Abstract Background Epidemiological investigations have shown that midlife elevated total serum cholesterol levels increase the risk of Alzheimer’s disease (AD). However, observational studies in late‐life subjects suggested that high cholesterol levels reduce the risk of dementia. Since hypometabolism is a pivotal marker of AD progression, understanding the impact of peripheral cholesterol levels over the brain’s metabolic state in older adults has become crucial to clarify these seemingly conflicting findings. Therefore, whether high serum cholesterol levels accelerate brain metabolic decline in older subjects remains a key question to be answered. Here, we investigated whether total serum cholesterol levels are associated with cerebral metabolic decline. We hypothesized that elevated total serum cholesterol levels accelerate metabolic decline in AD‐related brain regions. Method We evaluated 499 cognitively unimpaired and mild cognitive impaired elderly subjects (age>55 years) from Alzheimer's Disease Neuroimaging Initiative (ADNI) with available total serum cholesterol measurements at baseline and longitudinal [ 18 F]FDG‐PET scans. Voxel‐wise general linear modelling was used to assess the baseline association between total serum cholesterol levels and cerebral metabolic rate for glucose (CMRglc), as well as cholesterol’s impact over the 2‐year’s rate of metabolic decline. Age, gender, BMI, cholesterol treatment and diagnostic group were set as covariates for adjustment. APOEε4 genotype was used as both, a covariate or an interaction factor. Result No significant associations were observed at baseline. We found a negative correlation between cholesterol levels and the 2‐year CMRglc variation rates in brain regions affected in early stages of AD, which was driven by the interaction with APOEε4 status (Figure 1). Regions affected include the right precuneus (peak t(491) = ‐3.93, p<0.0001) and the right posterior cingulate gyrus (peak t(491) = ‐4.12, p<0.0001). Associations were greater in the right hemisphere. Conclusion Our results support the concept that the interaction of higher serum cholesterol levels with APOEε4 genotype accelerate metabolic decline in AD‐related brain regions. These findings complement cross‐sectional studies postulating that, even among older adults, serum cholesterol levels, in association with the APOEε4 status, might be related to brain functional changes similar to those observed in the development of AD.
Abstract Background Activation of microglial cells in the brain, more commonly known as neuroinflammation, has often been linked to the pathophysiology of Alzheimer’s disease (AD). However, how microglial activation is associated with longitudinal tau tangle accumulation and consequent cognitive decline is poorly understood. Here, we aimed to investigate whether baseline microglial activation impacts tau tangle deposition and cognitive decline in individuals across the AD continuum. Method We assessed 92 individuals from the TRIAD cohort (57 cognitively unimpaired and 35 cognitively impaired) with available baseline [11C]PBR28‐PET, a measure of microglial activation and [18F]NAV4694 Aß‐PET, and longitudinal [18F]MK6240 Tau‐PET (mean follow‐up time = 1.93 years) and Mini‐Mental State Exam (MMSE) (mean follow‐up time = 1.84 years). We performed voxel‐wise associations using linear regressions accounting for age and sex and adjusted for multiple comparisons using Random Field Theory (RFT) (p < 0.05). We used the cuneus and superior temporal cortex as a composite ROI for [11C]PBR28‐PET and Aß‐PET since these regions showed a higher association with longitudinal tau accumulation in the temporal meta‐ROI. Result Voxel‐wise analysis showed that baseline levels of [11C]PBR28‐PET alone are not sufficient to predict longitudinal tau tangle accumulation (Fig. 1a). However, the interaction between [11C]PBR28‐PET levels and Aß burden predicted an increased accumulation of tau tangle, mainly in the cuneus, inferior frontal and lateral occipital regions (Fig. 1b).Individuals with higher baseline [11C]PBR28‐PET and Aß‐PET levels present higher rates of longitudinal tau accumulation in the temporal meta‐ROI (ß = 0.36, t = 3.46, p = 0.0009; Fig. 1c). Similarly, while [11C]PBR28‐PET levels alone did not correlate with longitudinal changes in MMSE score (ß = ‐0.17, t = ‐1.69, p = 0.10), a significant interaction between [11C]PBR28‐PET and Aß‐PET levels on MMSE annual decline was observed (ß = ‐0.24, t = ‐2.25, p = 0.028; Fig. 1d). Conclusion We found that baseline levels of microglial activation was associated with longitudinal tau tangle accumulation and cognitive decline in individuals across the AD continuum in the presence of Aß burden. Our results indicate that microglial activation might act potentiating the deleterious effects of Aß on forthcoming tau tangle deposition.
