bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sex‐specific molecular profiling to understand pathology and identify causal genes and drug targets for Alzheimer’s disease
Authors: Sung Yun J , Do Anh , Timsina Jigyasha , Wang Lihua , Wang Fengxian , Cruchaga Carlos 
Published: December 2022

AbstractBackgroundAlzheimer’s disease (AD) is a highly heterogeneous multifactorial disease. Genetic influences on AD are strong as shown by several pathogenic genes and over 50 AD GWAS loci. There are also clear sex differences in AD risk and progression. Women are at a higher risk of developing AD and present faster progression. A recent GTEx study also highlights sex differences in the genetic regulation of gene expression. Despite these established sex differences, sex‐specific molecular findings in AD are still limited.MethodWe generated and performed analysis using 713 CSF proteomic data that passed QC obtained from Knight‐ADRC and DIAN samples. The sample includes 190 late onset clinical AD patients and 509 age‐ and gender‐matched cognitively healthy individuals. Age at lumbar puncture and surrogate variables (to account for unmeasured heterogeneity) were included as covariates. To evaluate sex‐specific molecular signatures, we performed sex‐stratified analysis by separately analyzing 333 males and 366 females.ResultIn male CSF tissues, 99 proteins significantly associated with late onset AD status (FDR P < 0.05) and 131 proteins in female CSF tissues. Those proteins were enriched in pathways involved in the endolysosome and proteasome pathway including HSP70. We identified 44 proteins presenting a suggestive evidence of sex‐specific effects on AD status. In particular, several proteins that were previously reported to be associated with AD risk show sex‐specific effects. Downregulation of LRIG3 improves cognitive impairment and alleviate neuronal damage in hippocampus tissues in AD rats through modulating the PI3K/Akt pathway. We found that LRIG3 was associated in males only and would not have been identified through a combined analysis that simply included sex as a covariate (P = 3.4×10‐3 in males; P > 0.5 in females; P = 0.067 in all).ConclusionThis study demonstrates that 800 proteins in 700 CSF tissues can provide a suggestive information on sex‐specific proteomic profiles. We will extend our sex‐specific analysis using our recently generated 7000 proteomic data to fully understand the biology of the disease and determine the role of sex in disease, create sex‐specific genes and prediction models. This will provides insights for clinically translatable interventions for prevention and treatment.
Evaluation of PVC methods on head‐to‐head FTP and MK6240
Authors: Dominguez Pablo Aguilar , Ward Tyler J. , Harrison Theresa M. , Landau Susan M. , Lopresti Brian J , Minhas Davneet S , Klunk William E , Jagust William J. , Baker Suzanne L. 
Published: December 2022

AbstractBackgroundMK6240 and Flortaucipir (FTP) are tau PET tracers with differing off‐target signal. Due to partial volume effects (PVE), this off‐target may spill into neighboring cortical regions. We applied different partial volume correction (PVC) methods to MK6240 and FTP images acquired in the same participants within two months to evaluate the performance of the methods within and between tracers.Method15 subjects (Table1) were scanned using MK6240 (SUVR calculated 70‐90min and 90‐110min) and FTP (80‐100min) on a Siemens Biograph mCT and smoothed to 8mm3 resolution. T1 MRIs were segmented using FreeSurfer 7.1. Five different PVC methods were tested: 2 compartment, 3 compartment, Van‐Cittert (VC), geometric transfer matrix (GTM) and region‐based voxelwise (RBV). Different ROI configurations were used when applying GTM/RBV. To determine how well the PVCed data fit the original data, PVCed images were smoothed back to the 8mm3 resolution and compared to the original image within SPM‐segmented gray matter, white matter, CSF, skull and meninges (Figure1).ResultVC fit the original data best after smoothing (Figure2), but had the highest correlation with the original data (Figure3c), meaning VC does not correct the data as much as the other methods and therefore does not address the PVE issue. 2 and 3 compartment fit the gray and white matter well (Figure2) while showing low R2 between the original and PVC data (Figure3). However, these methods assume zero signal outside the cortex, resulting in poor fit of extracortical areas and leading to over‐amplification of the cortical signal. GTM and RBV perform well for both tracers in all 5 regions. Their correlations between original and PVC data are between 2/3 compartment and VC, indicating these methods correct the data. PVC improves the correlation between FTP and MK6240; the highest correlation between tracers is achieved when the same PVC method is applied to both tracers (r2=0.9 for 2 and 3 compartment, r2=0.89 for GTM, r2=0.88 for RBV, r2=0.84 for VC, r2=0.80 for original).ConclusionGTM/RBV assume non‐zero signal outside the cortex resulting in a better fit of the original data, correct the data more than VC, and improve the correlation between MK6240 and FTP.
