bluuebunny/crossref_metadata_2025_split huggingface dataset:
Heterogeneous air pollutant exposure effects on episodic memory decline: Evidence from WHIMS‐ECHO study
Authors: Younan Diana , Wang Xinhui , Petkus Andrew J , Beavers Daniel P , Espeland Mark A , Chui Helena C , Resnick Susan M , Gatz Margaret , Kaufman Joel D , Wellenius Gregory , Whitsel Eric A , Manson JoAnn E , Chen Jiu‐Chiuan 
Published: December 2020

AbstractBackgroundEpisodic memory (EM) declines vary by age and underlying neuropathology. Whether exposure to ambient air pollutants contributes to the observed heterogeneity of EM decline in older people is unclear.MethodWe examined the associations between exposures and EM trajectories in the US‐based Women’s Health Initiative Memory Study‐Epidemiology of Cognitive Health Outcomes (WHIMS‐ECHO) cohort (N=2042, aged 74‐92 in 2008). EM was assessed annually (2008‐18) using the immediate recall total correct (3 trials) in the telephone‐based California Verbal Learning Test. Recent 3‐year average exposures to regional PM2.5 (particulate matter with aerodynamic diameter < 2.5μm) and NO2 (a proxy of traffic‐related air pollutants) and the corresponding remote 3‐year averages approximately at 10 years prior to inception were derived from yearly exposures estimated by regionalized national universal kriging. Latent‐class mixed models were used to classify EM trajectories and examine the exposure effect in each identified latent class. All models adjusted for social‐demographic factors, lifestyle, cardiovascular risk and other clinical covariates.ResultThere were two latent EM trajectories that differed by WHIMS‐ECHO baseline age (≤80 vs. oldest‐old 80+; Figure 1) and exposure characteristics (Table 1). In women aged ≤80 (N=836), greater EM decline was associated with higher PM2.5 in the subgroup with slow‐decline (76%; −0.18/year), but not in those with fast‐decline (24%; −1.75/year). The observed association, equivalent to 1‐2 years of aging, was stronger for recent 3‐year exposure (−0.151/year, per inter‐quartile‐range [IQR]=2.86µg/m3, p=0.02), as compared with remote exposure (−0.096/year, per IQR=3.28µg/m3, p=0.09). In the oldest‐old (aged>80; N=1206), EM decline (70% steady decline by −1.37/year; 30% no apparent decline) was not associated with recent or remote PM2.5 exposures. In both age groups, no appreciable NO2 effects on EM decline were observed, and neither PM2.5 nor NO2 exposures during recent or remote years affected the levels of EM trajectories. Adjusted models further accounting for propensity scores of exposures revealed very similar results.ConclusionThe accelerated EM declines associated with late‐life exposures to ambient air pollutants vary by age‐related cognitive trajectories, exposure time‐windows and pollution sources. The observed association was most consistent with PM2.5 exposure during recent years in older women aged ≤80 with slow EM decline.
Using cerebrospinal fluid amyloid‐beta(1‐42) in the memory clinic: Concordance with PET and use of biomarker ratios across immunoassays
Authors: Willemse Eline A.J. , Tijms Betty M. , Van Berckel Bart N.M. , Gannon Sara , Le Bastard Nathalie , Latham Jessica , Radwan Rachel R. , van Der Flier Wiesje , Scheltens Philip , Teunissen Charlotte E. 