Abstract Background Tau plays a prominent role in the synapse and has been shown to be closely related to neurodegeneration. Recent evidence show that cerebrospinal fluid (CSF) synaptic markers are related to CSF tau and degeneration. However, it is not clear whether synaptic dysfunction in combination with tau tangles is associated with neurodegeneration in the same brain regions. Our study aims to map in the brain the association of synaptic markers with tau and degeneration. Method We evaluated 147 individuals from the TRIAD cohort with CSF GAP43, SYT1, SNAP25, neurogranin (Ng), ptau217, neurofilament light protein (NfL) quantification, as well as tau positron emission tomography (PET), magnetic resonance imaging, and clinical assessments. One‐way ANOVA tested group differences on synaptic levels. Spearman correlation tested the association among synaptic proteins. Linear regressions tested the association between biomarkers. Voxel‐based morphometry (VBM) was considered as an index of neurodegeneration. Result We observed an increase in synaptic markers across age and the AD spectrum, reaching a plateau when cognition is affected (Figure 1A). Among the synaptic markers, SNAP25 showed the greatest magnitude of change, on average, between young, aged cognitively unimpaired, mild cognitive impaired, and AD individuals (mean 62.44%). (Figure 1B‐C). In addition, we found that all synaptic markers are significantly intercorrelated (Figure 1D). Moreover, CSF ptau217 correlated with all synaptic biomarkers, GAP43 (ß = ‐044), SYT1 (ß = ‐0.41), SNAP25 (ß = ‐0.59) and Ng (ß = ‐0.25). Conversely, CSF NfL was strongly but only correlated with GAP43 (ß = 0.62) (Figure 2). Voxel‐wise correlation revealed that tau tangles measured by [18F]‐MK6240 PET, was positively associated with SNAP25 in AD‐related regions, and VBM was associated with all synaptic markers, but more closely with GAP43. Furthermore, the interaction between synaptic markers and temporal meta‐ROI [18F]‐MK6240 on VBM was significant for Ng and GAP43 (Figure 3). Conclusion Our results support a heterogenous role of synaptic markers in the AD continuum and suggest that synaptic dysfunction in the presence of tau pathology may not be driving atrophy.
Abstract Background Previous studies have shown that microglial activation (MA) plays a key role in the pathophysiological and clinical progressions of Alzheimer’s disease (AD). However, little is known whether MA is also associated with the development of neuropsychiatric symptoms typically found in patients with AD. Thus, we aim to investigate here the association of MA with neuropsychiatric symptoms (NPS) of individuals across the AD continuum. Method We assessed 132 individuals (86 cognitively unimpaired (CU), 28 MCI, and 18 AD dementia) from the TRIAD cohort who underwent clinical assessments with the Neuropsychiatry Inventory Questionnaire (NPI‐Q), and had positron emission tomography (PET) for amyloid‐ß (Aß) ([18F]AZD4694), tau tangles ([18F]MK6240) and MA ([11C]PBR28) at the same visit. Regions were tailored using Desikan‐Killiany (DK) atlas. SUVRs were calculated using the cerebellum gray matter as a reference. Linear regression tested the association between biomarkers accounting for age, sex, and cognitive status. Result NPI‐Q total score was significantly associated with [11C]PBR28 in the cingulate, inferior temporal, and precuneus accounting for age, sex, and after false discovery rate (FDR) correction for multiple comparisons (Figures 1A, 1B, 1C). This association was independent of Aß and tau levels (Table 1). Notably, MA predicted neuropsychiatric dysfunction with higher magnitude than Aß or tau using PET values from overlap region (regional SUVR) and if we use a global measure for all tracers (global SUVR) (Figure 2A). When we stratify NPI‐Q domains (agitation, irritability, motor disturbance, disinhibition, elation, delusion, hallucinations, nighttime disturbance, depression, anxiety, apathy, and appetite disturbance) severity score, we found that the hyperactivity subdomain (agitation, irritability, motor disturbance, disinhibition, and elation) showed the larger contribution to the results (Figure 2B). Conclusion Our results suggest MA as a key element associated with neuropsychiatric dysfunction in AD independent of Aß and tau pathologies. These findings provide additional rationale for the therapeutics targeting glial cells activation in AD patients.
