bluuebunny/crossref_metadata_2025_split huggingface dataset:
Synonymous variant rs2405442 in PILRA is associated with Alzheimer's disease and affects RNA expression by destroying a ramp sequence
Authors: Miller Justin , McKinnon Lauren , Murcia Josue D Gonzalez , Kauwe John , Ridge Perry G 
Published: December 2020

AbstractBackgroundPaired Immunoglobulin‐like Type 2 Receptor Alpha (PILRA) recognizes specific O‐glycosylated proteins on various cell types, including microglia. A common missense mutation in PILRA, rs1859788, that likely causes the confirmed Alzheimer's disease risk locus at 7q21 is in linkage disequilibrium with synonymous variant rs2405442:T>C. This synonymous variant affects a ramp of rare codons at the beginning of PILRA, which may decrease overall transcriptional efficiency. Since reduced inhibitory signaling in microglia has a protective effect on Alzheimer's disease, we propose that destroying a ramp sequence in PILRA will likewise protect against Alzheimer's disease by reducing the amount of PILRA inhibitory receptor.MethodsWe recently developed an algorithm, ExtRamp, to identify ramp sequences within specific gene sequences. Using ExtRamp, we calculated the relative codon adaptiveness of PILRA using all isoforms in GRCh38. We then calculated the relative codon adaptiveness of PILRA with synonymous variant rs2405442:T>C and found that a ramp sequence is present in the reference isoforms but not the mutant isoforms. We predicted that although the mutant has a more common codon, it would have less overall mRNA expression because it lacks a ramp sequence. We performed biological validation using four sets of three qPCR replicates on the longest isoform to determine the transcriptional efficiency of the mutant versus the reference. We normalized the qPCR output by the number of cells.ResultsFigure 1 shows the relative adaptiveness of the longest PILRA reference isoform and the mutant gene averaged over a nine codon window. The mutant gene has a higher codon adaptiveness at the beginning of the gene sequence, which destroys the predicted ramp sequence. Figure 2 shows that our biological validation using qPCR confirms our prediction that the mutant gene has lower mRNA expression (p‐value = 4.45 × 10−9).ConclusionsAlthough higher GC content and more common codons generally increase mRNA levels, the ramp sequence appears to play an integral role in transcriptional efficiency in PILRA. Our results show that synonymous variant rs2405442:T>C directly impacts transcriptional efficiency in PILRA and indicates that synonymous variants should be considered in future association analyses for their causal effects on transcriptional efficiency by modifying ramp sequences.
Prediction of amyloid PET status using the LUMIPULSE G β‐amyloid ratio (1‐42/1‐40)
Authors: Willemse Eline A.J. , Tijms Betty M. , Van Berckel Bart N.M. , van Der Flier Wiesje , Gannon Sara , Bastard Nathalie Le , Radwan Rachel R. , Esquivel Rianne N. , Latham Jessica , Benina Natalya , Dickson Diana , Scheltens Philip , Teunissen Charlotte E. 
Published: December 2020

AbstractBackgroundCerebrospinal fluid (CSF) amyloid biomarker concentrations are valuable in the assessment of patients for Alzheimer’s dementia (AD). Automated platforms and the additional measurement of Aβ1‐40(Aβ40) with Aβ1‐42(Aβ42) has decreased preanalytical variability and increased diagnostic accuracy (Delaby, 2019; Hansson, 2019). Notably, the ratio Aβ42/Aβ40 significantly improves concordance with PET when compared to Aβ42 alone (Hansson, 2019). In this study Aβ42/Aβ40 concordance with PET was evaluated on the LUMIPULSE G1200 platform.MethodsA subset of biobanked individuals from the Amsterdam Dementia Cohort was included based on availability of visual amyloid‐PET reads using an FDA approved tracer (Florbetaben n=156; Flutemetamol n=71; Florbetapir n= 8) and CSF, and an age requirement of ≥50 years old (n=235). Demographics included 58% amyloid‐PET positive; mean age 63 years; 43% female; MMSE range 12‐30; and diagnoses: subjective cognitive decline (n=53), mild cognitive impairment (n=25), AD (n=105), or other dementias (n=52). CSF Aβ42 and Aβ40 were measured using Lumipulse G1200 (Fujirebio Diagnostics Inc., Malvern, PA). A cutoff for Aβ42/Aβ40 was established by maximizing the Youden index subject with constraint for sensitivity and specificity to differ by no more than 10 percentage points.ResultsAt a set cutoff of 646 pg/mL CSF Aβ42 sensitivity and specificity was 78.8% (CI 71.3% – 84.8%) and 80.6% (CI 71.7% – 87.2%) with an AUC of 0.854. CSF Aβ40 sensitivity and specificity was 51.8% (CI 43.5% – 60.0%) and 45.9% (CI 36.4% ‐ 55.8%) with an AUC of 0.401. The ratio Aβ42/Aβ40 at a set cutoff of 0.058 had a sensitivity and specificity of 94.2% (CI 88.9% ‐ 97.0%) and 90.8% (CI 83.5% and 95.1%) with an AUC of 0.952. Frequencies of patients within the AT(N) research framework were evaluated. With Aβ42 alone 127 patients were considered amyloid positive while use of the Aβ42/Aβ40 ratio included 138 amyloid positive patients.ConclusionThe Aβ42/Aβ40 ratio strongly correlates with PET status when measured on the LUMIPULSE G platform. In conjunction with clinical assessment the Aβ42/Aβ40 ratio can serve as a highly sensitive and specific biomarker to assess amyloid status.
