bluuebunny/crossref_metadata_2025_split huggingface dataset:
Accelerated brain ageing in participants with higher cerebrovascular lesion load across aging, cognitive impairment, and mixed presentation
Authors: Beheshti Iman , Potvin Olivier , Dadar Mahsa , Duchesne Simon 
Published: December 2023

AbstractBackgroundWe aimed to investigate the association between cortical brain age and white matter hyperintensities (WMH) in diverse forms of clinically‐defined vascular conditions. Our hypothesis was that a higher WMH burden would be associated with higher cortical brain age in all clinical subtypes.MethodWe used standardized MRI data obtained from participants in the COMPASS‐ND cohort of the CCNA. We used a standard linear support vector regression algorithm to estimate brain age, matching chronological age to cortical anatomical measurements obtained with the FreeSurfer toolbox on T1‐weighted MRI, sex, and intracranial volume. We calculated brain‐PAD (i.e., predicted brain age minus real age) and applied bias adjustment to remove age‐dependency in the estimates. A validated automated technique utilizing T2‐weighted and fluid attenuated inversion recovery MRIs was used to compute WMH loads.ResultParticipants included 107 CIE, 240 mild cognitive impairment (MCI), 115 vascular‐MCI (V‐MCI), 81 probable Alzheimer’s disease (AD), and 50 V‐AD. There was a significant difference in brain‐PAD [ F (4,599) = 56, P < 0.001, ANCOVA test] among groups, whilst adjusting for sex and chronological age. All four categories of patients exhibited a significantly higher mean brain‐PAD than CIE (P < 0.001), with the AD cohort having the highest brain‐PAD. There was a significant difference in WMH loads [ F (4,586) = 48, P < 0.001, ANCOVA test] between groups, whilst adjusting for sex and age. All cohorts showed a positive correlation between brain‐PAD and WMH, indicating accelerated ageing in people with higher WMHs. We observed moderate and significant correlations between brain‐PAD and WMH loads for V‐MCI (r = 0.34, P < 0.001) and V‐AD (r = 0.36, P = 0.001) but weaker, while significant correlations in CIE (r = 0.20, P = 0.035) and AD cohorts (r = 0.24, P = 0.025).ConclusionWe observed a positive and significant link between brain‐PAD and WMH in all categories. This finding indicates on the importance of treatment and prevention strategies for vascular risk factors, which might be able to slow down the progression of cerebrovascular lesions and delay the effect on cortical thickness.
Association of Technology Use with Subjective Cognitive Decline among Older Adults
Authors: Kiselica Andrew M , Stojanovic Marta , Waters Abigail , Benge Jared 
Published: December 2023

AbstractBackgroundOlder adults with a family history of Alzheimer’s disease (AD) have elevated rates of subjective cognitive decline (SCD). SCD is distressing for older adults and is a risk factor for future objective cognitive impairment, AD pathology, and AD diagnosis. Hence, it is important to understand whether lifestyle factors can mitigate SCD. One potentially protective factor is the use of technology‐based behavioral strategies for completing daily tasks. For example, in the face of subjective memory difficulties, an older adult may move from relying on memory to remember to pay bills to utilizing an automatic bill pay system. The current study examined whether greater use of technology for daily task completion was associated with fewer self‐reported cognitive symptoms, especially in individuals with family history of AD.MethodIndividuals over the age of 50 (range: 50‐85) completed surveys about their digital and analog approaches for completing daily tasks, as well as measures of physical activity and SCD. Participants with and without family histories of AD were matched on age, education, and sex. Multiple linear regression was used to assess main effects of family history and technology‐based behavioral strategy use on SCD. In addition, we examined whether family history of AD moderated this association.ResultThere was no main effect of technology‐based behavioral strategies on SCD (p = 0.259). However, there was an interaction effect between technology use and family history of AD on SCD. Specifically, in individuals with a family history of AD, more reliance on technology‐based behavioral strategies was associated with less SCD (t = ‐2.45, p = 0.018), but this relationship was not significant in individuals without family history of AD (t = 0.66, p = 0.511). This interaction remained significant even when controlling for another important protective lifestyle factor for SCD, physical activity.ConclusionOur findings suggest that technology‐based behavioral strategies are associated with less SCD in individuals with a family history of AD, independent of other protective lifestyle factors. Future recommendations provided by healthcare providers in the community to address subjective cognitive decline in cognitively unimpaired older adults might include focusing on technological assistance.
Minor neuropsychological deficits in SCD: Association with AD biomarkers and cognitive decline
Authors: Stark Melina , Wolfsgruber Steffen , Kleineidam Luca , Peters Oliver , Priller Josef , Schneider Anja , Fliessbach Klaus , Wiltfang Jens , Jessen Frank , Düzel Emrah , Bürger Katharina , Perneczky Robert , Teipel Stefan , Laske Christoph , Spottke Annika , Heneka Michael T. , Wagner Michael 
Published: December 2023