Published: December 2020

AbstractBackgroundShifting to automated cerebrospinal fluid biomarker platforms urges re‐evaluation of biomarker interpretation in the memory clinic setting. We aimed to evaluate whether different ratios of biomarkers, pTau:Aβ1– 42 and Aβ1– 42:Aβ1– 40, improved the discrimination of amyloid PET positivity in a retrospective cohort and whether this was dependent on the platform used.MethodCerebrospinal fluid samples of individuals in the biobank of the Amsterdam Dementia Cohort were selected based on availability of visual amyloid PET reads (n=288; n=133 florbetaben, n=86 Pittsburgh compound‐B, n=64 flutemetamol, n=5 florbetapir; 179 (62%) amyloid PET positive; age 63±7 years; 131(45%) female; MMSE 24±4; diagnoses subjective cognitive decline (n=58), mild cognitive impairment (n=42), Alzheimer’s disease (AD; n=145), or other dementias (n=43)). Aβ1– 42, Aβ1– 40 (Lumipulse only), pTau and tTau were measured using both Elecsys and Lumipulse automated platforms. Sensitivity and specificity percentages were determined based on optimal Youden’s index in receiver operating curve analyses with amyloid PET result as gold standard.ResultBoth pTau:Aβ1– 42 and Aβ1– 42:Aβ1– 40 ratios showed higher concordance with amyloid PET compared to Aβ1– 42 alone. Sensitivity and specificity percentages for Elecsys were 96% and 89% for pTau:Aβ1– 42 (cut‐point 0.020), 96% and 80% for Aβ1– 42:Aβ1– 40 (cut‐point 0.091), and 91% and 75% for Aβ1– 42 (cut‐point 1089 pg/ml). For Lumipulse, sensitivity and specificity percentages were 97% and 91% for pTau:Aβ1– 42 (cut‐point 0.072), 99% and 83% for Aβ1– 42:Aβ1– 40 (cut‐point 0.071), 91% and 73% for Aβ1– 42 (cut‐point 714 pg/ml). Areas under the curves were higher for pTau:Aβ1– 42 than Aβ1– 42:Aβ1– 40 using Elecsys (0.949 and 0.924, respectively, p‐value = 0.014) but comparable for both ratios using Lumipulse (0.956 and 0.942, respectively, p‐value = 0.20).ConclusionWe recommend the pTau:Aβ1– 42 ratio as optimal biomarker for use in memory clinic setting using the Elecsys platform, since it showed the strongest concordance with PET amyloid. For Lumipulse assays, pTau:Aβ1– 42 and Aβ1– 42:Aβ1– 40 perform equally well.
Synergistic effects of an innovative combination therapy on treating Alzheimer's disease involving modulation of gut dysbiosis
Authors: Shie Feng‐Shiun , Hsu Chia‐Yu , Chao Po‐Kuan , Hsueh Jung‐Tsung , Hsu John Tsu‐An , Su Ih‐Jen 
Published: December 2020

AbstractBackgroundChronic neuro‐inflammation leading to excessive neuronal damages predates the presence of clinical manifestations of Alzheimer’s disease (AD) by many years. Due to the complex pathogenesis of AD, a multi‐target regimen using a combination therapy is proposed to be essential to the effectiveness of AD therapy.MethodAn innovative combination therapy using a newly developed multi‐functional Aβ antibody (NP106) and a novel curcumin derivative (TML‐6) was applied to treat APP/PS1 mice. To investigate whether synergistic effects of combination therapy on reducing AD‐like pathology in APP/PS1 mice alter fecal microbiome, high‐throughput 16S rRNA sequencing was performed.ResultHere, we demonstrate that monotherapy with either weekly intraperitoneal injection of low‐dose NP106 or TML‐6‐supplemented diet for four months showed beneficial effects on counteracting AD‐like pathology in APP/PS1 mice, while combination therapy using these two drugs outperformed monotherapy and exerted synergistic effects on improving behavioral abnormality and reducing cerebral Aβ accumulation. Data from microbiome indicate that the gut bacterial community structure of APP/PS1 mice was different from those of wt littermates. Intriguingly, relative abundant genera of APP/PS1 mice can be restored by either monotherapy or combination therapy to levels similar to wt littermates, indicating that these treatments possessed prebiotic effects against gut dysbiosis. Importantly, analyses of samples diversity further reveal that combination therapy might lead to better normalization of microbiota.ConclusionCombination therapy exerted synergistic effects on attenuating AD‐like pathological features and behavioral deficits in APP/PS1 mice. Furthermore, the synergistic effects of combination therapy were concurrent with the normalization of gut microbiota.
