Plasma phosphorylated tau181 and neurodegeneration in Alzheimer's disease

We examined if plasma phosphorylated tau is associated with neurodegeneration in Alzheimer’s disease. We investigated 372 cognitively unimpaired participants, 554 mild cognitive impairment patients, and 141 Alzheimer’s disease dementia patients. Tau phosphorylated at threonine 181, regional cortical thickness (using magnetic resonance imaging) and hypometabolism (using fluorodeoxyglucose positron emission tomography) were measured longitudinally. High plasma tau was associated with hypometabolism and cortical atrophy at baseline and over time, and longitudinally increased tau was associated with accelerated atrophy, but these associations were only observed in Aβ‐positive participants. Plasma phosphorylated tau may identify and track processes linked to neurodegeneration in Alzheimer’s disease

Utility of plasma neurofilament light and total tau for clinical trials in Alzheimer's disease

INTRODUCTION: Several blood‐based biomarkers are associated with neuronal injury, but their utility in interventional clinical trials is unclear. This study retrospectively evaluated the utility of plasma neurofilament light (NfL) and total tau (t‐tau) in an 18‐month trial in mild Alzheimer's disease (AD). METHODS: Correlation and conditional independence analyses and Gaussian graphical models were used to investigate cross‐sectional and longitudinal relations between NfL, t‐tau, and clinical scales. RESULTS: NfL had a stronger association than t‐tau with clinical scales; t‐tau did not hold additional information to that given by NfL (P > 0.05 at all time points). NfL held independent information about shorter‐term (3‐ to 6‐month) progression beyond patient age and clinical scores. However, no meaningful gain in power was found when adjusting a longitudinal analysis of cognitive scores for baseline NfL. DISCUSSION: Plasma NfL is superior to t‐tau in mild AD. The ability of NfL to detect changes before clinical manifestations makes it a promising biomarker of drug response in trials of disease‐modifying drugs

Plasma glial fibrillary acidic protein detects Alzheimer pathology and predicts future conversion to Alzheimer dementia in patients with mild cognitive impairment.

INTRODUCTION: Plasma glial fibrillary acidic protein (GFAP) is a marker of astroglial activation and astrocytosis. We assessed the ability of plasma GFAP to detect Alzheimer's disease (AD) pathology in the form of AD-related amyloid-β (Aβ) pathology and conversion to AD dementia in a mild cognitive impairment (MCI) cohort. METHOD: One hundred sixty MCI patients were followed for 4.7 years (average). AD pathology was defined using cerebrospinal fluid (CSF) Aβ42/40 and Aβ42/total tau (T-tau). Plasma GFAP was measured at baseline and follow-up using Simoa technology. RESULTS: Baseline plasma GFAP could detect abnormal CSF Aβ42/40 and CSF Aβ42/T-tau with an AUC of 0.79 (95% CI 0.72-0.86) and 0.80 (95% CI 0.72-0.86), respectively. When also including APOE ε4 status as a predictor, the accuracy of the model to detect abnormal CSF Aβ42/40 status improved (AUC = 0.86, p = 0.02). Plasma GFAP predicted subsequent conversion to AD dementia with an AUC of 0.84 (95% CI 0.77-0.91), which was not significantly improved when adding APOE ε4 or age as predictors to the model. Longitudinal GFAP slopes for Aβ-positive and MCI who progressed to dementia (AD or other) were significantly steeper than those for Aβ-negative (p = 0.007) and stable MCI (p < 0.0001), respectively. CONCLUSION: Plasma GFAP can detect AD pathology in patients with MCI and predict conversion to AD dementia

Genetic effects on longitudinal cognitive decline during the early stages of Alzheimer's disease

Cognitive decline in early-stage Alzheimer’s disease (AD) may depend on genetic variability. In the Swedish BioFINDER study, we used polygenic scores (PGS) (for AD, intelligence, and educational attainment) to predict longitudinal cognitive change (measured by mini-mental state examination (MMSE) [primary outcome] and other cognitive tests) over a mean of 4.2 years. We included 260 β-amyloid (Aβ) negative cognitively unimpaired (CU) individuals, 121 Aβ-positive CU (preclinical AD), 50 Aβ-negative mild cognitive impairment (MCI) patients, and 127 Aβ-positive MCI patients (prodromal AD). Statistical significance was determined at Bonferroni corrected p value < 0.05. The PGS for intelligence (beta = 0.1, p = 2.9e−02) was protective against decline in MMSE in CU and MCI participants regardless of Aβ status. The polygenic risk score for AD (beta = − 0.12, p = 9.4e−03) was correlated with the rate of change in MMSE and was partially mediated by Aβ-pathology (mediation effect 20%). There was no effect of education PGS on cognitive measures. Genetic variants associated with intelligence mitigate cognitive decline independent of Aβ-pathology, while effects of genetic variants associated with AD are partly mediated by Aβ-pathology