Relationship between spectral analysis, SUV and SUV Pons ratio as a measure of cerebral glucose metabolic rate in Alzheimer's disease
Authors: Edison Paul , Raza Sanara , Femminella Grazia Daniela , Blunt Eleanor G , Carver Stefan , Livingston Nicholas R , Nowell Joseph , 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 , Nilforooshan Ramin , Macharouthu Ajayverma , Donaldson Andrew , Thacker Simon , Russell Gregor , Malik Naghma , Mate Vandana , Knight Lucy , Kshemendran Sajeev , Harrison John E , Brooks David J , Passmore Anthony Peter , Ballard Clive 
Published: December 2020

AbstractBackgroundCerebral metabolic rate of glucose (CMRglc) can be measured with fluorodeoxyglucose (FDG) positron emission tomography (PET) using arterial blood sampling. However, blood sampling is an invasive methodology for subjects. Standard uptake value (SUV) is a non‐invasive method to assess the cerebral glucose metabolism. Here we evaluate the relationship between semi‐quantitative measurements such as SUV and Pons ratio with a quantitative measurement such as Spectral analysis (SA) in order to assess their surrogate use in clinical trials.MethodsTo assess cerebral glucose metabolism by a non‐invasive and simplified method, the standardized uptake value (SUV) and used the Pons RATIO (where target region was divided by pons as a reference region, which requires no arterial input function or blood sampling. The SA was performed using blood sampling to create the input function. The study participants included 63 subjects (mean ± SD age, 71 ± 8 years) from the Evaluating Liraglutide in Alzheimer’s Disease (ELAD) trial. Regional CMRglucose (CMRglc) and regional SUV were measured using FDG PET, and the correlation between CMRglc and SUV together with CMRglc and Pons ratio were estimated for the temporal lobe, medial temporal lobe and hippocampusResultsIn this AD population, the SA and SUV in the temporal lobe (r=0.635, p= >0.001), medial temporal lobe (0.533, p= >0.001) and hippocampus (r=0.539, p= >0.001) had a positive correlation and were statistically significant. The correlation coefficient between SA and Pons ratio for the temporal lobe (r=0.166, p= 0.194), medial temporal lobe (r=0.146, p=0.146) and the hippocampus (r=0.193, p=0.129) was positive but not statistically significant.ConclusionThe SUV correlated better with cerebral glucose metabolic rate using SA than Pons RATIO in AD subjects, while Pons RATIO did not perform well. Therefore, SUV analysis maybe more reliable analysis method compared to Pons RATIO for FDG PET in clinical trials in AD subjects.
Basal forebrain atrophy and tau pathology are correlated in prodromal AD
Authors: Lo Yi‐Wen , Dore Vincent , Xia Ying , Fazlollahi Amir , Bourgeat Pierrick , Villemagne Victor L.L. , Rowe Christopher C. , Fripp Jurgen , Coulson Elizabeth J. 
