Tijms, Betty M.

Abstract Grey matter network topology is altered in Alzheimer’s disease and these alterations are related to cognitive decline. Understanding the biological underpinnings of loss of brain connectivity may provide insights into mechanisms related to developing Alzheimer’s dementia (i.e. dementia A+). We investigated which biological processes as measured in CSF proteomics were associated with loss of brain connections across the Alzheimer’s disease continuum. We included 347 individuals with abnormal CSF amyloid [mean age ± standard deviation (SD) 66 ± 8; 98 cognitively unimpaired—A+, 88 mild cognitive impairment—A+, 161 dementia A+] and 146 cognitively unimpaired individuals with normal CSF amyloid (mean age ± SD 62 ± 8) and available T1w MRI-scans and CSF proteomic data (3097 proteins using tandem mass tag spectrometry) from the Amsterdam Dementia Cohort. We used an automated pipeline to construct grey matter networks from 3D-T1 sequences and for each network, calculated the small-worldness coefficient, which we previously found to be robustly related to cognitive decline. Linear models were applied to test associations between CSF protein levels and connectivity measures using an interaction term for clinical stage while controlling for connectivity density, age and sex. We validated our results in data from the Alzheimer’s disease Neuroimaging Initiative (ADNI). Pathway enrichment analysis was performed for proteins associated with loss of brain connectivity (P < 0.05) using the Gene Ontology database. Individuals across the Alzheimer’s disease continuum had lower small-worldness coefficients compared with controls (ANOVA P < 0.001). In amyloid positive individuals, higher levels of 222 proteins and lower levels of 482 proteins were associated with lower small-worldness coefficients and were enriched for innate immune system and neuroplasticity pathways, respectively. Stratified for disease stage, most protein associations with lower small-worldness coefficients were found in mild cognitive impairment A+ (n = 527 proteins) and dementia A+ (n = 799 proteins) with considerable overlap (n = 239 proteins). Proteins in these stages were enriched for complement activation and synaptic integrity. In cognitive unimpairment A+, we found proteins enriched for processes involved in apoptosis. We did not find any enriched biological processes in controls. Repeating analyses in ADNI indicated that similar biological processes were associated with altered grey matter network connectivity. Higher CSF levels of proteins involved in immune responses and lower levels of proteins related to neuroplasticity were associated with lower small-worldness coefficients across the Alzheimer’s disease continuum. This suggests that preserving cognitive function in the presence of amyloid and prevention of dementia A+ may require therapies that strengthen synapses and targets the innate immune system in addition to amyloid and tau.

Abstract Cerebrospinal fluid (CSF) proteomics offers insights into molecular changes in aging and Alzheimer’s disease (AD). Key AD biomarkers, in particular amyloid-β (Aβ) and tau, in CSF are strongly associated with APOE genotype, the strongest genetic risk determinant of AD. To investigate how APOE genotype influences CSF proteome across AD pathology and age, we analyzed 362 neurology-related proteins and established AD biomarkers in CSF from 145 cognitively unimpaired participants in the Mayo Clinic Study of Aging. Importantly, our cohort is uniquely balanced across APOE genotypes, with similar representation of APOE2 carriers, APOE3/3 genotype, and APOE4 carriers. We identified several proteins, including lipid metabolism-related Lp-PLA2 and immune-related ITGAM, with strong APOE genotype-specific association. Notably, meta-analysis confirmed that ITGAM levels were consistently higher in APOE4 compared to APOE2 carriers across multiple cohorts and proteomic platforms. In addition, with increasing amyloid deposition, APOE4 carriers exhibited stronger immune responses, reflected by elevated ITGAM, TNF-α receptors, and IL-6, whereas APOE2 carriers showed attenuated responses. We further observed sex-specific effects among APOE2 carriers, characterized by distinct patterns in amyloid and CXCL11 levels. These findings suggest distinct mechanisms underlying APOE2’s protective and APOE4’s detrimental effects in brain aging and AD, paving for personalized diagnostics and interventions.