Serum neurofilament light in memory clinic practice
Authors: Willemse Eline A.J. , Teunissen Charlotte E. , Scheltens Philip , Vijverberg Everard 
Published: December 2020

AbstractBackgroundNeurofilament Light (NfL) is an accurate blood biomarker for axonal damage across the spectrum of neurology. Although serum NfL (sNfL) levels are increased in dementia, the diagnostic value of sNfL and appropriate use criteria need to be defined before implementation. The aim of this study is to evaluate the clinical use of sNfL in a tertiary memory clinic cohort.MethodThis single‐center implementation study included 111 patients that visited the Alzheimer Center Amsterdam between May–Oct 2019. In addition to routine neurological examination and cerebrospinal fluid (CSF) biomarker analysis, sNfL levels were analysed on Simoa (NfL‐Light kit, Quanterix) and reported together with normal values in relation to age. Six neurologists filled out a questionnaire regarding the added value of sNfL after discussing the results with the patient (n=5–43 questionnaires per neurologist). The clinical usefulness of sNfL was evaluated with regard to age (62±9 years) and diagnosis (subjective cognitive decline:n=31; Alzheimer’s disease:n=28; psychiatry:n=9; other diagnoses:n=43), whether diagnosis was uncertain (n=19), and whether CSF biomarker results were available (n=76). These results were translated into recommendations for use of sNfL in clinical practice.ResultSNfL was evaluated as useful diagnostic tool in 54% of the cases. SNfL was more often useful in patients below the age of 61 (60%,n=42); patients with a diagnosis of subjective cognitive decline (61%) or psychiatry (78%), in cases for which there was uncertainty regarding the diagnosis (68%) and less in Alzheimer’s disease (39%). Whether sNfL was regarded as useful, strongly depended on which neurologist filled out the questionnaire (ranging from 0–73% of useful cases per neurologist). Availability of CSF biomarker results at time of result discussion had no influence.ConclusionIn more than half of the evaluated cases, sNfL was perceived as useful diagnostic tool for memory clinic practice. SNfL analysis is mostly recommended as biomarker tool to exclude neurodegeneration in patients below 61 years old or in patients with suspected subjective cognitive decline, psychiatric disorders, or uncertainty regarding the diagnosis. The appreciation of sNfL as useful diagnostic tool was different across neurologists, probably due to differences in level of experience with biomarker sNfL.
Popular MRI de‐facing software does not sufficiently protect participants from re‐identification via face recognition
Authors: Schwarz Christopher G. , Kremers Walter K. , Therneau Terry M. , Sharp Richard R. , Gunter Jeffrey L. , Vemuri Prashanthi , Arani Arvin , Spychalla Anthony J. , Kantarci Kejal , Knopman David S. , Petersen Ronald C. , Jack Clifford R. 
Published: December 2020

AbstractBackgroundData shared for research use must be de‐identified to protect participant confidentiality. A recent study has shown that face imagery from brain imaging studies could be used to re‐identify anonymous participants via automatic face recognition, linking them with all study‐collected protected health information. Here we measure how well existing software for removing MRI face imagery protects against this privacy threat.MethodWe recruited 153 participants with existing 3D FLAIR MRI and photographed their face from 5 varying angles. We used surf_ice to reconstruct each participant’s face from their MRI and tested whether free, publicly‐available face recognition software (Microsoft Azure) could match their photos to their correct MRI. We tested two existing programs for removing face voxels in MRI: mri_deface (FreeSurfer) and pydeface (python package), and one in‐house implementation. We observed that existing methods often left participants’ eyes intact while removing most of their nose/mouth/chin. This prevented Azure from detecting the reconstructions as faces, therefore preventing its attempt of face recognition. We hypothesized that the retained eye imagery alone could be sufficient for face recognition, if a face were detected. Therefore, we also reconstructed each de‐faced MRI after applying novel pre‐processing that replaced any removed/missing face voxels with those from our population‐average template, e.g. replacing missing noses/mouths/chins with population‐average noses/mouths/chins, before testing face recognition.ResultsWithout any MRI de‐facing, Microsoft Azure correctly matched 78% of participants’ photos to their MRI. After mri_deface and pydeface, 8% and 6% were still identified, respectively. After preprocessing the de‐faced MRI to replace missing areas with those from a population‐average template prior to face reconstruction, the face imagery remaining in the de‐faced images was enough to allow re‐identification of 23% and 20% of participants, respectively. With an in‐house face removal implementation that removes the whole face from every image, 0% of faces were re‐identified from the standard reconstructions, and 5% were identified from the “missing‐parts‐replacement” reconstructions.ConclusionsEven after applying popular MRI de‐facing software, Microsoft Azure face recognition automatically re‐identified up to 23% of participants. Better de‐facing software is needed to remove more of the face more reliably, without affecting brain measurements from de‐faced images.