Published: December 2020

AbstractBackgroundDegeneration of cholinergic basal forebrain (BF) neurons play a role in the progression of Alzheimer’s disease (AD), with atrophy of the BF preceding cognitive dysfunction. Here, we investigated whether the presence of Ab and/or Tau PET tracer retention in cognitively normal elderly controls was associated with BF atrophy.MethodCognitively healthy participants (N = 134, 49% male) from the AIBL cohort underwent 3T MRI and amyloid (18F‐NAV4694), and Tau (18F‐MK6240) PET scans. PET images were spatially normalised and scaled to the cerebellar cortex using CapAIBL. An amyloid‐positive scan (Aβ+) was defined by cortical retention of ≥20 centiloids. Tau positivity (Tau+) was defined as the average mesial‐temporal lobe SUVR greater than two standard deviations of the Aβ‐ CN subjects for tau. BF volumes were generated from T1w MRIs using an SPM8‐based pipeline and cytoarchitectonic atlas that define BF nuclei Ch1 to 4. Models were corrected for intracranial volume, grey matter, gender and age. Statistical analysis was implemented in Rstudio using general linear models (GLM).ResultSubjects were placed into 3 groups (age, 75.03 ± 5.22 years, was not different between groups) consisting of 103 Aβ‐/Tau‐ subjects, 19 Aβ+/Tau‐ subjects and 12 Aβ+/Tau+ subjects. Although not cognitively impaired, the Aβ+/Tau+ cohort had lower MMSE and higher CDR scores than the Aβ‐/Tau‐ group. After correction for intracranial volume, gray matter and age, volumes of the entire BF and BF subregions were significantly smaller in the Aβ+/Tau+ group than the Aβ+/Tau‐ group (Figure 1, p < 0.05). BF volumes for NC Aβ‐/Tau‐ and NC Aβ+/Tau‐ groups were not different, indicating that BF atrophy and Tau pathology are strongly related.ConclusionThese findings are consistent with previous studies of cognitively impaired populations and suggest that in prodromal AD, atrophy in the BF occurs in parallel with the development of tau pathology and cognitive decline. We are investigating longitudinal changes in this population. Figure 1. Box plot representing comparisons between the three groups in terms of volumetry of BF (A) Ch4 sum ( Ch4 posterior and Ch4 anterior compartments) and (B) Ch1‐3 sum after the correction of intracranial volume and age by general linear models.
Colocalization of atrophy and tau improves AI classification of Alzheimer phenotypical variants
Authors: Damasceno Pablo F. , La Joie Renaud , Maia Pedro D. , Visani Adrienne , Iaccarino Leonardo , Strom Amelia , Edwards Lauren , Tempini Maria Luisa Gorno , Jagust William J. , Miller Bruce L. , Rabinovici Gil D. , Raj Ashish 
Published: December 2020

AbstractBackgroundThe high cognitive and anatomical heterogeneity in phenotypical variants of Alzheimer’s Disease imposes a formidable challenge for biomarker‐based discriminatory models. Motivated by recent findings (La Joie, 2020) suggesting a causal relationship between regional tau accumulation and future atrophy development, we used Machine Learning techniques to test whether colocalization of these two biomarkers could improve clinical differentiation of AD phenotypical variants.MethodWe investigated 85 amyloid‐positive patients with a clinical diagnosis of AD (MCI or dementia stage), including 17 patients with PCA, 16 with lvPPA, and 52 typical cases (tAD, Table 1). Patients received 3‐tesla MRI and PET with [11C]PIB and [18F]Flortaucipir. Thickness measurements were turned into age and sex adjusted z‐scores (Potvin, 2017). Standardized Uptake Value Ratio (SUVR) images were created using reference regions. Mean SUVR or cortical thickness values were extracted from 72 FreeSurfer regions (Figure 1). Random Forest (RF) and Support Vector Machines (SVM) were used for supervised classification of patients into one of the three diagnosis above. Four distinct models were compared: based on amyloid, atrophy, tau, or on the product between atrophy and tau (hence capturing the colocalization of these two biomarkers). Selected ROIs were acquired using SVM feature importance and used in a second SVM method (SVM‐ROIs).ResultFigure 2a shows the accuracy of the classification models as a function of the used input features. As a general trend: i) amyloid‐based models had the poorest classification accuracy; ii) tau‐based SVM models outperformed atrophy‐based predictions; iii) the product of atrophy and tau significantly improved accuracy; and iv) in SVM models, selection of a subset of regions consistently improved classifications over those models using all brain regions. Figure 2b shows the confusion matrix for the most accurate model (SVM on selected ROIs).ConclusionIn the cohort used in this study, the product of atrophy and tau provides a better classification feature than each feature considered in isolation. Our results suggest that the colocalization of these two biomarkers represent a valuable instrument for clinical decision‐making in phenotypical variants of AD. References: La Joie, et al. Science Translational Medicine (2020). Potvin, et al. Neuroimage (2017).