AbstractBackgroundThe clinical relevance and longitudinal impact of minor neuropsychological deficits (MND) in cognitively normal memory clinic patients with subjective cognitive decline (SCD) is still unclear. To address this, we set out to investigate whether MND in SCD patients are related to Alzheimer´s disease (AD) pathology and future cognitive decline. In addition, we wanted to assess whether SCD patients have an increased risk of neuropathology and cognitive decline even in the absence of MND.MethodWe included 233 cognitively normal, healthy control participants (HC) without subjective cognitive concerns and 439 memory clinic SCD patients with available baseline CSF and/or longitudinal cognitive data from the observational DZNE Longitudinal Cognitive Impairment and Dementia (DELCODE) study. Participants, who scored at least 0.5SD below the mean on a composite measure calculated from the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) neuropsychological assessment battery, were classified as having MND. We compared the resulting participant groups regarding their baseline CSF Aβ42/40, p‐tau181, t‐tau and Aβ42/p‐tau181 levels, longitudinal preclinical Alzheimer´s cognitive composite (PACC5) trajectories and risk of progression to incident mild cognitive impairment (MCI).ResultCompared to SCD patients without MND (n = 384), SCD patients with MND (n = 55, 12.5% of SCD sample) showed significantly elevated CSF AD biomarker levels, accelerated PACC5 decline and a higher risk of progression to incident MCI. Baseline MND had a positive predictive value of 57.0% (95%CI: 38.5‐75.4) and a negative predictive value of 86.0% (95%CI: 81.9‐90.1) for the progression to incident MCI within 3 years. SCD patients without MND showed increased PACC5 decline and a higher risk of incident MCI compared to HC participants without MND (n = 215), but did not differ from HC participants in their CSF biomarker levels.ConclusionMND are a risk factor for AD pathology and cognitive decline in memory clinic patients with SCD. The assessment of MND could thus provide useful information for AD risk stratification and enrichment in SCD samples. Importantly, SCD patients still have an increased risk for cognitive decline in the absence of MND.
Genome‐wide Association Study Meta‐analysis of Neurofilament light (NfL) levels in blood reveals novel loci related to neurodegeneration
Authors: Ahmad Shahzad , Imtiaz Mohammed Aslam , Mishra Aniket , Wang Ruiqi , Rivero Marisol Herrera‐ , Bis Joshua C , Fornage Myriam , Roshchupkin Gennady V. , Hofer Edith , Logue Mark W. , Longstreth W.T. , Xia Rui , Bouteloup Vincent , Mosley Thomas H. , Launer Lenore J J. , Khalil Michael , Kuhle Jens , Rissman Robert A , Chêne Geneviève , Dufouil Carole , Djoussé Luc , Lyons Michael J. , Mukamal Kenneth J , Stone William Seth , Franz Carol E , Schmidt Reinhold , Debette Stéphanie , Breteler Monique M.B. , Berger Klaus , Yang Qiong , Seshadri Sudha , Aziz N. Ahmad , Ghanbari Mohsen , Ikram M. Arfan 
Published: December 2023