Hallmarks of late‐onset Alzheimer’s disease in a humanized mouse model
Authors: Kotredes Kevin P. , Preuss Christoph , Pandey Ravi S. , Territo Paul R. , Oblak Adrian L. , Kaddurah‐Daouk Rima F. , Arnold Matthias , Seyfried Nicholas T. , Duong Duc , Williams Harriet M. , Lamb Bruce T. , Rizzo Stacey J. Sukoff , Carter Gregory W. , Sasner Michael , Howell Gareth 
Published: December 2020

AbstractBackgroundIf progress is to be made in the pursuit to cure Alzheimer’s Disease (AD), researchers and clinicians require better resources to understand this heterogenous pathology. Thus, the Model Organism Development and Evaluation for Late‐onset AD consortium (MODEL‐AD) is charged with creating, defining, and distributing novel mouse models of late‐onset AD (LOAD) carrying human‐relevant risk factors. Characterization of these variants in aging mice will reveal more appropriate disease pathways useful in the treatment of LOAD.Method
APOEe4 and Trem2*R47H variants, two primary risk factors of LOAD, were incorporated into the genome of C57BL/6J (B6J) mice. Cohorts of B6J.APOE4/Trem2*R47H mice were established on multiple sites and aged to 4, 8, 12, and 24 months. Behavior and wellness assays measured vigor, activity, coordination, cognition, and biometrics. In vivo MRI and PET cerebral imaging, to measure perfusion and metabolism, were also completed, as were post‐mortem analyses of blood chemistry, neuropathology, transcriptomics, metabolomics, and proteomics.ResultAs expected, mice expressing the humanized APOEe4 allele showed decreased plasma lipoprotein levels across all time points. Further, a novel splicing event was observed in mice expressing the Trem2*R47H variant, leading to a reduction of allele expression. In the absence of additional environmental or genetic risk factors, B6J.APOE4/Trem2*R47H mice did not display penetrant behavioral phenotypes, but did exhibit decreased survival probabilities by 24 months. Analysis of 6‐month‐old animals revealed differences in plasma metabolite levels and in brain proteomes compared to controls. Neuropathology analysis in brain tissue is in progress, as are cytokine panels in brain homogenates and plasma.ConclusionThe MODEL‐AD consortium has established B6J.APOE4/Trem2*R47H model to study the effects of two strong risk factors of LOAD. This strain serves as a platform for the further incorporation of human risk alleles of AD to more closely align phenotypes in the mouse to outcomes observed in the clinic. In accordance, the effect of a high fat diet is being evaluated in B6J.APOE4/Trem2*R47H mice. Ultimately, strains carrying combinations of risk factors that more closely align with human disease will be incorporated into the pre‐clinical testing core of MODEL‐AD to assess the potential of prioritized compounds to treat AD.
Exploring the shared genetic architecture of modifiable risk factors and related endophenotypes of Alzheimer’s disease: A genomic SEM study
Authors: Foote Isabelle F. , Jacobs Benjamin M. , Noyce Alastair J. , Korszun Ania , Bhui Kamaldeep S. , Marshall Charles R. 