Cerebrospinal fluid levels of growth associated protein 43 (GAP‐43) are associated with Alzheimer’s disease biomarkers, cognition, and functional changes
Authors: Bolton Corey J , Khan Omair A. , Liu Dandan , Moore Elizabeth E. , Houston Michelle , Pechman Kimberly R. , Blennow Kaj , Zetterberg Henrik , Hohman Timothy J. , Gifford Katherine A. , Jefferson Angela L. 
Published: December 2022

AbstractBackgroundGrowth‐associated protein 43 (GAP‐43) is a presynaptic protein involved in synaptic plasticity implicated in Alzheimer’s disease (AD). This study investigates cerebrospinal fluid (CSF) levels of GAP‐43 in relation to AD fluid biomarkers and clinical outcomes. To determine how associations change across disease states, interactions with cognitive and amyloid status are examined.MethodVanderbilt Memory and Aging Project participants free of clinical dementia or stroke (n=153, 72±7 years, 37% mild cognitive impairment (MCI)) underwent fasting lumbar puncture, 3T brain MRI, neuropsychological assessment, and informant‐reported functional activities questionnaire (FAQ). CSF GAP‐43, b‐amyloid1‐42 (Ab1‐42), tau, and phosphorylated‐tau (p‐tau) were analyzed in batch. An AD‐signature imaging composite was calculated by summing cortical thickness measurements from brain regions commonly affected by AD (Schwarz et al., 2016). Linear mixed effects models cross‐sectionally related GAP‐43 to CSF AD biomarkers, structural neuroimaging, cognitive, and FAQ variables adjusting for baseline age, sex, education, race/ethnicity, apolipoprotein E (APOE)‐e4 status, modified Framingham Stroke Risk Profile, and cognitive status. Follow‐up models assessed GAP‐43 x cognitive status (normal cognition (NC), MCI) and GAP‐43 x amyloid positivity interactions on outcomes.ResultIn main effect models, higher CSF GAP‐43 was associated with higher CSF tau (p<0.0001) and CSF p‐tau (p<0.0001); worse semantic fluency (p<0.02), visuospatial (p<0.007) and episodic memory performances (p=0.06); and worse FAQ scores (p=0.003). Remaining models were null (p‐values>0.23). GAP‐43 interacted with cognitive status on all CSF biomarkers, AD‐signature composite cortical thickness, visuospatial, executive functioning, and memory performances, and FAQ score (p‐values<0.06). CSF GAP‐43 interacted with amyloid positivity on CSF tau and CSF p‐tau, and memory composite score (p‐values<0.03). Stratified results suggested most associations were slightly stronger in participants with MCI (p‐values<0.02) versus NC and in amyloid positive (p‐values<0.02) versus negative participants.ConclusionWe found higher CSF GAP‐43 levels were associated with tau and p‐tau pathology, AD‐related neurodegeneration on MRI, and worse cognitive and functional scores. Results were more prominent in individuals at increased risk for AD‐type dementia (i.e., participants who were amyloid positive or have MCI). These findings extend prior work by highlighting the importance of synaptic dysfunction in the AD clinical syndrome.
Can Machine Learning advance or hinder statistical modelling of dementia risk in the Population.
