Which is to blame for cognitive decline in ageing: amyloid deposition, neurodegeneration or both?

This scientific commentary refers to ‘Effects of amyloid pathology and neurodegeneration on cognitive change in cognitively normal adults’, by Bilgel et al.. (doi:10.1093/brain/awy150). Amyloid deposition, one of the main neuropathological hallmarks of Alzheimer’s disease, begins decades before symptom onset and has been suggested to trigger the cascade of events leading to the disease (Hardy and Selkoe, 2002). Yet, this hypothesis has been challenged, primarily because anti-amyloid drugs have failed to reverse, or even slow down, cognitive decline in patients. The field has progressively shifted focus towards preclinical stages of Alzheimer’s disease, where downstream effects of amyloid deposition are more limited or could even be prevented. To understand the main drivers of cognitive decline in the preclinical stage of the disease, i.e. in cognitively normal elderly, two key questions remain: (i) are other pathological processes besides amyloid deposition, e.g. neurodegeneration, directly responsible for cognitive decline such that removing amyloid without addressing these would not result in cognitive improvement? and (ii) is there a dose effect of amyloid deposition such that removing only part of it could still lead to cognitive improvement? In other words, is cognitive decline related exclusively to amyloid status (the presence or absence of amyloid deposition based on a predefined threshold), or does the quantity of amyloid deposition also matter, with those with lower amyloid load showing less decline than those with greater amyloid load? These questions are crucial not only for the implementation of future clinical trials, but also for our understanding of the pathophysiology of the disease. In this issue of Brain, Bilgel and co-workers address both questions by assessing whether baseline amyloid deposition (status and dose) and neurodegeneration have independent and additive, or synergistic, effects on the cognitive trajectory of cognitively normal older adults (Bilgel et al., 2018). The National Institute on Aging-Alzheimer’s Association (NIA-AA) proposed research criteria to define the preclinical stages of Alzheimer’s disease according to biomarker positivity, and suggested categorizing cognitively normal adults according to the presence or absence of amyloidosis and neurodegeneration (Jack et al., 2012). An individual’s status is usually determined according to thresholds either pre-established in the literature or derived from data distribution. For amyloidosis, status is determined using the mean neocortical uptake obtained with amyloid-PET imaging (or amyloid levels in CSF), while for neurodegeneration, positivity is based on hippocampal atrophy and/or regional cerebral glucose hypometabolism measurements. Accordingly, individuals are divided into four categories: individuals with no evidence of neurodegeneration or amyloidosis (A−N−); individuals with amyloidosis but no evidence of neurodegeneration (A+N−); individuals with neurodegeneration but no evidence of amyloidosis (A−N+) and, finally, individuals with both amyloidosis and neurodegeneration (A+N+). According to the abovementioned amyloid hypotheses of Alzheimer’s disease, individuals with no abnormal biomarkers would be in stage 0 and are thus commonly used as controls free of Alzheimer’s disease-like pathology; individuals with only amyloid would be in stage 1, and those with both amyloid and neurodegeneration would be in a more advanced stage 2. By contrast, individuals with only neurodegeneration are referred to as SNAP (suspected non-Alzheimer’s disease pathophysiology) as they are not in the amyloid pathway of the disease. Taking advantage of longitudinal data, it is possible to assess the cognitive trajectories of these four categories of individuals and thereby further our understanding of the determinants of future cognitive decline. This issue has been tackled in many studies in the literature, as identifying the culprits for cognitive decline in ageing populations would also point to the main targets for preventive therapies. Results are unclear as to the independent effects of amyloid deposition or neurodegeneration alone, but most studies agree that the presence of both is associated with steeper cognitive decline compared to A−N− individuals. Several issues remain to be clarified: are there both independent and synergistic effects of amyloid and neurodegeneration? If so, which cognitive area(s)/test(s) are the most sensitive to these effects? And does the amount of amyloid deposition also predict cognitive decline over and above amyloid positivity? Bilgel et al. classified 171 cognitively normal older individuals from the Baltimore Longitudinal Study of Aging as neurodegeneration positive or negative, according to their baseline hippocampal volume (adjusted for total intracranial volume), and as amyloid positive or negative, according to the mean cortical binding from baseline amyloid-PET imaging (Fig. 1A). They then assessed the independent and additive versus synergistic effects of the presence of these biomarkers on cognitive change over time (mean follow-up period = 3.7 years) for 12 cognitive measures aimed at assessing various spheres of cognition (verbal and visual episodic memory subtests, executive functions and attention, language, visuospatial abilities and global cognition; Fig. 1B). In an attempt to restrict their analyses to the earliest (preclinical) stages of the disease, only visits where participants remained cognitively normal were included in the study. Using this approach, Bilgel and co-workers found that both amyloidosis and neurodegeneration were independently related to verbal episodic memory decline (mainly the immediate memory score from the California Verbal Learning Test), suggesting that not only isolated amyloidosis, but also SNAP is related to cognitive decline (Fig. 1C, left). In addition, when directly assessing the synergistic effect of amyloidosis and neurodegeneration, the authors found that when both biomarkers were abnormal, cognitive decline was steeper than would be expected if their independent effects were merely added for visual episodic memory (Benton Visual Retention Test), language (Boston Naming Test) and global cognition (Mini-Mental State Examination; MMSE) (Fig. 1C, middle). Schematic of experimental design and results in Bilgel et al. (A) Neuroimaging data. Cognitively normal (CN) participants underwent a structural MRI and an amyloid-PET scan at baseline to quantify hippocampal volume (as a proxy of neurodegeneration) and cerebral amyloid-β (Aβ) burden, respectively. They were then classified according to the presence or absence of abnormality on each