Published: December 2020

AbstractBackgroundThe mechanisms by which modifiable risk factors influence Alzheimer’s disease (AD) remain incompletely understood. Previous work using linkage disequilibrium score regression (LDSC) indicates that many of the modifiable risk factors have high genetic correlation with one another. Modelling this shared genetic architecture between risk factors, associated endophenotypes and AD could yield important insights into shared aetiological pathways.MethodWe used genomic structural equation modelling (SEM) to model the shared genetic architecture between potentially modifiable risk factors (including anxiety, depression, diabetes, education, hearing loss, hypertension, obesity, sleep, smoking) and related endophenotypes (including inflammatory markers and metabolites). Genomic SEM uses summary statistics from genome‐wide association studies (downloaded from leading consortia, the Neale lab group, CTG lab group and UK Biobank) to calculate the genetic covariance between traits using multivariable LDSC. We then used exploratory factor analysis (EFA) to model the patterns of shared genetic architecture into latent constructs.ResultMultivariable LDSC showed multiple correlations between traits. EFA showed that AD shares genetic covariance with circulating metabolites and cytokines, but not modifiable risk factors. A second factor showed loadings of age‐related hearing loss with cytokines and metabolites distinct from their variance with Alzheimer’s disease. A third factor comprised the majority of other risk factors including depression, insomnia and smoking, but not diabetes or obesity, which did not share significant covariance with any other AD‐related traits.ConclusionWe found high levels of shared genetic architecture between AD risk factors that were not shared with AD itself. This could suggest that rather than a direct causal effect on AD, risk factors exert their influence via a common intermediary (e.g. pro‐inflammatory states and/or cognitive reserve). Further work is required to establish model fit statistics, identify common SNPs related to these factors and conduct multivariable Mendelian randomisation to measure causal pathways that could lead to AD. This approach could provide important biological insights into the mechanisms underpinning the shared genetic architecture of AD risk factors, and explore how variation at these loci functionally impacts on AD pathophysiology, elucidating aetiological pathways, and ultimately prioritising targets for drug discovery and efforts to prevent AD.
Behavioural symptoms in Alzheimer’s disease are associated with white matter lesions (WML) volume and are independent of atrophy and hypometabolism
Authors: Femminella Grazia Daniela , van der Doef Thalia , Carver Stefan , Holmes Clive , Ritchie Craig W. , Lawrence Robert M , McFarlane Brady , Tadros George , Ridha Basil H , Bannister Carol , Walker Zuzana , Archer Hilary , Coulthard Elizabeth , Underwood Ben , Prasanna Aparna , Koranteng Paul , Karim Salman , Junaid Kehinde , McGuinness Bernadette , Passmore Anthony Peter , Nilforooshan Ramin , Macharouthu Ajayverma , Donaldson Andrew , Thacker Simon , Russell Gregor , Malik Naghma , Mate Vandana , Knight Lucy , Kshemendran Sajeev , Harrison John E. , Ballard Clive , Brooks David J. , Edison Paul 
Published: December 2020

AbstractBackgroundBehavioral and psychological symptoms of dementia (BPSD) are highly prevalent in Alzheimer’s disease (AD) and associated with adverse outcomes. There is increasing interest in identifying the risk factors for developing BPSD among AD patients. However, the pathophysiological mechanisms underlying BPSD remain poorly understood. Here we evaluated whether BPSD are associated with WMLs volume, brain atrophy and brain metabolism in AD subjects.Method130 AD subjects form the Evaluating Liraglutide in Alzheimer's Disease (ELAD) trial (NCT01843075) were enrolled. The presence of BPSD was evaluated with the Neuropsychiatric Inventory Questionnaire (NPI). FLAIR and T1‐weighted imaging were performed on 3T scans. WMLs was determined on FLAIR images using the Lesion Segmentation Tool (LST) in SPM12. Hippocampal and grey matter volumes were estimated using the FreeSurfer analysis on T1 MRI. Glucose metabolism (rCMRGlc) was calculated by parametric images generated using spectral analysis with an arterial plasma input function.ResultIn this AD population, NPI scores were correlated with WMLs volume (Spearman's rho=.197, p=.02), also after correcting for age. NPI scores were not correlated to hippocampal or grey matter volume and to rCMRGlc in frontal, temporal, parietal or occipital lobes. When looking at the NPI sub‐scores, WMLs volume was significantly associated with agitation/aggression (Spearman's rho=.177, p=.05) and with appetite and eating disorders (Spearman's rho=.233, p=.00).ConclusionIn this study of a clinical AD population, BPSD were associated with WMLs but not with brain atrophy and hypometabolism. This suggests that vascular changes and the prevention of vascular risk factors might play an important role in reducing the impact of BPSD in AD dementia.
