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A major unanswered question in the dementia field is whether cognitively unimpaired individuals who harbor both Alzheimer’s disease neuropathological hallmarks (that is, amyloid-β plaques and tau neurofibrillary tangles) can preserve their cognition over time or are destined to decline. In this large multicenter amyloid and tau positron emission tomography (PET) study ( n  = 1,325), we examined the risk for future progression to mild cognitive impairment and the rate of cognitive decline over time among cognitively unimpaired individuals who were amyloid PET-positive (A + ) and tau PET-positive (T + ) in the medial temporal lobe (A + T MTL + ) and/or in the temporal neocortex (A + T NEO-T + ) and compared them with A + T − and A − T − groups. Cox proportional-hazards models showed a substantially increased risk for progression to mild cognitive impairment in the A + T NEO-T + (hazard ratio (HR) = 19.2, 95% confidence interval (CI) = 10.9–33.7), A + T MTL + (HR = 14.6, 95% CI = 8.1–26.4) and A + T − (HR = 2.4, 95% CI = 1.4–4.3) groups versus the A − T − (reference) group. Both A + T MTL + (HR = 6.0, 95% CI = 3.4–10.6) and A + T NEO-T + (HR = 7.9, 95% CI = 4.7–13.5) groups also showed faster clinical progression to mild cognitive impairment than the A + T − group. Linear mixed-effect models indicated that the A + T NEO-T + ( β  = −0.056 ± 0.005, T  = −11.55, P  < 0.001), A + T MTL + ( β  = −0.024 ± 0.005, T  = −4.72, P  < 0.001) and A + T − ( β  = −0.008 ± 0.002, T  = −3.46, P  < 0.001) groups showed significantly faster longitudinal global cognitive decline compared to the A − T − (reference) group (all P  < 0.001). Both A + T NEO-T + ( P  < 0.001) and A + T MTL + ( P  = 0.002) groups also progressed faster than the A + T − group. In summary, evidence of advanced Alzheimer’s disease pathological changes provided by a combination of abnormal amyloid and tau PET examinations is strongly associated with short-term (that is, 3–5 years) cognitive decline in cognitively unimpaired individuals and is therefore of high clinical relevance. Abnormal amyloid and tau PET in cognitively unimpaired individuals is strongly associated with short-term cognitive decline and subsequent development of dementia.

Trials of monoclonal antibodies that target various forms of amyloid at different stages of Alzheimer's disease have had mixed results. We tested solanezumab, which targets monomeric amyloid, in a phase 3 trial involving persons with preclinical Alzheimer's disease. Persons 65 to 85 years of age with a global Clinical Dementia Rating score of 0 (range, 0 to 3, with 0 indicating no cognitive impairment and 3 severe dementia), a score on the Mini-Mental State Examination of 25 or more (range, 0 to 30, with lower scores indicating poorer cognition), and elevated brain amyloid levels on F-florbetapir positron-emission tomography (PET) were enrolled. Participants were randomly assigned in a 1:1 ratio to receive solanezumab at a dose of up to 1600 mg intravenously every 4 weeks or placebo. The primary end point was the change in the Preclinical Alzheimer Cognitive Composite (PACC) score (calculated as the sum of four z scores, with higher scores indicating better cognitive performance) over a period of 240 weeks. A total of 1169 persons underwent randomization: 578 were assigned to the solanezumab group and 591 to the placebo group. The mean age of the participants was 72 years, approximately 60% were women, and 75% had a family history of dementia. At 240 weeks, the mean change in PACC score was -1.43 in the solanezumab group and -1.13 in the placebo group (difference, -0.30; 95% confidence interval, -0.82 to 0.22; P = 0.26). Amyloid levels on brain PET increased by a mean of 11.6 centiloids in the solanezumab group and 19.3 centiloids in the placebo group. Amyloid-related imaging abnormalities (ARIA) with edema occurred in less than 1% of the participants in each group. ARIA with microhemorrhage or hemosiderosis occurred in 29.2% of the participants in the solanezumab group and 32.8% of those in the placebo group. Solanezumab, which targets monomeric amyloid in persons with elevated brain amyloid levels, did not slow cognitive decline as compared with placebo over a period of 240 weeks in persons with preclinical Alzheimer's disease. (Funded by the National Institute on Aging and others; A4 ClinicalTrials.gov number, NCT02008357.).

Biomarker discovery and development for clinical research, diagnostics and therapy monitoring in clinical trials have advanced rapidly in key areas of medicine — most notably, oncology and cardiovascular diseases — allowing rapid early detection and supporting the evolution of biomarker-guided, precision-medicine-based targeted therapies. In Alzheimer disease (AD), breakthroughs in biomarker identification and validation include cerebrospinal fluid and PET markers of amyloid-β and tau proteins, which are highly accurate in detecting the presence of AD-associated pathophysiological and neuropathological changes. However, the high cost, insufficient accessibility and/or invasiveness of these assays limit their use as viable first-line tools for detecting patterns of pathophysiology. Therefore, a multistage, tiered approach is needed, prioritizing development of an initial screen to exclude from these tests the high numbers of people with cognitive deficits who do not demonstrate evidence of underlying AD pathophysiology. This Review summarizes the efforts of an international working group that aimed to survey the current landscape of blood-based AD biomarkers and outlines operational steps for an effective academic–industry co-development pathway from identification and assay development to validation for clinical use.

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.