Benefits of home‐based dementia care on clinical and quality‐of‐life outcomes for persons living with dementia and their carers
Authors: Samus Quincy M , Amjad Halima , Huang Jin , Leoutsakos Jeannie‐Marie S , Lyketsos Constantine , Johnston Deirdre 
Published: December 2020

AbstractBackgroundScalable, home‐based approaches for the delivery of comprehensive dementia care that bridge medical, social, and supportive care services are needed to improve outcomes and care value for persons living with dementia (PWD) and family carers. We present results from a CMS‐funded Healthcare Innovations Award evaluating the 18‐month impact of a comprehensive home‐based dementia care coordination program, MIND at Home, on clinical and quality of life outcomes in a racially and socioeconomically diverse group of community‐living PWD and family carers.MethodsThis is a prospective, within‐subject, study to evaluate change (baseline to 9‐months, and baseline to 18‐months) in clinical and quality of life outcomes among 342 PWD in Maryland who received MIND at Home. Within‐group changes on dementia‐related unmet care needs (59 items total, 13 domains), neuropsychiatric behaviors (NPI), quality of life (QOL‐AD), carer burden (Zarit Burden Interview), carer depression (PHQ‐9) were assessed using longitudinal mixed effects models. We also tested whether change in outcomes over time was moderated by baseline cognition, living arrangement, or race (Caucasian vs. Non‐Caucasian) in post hoc analyses.ResultsIn adjusted linear mixed models, PWD and carers had significantly fewer total percent unmet dementia‐related needs (PWD: β=‐19.95, SE 0.7, p<0.001; CG: β =‐28.30 SE 0.9, p<0.001) and percent unmet in all need subdomains (all p<0.01), fewer behavioral symptoms (NPI) (β = ‐5.16, SE 1.4, p<0.001), reduced caregiver burden (ZBI short) (β = ‐1.69, SE 0.6, p=0.005), and reduced caregiver distress with behavioral symptoms (β = ‐1.63, SE 0.8), p=0.03) from baseline to 18 months. Carer depression trended towards improvement (PHQ‐9, β = ‐0.63, SE 0.37, p=0.086). No statistically significant change in QOL‐AD scores were observed. Most interaction effects were not significant, however non‐Caucasians tended to benefit more on several outcomes compared to Caucasians.ConclusionMIND is a scalable home‐based dementia model of care that promotes evidence‐based dementia care strategies addressing range of key medical, social, and environmental determinants of health. This quasi‐experimental analysis provides new evidence of effectiveness for improving outcomes for both PWD and their caregivers over time to and contributes important new longitudinal data to support the program’s value proposition to potential adopters.
The Alzheimer’s disease sequencing project–follow up study (ADSP‐FUS): Increasing ethnic diversity in Alzheimer’s genetics research with addition of potential new cohorts
Authors: Mena Pedro Ramon , Kunkle Brian W. , Faber Kelley , Celis Katrina , Adams Larry D. , Foroud Tatiana M. , Reyes‐Dumeyer Dolly , Kuzma Amanda B. , Naj Adam C. , Martin Eden R , Dalgard Clifton , Schellenberg Gerard D. , Wang Li‐San , Vance Jeffery M. , Cuccaro Michael L. , Mayeux Richard , Vardarajan Badri N. , Pericak‐Vance Margaret A. 
