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Use of selective in-vivo tau imaging will enable improved understanding of tau aggregation in the brain, facilitating research into causes, diagnosis, and treatment of major tauopathies such as Alzheimer's disease, progressive supranuclear palsy, corticobasal syndrome, chronic traumatic encephalopathy, and some variants of frontotemporal lobar degeneration. Neuropathological studies of Alzheimer's disease show a strong association between tau deposits, decreased cognitive function, and neurodegenerative changes. Selective tau imaging will allow the in-vivo exploration of such associations and measure the global and regional changes in tau deposits over time. Such imaging studies will comprise non-invasive assessment of the spatial and temporal pattern of tau deposition over time, providing insight into the role tau plays in ageing and helping to establish the relation between cognition, genotype, neurodegeneration, and other biomarkers. Once validated, selective tau imaging might be useful as a diagnostic, prognostic, and progression biomarker, and a surrogate marker for the monitoring of efficacy and patient recruitment for anti-tau therapeutic trials.

Glial fibrillary acidic protein (GFAP), an astrocytic cytoskeletal protein, can be measured in blood samples, and has been associated with Alzheimer’s disease (AD). However, plasma GFAP has not been investigated in cognitively normal older adults at risk of AD, based on brain amyloid-β (Aβ) load. Cross-sectional analyses were carried out for plasma GFAP and plasma Aβ1–42/Aβ1–40 ratio, a blood-based marker associated with brain Aβ load, in participants (65–90 years) categorised into low (Aβ−, n = 63) and high (Aβ+, n = 33) brain Aβ load groups via Aβ positron emission tomography. Plasma GFAP, Aβ1–42, and Aβ1–40 were measured using the Single molecule array (Simoa) platform. Plasma GFAP levels were significantly higher (p < 0.00001), and plasma Aβ1–42/Aβ1–40 ratios were significantly lower (p < 0.005), in Aβ+ participants compared to Aβ− participants, adjusted for covariates age, sex, and apolipoprotein E-ε4 carriage. A receiver operating characteristic curve based on a logistic regression of the same covariates, the base model, distinguished Aβ+ from Aβ− (area under the curve, AUC = 0.78), but was outperformed when plasma GFAP was added to the base model (AUC = 0.91) and further improved with plasma Aβ1–42/Aβ1–40 ratio (AUC = 0.92). The current findings demonstrate that plasma GFAP levels are elevated in cognitively normal older adults at risk of AD. These observations suggest that astrocytic damage or activation begins from the pre-symptomatic stage of AD and is associated with brain Aβ load. Observations from the present study highlight the potential of plasma GFAP to contribute to a diagnostic blood biomarker panel (along with plasma Aβ1–42/Aβ1–40 ratios) for cognitively normal older adults at risk of AD.

Studies of rodent models of Alzheimer’s disease (AD) and of human tissues suggest that the retinal changes that occur in AD, including the accumulation of amyloid beta (Aβ), may serve as surrogate markers of brain Aβ levels. As Aβ has a wavelength-dependent effect on light scatter, we investigate the potential for in vivo retinal hyperspectral imaging to serve as a biomarker of brain Aβ. Significant differences in the retinal reflectance spectra are found between individuals with high Aβ burden on brain PET imaging and mild cognitive impairment ( n  = 15), and age-matched PET-negative controls ( n  = 20). Retinal imaging scores are correlated with brain Aβ loads. The findings are validated in an independent cohort, using a second hyperspectral camera. A similar spectral difference is found between control and 5xFAD transgenic mice that accumulate Aβ in the brain and retina. These findings indicate that retinal hyperspectral imaging may predict brain Aβ load. The use of PET for detection of Aβ in the brain in AD has limitations; studies also indicate that retinal changes, including Aβ deposition, occur in AD. Here the authors demonstrate the potential to use in vivo retinal hyperspectral imaging as a surrogate for brain accumulation of Aβ.

