Chen, Kewei

AbstractBackgroundAPOE4 gene dose, the number of apolipoprotein E ‐ɛ4 (APOE4) ɛ4 alleles in a person’s genotype, is associated with higher Alzheimer’s disease (AD) risk and younger median age at dementia onset. We previously found relationships between PET, plasma, and measurements of core AD CSF biomarkers in APOE4 gene dose. Here, we characterize longitudinal neurofilament light chain (NfL) changes in the Arizona APOE cohort, including cognitively unimpaired participants, with longitudinal plasma NfL measurements in APOE4 homozygotes (HMs), heterozygotes (HTs) and non‐carriers (NCs).MethodPlasma NfL measurements were performed at the University of Gothenburg using a Single molecule array (Simoa) immunoassay. Annual log‐transformed plasma NfL changes were compared with linear‐tend and univariate ANOVA adjusted for age, sex, and education in 45 HMs, 107 HTs, and 201 NCs who were cognitively unimpaired, 47‐85 years old and did not differ significantly in their age, sex, or educational level. Pearson r correlations with age and annual log‐transformed NfL changes were assessed in 353 participants with at least 2 visits (range 2‐6 visits with an average of ∼3) and an average follow‐up of ∼7 years (range 1‐12 years).ResultAnnual log‐transformed NfL changes were linearly associated with APOE4 gene dose (linear trend HM>HT>NC, P=0.01) and positively associated with age (r = 0.31, P<0.0001). Pairwise comparisons from the univariate ANOVA adjusted for age, sex, and education (P=0.001) showed significantly higher annual NfL changes in HM and HT groups versus NC controls (P=0.01).ConclusionAge and APOE4 gene dose are associated with greater annual plasma NfL changes in CU subjects close to their estimated ages at clinical onset. Studies are needed to better elucidate relationships between different Aβ, tau, inflammatory, and other CSF, or blood‐based biomarkers, and APOE4 gene dose at other ages and in larger subject groups.

AbstractBackgroundWe previously reported that in our autopsy‐confirmed cohort of Dementia with Lewy Bodies (DLB), Parkinson’s Disease Dementia (PDD) and Alzheimer’s Disease (AD), DLB with parkinsonism (DLB+Park) predicted faster progression of UPDRS‐III scores compared to PDD, DLB without parkinsonism (DLB‐Park), and AD. This study examined annualized rate of change of UPDRS‐III subscales in our cohort.MethodsData included were from participants in Arizona Study of Aging and Neurodegenerative Diseases (AZSAND) with clinicopathologic diagnosis of DLB (n = 48), PDD (n = 98) or pure AD (n = 48), and longitudinal movement exams. Mixed effects model (accounting for age, education, and sex) was used to determine predicted mean UPDRS‐III and subscale scores (0‐4) for all groups. Individual UPDRS‐III subscale scores were derived for: Body Bradykinesia, Limb Bradykinesia, Gait, Postural Instability, Limb Rest Tremor, Neck Rigidity and Limb Rigidity. Where applicable, scores for multiple limbs involved were averaged.ResultsLongitudinal data was analyzed for over 1430‐participant years. Parkinsonism was present in 65.6% of DLB participants. PDD participants were more likely to be treated with dopaminergic agents (87.8%) compared to (p<0.001) DLB+Park (57.6%), DLB‐park (0%) and AD (2.1%). Frequency (29.4% vs. 29.0%) and severity (mean±SE = 0.4±0.07 and 0.4±0.12) of Limb Rest Tremor was similar for PDD and DLB+park. Limb Bradykinesia frequency and severity were PDD: 100%, 2.4±0.11, DLB+Park: 96.8%, 2.1±0.18, DLB‐Park: 20%, 0.4±0.23, and AD: 62%, 1.1±0.16. Frequency of gait abnormality was 98.5% in PDD, 90.3% in DLB+park, 60% in DLB‐Park and 57.8% in AD. Rigidity was most frequent in the PDD group (88.2%), followed by DLB+Park (71%), AD (19.6%), and none DLB‐park (0%). Over 8 years trajectories for limb bradykinesia and gait were greater for DLB+park than PDD (Figure 1, Cohen’s d range 0.89‐2.18 and 0.63‐1.99 over 8 years, p<0.001).ConclusionOur results suggest that DLB+Park group have faster motor progression predominantly related to gait abnormalities and limb bradykinesia. Future analyses will explore more convergent data, integrated effects of various measures and their differential contributions in this cohort.

Background: Older age is a major risk factor for severe COVID-19 disease which has been associated with a variety of neurologic complications, both acutely and chronically. Objective: We sought to determine whether milder COVID-19 disease in older vulnerable individuals is also associated with cognitive and behavioral sequelae. Methods: Neuropsychological, behavioral, and clinical outcomes before and after contracting COVID-19 disease, were compared in members of two ongoing longitudinal studies, the Arizona APOE Cohort and the national Alzheimer’s Disease Research Center (ADRC). Results: 152 APOE and 852 ADRC cohort members, mean age overall roughly 70 years, responded to a survey that indicated 21 APOE and 57 ADRC members had contracted COVID-19 before their ensuing (post-COVID) study visit. The mean interval between test sessions that preceded and followed COVID was 2.2 years and 1.2 years respectively for the APOE and ADRC cohorts. The magnitude of change between the pre and post COVID test sessions did not differ on any neuropsychological measure in either cohort. There was, however, a greater increase in informant (but not self) reported cognitive change in the APOE cohort (p = 0.018), but this became nonsignificant after correcting for multiple comparisons. Conclusion: Overall members of both cohorts recovered well despite their greater age-related vulnerability to more severe disease.