AbstractBackgroundNeurofilament light chain (NfL) levels in circulation have been established as a sensitive biomarker of neuro‐axonal damage across a range of neurodegenerative disorders. Elucidation of the genetic architecture of blood NfL levels and its genetic correlation with neurological traits could therefore provide new insights into shared molecular mechanisms underlying neurodegenerative disorders.MethodTo identify the genetic variations underlying blood NfL levels, we conducted an ancestry‐specific meta‐analyses of genome‐wide association studies (GWAS) based on 18,532 participants from 11 cohorts of European and 1142 participants (3 cohorts) of African‐American ancestry. In the post‐GWAS analyses, we performed expression quantitative trait loci (eQTL) analysis, LD‐regression, and genetic risk score (GRS) association analysis with neurological traits.ResultIn the European ancestry GWAS meta‐analysis, we identified two genome‐wide significant (P< 5×10−8) loci at 16p12 (UMOD), and 17q24 (SLC39A11). In the African‐American ancestry GWAS meta‐analysis, we identified three novel loci at 1q43 (FMN2), 12q14, and 12q21. Genetic correlation based on the European ancestry meta‐analysis with neurological traits showed a strong genetic correlation of NfL with Alzheimer’s disease (AD) (rg = 0.32, P = 1.74×10−6), total‐tau (rg = 2.01, P = 1.03×10−6), amyloid‐beta (Aβ)‐40 (rg = 0.80, P = 6.92×10−6),and Aβ‐42 (rg = 1.03, P = 4.39×10−5). A higher genetic risk score based on NfL‐associated genetic variants was also related to increased plasma levels of total‐tau (P = 1.97×10−4), Aβ‐ 40 (P = 2.24×10−5), Aβ‐42 (P = 2.92×10−4) in the Rotterdam Study.ConclusionThis large‐scale GWAS meta‐analysis revealed multiple novel genetic loci of NfL levels in blood in participants from European and African‐American ancestry. Significant genetic correlation of genes underlying NfL with AD, Aβ‐42, and total‐tau may indicate a common underlying pathway of neurodegeneration.
Proteome‐wide effects and causality of familial Parkinson’s disease
Authors: Yu Lai Mei‐ , Benitez Bruno A. 
Published: December 2023

AbstractBackgroundThe causal mediators of phenotype‐associated genetic variation in ∼75 genetic loci associated with Parkinson’s disease (PD) risk, including LRRK2 and GBA1 key loci, remain unresolved. We integrated the cerebrospinal fluid (CSF) proteome and genomic data to uncover causal associations in genetically‐defined PD.MethodsCSF proteome from the Parkinson’s Progression Markers Initiative (PPMI) cohort (N = 1158) was quantified using the 5K SOMAscan assay. We integrated whole genome sequencing data of 683 samples to identify genetic variants associated with protein levels (protein quantitative trait loci; pQTLs). pQTLs within a region of ± 1Mb from the sentinel SNP as cis‐pQTLs, otherwise, as trans‐pQTLs. We sought pleiotropic locus associated with multiple proteins. We used the latest meta‐analysis from Nalls et al.[1] as an outcome to implement two‐sample Mendelian randomization using TwoSampleMR[2]. We identified colocalized proteins for PD using coloc[3,4,5]. We identified associated proteins with sporadic PD (sPD), and PD with mutations in LRRK2, and GBA1. The pathway analysis was performed using pathfindR[6]. We built elastic‐net logistic regression models to select protein combinations for sPD, LRRK2+ PD, and GBA1+ PD prediction using glmnet[7]. Plasma proteome data from the Penn Udall and Multi‐site PDBP were used as replication cohorts[8].ResultsWe found 701 cis‐ and 420 trans‐pQTLs, and 23 causal proteins; eight of which colocalized with PD risk loci. We found 92 pleiotropic loci, including the APOE and LRRK2 loci. We found 59, 271, and 127 proteins associated with sPD, LRRK2+ PD, and GBA1+ PD, respectively. The predictive models with 33, 154, and 66 proteins for precisely classifying sPD vs. controls (AUC = 0.835; PR‐AUC = 0.898; F1‐score = 0.852), LRRK2+ PD vs. sPD (AUC = 0.95; PR‐AUC = 0.918; F1‐score = 0.828), and GBA1+ PD vs. sPD (AUC = 0.877; PR‐AUC = 0.776; F1‐score = 0.688), respectively. LRRK2+ and GBA1+ PD associated proteins were enriched for dopaminergic synapse, proteasome, and lysosome pathways, and sPD‐associated proteins were enriched for neurotrophin, endocytosis, and chemokine signaling. We found 25 PD‐associated proteins in the Upenn Udall Discovery cohort and replicated 11 proteins in the PDBP replication cohort.ConclusionsOur systems biology approach discovered causal PD proteins which could be used as drug targets and offers a novel methodology to improve our understanding of PD pathogenesis and prediction.
Integration of cerebrospinal fluid proteomics and clinical scaled scores for subtyping sporadic Parkinson’s disease
Authors: Lai Mei‐Yu , Palanisamy Divya , Benitez Bruno A. 
Published: December 2023

