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New insights into Alzheimer’s disease via longitudinal neuroimaging

Alzheimer's effect - lonely feeling.

Until fairly recently, the brains of patients with Alzheimer’s disease (AD) and other forms of dementia could only be studied by post mortem examination. This all changed with the development of neuroimaging techniques, allowing the features of dementia to be studied in living individuals. Today, neuroimaging ‘biomarkers’ can track the progression of AD over time, and are proving increasingly valuable to characterise subtypes of the disease. This was the topic of a session at AAIC 2016, chaired by Nick Fox (UCL Institute of Neurology, United Kingdom) and Alexandre Bejanin (Université de Caen, France), and entitled, ‘Neuroimaging: longitudinal progression of neurodegeneration using neuroimaging’.

Brain atrophy in familial AD – a distinct pattern

 

In order for therapies to show a disease-modifying effect in AD, experts believe that they must be administered very early in the disease course – prior to significant pathology or neurodegeneration. To test potentially disease-modifying therapies in clinical trials, then, individuals must be identified in the preclinical stages of the disease.

Preclinical diagnosis is challenging – current biomarkers are good predictors of disease progression, but the presence of a biomarker does not guarantee the development of dementia. More sensitive biomarkers are needed, and familial AD may provide the key to their development. In familial AD – an inherited form of early-onset AD – individuals carrying the autosomal-dominant mutation will always progress to AD dementia.

The Dominantly Inherited Alzheimer Network (DIAN) study enrolled individuals with familial AD for serial magnetic resonance imaging (MRI) scans. Using these scans, David Cash, of the UCL Centre for Medical Image Computing, United Kingdom, devised a technique to measure grey matter volume changes across the entire brains of the participants.

Over time, individuals with AD clinical symptoms showed widespread brain atrophy. The same pattern of atrophy was observed in presymptomatic individuals who were expected to show symptoms within 5 years, albeit with much smaller changes over time. Presymptomatic individuals who were further from onset of symptoms showed no atrophy.

Thus, longitudinal MRI in familial AD revealed a distinct pattern of brain atrophy that increased with disease severity. Potentially, this volumetric MRI technique could be used in future clinical trials, to help identify suitable candidates for disease-modifying therapies.

 

Brain atrophy in AD variants – further distinct patterns

 

MRI reveals localised brain atrophy in variants of AD

The most common variant of AD is ‘late-onset AD’, named in contrast to ‘early-onset AD’ that occurs in individuals under the age of 65. Other variants include a language disorder known as ‘logopenic variant primary progressive aphasia (PPA)’, and a visual disorder known as ‘posterior cortical atrophy’.

Due to their different natures, it might be expected that variants of AD will show different patterns of brain atrophy in serial MRI scans. This is what Gautam Tammewar, of the University of California – San Francisco, USA, set out to prove.

On their initial MRI scans, each variant of AD showed a distinct, localised pattern of atrophy compared to healthy controls. Over time, the atrophy patterns extended and began to converge – for early-onset AD, logopenic-variant PPA, and posterior cortical atrophy.

The exception was late-onset AD, in which no significant new atrophy was observed over time. Thus, late-onset AD may be a less aggressive form of AD than the other variants considered.

 

Brain metabolic changes in frontotemporal dementia – another distinct pattern

 

PET reveals localised reductions in brain metabolism in FTD

Frontotemporal dementia (FTD) is a relatively uncommon clinical syndrome characterised by degeneration of the frontal and temporal lobes – parts of the brain that are associated with behaviour and language. As for AD, FTD can be split into variants, termed behavioural variant FTD, non-fluent/agrammatic variant PPA, and semantic variant PPA.

FTD is another disease area that would benefit from sensitive biomarkers, to monitor disease progression, and to identify candidates for trials of potentially disease-modifying therapies. Alexandre Bejanin described the Neuroimaging In Frontotemporal Dementia (NIFD) study, designed to assess the ability of positron emission tomography (PET) to detect brain changes over time in FTD. In contrast to MRI, which measures structural changes in the brain, PET measures functional changes based on the uptake of a tracer (in this case, 18F-fludeoxyglucose, or FDG, to measure metabolism).

