Citations

AbstractBackgroundGrey matter covariance networks become disrupted early in Alzheimer’s disease (AD), when amyloid starts aggregating, and before the onset of irreversible atrophy. However, the precise relationship of grey matter network breakdown and the AD pathophysiological process remains unclear. Here, we studied to what extent shared familial (genes and family environment) or environmental factors unique to a twin contribute to these relationships in a sample of older monozygotic twins with intact cognition.MethodFrom the EMIF‐AD PreclinAD study we included monozygotic twins who had at least an MRI scan (n=197) and CSF available (n=124). We assessed the upper limit of genetic contribution to grey matter network measures (size, degree, connectivity density, clustering, path length, small‐world measures) with within‐twin pair correlations. Associations between CSF AD markers (Aβ42/40, t‐tau, p‐tau) and other markers related to AD pathophysiology (neurogranin, Aβ38, Aβ40, BACE1) with grey matter network disruptions were assessed with linear mixed models. Twin analyses (cross‐twin cross‐trait, twin difference analyses) were used to assess the contribution of shared familial and/or unique environmental factors to these associations.ResultTwenty individuals (16%) had abnormal CSF Aβ42/40 levels. Grey matter network measures were highly correlated within twin pairs (r=0.54‐0.97; all p<0.001, Fig.1), suggesting a moderate to strong genetic background. Across the whole sample, more abnormal Aβ42/40 ratios were moderately related to lower connectivity density (β±SE = 0.176±0.09, p=0.055, Fig.2). The strongest associations with grey matter network measures were observed for t‐tau and p‐tau levels (range β=‐0.344‐0.182), followed by Aβ production markers (Aβ38, Aβ40, BACE1) (range β=‐0.202‐0.165). We observed no relationships between neurogranin and grey matter network measures. Cross‐twin cross‐trait analyses showed that levels for t‐tau, p‐tau and Aβ38 in one twin were associated with grey matter network measures in their co‐twin (r=‐0.27 to ‐0.21; p range=0.02 to 0.07, Fig.3). Within‐pair differences in BACE1 were related to within‐pair differences in normalized path length values (r=0.35; p=0.01; Fig.4).ConclusionOur results suggest a shared familial background for the relationship of grey matter network disruptions and AD‐related processes as measured in CSF. In addition, unique environmental factors influencing BACE1 may also affect grey matter network disruptions.

AbstractIndividuals with prodromal Alzheimer’s disease show considerable variability in rates of cognitive decline, which hampers the ability to detect potential treatment effects in clinical trials. Prognostic markers to select those individuals who will decline rapidly within a trial time frame are needed. Brain network measures based on grey matter covariance patterns have been associated with future cognitive decline in Alzheimer’s disease. In this longitudinal cohort study, we investigated whether cut-offs for grey matter networks could be derived to detect fast disease progression at an individual level. We further tested whether detection was improved by adding other biomarkers known to be associated with future cognitive decline [i.e. CSF tau phosphorylated at threonine 181 (p-tau181) levels and hippocampal volume]. We selected individuals with mild cognitive impairment and abnormal CSF amyloid β1–42 levels from the Amsterdam Dementia Cohort and the Alzheimer’s Disease Neuroimaging Initiative, when they had available baseline structural MRI and clinical follow-up. The outcome was progression to dementia within 2 years. We determined prognostic cut-offs for grey matter network properties (gamma, lambda and small-world coefficient) using time-dependent receiver operating characteristic analysis in the Amsterdam Dementia Cohort. We tested the generalization of cut-offs in the Alzheimer’s Disease Neuroimaging Initiative, using logistic regression analysis and classification statistics. We further tested whether combining these with CSF p-tau181 and hippocampal volume improved the detection of fast decliners. We observed that within 2 years, 24.6% (Amsterdam Dementia Cohort, n = 244) and 34.0% (Alzheimer’s Disease Neuroimaging Initiative, n = 247) of prodromal Alzheimer’s disease patients progressed to dementia. Using the grey matter network cut-offs for progression, we could detect fast progressors with 65% accuracy in the Alzheimer’s Disease Neuroimaging Initiative. Combining grey matter network measures with CSF p-tau and hippocampal volume resulted in the best model fit for classification of rapid decliners, increasing detecting accuracy to 72%. These data suggest that single-subject grey matter connectivity networks indicative of a more random network organization can contribute to identifying prodromal Alzheimer’s disease individuals who will show rapid disease progression. Moreover, we found that combined with p-tau and hippocampal volume this resulted in the highest accuracy. This could facilitate clinical trials by increasing chances to detect effects on clinical outcome measures.