AbstractBackgroundMolecular pathways and networks associated with sporadic PD (sPD) subtypes are still unclear. We applied a systems biology approach to identifying proteomic signatures associated with sPD, cognitive and motor scaled scores, brain volumetric measurements, and cerebrospinal fluid (CSF) phosphorylated‐tau (p‐tau), total‐tau (t‐tau), alpha‐synuclein (a‐syn), and amyloid‐beta (a‐beta) levels.MethodsWe used CSF proteomic data of sPD from the Parkinson’s Progression Markers Initiative (PPMI) cohort (n = 377 sPD vs. 182 controls) and built an elastic‐net logistic regression model, adjusting for age, sex, and principal components, for sPD risk prediction using glmnet[1]. We performed correlation of baseline proteome with cognitive (MoCA) and motor assessments (UPDRS‐I, II, and III), neuroimaging data (Dopamine transporter scan; DaTscan of mean bilateral caudate, putamen, and striatum thickness (mm)), and CSF biomarkers. We implemented weighted gene co‐expression network analysis (WGCNA) to group co‐expression CSF proteins for sPD and to subtype sPD by CSF biomarkers using WGCNA[2] and ConsensusClusterPlus[3].ResultsWe found a CSF proteomic signature (59 proteins) differentially expressed in sPD compared to controls linked to neurotrophin, endocytosis, and chemokine signaling. There are 82 and 68 proteins differentially expressed in UPDRS‐II and UPDRS‐III scores. Three, 13, and 11 proteins were associated with caudate, putamen, and striatum volumes. We found proteins correlated with p‐tau (n = 1394), t‐tau (n = 1577), a‐syn (n = 1427), and a‐beta (n = 847) levels. We implemented a predictive model with 33 proteins for sPD (AUC = 0.83) that outperformed the models with UPDRS‐I (AUC = 0.71), MoCA (AUC = 0.67), p‐tau (AUC = 0.56), t‐tau (AUC = 0.61), a‐syn (AUC = 0.54), and a‐beta (AUC = 0.57) but underperformed UPDRS‐III (AUC = 0.99) or putamen (AUC = 0.99). WGCNA indicates CSF biomarkers are highly correlated to six protein modules for subtyping sPD: each module was enriched for different pathways, ME1 (SNARE and MAPK signaling), ME9 (autophagy), ME7 (lysosome and MAPK), ME6 (immune response), ME10 (proteostasis and phagocytosis), and ME2 (longevity regulating and mTOR). Those modules defined two subtypes of sPD by p‐tau levels (9.93 pg/mL, AUC = 0.909). MoCA (p = 0.035), t‐tau (p = 3.2×10−60), a‐beta (p = 1.7×10−22), and a‐syn (p = 4.6×10−32) were significantly lower in the subtype with low levels of p‐tau.ConclusionsIntegrating molecular profiling and clinical scale scores improves the prediction of sPD risk and identifies sPD subtypes.
Integrated Memory Care: Primary Care for Persons Living with Dementia
Authors: Clevenger Carolyn K 
Published: December 2023

AbstractThe Integrated Memory Care (IMC) program is a nurse practitioner led, physician‐supported primary care model which serves individuals living with Alzheimer’s disease and related dementias (ADRD) and their care partners living in the community. Individualized care plans align with patients’ goals, and patients benefit from care coordination provided by an expert team that includes nurses, social workers, occupational therapists, and dementia care assistants, all working directly with primary care providers. The IMC adds a palliative care perspective as well, emphasizing aggressive symptom management and advance care planning, thereby providing holistic care for individuals living with ADRD.Over 2000 patients and family care partners have received care at IMC since 2015. The average age of the patient population is 78.6 years; 65% of patients are female; 42% of patients are non‐white. The practice has developed and tested dementia‐specific workflows that can be implemented in a fee‐for‐service primary care environment. Successful completion of dementia quality measures, such as documentation of dementia type and stage, as well as assessment of cognitive, functional, and behavioral symptoms, is >90%. The ambulatory care sensitive admission rate of patients enrolled in the program is 0.5‐0.9%, well below published rates of 13% to 15%. In a case‐matched control group of patients, the IMC relative risk of hospitalization was one‐half those managed in usual primary care.The IMC represents a novel model of care for individuals living with ADRD and their care partners, achieving superior patient outcomes and representing a financially sustainable model for this growing population. Its primary care systems and processes provide an integrated pathway to quality care, thereby facilitating the identification of patients in the primary care setting who are living with ADRD.
Predicted Brain Age Difference Scores at Age 56 Are Associated with Executive Function Changes Across 12 Years
Authors: Gustavson Daniel E. , Elman Jeremy A. , Reynolds Chandra A. , Eyler Lisa T. , Fennema‐Notestine Christine , Gillespie Nathan A. , Neale Michael C. , Panizzon Matthew S. , Lyons Michael J. , Franz Carol E , Kremen William S. 
Published: December 2023