Published: December 2020

AbstractBackgroundThe ADSP‐FUS is a National Institute on Aging (NIA) initiative focused on identifying genetic risk and protective variants for late‐onset Alzheimer Disease (LOAD). A major concern in AD genetic studies is a lack of racial‐ethnic diversity. The ADSP‐FUS collects and sequences existing both ethnically diverse and unique cohorts with extensive clinical data to expand the utility of new discoveries for individuals from all populations. Additional multi‐ethnic cohorts are presently being recruited (e.g. Amerindian, Korean and Indian).MethodThe cohorts consist of participants from studies of AD, dementia, and aging‐related conditions. Clinical classification (i.e., AD, dementia, and non‐affected) is implemented using algorithms based on a minimal set of criteria derived from standard measures (e.g., global cognitive screeners, dementia rating scales, etc.) and pertinent clinical history. Data dictionaries are generated for each cohort by clinical staff at Columbia University and University of Miami (UM). In total, ADSP‐FUS intends to sequence over 40,000 individuals. Existing biospecimens were obtained and processed through the National Centralized Repository for Alzheimer’s (NCRAD), the primary site for preparation and allocation of DNA, which is then delivered to the Uniformed Services University of the Health Sciences (USUHS) for whole genome sequencing (WGS). The resulting raw sequence data is delivered to the Genome Center for Alzheimer’s Disease (GCAD) for processing and harmonization followed by quality control analysis at University of Pennsylvania and University of Miami into analysis‐ready genotype data. The final step is delivery of clinical, genotype and sequence data to the NIA Genetics of Alzheimer Disease Data Storage Site (NIAGADS), which serves as the ASDP‐FUS data storage, management and sharing center.ResultsOver 30,000 samples have been ascertained and are distributed as follows: 7,896 with African ancestry; 9,475 with Hispanic ancestry; 13,531 with non‐Hispanic white ancestry (1,400 EOAD and 3,745 autopsy) and 89 with Amerindian ancestry.ConclusionThe ADSP‐FUS is designed to enhance ongoing efforts for the identification of shared and novel genetic risk factors for LOAD among different populations. This project will expand our current knowledge of potential genetic risk and protective variants for LOAD across all populations with the hope of developing new treatments.
Differential protein expression identified by digital spatial profiling (DSP) in the hippocampi of resilient individuals
Authors: Walker Jamie M. , Dehkordi Shiva Kazempour , Richardson Timothy , Zare Habil , Orr Miranda E. 
Published: December 2020

AbstractBackgroundAn intriguing group of individuals, termed “resilient”, demonstrate neuropathologic changes consistent with Alzheimer disease (AD), yet display no cognitive impairment. Studies have reported less neuronal loss overall, less gliosis, and fewer comorbidities (such as cerebrovascular disease) in these individuals. We sought to analyze protein expression in and around normal and neurofibrillary tangle (NFT)‐bearing neurons in these individuals as compared to those with cognitive decline.MethodUtilizing Nanostring’s GeoMx™ Digital Spatial Profiling (DSP) technology, we compared individuals with AD neuropathologic change and dementia (n = 6) to individuals with AD neuropathologic change and no dementia (n = 8). The DSP panel allowed for spatial analysis of the level of expression of 89 different proteins in multiple regions of interest on formalin‐fixed paraffin embedded sections. We analyzed hippocampal sections, with our regions of interest (ROIs) being NFT‐bearing neurons, non‐NFT‐bearing neurons, and their immediate neuronal microenvironments (50 μm diameter surrounding the neuronal cell body) (Figure 1).ResultAnalyses identified 11 proteins having differential expression in tangle‐bearing neurons when comparing resilient to demented individuals. These included IDH1, GFAP, CD68, IDE, SYP and Histone H3. In addition, IDH1, CD68 and SYP were differentially expressed in the environment of normal and tangle‐bearing neurons when comparing resilient to demented individuals. IDH1 displayed significantly lower expression in the resilient individuals. It is thought that IDH1 is upregulated under energetic and oxidative stress. Similarly, PINK1 (which is upregulated in response to mitochondrial dysfunction and oxidative stress) displayed lower expression in the environment of normal and tangle‐bearing neurons in the resilient. Therefore, we hypothesize that there is less energetic and oxidative stress in the resilient individuals. Synaptophysin (SYP) is increased in the resilient, which likely indicates better maintenance of neuronal synapses and synaptic connections. Furthermore, neurofilament light chain (NEFL) and ubiquitin c‐terminal hydrolase (Park5) expression were higher in the resilient compared to the demented in the environment of NFTs. These differences all suggest healthier intact axons, dendrites and synapses in the resilient.ConclusionIn conclusion, resilient individuals display protein expression patterns suggestive of an environment containing less energetic and oxidative stress, which in turn results in maintenance of neurons and their synaptic connections.