Similar to most chronic diseases, Alzheimer's disease (AD) develops slowly from a preclinical phase into a fully expressed clinical syndrome. We aimed to use longitudinal data to calculate the rates of amyloid β (Aβ) deposition, cerebral atrophy, and cognitive decline. In this prospective cohort study, healthy controls, patients with mild cognitive impairment (MCI), and patients with AD were assessed at enrolment and every 18 months. At every visit, participants underwent neuropsychological examination, MRI, and a carbon-11-labelled Pittsburgh compound B (11C-PiB) PET scan. We included participants with three or more 11C-PiB PET follow-up assessments. Aβ burden was expressed as 11C-PiB standardised uptake value ratio (SUVR) with the cerebellar cortex as reference region. An SUVR of 1·5 was used to discriminate high from low Aβ burdens. The slope of the regression plots over 3–5 years was used to estimate rates of change for Aβ deposition, MRI volumetrics, and cognition. We included those participants with a positive rate of Aβ deposition to calculate the trajectory of each variable over time. 200 participants (145 healthy controls, 36 participants with MCI, and 19 participants with AD) were assessed at enrolment and every 18 months for a mean follow-up of 3·8 (95% CI CI 3·6–3·9) years. At baseline, significantly higher Aβ burdens were noted in patients with AD (2·27, SD 0·43) and those with MCI (1·94, 0·64) than in healthy controls (1·38, 0·39). At follow-up, 163 (82%) of the 200 participants showed positive rates of Aβ accumulation. Aβ deposition was estimated to take 19·2 (95% CI 16·8–22·5) years in an almost linear fashion—with a mean increase of 0·043 (95% CI 0·037–0·049) SUVR per year—to go from the threshold of 11C-PiB positivity (1·5 SUVR) to the levels observed in AD. It was estimated to take 12·0 (95% CI 10·1–14·9) years from the levels observed in healthy controls with low Aβ deposition (1·2 [SD 0·1] SUVR) to the threshold of 11C-PiB positivity. As AD progressed, the rate of Aβ deposition slowed towards a plateau. Our projections suggest a prolonged preclinical phase of AD in which Aβ deposition reaches our threshold of positivity at 17·0 (95% CI 14·9–19·9) years, hippocampal atrophy at 4·2 (3·6–5·1) years, and memory impairment at 3·3 (2·5–4·5) years before the onset of dementia (clinical dementia rating score 1). Aβ deposition is slow and protracted, likely to extend for more than two decades. Such predictions of the rate of preclinical changes and the onset of the clinical phase of AD will facilitate the design and timing of therapeutic interventions aimed at modifying the course of this illness. Science and Industry Endowment Fund (Australia), The Commonwealth Scientific and Industrial Research Organisation (Australia), The National Health and Medical Research Council of Australia Program and Project Grants, the Austin Hospital Medical Research Foundation, Victorian State Government, The Alzheimer's Drug Discovery Foundation, and the Alzheimer's Association.

Changes to lipid metabolism are tightly associated with the onset and pathology of Alzheimer’s disease (AD). Lipids are complex molecules comprising many isomeric and isobaric species, necessitating detailed analysis to enable interpretation of biological significance. Our expanded targeted lipidomics platform (569 species across 32 classes) allows for detailed lipid separation and characterisation. In this study we examined peripheral samples of two cohorts (AIBL, n  = 1112 and ADNI, n  = 800). We are able to identify concordant peripheral signatures associated with prevalent AD arising from lipid pathways including; ether lipids, sphingolipids (notably GM 3 gangliosides) and lipid classes previously associated with cardiometabolic disease (phosphatidylethanolamine and triglycerides). We subsequently identified similar lipid signatures in both cohorts with future disease. Lastly, we developed multivariate lipid models that improved classification and prediction. Our results provide a holistic view between the lipidome and AD using a comprehensive approach, providing targets for further mechanistic investigation. The onset and pathology of Alzheimer’s disease (AD) is associated with changes to lipid metabolism. Here, the authors analysed 569 lipids from 32 classes and subclasses in two independent patient cohorts to identify key lipid pathways to link the plasma lipidome with AD and the future onset of AD.