Astrocytes, a major class of glial cells, regulate neurotransmitter systems, synaptic processing, ion homeostasis, antioxidant defenses and energy metabolism. Astrocyte cultures derived from rodent brains have been extensively used to characterize astrocytes' biochemical, pharmacological and morphological properties. The aims of this study were to develop a protocol for routine preparation and to characterize a primary astrocyte culture from the brains of adult (90 days old) Wistar rats. For this we used enzymatic digestion (trypsin and papain) and mechanical dissociation. Medium exchange occurred from 24 h after obtaining a culture and after, twice a week up to reach the confluence (around the 4th to 5th week). Under basal conditions, adult astrocytes presented a polygonal to fusiform and flat morphology. Furthermore, approximately 95% the cells were positive for the main glial markers, including GFAP, glutamate transporters, glutamine synthetase and S100B. Moreover, the astrocytes were able to take up glucose and glutamate. Adult astrocytes were also able to respond to acute H2O2 exposure, which led to an increase in reactive oxygen species (ROS) levels and a decrease in glutamate uptake. The antioxidant compound resveratrol was able to protect adult astrocytes from oxidative damage. A response of adult astrocytes to an inflammatory stimulus with LPS was also observed. Changes in the actin cytoskeleton were induced in stimulated astrocytes, most likely by a mechanism dependent on MAPK and Rho A signaling pathways. Taken together, these findings indicate that the culture model described in this study exhibits the biochemical and physiological properties of astrocytes and may be useful for elucidating the mechanisms related to the adult brain, exploring changes between neonatal and adult astrocytes, as well as investigating compounds involved in cytotoxicity and cytoprotection.
Abstract Background Tau PET provides continuous measurements of tau tangle pathology in the human brain. However, establishing cutoffs is crucial for selecting individuals for treatment in clinical trials or practice. In the absence of postmortem data, PET cutoffs must be established using statistical methods based on what is considered normal tracer uptake. In this study, we tested the impact of various methods to determine tau positivity using two different tau PET tracers in individuals scanned head‐to‐head. Methods We studied 147 individuals from Head‐to‐Head Harmonization of Tau Tracers in Alzheimer's Disease (HEAD) study with tau tangle PET scans with [ 18 F]Flortaucipir and [ 18 F]MK‐6240, and amyloid‐β (Aβ) PET. Tau deposition was measured with the standardized uptake value ratio (SUVR) of each agent in the Medial Temporal Lobe (MTL) and the Entorhinal Cortex (EC). To determine Tau positivity three different methods were used: >2.5 standard deviations (SD) than the mean of the young, >1.5 SD mean of the cognitively unimpaired (CU) and >1.5 SD mean of CUAβ‐. Results Demographic characteristics of the study population are reported in Table 1. Using the cutoff >2.5 SD mean of young, [ 18 F]Flortaucipir was positive in 35 (23.8%) and 69 (46.9%) individuals in the EC and MTL, respectively. [ 18 F]MK‐6240 was positive in 49 (33.3%) and 58 (39.5%) individuals in EC and in MTL. Using >1.5 SD mean of CUAβ‐, [ 18 F]Flortaucipir was positive in 38 (25.9%) and 45 (30.6%) individuals in EC and MTL, while [ 18 F]MK‐6240 was positive in 51 (34.7%) and 50 (34.0%) in EC and in MTL (Figure 1). Conclusions Our findings indicate variations in tau positivity when employing different methods based on either the young or CUAβ‐. [ 18 F]Flortaucipir exhibited a higher rate of positive results when the method based on young individuals was applied in the MTL. Conversely, [ 18 F]MK‐6240 showed more consistent and generally higher positivity when other methods were used for cutoff determination and/or in the EC region. Further research with a larger sample size is required to gain a better understanding of the optimal cutoff determination methods for these tracers.
Abstract Background The HEAD study aims to collect a large dataset of multiple tau‐PET tracers to provide robust anchor values for tau‐PET harmonization. Here, we tested the hypothesis that anchoring two tau tracer uptake values using head‐to‐head measurements has the potential to generate an accurate universal tau‐PET scale, named Uniτ(tau). Methods We assessed 200 individuals across the aging and AD spectrum (Training: HEAD data freeze 2.0, n=185; Testing: UPitt dataset (Gogola et al.), n=15) with [ 18 F]Flortaucipir and [ 18 F]MK‐6240 tau‐PET. SUVRs were processed to a common 8mm FWHM, with inferior cerebellar gray matter as the reference region (Pascoal et al.). Uniτ explored two anchoring/harmonization methods. First, we examined within‐tracer anchoring by creating anchor values based on the mean SUVR of Youngs (<25 years) and 95th percentile voxels from cognitively impaired individuals. Second, we explored within‐ plus between‐tracer anchoring, employing linear (e.g., piecewise) and non‐linear regressions. To address the inherent problem of discontinuity of piecewise transformations, we implemented two smoothing methods at the inflection point between equations, transforming them into a continuous function. Results Uniτ scale anchoring within‐tracer resulted in similar estimates for high values, but less accurate in the lower range (Figure 1,2). Anchoring within‐ plus between‐tracer improved estimate consistency, with the piecewise transformation generating the best results. The piecewise smoothing equation yielded estimates comparable to those obtained from the piecewise method without smoothing. This allowed for the use of a single formula. In addition, this leads to more robust results when the goal is to study longitudinal changes in the scale (data not shown). UPitt testing dataset showed similar results to the training set (Figure 3). Conclusion Our preliminary findings suggest that anchoring tau‐PET values both within and between tracers has the potential to harmonize tau‐PET tracers, while preserving their characteristics. Currently, piecewise smoothing is the preferred method for Uniτ, but we are continuously fine‐tuning scale parameters as we acquire more data. The final scale parameters will be determined based on extensive training and testing data from multiple tracers. This cautious methodology holds the promise of delivering reliable, robust, and reproducible results, ensuring safe usage of the scale in clinical trials, and potentially paving the way for future use in clinical practice.