Authors: Richardson Connor Duncan 
Published: December 2022

AbstractBackgroundMachine learning and Artificial intelligence is a rapidly growing area of health research in predicting individual and population disease risk. Predicting dementia in the population using neuropathology data is particularly challenged due to the lack of brain donation recourses. Machine learning is growing in popularity due to an increased focus on collecting health data and its powerful ability to compute complex statistics using smaller datasets while handling missing data.There is debate in the research community on the reliability of machine learning for predicting dementia. The thinking behind many machine learning techniques emphasizes use of very large amounts of data, whereas more classical statistical techniques emphasize a prediction of risk based on more specific and targeted data often resulting in smaller sample sizes.If these techniques complement or contradict each other’s ability to accurately predict risk of dementia in the population is yet unknown. Understanding this is paramount for the future of dementia research considering the ever‐growing integration between Neuropathology, data and statistical scienceMethodWe applied machine learning techniques for feature ranking and classification as an unbiased comparison of neuropathological features and assessment of their diagnostic performance using a cohort (n = 507) from the Cognitive Function and Ageing Studies (CFAS). This could then be compared to more traditional statistical modelling using logistic regression.ResultDecision Tree Classification gave clear and easy to interpret roots for dementia risk, visualising a hierarchy from pathologies that is less clear than logistic regression and odds ratios. It produces an individualised look at dementia risk based on specific combinations of pathology that is unclear in the statistical regression model however, does not deal with missing data well.The random forest model is strong in predicting cases of dementia however it misclassifies healthy cases as dementia cases 52% of the time.TDP 43 contributes the most to the model and Lewy bodies contributes the least.ConclusionMachine learning has the potential to analyse neuropathology of dementia in sophisticated ways, however almost half of dementia cases remained misclassified. This points towards a more complex model of dementia pathology that requires further neuropathological assessments and collection of high quality cohort data to analyse.
Indigenous cultural understandings of Alzheimer’s disease and related dementias – research and engagement (ICARE): relationships between cultural strengths, education and stigma
Authors: Lambrou Nickolas H. , Blind Melissa J , Ketcher Dana , Pederson Collette , Johnson January , Hill Annamarie , Dertinger Melinda , Chiovitte Jessica , Trudeau Rhonda , Pitawanakwat Karen , Strong Lois , Summers Marlene , Martin Wesley , Warry Wayne , Lewis Jordan P , Zuelsdorff Megan , Gleason Carey E. , Jacklin Kristen 
Published: December 2022

AbstractBackgroundEvidence indicates Alzheimer’s disease and related dementias (ADRD) and chronic diseases considered risk factors for developing ADRD disproportionately affect Indigenous people. However, little is known regarding lived experiences, needs and resiliencies across the ADRD disease trajectory in Indigenous communities. This work highlights preliminary data from the ICARE study (R56 AG 62307); a project that explored factors impacting ADRD across the disease trajectory. This research focuses on thematic trends and relationships between education, cultural strengths and stigma emerging from the analysis of Sequential Focus Groups (SFGs) data.MethodCommunity‐based participatory research and phenomenology guided qualitative study design, recruitment, data collection and analysis. We conducted SFGs in four tribal community sites in the US and Canada. All participants had professional health care experience working with Indigenous people with dementia (PWD). Each site held between 4‐5 sequential virtual focus groups with consistent participants lasting between 30‐90 minutes. Community‐based researchers led these sessions focused on understandings within the contexts of early, mid and late‐stage dementia.ResultParticipants (N = 17) were aged 18+, and worked with Indigenous PWD (M = 11.09 years; SD = 9.25). Additionally, 41% reported caring for a loved one with dementia. Preliminary analysis suggests thematic shifts within the contexts of early, mid, and late‐stage ADRD, including cultural understandings of ADRD. Participants described a need for education in early‐stage, service access in middle‐stage, and safety and retrospective recommendations for education in late‐stage. Notably, aspects of stigma appeared thematically through each stage, and appeared antithetical to other consistent subthemes of community needs (e.g., education) and cultural strengths (e.g., connectedness, storytelling).ConclusionSFGs revealed nuances of cultural and community level factors influencing the impact of ADRD across the disease trajectory in four diverse Indigenous communities. Preliminary data provide lines of inquiry for ICARE R01 NIH (R01AG062307), such as exploring culturally appropriate education and outreach as inroads to developing effective interventions and reducing stigma, isolation and the burden of ADRD on AI/FN communities. Notions of stigma require further investigation and may reflect Western ideology and disconnection from traditional teachings resulting from colonization.
Exploration of visual hallucinations – FDG‐PET associations in DLB and MCI‐LB with and without visual hallucinations
Authors: Christine Cliatt Brown J , Miyagawa Toji , Przybelski Scott A. , Min Paul H , Jordan Lennon , Lesnick Timothy G. , Graff‐Radford Jonathan , Jones David T. , Savica Rodolfo , Botha Hugo , Ramanan Vijay K , Knopman David S. , Petersen Ronald C. , Fields Julie A. , Machulda Mary M. , Forsberg Leah K. , Diaz‐Galvan Patricia , Li Wentao , Jack Clifford R. , Kantarci Kejal , Lowe Val J. , Boeve Bradley F. 