Background: Amyloid-β (Aβ) plaques and tau protein tangles in the brain are the defining ‘A’ and ‘T’ hallmarks of Alzheimer’s disease (AD), and together with structural atrophy detectable on brain magnetic resonance imaging (MRI) scans as one of the neurodegenerative (‘N’) biomarkers comprise the “ATN framework” of AD. Current methods to detect Aβ/tau pathology include cerebrospinal fluid (invasive), positron emission tomography (PET; costly and not widely available), and blood-based biomarkers (promising but mainly still in development). Objective: To develop a non-invasive and widely available structural MRI-based framework to quantitatively predict the amyloid and tau measurements. Methods: With MRI-based hippocampal multivariate morphometry statistics (MMS) features, we apply our Patch Analysis-based Surface Correntropy-induced Sparse coding and max-pooling (PASCS-MP) method combined with the ridge regression model to individual amyloid/tau measure prediction. Results: We evaluate our framework on amyloid PET/MRI and tau PET/MRI datasets from the Alzheimer’s Disease Neuroimaging Initiative. Each subject has one pair consisting of a PET image and MRI scan, collected at about the same time. Experimental results suggest that amyloid/tau measurements predicted with our PASCP-MP representations are closer to the real values than the measures derived from other approaches, such as hippocampal surface area, volume, and shape morphometry features based on spherical harmonics. Conclusion: The MMS-based PASCP-MP is an efficient tool that can bridge hippocampal atrophy with amyloid and tau pathology and thus help assess disease burden, progression, and treatment effects.

AbstractIntroductionMultiple positron emission tomography (PET) tracers are available for amyloid imaging, posing a significant challenge to consensus interpretation and quantitative analysis. We accordingly developed and validated a deep learning model as a harmonization strategy.MethodA Residual Inception Encoder‐Decoder Neural Network was developed to harmonize images between amyloid PET image pairs made with Pittsburgh Compound‐B and florbetapir tracers. The model was trained using a dataset with 92 subjects with 10‐fold cross validation and its generalizability was further examined using an independent external dataset of 46 subjects.ResultsSignificantly stronger between‐tracer correlations (P < .001) were observed after harmonization for both global amyloid burden indices and voxel‐wise measurements in the training cohort and the external testing cohort.DiscussionWe proposed and validated a novel encoder‐decoder based deep model to harmonize amyloid PET imaging data from different tracers. Further investigation is ongoing to improve the model and apply to additional tracers.

AbstractBackgroundAPOE4 gene dose, the number of apolipoprotein E ‐ɛ4 (APOE4) ɛ4 alleles in a person’s genotype, is associated with higher Alzheimer’s disease (AD) risk and younger median age at dementia onset. We previously found relationships between PET, plasma, and CSF measurements of amyloid‐β (Aβ) pathophysiology in APOE4 gene dose. Here, we characterize the core AD CSF biomarkers (Aβ42/40, pTau181, tTau), the neurodegeneration marker neurofilament light chain (NfL) and the glial biomarkers soluble TREM2 (sTREM2), and glial fibrillary acidic protein (GFAP) measurements in age‐matched 48‐70 year‐old cognitively unimpaired (CU) APOE4 homozygotes (HMs), heterozygotes (HTs) and non‐carriers (NCs) from the Arizona cohort.MethodCSF biomarker measurements were performed at the University of Gothenburg using Lumipulse and ELISA immunoassays. CSF biomarker measurements were characterized and compared using linear‐tend ANOVAs in 12 HMs, 17 HTs, and 20 NCs who were CU, 48‐70 years old and did not differ significantly in their age, sex, or educational level.ResultAs expected, CSF Aβ42/40 ratios were inversely associated with APOE4 gene dose (linear trend, HM<HT<NC, p<0.05), and tTau/Aβ42 and pTau181/Aβ42 ratios were directly associated with APOE4 gene dose (HM>HT>NC p<0.05). Non‐significant trends suggested that CSF sTREM2 and sTREM2/pTau181 measurements were inversely associated with APOE4 gene dose (HM<HT≤NC, (0.05<p≤0.06). We failed to detect significant differences or linear trends in CSF pTau181, tTau, GFAP, or NfL measurements among these small subject groups.ConclusionAPOE4 gene dose is associated with measures of CSF Aβ42/40 and Aβ‐related tau pathophysiology, reflecting higher Aβ plaque burden in CU subjects close to their estimated ages at clinical onset. Additional studies are needed to clarify relationships between different Aβ, tau, neurodegenerative, inflammatory, and other CSF, or blood‐based biomarkers, and APOE4 gene dose in larger subject groups and at other ages.