Compared to healthy controls, each variant of FTD showed a distinct pattern of reduced metabolism. Over time, the regions of reduced metabolism spread across the brain, until the variants ultimately converged. Consequently, FDG-PET appears to be a sensitive biomarker for FTD, with potential applications in future clinical trials.

 

A new tau PET tracer – a promising biomarker

 

Tau PET biomarkers reveal AD phenotypes associated with faster disease progression

The aggregation of tau protein into neurofibrillary tangles is a hallmark of AD, increasing over time, and correlating with cognitive impairment. Consequently, tau is a known biomarker, which can be accurately measured using a new PET tracer that targets neurofibrillary tangles. Sergey Shcherbinin, of Eli Lilly and Company, USA, used this tracer to investigate changes in tau levels over 9 months among patients with amyloid-positive mild cognitive impairment (MCI) or AD.

Individual patients were found to accumulate tau at very different rates, with some showing a large increase (termed ‘progressors’), and others showing little change (termed ‘non-progressors’). The brain regions in which tau accumulated also varied from patient to patient.

Working backwards, progressors and non-progressors were compared at the start of the study. Progressors tended to be younger than non-progressors, and to have worse cognitive symptoms. In addition, progressors had a greater tau burden at the start of the study, and more advanced AD.

Thus, tau PET offers new insights into AD progression, with potential value in future clinical trials.

 

Biomarkers clarify the nature of SNAP

 

SNAP is not a preclinical stage of AD

As discussed already, AD pathology is thought to begin many years before a patient shows clinical symptoms. According to the National Institute on Aging and the Alzheimer’s Association, preclinical AD begins with amyloidosis (Stage 1), followed by evidence of neurodegeneration (Stage 2), and then subtle cognitive decline (Stage 3).

However, over 20% of clinically normal individuals aged >65 years show biomarker evidence of neurodegeneration, without any amyloidosis. These individuals are said to have ‘suspected non-Alzheimer pathophysiology’ (SNAP). Using various longitudinal biomarkers, Brian Gordon, of Washington University in St. Louis School of Medicine, USA, set out to investigate if SNAP could be a form of preclinical AD.

Individuals with SNAP were found to be indistinguishable from those with no pathology, in terms of amyloid accumulation and brain atrophy over time. In contrast, Stages 1–3 showed increased amyloid accumulation, and Stages 2–3 showed accelerated brain atrophy. Thus, rather than being a stage of AD, SNAP may arise due to other morbidities (such as cerebrovascular disease), or simply inherent variability in brain structure.

 

A new technique to sequence AD biomarker progression

 

Amyloidosis is not necessarily the first biomarker in AD

Models of AD generally agree that its pathology starts with the aggregation of amyloid, followed by neurodegeneration, and finally progressing to cognitive symptoms. This is an oversimplification, however, and different patients may follow very different paths of progression. Using data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, Laurel Beckett, of the University of California, USA, developed a mathematical technique to categorise patients according to the order in which their AD biomarkers appeared and progressed.

A simple example looked at three biomarkers – amyloidosis, brain atrophy, and brain metabolism (as measured by FDG-PET). Patients fell into three groups – those with the pattern of amyloidosis, atrophy, then reduced metabolism; those with amyloidosis and reduced metabolism together, followed by atrophy; and those in whom amyloidosis came later, and was therefore not driving the disease.

So, this analysis technique is able to distinguish patients with different trajectories of AD progression. The observed heterogeneity has important implications for the early diagnosis of AD.

Our correspondent’s highlights from the symposium are meant as a fair representation of the scientific content presented. The views and opinions expressed on this page do not necessarily reflect those of Lundbeck.

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