Fractional anisotropy and tau‐PET show parallel changes in amnestic AD, MCI, and controls in the medial temporal lobe
Authors: Carlson Mackenzie L , Toueg Tyler , Corso Nicole , Khalighi Mehdi , DiGiacomo Phillip , Zaharchuk Greg , James Michelle L , Greicius Michael D , Mormino Elizabeth C , Zeineh Michael 
Published: December 2020

AbstractBackgroundMicrostructural tissue alterations in Alzheimer disease (AD) contribute to decreased fractional anisotropy (FA) in white matter (Sexton et al. 2011), which may be secondary to tau‐based neurodegeneration that starts in the medial temporal lobe (MTL). Here, we show that tau accumulation in the entorhinal and perirhinal cortices (ERC/PRC) – the first area of the MTL known to accumulate tau pathology (Braak & Braak, 1991) – correlates with parahippocampal gyral FA, and both are altered in AD and mild cognitive impairment (MCI) compared with control subjects.Method21 subjects, 5 amyloid‐positive AD (3 amnestic), 5 amyloid‐positive MCI (4 amnestic), and 11 healthy control, underwent a dynamic 90‐minute tau‐PET scan on a 3T PET‐MR after 5mCi intravenous injection of PI‐2620 (Life Molecular Imaging, Inc.). During the PET acquisition, coronal‐oblique T2‐weighted fast‐spin‐echo (TR 14111ms, TE 102.4ms, FA 111, 0.43x0.43x1.9mm resolution, 3m24s) and DTI (6b=0 s/mm2, 60 b=1000s/mm2 directions, 1.6‐2.0mm isotropic) were acquired. Standardized uptake value ratio (SUVr) maps from 60‐90 were normalized to the inferior cerebellar cortex. DTI and PET were registered to coronal T2 MR image space. Hippocampal subfields (CA1, dentate gyrus and CA2‐4 (DG), subiculum, entorhinal/perirhinal cortex, and parahippocampal gyrus) were segmented using Automated Segmentation of Hippocampal Subfields (Yushkevich et al, 2010) with a tailored atlas (Parivash et al, 2019). Mean subfield SUVr and FA were computed after subfields were 1‐voxel 2D‐eroded in the coronal‐oblique plane. Statistics utilized a linear regression model correcting for age.ResultSUVr differences were present between amnestic AD/MCI (N=7) and controls (N=11) in the ERC/PRC (p<0.001), more than in other subfields (Figure 1A). Amnestic AD/MCI demonstrated significantly lower FA than controls in the parahippocampal gyrus (p =0.04) (Figure 1B). FA in the parahippocampal gyrus correlates strongly (R=‐0.60) with ERC/PRC tau SUVr in amnestic AD, amnestic MCI, and controls and shows clear clustering by subject group (Figure 2).ConclusionThe correlation between parahippocampal FA and tau SUVr in the ERC/PRC indicate that simultaneous in vivo PET/MRI measurements of early‐stage tau accumulation and white matter degradation may be useful to interrogate the relationship between tau accumulation and MTL white matter microstructure.