Published: December 2022

AbstractBackgroundSeveral hypotheses exist regarding the neurologic substrate underlying visual hallucinations (VH) in Dementia with Lewy Bodies (DLB). Studies have suggested an association with regional cerebral hypometabolism in the primary visual or visual association cortices on FDG‐PET. We compared regional cerebral glucose metabolism and posterior cingulate island sign (CIS) ratios in DLB and in Mild Cognitive Impairment with Lewy Bodies (MCI‐LB) participants with (VH+) and without (VH‐) VH.MethodParticipants with clinically probable DLB or MCI‐LB and an FDG‐PET scan were identified from the Mayo ADRC database. Responses on the Visual Hallucinations and Delusions Questionnaire and Neuropsychiatric Inventory were used to assess presence or absence of VH. Standardized uptake value ratios (SUVr) were used to calculate regional cerebral glucose hypometabolism in the primary visual cortex (PVC), lateral occipital cortex (LOC), and whole occipital cortex (WOC). The CIS ratio was defined as posterior cingulate divided by the sum of the precuneus plus cuneus FDG‐PET uptake ratio.ResultTwo cohorts were identified (27 DLB and 18 MCI‐LB, 87% male). Eighteen DLB participants were VH+ and 9 were VH‐, whereas 5 MCI‐LB were VH+ and 13 were VH‐. There were no statistically significant differences in demographic (age, sex, education) and clinical features (MoCA, CDR, presence of other core DLB features, and UPDRS) between VH+ vs VH‐ in those with DLB, and between VH+ vs VH‐ in those with MCI‐LB. Similarly, there were no statistically significant differences in DLB between VH+ vs VH‐ in PVC (1.55±0.14 vs 1.58±0.15), LOC (1.22±0.16 vs 1.28±0.12), WOC (1.29±0.15 vs 1.34±0.11), or CIS ratio (1.16±0.08 vs 1.14±0.10), nor in MCI‐LB between VH+ vs VH‐ in PVC (1.68±0.19 vs 1.58±0.14), LOC (1.40±0.21 vs 1.32±0.14), WOC (1.45±0.20 vs 1.37±0.14), or CIS ratio (1.17±0.08 vs 1.19±0.10) (P>0.05 for all comparisons).ConclusionThese findings do not support a direct correlation between regional cerebral glucose metabolism and VH in DLB or MCI‐LB. Further work in DLB and MCI‐LB with larger numbers is warranted. Other neurologic substrates and mechanisms, such as REM sleep intrusions during wakefulness, neurotransmitter dysfunction, and disruption of functional connectivity across brain regions should be explored as contributors to VH in DLB.
Identification of Genetic Variants and Serum Metabolites Associated with Blood‐derived Mitochondrial DNA Copy Number
Authors: Tong Tong , Farrell John , Zhu Congcong , Khurshid Zainab , Martin Eden R. , Bush William S. , Pericak‐Vance Margaret A. , Wang Li‐San , Schellenberg Gerard D. , Haines Jonathan L. , Lunetta Kathryn L. , Farrer Lindsay A. , Zhang Xiaoling , Project Disease Sequencing 
Published: December 2022

AbstractBackgroundMitochondrial DNA Copy Number (mtDNA‐CN) can be used to estimate mitochondrial function and it varies across tissues and cell types. Blood‐derived mtDNA‐CN has been studied in aging‐related diseases including Alzheimer’s Disease (AD). However, little is known about the genetic contribution to variation of blood‐derived mtDNA‐CN and how this variation affects the level of serum metabolites.MethodWe developed a fast computational pipeline to accurately estimate mtDNA‐CN using whole genome sequence data from blood samples from European ancestry (EA, 2,885 AD cases and 2,777 controls), African American (AA, 1,118 AD cases and 1,725 controls), and Caribbean Hispanic (CH, 1,022 AD cases and 1,949 controls) participants of the Alzheimer’s Disease Sequencing Project (ADSP). In the combined sample and within each population group. We tested association of blood‐derived mtDNA‐CN with 17,569,705 variants (minor allele count≥20) genome‐wide using a linear mixed effect model including covariates for age, sex, sequencing center, PCR, AD status, polygenic risk score of blood cell count, and principal components of ancestry. We also tested the associations of