Background: Altered cerebral glucose metabolism, especially prominent in APOE ɛ4 carriers, occurs years prior to symptoms in Alzheimer’s disease (AD). We recently found an association between a higher ratio of plasma apolipoprotein E4 (apoE4) over apoE3, and cerebral glucose hypometabolism in cognitively healthy APOE ɛ3/ɛ4 subjects. Plasma apoE does not cross the blood-brain barrier, hence we speculate that apoE is linked to peripheral glucose metabolism which is known to affect glucose metabolism in the brain. Objective: Explore potential associations between levels of plasma insulin and glucose with previously acquired plasma apoE, cerebral metabolic rate of glucose (CMRgl), gray matter volume, and neuropsychological test scores. Methods: Plasma insulin and glucose levels were determined by ELISA and a glucose oxidase assay whereas apoE levels were earlier quantified by mass-spectrometry in 128 cognitively healthy APOE ɛ3/ɛ4 subjects. Twenty-five study subjects had previously undergone FDG-PET and structural MRI. Results: Lower plasma apoE3 associated with higher plasma glucose but not insulin in male subjects and subjects with a body mass index above 25. Negative correlations were found between plasma glucose and CMRgl in the left prefrontal and bilateral occipital regions. These associations may have functional implications since glucose levels in turn were negatively associated with neuropsychological test scores. Conclusion: Plasma apoE3 but not apoE4 may be involved in insulin-independent processes governing plasma glucose levels. Higher plasma glucose, which negatively affects brain glucose metabolism, was associated with lower plasma apoE levels in APOE ɛ3/ɛ4 subjects. High plasma glucose and low apoE levels may be a hazardous combination leading to an increased risk of AD.

Background: Besides their other roles, brain imaging and other biomarkers of Alzheimer’s disease (AD) have the potential to inform a cognitively unimpaired (CU) person’s likelihood of progression to mild cognitive impairment (MCI) and benefit subject selection when evaluating promising prevention therapies. We previously described that among baseline FDG-PET and MRI measures known to be preferentially affected in the preclinical and clinical stages of AD, hippocampal volume was the best predictor of incident MCI within 2 years (79%sensitivity/78%specificity), using standard automated MRI volumetric algorithmic programs, binary logistic regression, and leave-one-out procedures. Objective: To improve the same prediction by using different hippocampal features and machine learning methods, cross-validated via two independent and prospective cohorts (Arizona and ADNI). Methods: Patch-based sparse coding algorithms were applied to hippocampal surface features of baseline TI-MRIs from 78 CU adults who subsequently progressed to amnestic MCI in approximately 2 years (“progressors”) and 80 matched adults who remained CU for at least 4 years (“nonprogressors”). Nonprogressors and progressors were matched for age, sex, education, and apolipoprotein E4 allele dose. We did not include amyloid or tau biomarkers in defining MCI. Results: We achieved 92%prediction accuracy in the Arizona cohort, 92%prediction accuracy in the ADNI cohort, and 90%prediction accuracy when combining the two demographically distinct cohorts, as compared to 79%(Arizona) and 72%(ADNI) prediction accuracy using hippocampal volume. Conclusion: Surface multivariate morphometry and sparse coding, applied to individual MRIs, may accurately predict imminent progression to MCI even in the absence of other AD biomarkers.

Background: Whether brain-derived neurotrophic factor (BDNF) Met carriage impacts the risk or progression of Alzheimer’s disease (AD) is unknown. Objective: To evaluate the interaction of BDNF Met and APOE4 carriage on cerebral metabolic rate for glucose (CMRgl), amyloid burden, hippocampus volume, and cognitive decline among cognitively unimpaired (CU) adults enrolled in the Arizona APOE cohort study. Methods: 114 CU adults (mean age 56.85 years, 38% male) with longitudinal FDG PET, magnetic resonance imaging, and cognitive measures were BDNF and APOE genotyped. A subgroup of 58 individuals also had Pittsburgh B (PiB) PET imaging. We examined baseline CMRgl, PiB PET amyloid burden, CMRgl, and hippocampus volume change over time, and rate of change in cognition over an average of 15 years. Results: Among APOE4 carriers, BDNF Met carriers had significantly increased amyloid deposition and accelerated CMRgl decline in regions typically affected by AD, but without accompanying acceleration of cognitive decline or hippocampal volume changes and with higher baseline frontal CMRgl and slower frontal decline relative to the Val/Val group. The BDNF effects were not found among APOE4 non-carriers. Conclusion: Our preliminary studies suggest that there is a weak interaction between BDNF Met and APOE4 on amyloid-β plaque burden and longitudinal PET measurements of AD-related CMRgl decline in cognitively unimpaired late-middle-aged and older adults, but with no apparent effect upon rate of cognitive decline. We suggest that cognitive effects of BDNF variants may be mitigated by compensatory increases in frontal brain activity—findings that would need to be confirmed in larger studies.