biomarker (‘N’ for neurodegeneration and ‘A’ for amyloid-β deposition). This dichotomous categorization results in four experimental groups: A−N−, A−N+, A+N− and A+N+. (B) Cognitive assessment. Participants underwent a neuropsychological battery covering several cognitive domains, both at baseline and longitudinally. (C) Schematic representation of the effects of amyloidosis and neurodegeneration on cognitive decline. The four groups were compared to assess the independent and synergistic effects of amyloidosis (A+) and neurodegeneration (N+) on prospective cognitive decline. Left: The presence of amyloid-β deposition and neurodegeneration (A+N− and A−N+) was independently related to steeper decline in verbal episodic memory when compared to A−N− individuals. Middle: Amyloid-β deposition and neurodegeneration also act synergistically (A+N+) and worsen the decline in other cognitive domains including visual episodic memory, language (assessed by the Boston Naming Test), and global cognition. Right: The load of cortical amyloid-β deposition, and not only the mere presence of amyloidosis, is important as, in A+ individuals, the cognitive decline worsens as the level of cortical amyloid-β increases. Next, Bilgel et al. tested whether, in addition to the presence of amyloid deposition, the amount of amyloid matters. They assessed the links between baseline cortical amyloid load and subsequent cognitive decline within amyloid-positive individuals, adjusting for baseline neurodegeneration, and found a dose-dependent effect of amyloid deposition on language (Boston Naming Test), visuospatial abilities (Card Rotation Test) and global cognition (MMSE). Those with a greater amount of amyloid deposition showed greater cognitive decline than those with a lesser amount (Fig. 1C, right). These findings are in line with those of a recent study showing that the level of amyloid deposition is a better predictor of cognitive decline than amyloid positive or negative status (Farrell et al., 2017). As the global population ages, preventing cognitive decline in the asymptomatic elderly becomes a major challenge. Yet, using cognitive decline as the endpoint (as in the Bilgel et al. study), instead of conversion to a specific defined clinical diagnosis, also has disadvantages. The absence of information as to the clinical outcomes of the participants makes it difficult to determine the clinical relevance of the cognitive decline, and how specific the findings are to preclinical Alzheimer’s disease. Moreover, the fact that the analyses were restricted to visits where participants remained cognitively normal might have induced a bias towards the period where, or the participants in whom, reserve mechanisms were most able to prevent or delay cognitive decline. It would be interesting to run the same analyses with the full dataset to obtain an overall picture of predictors of cognitive decline in general. Additionally, this study focused on hippocampal atrophy and global cortical amyloid load. However, examining atrophy and amyloid deposition in other brain regions, i.e. by running regional or voxel-wise analyses, would also be useful. While the hippocampus is likely the most sensitive and recognized biomarker of atrophy in preclinical Alzheimer’s disease, cognitive decline might be the result of neurodegeneration elsewhere in the brain too. Moreover, regional analyses might be more sensitive to relationships between amyloid deposition and cognitive changes, as slight and localized amyloid increases can be detected (Chételat et al., 2011). Finally, the NIA-AA research criteria have recently been updated to include tau biomarker status as an additional category (T+/T−), leading to the so-called ATN classification scheme (Jack et al., 2018). In this context, it would be valuable to replicate these analyses with a measure of tau pathology included, and to consider the interplay between amyloidosis, tau pathology and neurodegeneration in order to obtain a more comprehensive picture. AN (or ATN) categorization: Classification scheme proposed to categorize individuals based on binarized Alzheimer’s disease biomarkers; i.e. on whether or not they present the pathological hallmarks of Alzheimer’s disease. ‘A’ refers to the amyloid biomarker and is quantified using amyloid-PET or CSF Aβ42 levels, while ‘N’ refers to neurodegeneration or neuronal injury, measured with 18F-fluorodeoxyglucose-PET, structural MRI (usually hippocampal volume) or CSF total-tau levels. Each biomarker category is rated as positive (‘+’; abnormal) or negative (‘−’; normal) according to a predefined threshold. In a revised version of the classification scheme, ‘T’ has been added to further account for the presence or absence of tau pathology measured with tau-PET or CSF phospho-tau levels. Cognitive reserve: Reserve is a heuristic to help explain individual differences in brain health and cognition relative to ageing and brain disease. Higher reserve is defined by a higher level of cognitive performance than expected based on the state of the brain. Cognitive reserve refers to an active model of reserve where pre-existing or new cognitive/brain processes attempt to cope with brain damage in order to maintain function. This ability is influenced by different factors including education and IQ, which are therefore often used as proxies of reserve. Synergistic effect: Interaction or co-operation between two factors (in this case amyloid accumulation and neurodegeneration) such that their combined effect is greater than the simple addition of their separate effects. Overall, this study suggests that amyloidosis and neurodegeneration have both independent and synergistic effects on cognitive trajectories. In addition, the dose effect of amyloidosis on cognitive decline suggests that not only the presence but also the amount of amyloid deposition influences cognitive decline. These findings have implications for future clinical trials. First, they suggest that cognitively normal individuals with both neurodegeneration and amyloid (and especially those with larger amounts of amyloid) should be preferentially targeted given their increased risk of decline. They also indicate that treatments should target both the amyloid and the neurodegenerative pathological processes, and give indications as to which cognitive tests should be used to detect an effect of treatment. These findings are thus important for our understanding of the physiopathology of Alzheimer’s disease and of the use of biomarkers for clinical diagnosis, by suggesting that neurodegeneration and amyloidosis act both independently and synergistically (Chételat, 2013). Inserm, Région Normandie, Fondation d'entreprise MMA des Entrepreneurs du Futur.

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