AbstractBackgroundPredicted brain age difference (PBAD) scores are novel metrics which compare chronological age to age predicted from neuroimaging data. PBADs are highly relevant to Alzheimer’s disease (AD) and cognitive aging. Individuals with AD have predicted brain ages about 10 years older than their chronological age (Franke et al. 2010). Less is known about PBADs in midlife, but recent work on the sample studied here has demonstrated that PBADs are highly heritable (59‐75%), highly stable between middle‐ and early‐old age, and moderated by modifiable lifestyle behaviors earlier in midlife (age 40). The current study examined how midlife PBAD (mean age 56) predicted executive functions concurrently and longitudinally into early old age (mean age 68).MethodWe examined 779 non‐demented men in the Vietnam Era Twin Study of Aging (VETSA) who completed cognitive and neuroimaging assessments at up to three assessments (M = 56, 62, and 68 years). PBADs were based on two methods (Liem et al., 2017, Cole et al. 2019) which include information about grey matter or both gray and white matter. Executive function was based on six tasks spanning inhibition, shifting and working memory. Analyses controlled for age, diabetes, hypertension, and APOE‐ε4 dosage.ResultLatent growth models revealed that greater PBAD at age 56 (i.e., greater brain age than chronological age) was associated with worse executive functioning at baseline and greater decline in executive functioning over the following 12 years. The latter association was observed only for PBADs that included white matter (Cole et al., 2019) and persisted even when excluding N = 61 individuals with amnestic mild cognitive impairment (MCI). Subsequent analyses indicated that associations were not moderated by APOE allele status or cognitive reserve (general cognitive ability at mean age 20).ConclusionOur findings indicate that PBAD is associated with executive functioning in midlife and predicts executive function changes into early old age. They also suggest that PBADs capturing more information about brain health are better predictors of executive function decline across midlife into early‐old age. Finally, the persistence of these effects within cognitively normal subjects suggests PBADs are important predictors of cognitive decline before the onset of MCI/AD.
Chasing the cell type‐specific molecular networks involved in Alzheimer’s Disease
Authors: Lopes Katia de Paiva , Vialle Ricardo A , Green Gilad Sahar , Fujita Masashi , Wang Yanling , Gaiteri Chris , Menon Vilas , De Jager Philip L , Habib Naomi , Tasaki Shinya , Bennett David A. A 
Published: December 2023

AbstractBackgroundAlzheimer’s disease (AD) is the primary cause of dementia, characterized by accumulation of neuritic plaques and neurofibrillary tangles, accompanied by neuronal loss and gliosis. Single‐cell profiling shows that AD involves a complex interplay of every major brain cell type. However, the molecular mechanisms responsible for cell‐type‐specific transcriptional changes remain to be defined.MethodWe used systems biology methods to analyze bulk RNASeq from DLPFC tissues of 1,210 participants and snRNASeq of 424 individuals from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP). We identified modules of co‐regulated genes, assigned them to coherent cellular processes, and assessed which modules were associated with AD traits such as cognitive decline, tangle density, and β‐amyloid deposition.ResultFrom the bulk RNASeq, we found 34 coexpressed modules with at least 30 genes. These modules were enriched for GO terms such as Lipid metabolism (M09), Apoptosis (M32), and Mitochondria (M24). Because variation in cell type proportions can drive coexpression patterns between genes, we verified that changes in cell proportion did not account for the module trait associations. Integrating the bulk modules with cell counts, we observed that 38% (13) of all modules had their variance significantly explained by specific cell‐type counts, including 41% (7) of the modules enriched for cell‐specific gene makers. Eight modules were associated with AD traits, where module M03 showed the strongest association with cognitive decline (P‐value = 2.56×10−19). This module was not enriched for a particular cell type, suggesting preservation across brain cells. Next, we constructed networks by each cell type and performed Bayesian inference to predict distinct directional relationships between AD traits and modules in each cell‐type network. The strongest associations with pathology were found in mic_M46 (tangles) enriched with genes related to negative regulation of T cell activation, and mic_M45 (β‐amyloid) enriched for transcription factors. By contrast, Oli_M14 (e.g. genes UMAD1, TPCN1, BCL2L1), enriched with bulk M03 genes, was the best module directly associated with cognitive decline after conditioning on all other correlated modules.ConclusionOur findings show that single‐cell co‐expression networks of the aging brain can highlight distinct molecular programs involved in cognition and pathology accumulation.