. Synergistic effect of β-amyloid and neurodegeneration on cognitive decline in clinically normal individuals. JAMA Neurol. 2014 Nov;71(11):1379-85. PubMed.

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  1. This is a very nice study: informative, useful, and timely. It shows that while both neurodegeneration and amyloid deposition independently have impacts on the global cognitive trajectory at follow-up, they have a synergistic effect such that their concomitant presence accelerates cognitive decline. This study emphasizes the view that the biomarkers may be considered independently (rather than sequentially), with "the additive presence of each [biomarker] causing an incremental increase in the risk of (...) progress to AD" (see Chételat, 2013).

    Of course, we have to keep in mind that the findings may differ with longer follow-up because the predictive value of each biomarker, and any combination of biomarkers, is likely highly dependent on the duration of the observation. Moreover, different interpretations may be proposed; this study obviously fuels the debate about what should be considered preclinical AD versus what should not. The answer depends on what are considered the physiopathological processes of the disease, and also on its definition, i.e., what should be called "AD" at this early stage.

    References:

    . Alzheimer disease: Aβ-independent processes-rethinking preclinical AD. Nat Rev Neurol. 2013 Mar;9(3):123-4. PubMed.

  2. In this elegant study, Beth Mormino, Reisa Sperling, and their colleagues support the hypothesis that biomarker measurements of fibrillar amyloid deposition in cognitively unimpaired older adults are associated with an indicator of subsequent cognitive decline. They also support the hypothesis that biomarker evidence of both fibrillar amyloid deposition and neurodegeneration is associated with an even greater rate of cognitive decline, and that there may be prognostic value in the previously proposed biomarker stages of preclinical Alzheimer’s disease (AD).

    Larger samples and longer follow-up durations are needed to further clarify the extent to which these preclinical stages provide a prognostic indicator of decline and clinical progression. Findings from preclinical AD trials like the ADCS A4 trial are needed to clarify when different amyloid-modifying agents and other investigational agents need to be given in the earlier preclinical AD stages in order to have the most profound therapeutic effect.

    The Alzheimer’s Prevention Initiative Autosomal Dominant (API ADAD) and Apolipoprotein E4 (API APOE4) trials are evaluating different amyloid-modifying agents in cognitively unimpaired persons who, based on their age and genetic background, are at the highest imminent risk for the clinical onset of AD. Since about a third of the individuals in these trials are not expected to meet PET evidence consistent with moderate to frequent neuritic plaques at the time of enrollment, the API trials will evaluate these agents and test the amyloid hypothesis even earlier in the preclinical course of AD, complementing the important efforts now being undertaken in the ADCS A4 and DIAN trials.

  3. This is a really nice contribution from a group that is obviously at the forefront of work in the preclinical AD domain. A couple of things are unique about this study, but I should note that even the aspects that are consistent with other prior work serve as important replication of the longitudinal implications of different preclinical subtypes, on which we still have very limited data at this point.

    This study is consistent with the notion that the presence of amyloid plus AD-like neurodegenerative change—in this case hippocampal atrophy and/or a signature of FDG-PET measured hypometabolism—represents a likely later preclinical stage relative to the presence of cerebral amyloid without neurodegenerative change, and thus is more likely to be associated with near-term cognitive change.  This has important implications for clinical trial design, as enrichment in this group may enhance the ability to detect a treatment effect using cognitive outcome measures. 

    Another important finding of this report is that SNAP, neurodegeneration in the absence of amyloid, is not a completely benign state, and that these individuals do display some evidence of longitudinal cognitive change relative to those without amyloid and neurodegeneration. As the authors note, this is likely a mixed group with regard to etiology of the neurodegenerative changes they are displaying.  My suspicion is that this heterogeneity in etiology likely results in heterogeneity in outcomes in this group and that further work will be needed to explore who among the SNAP group are more likely to display future cognitive change.

    One of the most interesting findings of this study was that there was a trend for sub-threshold PiB uptake to correlate with neurodegeneration.  In other words, amongst the amyloid-negative individuals, those closer to the threshold of positivity were more likely to display evidence of either hippocampal atrophy or hypometabolism.  This is an interesting finding, as it may suggest that there is some “signal” to the presence of amyloid even below a relatively liberal cutoff and that some in the SNAP group may represent individuals with cerebral amyloid, but at levels lower than typically constitutes a positive amyloid scan. This could reflect that these individuals—mostly non-ApoE4 carriers—have a lower threshold of amyloid required for neurodegenerative changes to occur. Understanding this relationship may have important implications for disease pathophysiology.

  4. This study by Mormino et al. addresses important questions using an elegant methodology. In order to use cognition as an endpoint in clinical trials enrolling cognitively normal older adults, it is important to assess the relationship between the AD biomarkers and cognitive changes and, more importantly, to assess if sufficient cognitive change can be detected during a short follow-up.

    The threshold the authors used to assess amyloid positivity was derived from a Gaussian Mixture Model approach and was set at a standard uptake value ratio of 1.196, which is lower than the widely used SUVR thresholds of 1.4 -1.5 and supports the findings we presented at the last AAIC conference (see Jul 2014 news story). The fact that almost identical “early thresholds” were found in two independent cohorts gives strong support to the idea that amyloid can be detected with PIB-PET imaging long before individuals reach the widely used SUVR cutoffs of 1.4-1.5, and that some subjects classified as PiB-negative in previous studies might in fact have been early amyloid accumulators.

    Looking subthreshold for amyloid positivity is an interesting and novel approach of this paper that will need further investigation. While fixed thresholds are often used in research, it is possible that thresholds vary from one person to another depending on other risks and protective factors. Our previous work suggests, for instance, that less amyloid is needed to detect an association between amyloid and cortical thickness in subjects presenting a vascular burden (see Villeneuve et al., 2014).

    Finally, we need to remember that PiB is a tracer that binds to amyloid, but it is not amyloid per se. Some subjects might therefore already have substantial levels of amyloid before reaching positive PiB-thresholds.

    References:

    . Vascular risk and Aβ interact to reduce cortical thickness in AD vulnerable brain regions. Neurology. 2014 Jul 1;83(1):40-7. Epub 2014 Jun 6 PubMed.

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