive impairment from those with normal cognition and were associated with worse performance on all neuropsychological tests assessed. GFAP had similar accuracy in detecting those with cognitive impairment compared with p-tau181 and NfL, however, a panel of all three biomarkers was optimal. These results support the utility of plasma GFAP in AD detection and suggest the pathological processes it represents might play an integral role in the pathogenesis of AD.
Blood-based biomarkers offer a more feasible alternative to Alzheimer’s disease (AD) detection, management, and study of disease mechanisms than current in vivo measures. Given their novelty, these plasma biomarkers must be assessed against postmortem neuropathological outcomes for validation. Research has shown utility in plasma markers of the proposed AT(N) framework, however recent studies have stressed the importance of expanding this framework to include other pathways. There is promising data supporting the usefulness of plasma glial fibrillary acidic protein (GFAP) in AD, but GFAP-to-autopsy studies are limited. Here, we tested the association between plasma GFAP and AD-related neuropathological outcomes in participants from the Boston University (BU) Alzheimer’s Disease Research Center (ADRC).
Participants and Methods:
This sample included 45 participants from the BU ADRC who had a plasma sample within 5 years of death and donated their brain for neuropathological examination. Most recent plasma samples were analyzed using the Simoa platform. Neuropathological examinations followed the National Alzheimer’s Coordinating Center procedures and diagnostic criteria. The NIA-Reagan Institute criteria were used for the neuropathological diagnosis of AD. Measures of GFAP were log-transformed. Binary logistic regression analyses tested the association between GFAP and autopsy-confirmed AD status, as well as with semi-quantitative ratings of regional atrophy (none/mild versus moderate/severe) using binary logistic regression. Ordinal logistic regression analyses tested the association between plasma GFAP and Braak stage and CERAD neuritic plaque score. Area under the curve (AUC) statistics from receiver operating characteristics (ROC) using predicted probabilities from binary logistic regression examined the ability of plasma GFAP to discriminate autopsy-confirmed AD status. All analyses controlled for sex, age at death, years between last blood draw and death, and APOE e4 status.
Results:
Of the 45 brain donors, 29 (64.4%) had autopsy-confirmed AD. The mean (SD) age of the sample at the time of blood draw was 80.76 (8.58) and there were 2.80 (1.16) years between the last blood draw and death. The sample included 20 (44.4%) females, 41 (91.1%) were White, and 20 (44.4%) were APOE e4 carriers. Higher GFAP concentrations were associated with increased odds for having autopsy-confirmed AD (OR=14.12, 95% CI [2.00, 99.88], p=0.008). ROC analysis showed plasma GFAP accurately discriminated those with and without autopsy-confirmed AD on its own (AUC=0.75) and strengthened as the above covariates were added to the model (AUC=0.81). Increases in GFAP levels corresponded to increases in Braak stage (OR=2.39, 95% CI [0.71-4.07], p=0.005), but not CERAD ratings (OR=1.24, 95% CI [0.004, 2.49], p=0.051). Higher GFAP levels were associated with greater temporal lobe atrophy (OR=10.27, 95% CI [1.53,69.15], p=0.017), but this was not observed with any other regions.
Conclusions:
The current results show that antemortem plasma GFAP is associated with non-specific AD neuropathological changes at autopsy. Plasma GFAP could be a useful and practical biomarker for assisting in the detection of AD-related changes, as well as for study of disease mechanisms.