Brain amyloid β (Aβ) deposition and neurodegeneration have been documented in about 50–60% of cognitively healthy elderly individuals (aged 60 years or older). The long-term cognitive consequences of the presence of Alzheimer's disease pathology and neurodegeneration, and whether they have an independent or synergistic effect on cognition, are unclear. We aimed to characterise the long-term clinical and cognitive trajectories of healthy elderly individuals using a two-marker (Alzheimer's disease pathology and neurodegeneration) imaging construct. Between Nov 3, 2006, and Nov 25, 2014, 573 cognitively healthy individuals in Melbourne and Perth, Australia, (mean age 73·1 years [SD 6·2]; 58% women) were enrolled in the Australian Imaging, Biomarker and Lifestyle (AIBL) study. Alzheimer's disease pathology (A) was determined by measuring Aβ deposition by PET, and neurodegeneration (N) was established by measuring hippocampal volume using MRI. Individuals were categorised as A−N−, A+N−, A+N+, or suspected non-Alzheimer's disease pathophysiology (A−N+, SNAP). Clinical progression, hippocampal volume, standard neuropsychological tests, and domain-specific and global cognitive composite scores were assessed over 6 years of follow-up. Linear mixed effect models and a Cox proportional hazards model of survival were used to evaluate, compare, and contrast the clinical, cognitive, and volumetric trajectories of patients in the four AN categories. 50 (9%) healthy individuals were classified as A+N+, 87 (15%) as A+N−, 310 (54%) as A−N−, and 126 (22%) as SNAP. APOE ε4 was more frequent in participants in the A+N+ (27; 54%) and A+N− (42; 48%) groups than in the A−N− (66; 21%) and SNAP groups (23; 18%). The A+N− and A+N+ groups had significantly faster cognitive decline than the A−N− group (0·08 SD per year for AIBL-Preclinical AD Cognitive Composite [PACC]; p<0·0001; and 0·25; p<0·0001; respectively). The A +N+ group also had faster hippocampal atrophy than the A−N− group (0·04 cm3 per year; p=0·02). The SNAP group generally did not show significant decline over time compared with the A−N− group (0·03 SD per year [p=0·19] for AIBL-PACC and a 0·02 cm3 per year increase [p=0·16] for hippocampal volume), although SNAP was sometimes associated with lower baseline cognitive scores (0·20 SD less than A−N− for AIBL-PACC). Within the follow-up, 24% (n=12) of individuals in the A+N+ group and 16% (n=14) in the A+N− group progressed to amnestic mild cognitive impairment or Alzheimer's disease, compared with 9% (n=11) in the SNAP group. Brain amyloidosis, a surrogate marker of Alzheimer's disease pathology, is a risk factor for cognitive decline and for progression from preclinical stages to symptomatic stages of the disease, with neurodegeneration acting as a compounding factor. However, neurodegeneration alone does not confer a significantly different risk of cognitive decline from that in the group with neither brain amyloidosis or neurodegeneration. CSIRO Flagship Collaboration Fund and the Science and Industry Endowment Fund (SIEF), National Health and Medical Research Council, the Dementia Collaborative Research Centres programme, McCusker Alzheimer's Research Foundation, and Operational Infrastructure Support from the Government of Victoria.

Many neurodegenerative diseases are neuropathologically characterized by neuronal loss, gliosis, and the deposition of misfolded proteins such as beta-amyloid (Abeta) plaques and tau tangles in Alzheimer’s disease (AD). In postmortem AD brains, reactive astrocytes and activated microglia are observed surrounding Abeta plaques and tau tangles. These activated glial cells secrete pro-inflammatory cytokines and reactive oxygen species, which may contribute to neurodegeneration. Therefore, in vivo imaging of glial response by positron emission tomography (PET) combined with Abeta and tau PET would provide new insights to better understand the disease process, as well as aid in the differential diagnosis, and monitoring glial response disease-specific therapeutics. There are two promising targets proposed for imaging reactive astrogliosis: monoamine oxidase-B (MAO-B) and imidazolin2 binding site (I2BS), which are predominantly expressed in the mitochondrial membranes of astrocytes and upregulated in various neurodegenerative conditions. PET tracers targeting to these MAO-B and I2BS have been evaluated in humans. [18F]THK-5351, which was originally designed to target tau aggregates in AD, showed high affinity to MAO-B and clearly visualized reactive astrocytes in progressive supranuclear palsy (PSP). However, the lack of selectivity of [18F]THK-5351 binding to both MAO-B and tau, severely limits its clinical utility as a biomarker. Recently, [18F]SMBT-1 was developed as a selective and reversible MAO-B PET tracer derived through the optimization of [18F]THK-5351. In this review, we will summarize the strategy for molecular imaging of reactive astrogliosis and clinical studies using MAO-B and I2BS PET tracers.

Background The Centiloid scale was developed to standardise the results of beta-amyloid (Aβ) PET. We aimed to determine the Centiloid unit (CL) thresholds for CERAD sparse and moderate-density neuritic plaques, Alzheimer’s disease neuropathologic change (ADNC) score of intermediate or high probability of Alzheimer’s Disease (AD), final clinicopathological diagnosis of AD, and expert visual read of a positive Aβ PET scan. Methods Aβ PET results in CL for 49 subjects were compared with post-mortem findings, visual read, and final clinicopathological diagnosis. The Youden Index was used to determine the optimal CL thresholds from receiver operator characteristic (ROC) curves. Results A threshold of 20.1 CL (21.3 CL when corrected for time to death, AUC 0.97) yielded highest accuracy in detecting moderate or frequent plaque density while < 10 CL was optimal for excluding neuritic plaque. The threshold for ADNC intermediate or high likelihood AD was 49.4 CL (AUC 0.98). Those cases with a final clinicopathological diagnosis of AD yielded a median CL result of 87.7 (IQR ± 42.2) with 94% > 45 CL. Positive visual read agreed highly with results > 26 CL. Conclusions Centiloid values < 10 accurately reflected the absence of any neuritic plaque and > 20 CL indicated the presence of at least moderate plaque density, but approximately 50 CL or more best confirmed both neuropathological and clinicopathological diagnosis of Alzheimer’s disease.