Understanding Alzheimer’s disease (AD) heterogeneity is important for understanding the underlying pathophysiological mechanisms of AD. However, AD atrophy subtypes may reflect different disease stages or biologically distinct subtypes. Here we use longitudinal magnetic resonance imaging data (891 participants with AD dementia, 305 healthy control participants) from four international cohorts, and longitudinal clustering to estimate differential atrophy trajectories from the age of clinical disease onset. Our findings (in amyloid-β positive AD patients) show five distinct longitudinal patterns of atrophy with different demographical and cognitive characteristics. Some previously reported atrophy subtypes may reflect disease stages rather than distinct subtypes. The heterogeneity in atrophy rates and cognitive decline within the five longitudinal atrophy patterns, potentially expresses a complex combination of protective/risk factors and concomitant non-AD pathologies. By alternating between the cross-sectional and longitudinal understanding of AD subtypes these analyses may allow better understanding of disease heterogeneity. Different types of atrophy in Alzheimer’s disease may reflect different disease stages or biologically distinct subtypes. Here the authors use longitudinal neuroimaging data to demonstrate five distinct patterns of atrophy with different demographical and cognitive characteristics.

Alzheimer disease (AD) is the most common cause of dementia in older individuals (>65 years) and has a long presymptomatic phase. Preventive therapies for AD are not yet available, and potential disease-modifying therapies targeting amyloid-β plaques in symptomatic stages of AD have only just been approved in the United States. Small-molecule inhibitors of β-site amyloid precursor protein (APP)-cleaving enzyme 1 (BACE1; also known as β-secretase 1) reduce the production of amyloid-β peptide and are among the most advanced drug candidates for AD. However, to date all phase II and phase III clinical trials of BACE inhibitors were either concluded without benefit or discontinued owing to futility or the occurrence of adverse effects. Adverse effects included early, mild cognitive impairment that was associated with all but one inhibitor; preliminary results suggest that the cognitive effects are non-progressive and reversible. These discontinuations have raised questions regarding the suitability of BACE1 as a drug target for AD. In this Perspective, we discuss the status of BACE inhibitors and suggest ways in which the results of the discontinued trials can inform the development of future clinical trials of BACE inhibitors and related secretase modulators as preventative therapies. We also propose a series of experiments that should be performed to inform ‘go–no-go’ decisions in future trials with BACE inhibitors and consider the possibility that low levels of BACE1 inhibition could avoid adverse effects while achieving efficacy for AD prevention.To date, all phase III trials of β-site amyloid precursor protein (APP)-cleaving enzyme (BACE) inhibitors for Alzheimer disease were either discontinued or produced negative results. Here the authors present their opinion that BACE inhibitors still hold promise as a preventative therapy for Alzheimer disease and outline a series of experiments to inform future trials.

In the context of global ageing, the prevalence of neurodegenerative diseases and dementia, such as Alzheimer’s disease (AD), is increasing. However, the current symptomatic and disease-modifying therapies have achieved limited benefits for neurodegenerative diseases in clinical settings. Halting the progress of neurodegeneration and cognitive decline or even improving impaired cognition and function are the clinically meaningful goals of treatments for neurodegenerative diseases. Ageing is the primary risk factor for neurodegenerative diseases and their associated comorbidities, such as vascular pathologies, in elderly individuals. Thus, we aim to elucidate the role of ageing in neurodegenerative diseases from the perspective of a complex system, in which the brain is the core and peripheral organs and tissues form a holistic network to support brain functions. During ageing, the progressive deterioration of the structure and function of the entire body hampers its active and adaptive responses to various stimuli, thereby rendering individuals more vulnerable to neurodegenerative diseases. Consequently, we propose that the prevention and treatment of neurodegenerative diseases should be grounded in holistic antiageing and rejuvenation means complemented by interventions targeting disease-specific pathogenic events. This integrated approach is a promising strategy to effectively prevent, pause or slow down the progression of neurodegenerative diseases.

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.

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.

: Centiloid was introduced to harmonise β-Amyloid (Aβ) PET quantification across different tracers, scanners and analysis techniques. Unfortunately, Centiloid still suffers from some quantification disparities in longitudinal analysis when normalising data from different tracers or scanners. In this work, we aim to reduce this variability using a different analysis technique applied to the existing calibration data. : All PET images from the Centiloid calibration dataset, along with 3762 PET images from the AIBL study were analysed using the recommended SPM pipeline. The PET images were SUVR normalised using the whole cerebellum. All SUVR normalised PiB images from the calibration dataset were decomposed using non-negative matrix factorisation (NMF). The NMF coefficients related to the first component were strongly correlated with global SUVR and were subsequently used as a surrogate for Aβ retention. For each tracer of the calibration dataset, the components of the NMF were computed in a way such that the coefficients of the first component would match those of the corresponding PiB. Given the strong correlations between the SUVR and the NMF coefficients on the calibration dataset, all PET images from AIBL were subsequently decomposed using the computed NMF, and their coefficients transformed into Centiloids. : Using the AIBL data, the correlation between the standard Centiloid and the novel NMF-based Centiloid was high in each tracer. The NMF-based Centiloids showed a reduction of outliers, and improved longitudinal consistency. Furthermore, it removed the effects of switching tracers from the longitudinal variance of the Centiloid measure, when assessed using a linear mixed effects model. : We here propose a novel image driven method to perform the Centiloid quantification. The methods is highly correlated with standard Centiloids while improving the longitudinal reliability when switching tracers. Implementation of this method across multiple studies may lend to more robust and comparable data for future research.