AbstractIntroductionHarboring two copies of the apolipoprotein E (APOE) ε2 allele strongly protects against Alzheimer's disease (AD). However, the effect of this genotype on gray matter (GM) volume in cognitively unimpaired individuals has not yet been described.MethodsMulticenter brain magnetic resonance images (MRIs) from cognitively unimpaired ε2 homozygotes were matched (1:1) against all other APOE genotypes for relevant confounders (n = 223). GM volumes of ε2 genotypic groups were compared to each other and to the reference group (APOE ε3/ε3).ResultsCarrying at least one ε2 allele was associated with larger GM volumes in brain areas typically affected by AD and also in areas associated with cognitive resilience. APOE ε2 homozygotes, but not APOE ε2 heterozygotes, showed larger GM volumes in areas related to successful aging.DiscussionIn addition to the known resistance against amyloid‐β deposition, the larger GM volumes in key brain regions may confer APOE ε2 homozygotes additional protection against AD‐related cognitive decline.

Abstract Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer’s disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer’s disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset −9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We found that greater amyloid accumulation at baseline was associated with faster decline of small worldness over time, and decline in grey matter network measures over time was accompanied by decline in brain metabolism, cortical thinning and cognitive decline. In summary, network measures decline in autosomal dominant Alzheimer’s disease, which is alike sporadic Alzheimer’s disease, and the properties show decline over time prior to estimated symptom onset. These data suggest that single-subject networks properties obtained from structural MRI scans form an additional non-invasive tool for understanding the substrate of cognitive decline and measuring progression from preclinical to severe clinical stages of Alzheimer’s disease.

AbstractIntroductionWe aimed to describe clinician‐patient communication in the diagnostic process of memory clinics, specifically clinician behavior known to facilitate knowledgeable participation of patients during consultations.MethodsIn this multicenter, observational study, we audio‐recorded routine diagnostic consultations of 41 clinicians and 136 patients/caregivers at eight memory clinics. Patients/caregivers completed surveys after each audiotaped consultation. We used a study‐specific coding scheme to categorize communication behavior.ResultsClinicians often provided information on (results of) diagnostic testing. They infrequently invited questions and/or checked understanding. Clinician behavior to involve patients in decision‐making about diagnostic testing was limited. Of note, patients/caregivers rarely expressed their information or involvement preferences. Yet, approximately, one quarter of them would have liked to receive more information.DiscussionInvolving patients more explicitly by means of shared decision‐making could benefit the quality of care provided in memory clinics because it enables clinicians to attune the diagnostic workup to the individual patient's needs.

Abstract In 2011, the National Institute on Aging and Alzheimer's Association created separate diagnostic recommendations for the preclinical, mild cognitive impairment, and dementia stages of Alzheimer's disease. Scientific progress in the interim led to an initiative by the National Institute on Aging and Alzheimer's Association to update and unify the 2011 guidelines. This unifying update is labeled a “research framework” because its intended use is for observational and interventional research, not routine clinical care. In the National Institute on Aging and Alzheimer's Association Research Framework, Alzheimer's disease (AD) is defined by its underlying pathologic processes that can be documented by postmortem examination or in vivo by biomarkers. The diagnosis is not based on the clinical consequences of the disease (i.e., symptoms/signs) in this research framework, which shifts the definition of AD in living people from a syndromal to a biological construct. The research framework focuses on the diagnosis of AD with biomarkers in living persons. Biomarkers are grouped into those of β amyloid deposition, pathologic tau, and neurodegeneration [AT(N)]. This ATN classification system groups different biomarkers (imaging and biofluids) by the pathologic process each measures. The AT(N) system is flexible in that new biomarkers can be added to the three existing AT(N) groups, and new biomarker groups beyond AT(N) can be added when they become available. We focus on AD as a continuum, and cognitive staging may be accomplished using continuous measures. However, we also outline two different categorical cognitive schemes for staging the severity of cognitive impairment: a scheme using three traditional syndromal categories and a six‐stage numeric scheme. It is important to stress that this framework seeks to create a common language with which investigators can generate and test hypotheses about the interactions among different pathologic processes (denoted by biomarkers) and cognitive symptoms. We appreciate the concern that this biomarker‐based research framework has the potential to be misused. Therefore, we emphasize, first, it is premature and inappropriate to use this research framework in general medical practice. Second, this research framework should not be used to restrict alternative approaches to hypothesis testing that do not use biomarkers. There will be situations where biomarkers are not available or requiring them would be counterproductive to the specific research goals (discussed in more detail later in the document). Thus, biomarker‐based research should not be considered a template for all research into age‐related cognitive impairment and dementia; rather, it should be applied when it is fit for the purpose of the specific research goals of a study. Importantly, this framework should be examined in diverse populations. Although it is possible that β‐amyloid plaques and neurofibrillary tau deposits are not causal in AD pathogenesis, it is these abnormal protein deposits that define AD as a unique neurodegenerative disease among different disorders that can lead to dementia. We envision that defining AD as a biological construct will enable a more accurate characterization and understanding of the sequence of events that lead to cognitive impairment that is associated with AD, as well as the multifactorial etiology of dementia. This approach also will enable a more precise approach to interventional trials where specific pathways can be targeted in the disease process and in the appropriate people.