Background Blood-based biomarkers are gaining grounds for the detection of Alzheimer’s disease (AD) and related disorders (ADRDs). However, two key obstacles remain: the lack of methods for multi-analyte assessments and the need for biomarkers for related pathophysiological processes like neuroinflammation, vascular, and synaptic dysfunction. A novel proteomic method for pre-selected analytes, based on proximity extension technology, was recently introduced. Referred to as the NULISAseq CNS disease panel, the assay simultaneously measures ~ 120 analytes related to neurodegenerative diseases, including those linked to both core (i.e., tau and amyloid-beta (Aβ)) and non-core AD processes. This study aimed to evaluate the technical and clinical performance of this novel targeted proteomic panel. Methods The NULISAseq CNS disease panel was applied to 176 plasma samples from 113 individuals in the MYHAT-NI cohort of predominantly cognitively normal participants from an economically underserved region in southwestern Pennsylvania, USA. Classical AD biomarkers, including p-tau181, p-tau217, p-tau231, GFAP, NEFL, Aβ40, and Aβ42, were independently measured using Single Molecule Array (Simoa) and correlations and diagnostic performances compared. Aβ pathology, tau pathology, and neurodegeneration (AT(N) statuses) were evaluated with [ 11 C] PiB PET, [ 18 F]AV-1451 PET, and an MRI-based AD-signature composite cortical thickness index, respectively. Linear mixed models were used to examine cross-sectional and Wilcoxon rank sum tests for longitudinal associations between NULISA and neuroimaging-determined AT(N) biomarkers. Results NULISA concurrently measured 116 plasma biomarkers with good technical performance (97.2 ± 13.9% targets gave signals above assay limits of detection), and significant correlation with Simoa assays for the classical biomarkers. Cross-sectionally, p-tau217 was the top hit to identify Aβ pathology, with age, sex, and APOE genotype-adjusted AUC of 0.930 (95%CI: 0.878–0.983). Fourteen markers were significantly decreased in Aβ-PET + participants, including TIMP3, BDNF, MDH1, and several cytokines. Longitudinally, FGF2, IL4, and IL9 exhibited Aβ PET-dependent yearly increases in Aβ-PET + participants. Novel plasma biomarkers with tau PET-dependent longitudinal changes included proteins associated with neuroinflammation, synaptic function, and cerebrovascular integrity, such as CHIT1, CHI3L1, NPTX1, PGF, PDGFRB, and VEGFA; all previously linked to AD but only reliable when measured in cerebrospinal fluid. The autophagosome cargo protein SQSTM1 exhibited significant association with neurodegeneration after adjusting age, sex, and APOE ε4 genotype. Conclusions Together, our results demonstrate the feasibility and potential of immunoassay-based multiplexing to provide a comprehensive view of AD-associated proteomic changes, consistent with the recently revised biological and diagnostic framework. Further validation of the identified inflammation, synaptic, and vascular markers will be important for establishing disease state markers in asymptomatic AD.

Suspected non-Alzheimer disease pathophysiology (SNAP) is a biomarker-based concept that applies to individuals with normal levels of amyloid-β biomarkers in the brain, but in whom biomarkers of neurodegeneration are abnormal. Clinically normal and mildly impaired individuals with SNAP are at increased risk of poor clinical and cognitive outcomes. In this Perspectives article, Clifford Jack and colleagues describe the available data on SNAP and address topical controversies in the field. Suspected non-Alzheimer disease pathophysiology (SNAP) is a biomarker-based concept that applies to individuals with normal levels of amyloid-β biomarkers in the brain, but in whom biomarkers of neurodegeneration are abnormal. The term SNAP has been applied to clinically normal individuals (who do not meet criteria for either mild cognitive impairment or dementia) and to individuals with mild cognitive impairment, but is applicable to any amyloid-negative, neurodegeneration-positive individual regardless of clinical status, except when the pathology underlying neurodegeneration can be reliably inferred from the clinical presentation. SNAP is present in ∼23% of clinically normal individuals aged >65 years and in ∼25% of mildly cognitively impaired individuals. APOE* ε 4 is underrepresented in individuals with SNAP compared with amyloid-positive individuals. Clinically normal and mildly impaired individuals with SNAP have worse clinical and/or cognitive outcomes than individuals with normal levels of neurodegeneration and amyloid-β biomarkers. In this Perspectives article, we describe the available data on SNAP and address topical controversies in the field.