Background The association between lifestyle factors and Alzheimer's disease (AD) pathophysiology remains incompletely understood. Objective The aim of this study was to assess the association of alcohol consumption, smoking behavior, sleep quality and physical, cognitive, and social activity with cerebral amyloid pathology. Methods For this cross-sectional study, we selected participants from the Amyloid Biomarker Study data pooling initiative. We used generalized estimating equations to assess associations of dichotomized lifestyle measures with amyloid pathology. Results We included 9171 participants with normal cognition (NC) and 2555 participants with mild cognitive impairment (MCI) from the Amyloid Biomarker Study. Of participants with NC, 58% were women, 34% were APOE ε4 carrier, and 27% had amyloid pathology. Of participants with MCI, 48% were women, 47% were APOE ε4 carrier, and 57% had amyloid pathology. In NC, cognitively active participants were less likely to have amyloid pathology (OR = 0.77, 95%CI 0.66–0.89, p < 0.001). In MCI, participants who had ever smoked or had sleep problems were less likely to have amyloid pathology (OR = 0.85, 95%CI 0.73–0.99, p = 0.029; OR = 0.62, 95%CI 0.45–0.86, p = 0.004). Conclusions In NC, cognitive activity was associated with a lower frequency of amyloid pathology. In MCI, favorable lifestyle behaviors were not associated with a lower frequency of amyloid pathology. The results of the current study contribute to the broader evidence base on lifestyle and AD by further characterizing the role of lifestyle behaviors in AD pathology across different clinical stages.

ABSTRACT Genetic variation at the TMEM106B locus has been associated with Alzheimer’s disease (AD) and related neurodegenerative phenotypes, yet its haplotypic architecture remains incompletely resolved. We refined the genetic landscape of TMEM106B by integrating AD-GWAS summary statistics (∼978K individuals) with haplotype analyses in 5,873 Dutch genomes including AD-cases, age-matched controls, and cognitively healthy centenarians. In addition to the known linkage disequilibrium (LD) block defining risk and protective haplotypes characterized by the amino acid substitution at residue 185 (Threonine/Serine), we identify a second conditionally independent LD block. Together these form four haplotypes (T1-T4), with T3 strongly enriched in centenarians. Long-read whole-genome sequencing in 493 individuals reveals haplotype-linked structural variation and methylation patterns, including a ∼19 Kbp genomic rearrangement carried by T3. These findings indicate that the TMEM106B locus comprises multiple haplotypes defined by methylation patterns and structural variants, offering insights into GWAS associations with AD risk and preserved cognition at extreme age.

Importance Depressive symptoms are associated with cognitive decline in older individuals. Uncertainty about underlying mechanisms hampers diagnostic and therapeutic efforts. This large-scale study aimed to elucidate the association between depressive symptoms and amyloid pathology. Objective To examine the association between depressive symptoms and amyloid pathology and its dependency on age, sex, education, and APOE genotype in older individuals without dementia. Design, Setting, and Participants Cross-sectional analyses were performed using data from the Amyloid Biomarker Study data pooling initiative. Data from 49 research, population-based, and memory clinic studies were pooled and harmonized. The Amyloid Biomarker Study has been collecting data since 2012 and data collection is ongoing. At the time of analysis, 95 centers were included in the Amyloid Biomarker Study. The study included 9746 individuals with normal cognition (NC) and 3023 participants with mild cognitive impairment (MCI) aged between 34 and 100 years for whom data on amyloid biomarkers, presence of depressive symptoms, and age were available. Data were analyzed from December 2022 to February 2024. Main Outcomes and Measures Amyloid-β1-42 levels in cerebrospinal fluid or amyloid positron emission tomography scans were used to determine presence or absence of amyloid pathology. Presence of depressive symptoms was determined on the basis of validated depression rating scale scores, evidence of a current clinical diagnosis of depression, or self-reported depressive symptoms. Results In individuals with NC (mean [SD] age, 68.6 [8.9] years; 5664 [58.2%] female; 3002 [34.0%] APOE ε4 carriers; 937 [9.6%] had depressive symptoms; 2648 [27.2%] had amyloid pathology), the presence of depressive symptoms was not associated with amyloid pathology (odds ratio [OR], 1.13; 95% CI, 0.90-1.40; P = .29). In individuals with MCI (mean [SD] age, 70.2 [8.7] years; 1481 [49.0%] female; 1046 [44.8%] APOE ε4 carriers; 824 [27.3%] had depressive symptoms; 1668 [55.8%] had amyloid pathology), the presence of depressive symptoms was associated with a lower likelihood of amyloid pathology (OR, 0.73; 95% CI 0.61-0.89; P = .001). When considering subgroup effects, in individuals with NC, the presence of depressive symptoms was associated with a higher frequency of amyloid pathology in APOE ε4 noncarriers (mean difference, 5.0%; 95% CI 1.0-9.0; P = .02) but not in APOE ε4 carriers. This was not the case in individuals with MCI. Conclusions and Relevance Depressive symptoms were not consistently associated with a higher frequency of amyloid pathology in participants with NC and were associated with a lower likelihood of amyloid pathology in participants with MCI. These findings were not influenced by age, sex, or education level. Mechanisms other than amyloid accumulation may commonly underlie depressive symptoms in late life.