Prediction of future Alzheimer's disease dementia using plasma phospho-tau combined with other accessible measures

A combination of plasma phospho-tau (P-tau) and other accessible biomarkers might provide accurate prediction about the risk of developing Alzheimer’s disease (AD) dementia. We examined this in participants with subjective cognitive decline and mild cognitive impairment from the BioFINDER (n = 340) and Alzheimer’s Disease Neuroimaging Initiative (ADNI) (n = 543) studies. Plasma P-tau, plasma Aβ42/Aβ40, plasma neurofilament light, APOE genotype, brief cognitive tests and an AD-specific magnetic resonance imaging measure were examined using progression to AD as outcome. Within 4 years, plasma P-tau217 predicted AD accurately (area under the curve (AUC) = 0.83) in BioFINDER. Combining plasma P-tau217, memory, executive function and APOE produced higher accuracy (AUC = 0.91, P < 0.001). In ADNI, this model had similar AUC (0.90) using plasma P-tau181 instead of P-tau217. The model was implemented online for prediction of the individual probability of progressing to AD. Within 2 and 6 years, similar models had AUCs of 0.90–0.91 in both cohorts. Using cerebrospinal fluid P-tau, Aβ42/Aβ40 and neurofilament light instead of plasma biomarkers did not improve the accuracy significantly. The clinical predictions by memory clinic physicians had significantly lower accuracy (4-year AUC = 0.71). In summary, plasma P-tau, in combination with brief cognitive tests and APOE genotyping, might greatly improve the diagnostic prediction of AD and facilitate recruitment for AD trials

Plasma GFAP is an early marker of amyloid-β but not tau pathology in Alzheimer's disease

Although recent clinical trials targeting amyloid-β (Aβ) in Alzheimer's disease (AD) have shown promising results, there is increasing evidence suggesting that understanding alternative disease pathways that interact with Aβ metabolism and amyloid pathology might be important to halt the clinical deterioration. In particular, there is evidence supporting a critical role of astroglial activation and astrocytosis in AD. However, to this date, no studies have assessed whether astrocytosis is independently related to either Aβ or tau pathology, respectively, in vivo. To address this question, we determined the levels of the astrocytic marker glial fibrillary acidic protein (GFAP) in plasma and cerebrospinal fluid (CSF) of 217 Aβ-negative cognitively unimpaired individuals, 71 Aβ-positive cognitively unimpaired individuals, 78 Aβ-positive cognitively impaired individuals, 63 Aβ-negative cognitively impaired individuals and 75 patients with a non-AD neurodegenerative disorder from the Swedish BioFINDER-2 study. Subjects underwent longitudinal Aβ (18F-flutemetamol) and tau (18F-RO948) positron emission tomography (PET) as well as cognitive testing. We found that plasma GFAP concentration was significantly increased in all Aβ-positive groups compared with subjects without Aβ pathology (p < 0.01). In addition, there were significant associations between plasma GFAP with higher Aβ-PET signal in all Aβ-positive groups, but also in cognitively normal individuals with normal Aβ values (p < 0.001), which remained significant after controlling for tau-PET signal. Furthermore, plasma GFAP could predict Aβ-PET positivity with an area under the curve of 0.76, which was greater than the performance achieved by CSF GFAP (0.69) and other glial markers (CSF YKL-40: 0.64, sTREM2: 0.71). Although correlations were also observed between tau-PET and plasma GFAP, these were no longer significant after controlling for Aβ-PET. In contrast to plasma GFAP, CSF GFAP concentration was significantly increased in non-AD patients compared to other groups (p < 0.05) and correlated with Aβ-PET only in Aβ-positive cognitively impaired individuals (p = 0.005). Finally, plasma GFAP was associated with both longitudinal Aβ-PET and cognitive decline, and mediated the effect of Aβ-PET on tau-PET burden, suggesting that astrocytosis secondary to Aβ aggregation might promote tau accumulation. Altogether, these findings indicate that plasma GFAP is an early marker associated with brain Aβ pathology but not tau aggregation, even in cognitively normal individuals with a normal Aβ status. This suggests that plasma GFAP should be incorporated in current hypothetical models of AD pathogenesis and be used as a non-invasive and accessible tool to detect early astrocytosis secondary to Aβ pathology

Plasma neurofilament light is a predictor of neurological outcome 12 h after cardiac arrest