mtDNA‐CN and levels of 175 serum metabolites in 1,521 subjects from Alzheimer’s Disease Neuroimaging Initiative.ResultIn the total sample, we identified genome‐wide significant (GWS) associations with variants from 24 independent loci. The top‐ranked SNP (rs1455130415 in NDUFS8, (p=3.25x10‐42) was also GWS in EAs (p=3.96x10‐28) and AAs (p=2.20x10‐27). SNP rs997412864 (p=5.13x10‐9) in GCAT was found associated with mtDNA‐CN in EAs, but not in other groups. Association of SNP rs1237233197 (p=1.40x10‐8) in NDUFS7 was specific to AAs. Measurements of two long‐chain acylcarnitine, C14:2‐OH (p=4.91x10‐7) and C18:2 (p=1.81x10‐5), were negatively associated with mtDNA‐CN after Bonferroni correction (P<2.9x10‐4). Ornithine, a non‐proteinogenic amino acid, was also negatively associated with mtDNA‐CN (p=2.32x10‐4).ConclusionWe identified GWS association of blood‐derived mtDNA‐CN with a variant in a nuclear‐encoded mitochondrial gene, NDUFS8, which encodes a subunit of Complex I. In addition, mtDNA‐CN was significantly associated with two acylcarnitine metabolites and ornithine whose levels were previously reported to be decreased in blood of AD patients. These results provide more insight about the role of mitochondrial function in AD pathogenesis and suggest that blood‐derived mtDNA‐CN may be a useful biomarker for AD.
APOE4 genotype associations with longitudinal change in hippocampal microstructure
Authors: Zhu Alyssa H , Nir Talia M , Gari Iyad Ba , Dixon Daniel , Islam Tasfiya , Villalon‐Reina Julio E , Thompson Paul M , Jahanshad Neda 
Published: December 2022

AbstractBackgroundApolipoprotein E4 (APOE4) is a major risk factor for late‐onset Alzheimer’s disease (AD). Associations between APOE4 genotype and hippocampal atrophy have been reported, but few longitudinal studies have evaluated microstructural changes. There is evidence that risk conferred by APOE4 may be partially driven by factors such as cerebral iron burden, neuronal injury, and inflammation. Brain iron concentration and other microstructural properties may be detected in vivo using quantitative susceptibility mapping (QSM) and diffusion‐weighted MRI (dMRI). Here, we characterized the effects of APOE4 genotype on longitudinal changes in hippocampal QSM and dMRI microstructural measures in a population‐based sample of adults from the UK Biobank (UKB).Method598 UKB participants of European ancestry (baseline age: 48‐78 years) with baseline and ∼2 year follow‐up brain MRI data were analyzed (Figure 1A). Four dMRI measures were derived from diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI; Figure 1B). Median dMRI and QSM hippocampal measures were extracted and averaged across hemispheres. For comparison with microstructure, bilateral hippocampal volume was also estimated. Linear regressions were used to test associations between change in six hippocampal measures (FollowUp‐Baseline) and number of E4 alleles (e3e3/e3e4/e4e4) adjusting for the time‐interval between scans, baseline MRI measure, age, sex, age‐by‐sex, age2, age2‐by‐sex, education, and the first 4 genetic ancestry principal components. Age‐dependent effects were explored in post‐hoc analyses including a sensitivity analysis stratifying associations by age (< 65 or ≥ 65 yrs) and evaluation of APOE4‐by‐age interactions.ResultCompared to e3e3 homozygotes, E4 carriers had significantly greater increases in MD and ISOVF and greater decreases in ND (p<critical FDR threshold; q=0.05; Table 1). E4 carriers ≥65 had nominally greater increases in MD and ISOVF and greater decrease in volume (p<0.05); APOE4‐by‐age interactions were found for changes in ISOVF and volume (p<0.05; Figure 2).ConclusionDisease risk conferred by the APOE4 genotype may be linked with greater inflammation (greater diffusivity) or neuronal loss. E4 associations with atrophy were only found in the subset of older participants, suggesting that detection of microstructural changes may precede volumetric changes in the hippocampus.