Abstract The grey matter of the brain develops and declines in coordinated patterns during the lifespan. Such covariation patterns of grey matter structure can be quantified as grey matter networks, which can be measured with magnetic resonance imaging. In Alzheimer’s disease, the global organization of grey matter networks becomes more random, which is captured by a decline in the small-world coefficient. Such decline in the small-world value has been robustly associated with cognitive decline across clinical stages of Alzheimer’s disease. The biological mechanisms causing this decline in small-world values remain unknown. Cerebrospinal fluid (CSF) protein biomarkers are available for studying diverse pathological mechanisms in humans and can provide insight into decline. We investigated the relationships between 10 CSF proteins and small-world coefficient in mutation carriers (N = 219) and non-carriers (N = 136) of the Dominantly Inherited Alzheimer Network Observational study. Abnormalities in Amyloid beta, Tau, synaptic (Synaptosome associated protein-25, Neurogranin) and neuronal calcium-sensor protein (Visinin-like protein-1) preceded loss of small-world coefficient by several years, while increased levels in CSF markers for inflammation (Chitinase-3-like protein 1) and axonal injury (Neurofilament light) co-occurred with decreasing small-world values. This suggests that axonal loss and inflammation play a role in structural grey matter network changes.

AbstractBackgroundLower gray matter connectivity measures are related to faster cognitive decline in prodromal Alzheimer’s Disease (AD). We investigated the biological underpinnings responsible for this loss of brain connections using cerebrospinal fluid (CSF) proteomics.MethodData from ADNI was used to select 82 individuals with MCI and abnormal CSF amyloid (mean age±SD 74±7) and 37 controls with normal amyloid levels (mean age±SD 75±5), for whom repeated MRI‐scans (median of 6 scans over 1.5 follow‐up years) and baseline CSF proteomic data (in total 306 proteins) were available. We used an automated pipeline (Tijms, Series et al. 2012) to construct gray matter networks from 3D‐T1 sequences and calculate connectivity density and gamma – measures previously found to be most strongly related to cognitive decline (Dicks, Vermunt et al. 2020). Linear mixed models were applied to test associations between baseline CSF protein levels and repeated connectivity metrics, controlling for intracranial volume, age and sex. Proteins that demonstrated significant associations (p<0.05) were identified and examined for their biological relevance according to Gene Ontology.ResultConnectivity density declined over time with ‐0.8% per year (pMCI = 0.001; figure 1), which did not differ from controls (pdif = 0.15). Gamma declined faster in MCI than in controls (bMCI = ‐0.016 vs. bCN = ‐0.007, pdif <0.001; figure 1). Higher levels of 99/108 proteins were associated with faster decline in connectivity density (a.o. tau and neuroprentaxins). These proteins were involved in regulation of synapse organization (GO:0050807; pFDR = 7.00E‐07; figure 2). Higher levels of 9/108 proteins associated with less decline and were involved in chemotaxis (GO:0048247; pFDR = 1.40E‐02). Similarly, 25 out 30 proteins (83%) associated with alterations in gamma over time, showed associations of higher levels with less gamma decline and were also involved in chemotaxis (GO:0050926; pFDR = 9.42E‐05; figure 3). There were no protein‐connectivity relationships in controls.ConclusionProteins involved in synapse organization were associated with faster decline in connectivity density over time in patients, while chemotaxis was associated with less decline. This suggests that synaptic degeneration may predict disease progression while immune responses may serve as a resilience mechanism in AD.