Background: Previous studies have reported high prognostic accuracy of circulating neurofilament light (NfL) at 24–72 h after out-of-hospital cardiac arrest (OHCA), but performance at earlier time points and after in-hospital cardiac arrest (IHCA) is less investigated. We aimed to assess plasma NfL during the first 48 h after OHCA and IHCA to predict long-term outcomes. Methods: Observational multicentre cohort study in adults admitted to intensive care after cardiac arrest. NfL was retrospectively analysed in plasma collected on admission to intensive care, 12 and 48 h after cardiac arrest. The outcome was assessed at two to six months using the Cerebral Performance Category (CPC) scale, where CPC 1–2 was considered a good outcome and CPC 3–5 a poor outcome. Predictive performance was measured with the area under the receiver operating characteristic curve (AUROC). Results: Of 428 patients, 328 (77%) suffered OHCA and 100 (23%) IHCA. Poor outcome was found in 68% of OHCA and 55% of IHCA patients. The overall prognostic performance of NfL was excellent at 12 and 48 h after OHCA, with AUROCs of 0.93 and 0.97, respectively. The predictive ability was lower after IHCA than OHCA at 12 and 48 h, with AUROCs of 0.81 and 0.86 (p ≤ 0.03). AUROCs on admission were 0.77 and 0.67 after OHCA and IHCA, respectively. At 12 and 48 h after OHCA, high NfL levels predicted poor outcome at 95% specificity with 70 and 89% sensitivity, while low NfL levels predicted good outcome at 95% sensitivity with 71 and 74% specificity and negative predictive values of 86 and 88%. Conclusions: The prognostic accuracy of NfL for predicting good and poor outcomes is excellent as early as 12 h after OHCA. NfL is less reliable for the prediction of outcome after IHCA

Tau-PET is superior to phospho-tau when predicting cognitive decline in symptomatic AD patients

Introduction: Biomarkers for the prediction of cognitive decline in patients with amnestic mild cognitive impairment (MCI) and amnestic mild dementia are needed for both clinical practice and clinical trials. Methods: We evaluated the ability of tau-PET (positron emission tomography), cortical atrophy on magnetic resonance imaging (MRI), baseline cognition, apolipoprotein E gene (APOE) status, plasma and cerebrospinal fluid (CSF) levels of phosphorylated tau-217, neurofilament light (NfL), and amyloid beta (Aβ)42/40 ratio (individually and in combination) to predict cognitive decline over 2 years in BioFINDER-2 and Alzheimer's Disease Neuroimaging Initiative (ADNI). Results: Baseline tau-PET and a composite baseline cognitive score were the strongest independent predictors of cognitive decline. Cortical thickness and NfL provided some additional information. Using a predictive algorithm to enrich patient selection in a theoretical clinical trial led to a significantly lower required sample size. Discussion: Models including baseline tau-PET and cognition consistently provided the best prediction of change in cognitive function over 2 years in patients with amnestic MCI or mild dementia

Tau PET correlates with different Alzheimer's disease-related features compared to CSF and plasma p-tau biomarkers

PET, CSF and plasma biomarkers of tau pathology may be differentially associated with Alzheimer's disease (AD)-related demographic, cognitive, genetic and neuroimaging markers. We examined 771 participants with normal cognition, mild cognitive impairment or dementia from BioFINDER-2 (n = 400) and ADNI (n = 371). All had tau-PET ([18F]RO948 in BioFINDER-2, [18F]flortaucipir in ADNI) and CSF p-tau181 biomarkers available. Plasma p-tau181 and plasma/CSF p-tau217 were available in BioFINDER-2 only. Concordance between PET, CSF and plasma tau biomarkers ranged between 66 and 95%. Across the whole group, ridge regression models showed that increased CSF and plasma p-tau181 and p-tau217 levels were independently of tau PET associated with higher age, and APOEɛ4-carriership and Aβ-positivity, while increased tau-PET signal in the temporal cortex was associated with worse cognitive performance and reduced cortical thickness. We conclude that biofluid and neuroimaging markers of tau pathology convey partly independent information, with CSF and plasma p-tau181 and p-tau217 levels being more tightly linked with early markers of AD (especially Aβ-pathology), while tau-PET shows the strongest associations with cognitive and neurodegenerative markers of disease progression

Relevance of biomarkers across different neurodegenerative

Background: The panel of fluid- and imaging-based biomarkers available for neurodegenerative disease research is growing and has the potential to close important gaps in research and the clinic. With this growth and increasing use, appropriate implementation and interpretation are paramount. Various biomarkers feature nuanced differences in strengths, limitations, and biases that must be considered when investigating disease etiology and clinical utility. For example, neuropathological investigations of Alzheimer’s disease pathogenesis can fall in disagreement with conclusions reached by biomarker-based investigations. Considering the varied strengths, limitations, and biases of different research methodologies and approaches may help harmonize disciplines within the neurodegenerative disease field. Purpose of review: Along with separate review articles covering fluid and imaging biomarkers in this issue of Alzheimer’s Research and Therapy, we present the result of a discussion from the 2019 Biomarkers in Neurodegenerative Diseases course at the University College London. Here, we discuss themes of biomarker use in neurodegenerative disease research, commenting on appropriate use, interpretation, and considerations for implementation across different neurodegenerative diseases. We also draw attention to areas where biomarker use can be combined with other disciplines to understand issues of pathophysiology and etiology underlying dementia. Lastly, we highlight novel modalities that have been proposed in the landscape of neurodegenerative disease research and care