Abstract Background The HEAD study aims to collect a large dataset of multiple tau‐PET tracers to provide robust anchor values for tau‐PET harmonization. Here, we tested the hypothesis that anchoring two tau tracer uptake values using head‐to‐head measurements has the potential to generate an accurate universal tau‐PET scale, named Uniτ(tau). Methods We assessed 200 individuals across the aging and AD spectrum (Training:HEAD data freeze 2.0, n=185; Testing:UPitt dataset (Gogola et al.), n=15) with [18F]Flortaucipir and [18F]MK‐6240 tau‐PET. SUVRs were processed to a common 8mm FWHM, with inferior cerebellar gray matter as the reference region (Pascoal et al.). Uniτ explored two anchoring/harmonization methods. First, we examined within‐tracer anchoring by creating anchor values based on the mean SUVR of Youngs (<25 years) and 95th percentile voxels from cognitively impaired individuals. Second, we explored within‐ plus between‐tracer anchoring, employing linear (e.g., piecewise) and non‐linear regressions. To address the inherent problem of discontinuity of piecewise transformations, we implemented two smoothing methods at the inflection point between equations, transforming them into a continuous function. Results Uniτ scale anchoring within‐tracer resulted in similar estimates for high values, but less accurate in the lower range (Figure 1,2). Anchoring within‐ plus between‐tracer improved estimate consistency, with the piecewise transformation generating the best results. The piecewise smoothing equation yielded estimates comparable to those obtained from the piecewise method without smoothing. This allowed for the use of a single formula. In addition, this leads to more robust results when the goal is to study longitudinal changes in the scale (data not shown). UPitt testing dataset showed similar results to the training set (Figure 3). Conclusion Our preliminary findings suggest that anchoring tau‐PET values both within and between tracers has the potential to harmonize tau‐PET tracers, while preserving their characteristics. Currently, piecewise smoothing is the preferred method for Uniτ, but we are continuously fine‐tuning scale parameters as we acquire more data. The final scale parameters will be determined based on extensive training and testing data from multiple tracers. This cautious methodology holds the promise of delivering reliable, robust, and reproducible results, ensuring safe usage of the scale in clinical trials, and potentially paving the way for future use in clinical practice.
Food processing greatly contributed to increased food safety, diversity, and accessibility. However, the prevalence of highly palatable and highly processed food in our modern diet has exacerbated obesity rates and contributed to a global health crisis. While accumulating evidence suggests that chronic consumption of such foods is detrimental to sensory and neural physiology, it is unclear whether its short-term intake has adverse effects. We assessed how short-term consumption (<2 months) of three diets varying in composition and macronutrient content influence olfaction and brain metabolism in mice. The diets tested included a grain-based standard chow diet (CHOW; 54% carbohydrate, 32% protein, 14% fat; #8604 Teklad Rodent diet, Envigo Inc.), a highly processed control diet (hpCTR; 70% carbohydrate, 20% protein, 10% fat; #D12450B, Research Diets Inc.), and a highly processed high-fat diet (hpHFD; 20% carbohydrate, 20% protein, 60% fat; #D12492, Research Diets Inc.). We performed behavioral and metabolic phenotyping, electro-olfactogram (EOG) recordings, brain glucose metabolism imaging, and mitochondrial respirometry in different brain regions. We also performed RNA-sequencing (RNA-seq) in the nose and across several brain regions, and conducted differential expression analysis, gene ontology, and network analysis. We show that short-term consumption of the two highly processed diets, but not the grain-based diet, regardless of macronutrient content, adversely affects odor-guided behaviors, physiological responses to odorants, transcriptional profiles in the olfactory mucosa and brain regions, and brain glucose metabolism and mitochondrial respiration. Even short periods of highly processed food consumption are sufficient to cause early olfactory and brain abnormalities, which has the potential to alter food choices and influence the risk of developing metabolic disease.
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