AbstractBackgroundBrain amyloid‐β (Aβ) is the pathological hallmark of Alzheimer’s disease (AD). In logistic disease models, Aβ accumulation is a sigmoid function of time‐since‐disease‐onset (TSDO) (figure 1). Previous positron emission tomography (PET)‐based models vary accumulation onset(t50) and duration(r) globally; capacity(K) and baseline(NS) regionally (Whittington2018). We confirm existing approaches and propose a more powerful ICA‐based approach to quantify disease severity and estimate TSDO.MethodWe used 1071 18F‐florbetapir standard uptake value ratio (SUVR) images from the ADNI‐2 study (adni.loni.usc.edu/data‐samples/data‐types/pet). Images were mapped into MNI space. Averages were extracted using the Harvard‐Oxford brain‐atlas. Whole‐brain tracer‐specific sigmoid parameters (Jack2013) obtained from the literature were used to estimate TSDO.Of 16 models of regional Aβ accumulation (each of the 4 regional sigmoid parameters varied either regionally or globally), the optimal Bayesian information criterion was found with global t50 and r, and regional NS and K (figure 1) with global values r = 6.16y and t50 = 4.10y.Linearised maps of NS and K were obtained by regressing the SUVR maps onto the global sigmoid. We also estimated these maps as independent components, using a 2‐component ICA on the SUVR maps. Both outcomes were used to quantify Aβ accumulation from SUVR images as weighting factors of the accumulation map.We compared the weights from the logistic model and the ICA model in ADNI, using effect size measured with Hedges' g between cognitively normal (CN), subjective memory complaints (SMC), mild cognitive impairment (EMCI/MCI/LMCI) and AD groups. We compared 3 longitudinal visits (N = 112) in the OASIS‐3 study (see www.oasis‐brains.org) with both methods, global SUVR and Centiloid (Klunk2015) using 11C‐PiB PET SUVR images.ResultMaps of accumulation capacity from both models had spatial correlation of 0.86 (figure 2); baseline maps had spatial correlation of 0.95. Hedges' g between ADNI groups was 2.25 for K, and 2.42 for ICA (1.46 for global SUVR). In OASIS‐3, Hedges' g between visits was 1.24 for K, 1.46 for ICA (global SUVR 0.15, Centiloid 0.4).ConclusionWe demonstrate that linear accumulation models can be used to quantify brain Aβ with PET; maps obtained by ICA yield larger effect sizes than the logistic method for differentiating groups and measuring changes between visits.

AbstractINTRODUCTIONAt the Alzheimer's Association's APOE and Immunity virtual conference, held in October 2021, leading neuroscience experts shared recent research advances on and inspiring insights into the various roles that both the apolipoprotein E gene (APOE) and facets of immunity play in neurodegenerative diseases, including Alzheimer's disease and other dementias.METHODSThe meeting brought together more than 1200 registered attendees from 62 different countries, representing the realms of academia and industry.RESULTSDuring the 4‐day meeting, presenters illuminated aspects of the cross‐talk between APOE and immunity, with a focus on the roles of microglia, triggering receptor expressed on myeloid cells 2 (TREM2), and components of inflammation (e.g., tumor necrosis factor α [TNFα]).DISCUSSIONThis manuscript emphasizes the importance of diversity in current and future research and presents an integrated view of innate immune functions in Alzheimer's disease as well as related promising directions in drug development.

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.