bluuebunny/crossref_metadata_2025_split huggingface dataset:
Synaptic dysfunction reflected in CSF: serial CSF sampling reveals trajectories of potential synaptic biomarkers in early AD stages
Authors: Duits Flora H. , Nilsson Johanna , Teunissen Charlotte E. , Zetterberg Henrik , Blennow Kaj , Scheltens Philip , van der Flier Wiesje M. , Brinkmalm Ann 
Published: December 2022

AbstractBackgroundSynaptic dysfunction plays an important role in Alzheimer’s disease (AD) and cognitive decline. Using serial CSF sampling we investigated longitudinal trajectories of synaptic proteins, including SYT1, SNAP25, neurogranin, synucleins, neuronal pentraxins (NPTX) and three 14‐3‐3 proteins, in AD.MethodsWe included 150 subjects with subjective cognitive decline (SCD; 62±7 years), mild cognitive impairment (MCI; 66±8 years) or AD dementia (64±7 years) from the Amsterdam dementia cohort, based on availability of CSF at two time points and clinical follow‐up of more than one year (median[interquartile range] 3.3[2.1‐5.1] years). Synaptic proteins were analyzed using two different mass spectrometry‐based assays. We used linear mixed models to assess whether baseline protein levels and slopes over time differed between groups (first analysis), and to assess associations of protein levels with cognitive function at baseline and decline over time, measured by repeated MMSE (second analysis). We first compared groups according to baseline diagnosis. Secondly, non‐demented subjects were compared based on CSF AD profile (i.e. abnormal Ab42 and tau levels; 60 AD‐ and 40 AD+ subjects). All protein levels were transformed to Z‐scores for comparison of effect sizes and were evaluated in separate models.ResultsAt baseline levels of SNAP25, neurogranin, beta‐synuclein and 14‐3‐3 proteins were higher in AD dementia than in SCD and MCI (figure 1), and in non‐demented AD+ versus AD‐ subjects. NPTX‐2 levels were lower in AD compared with SCD. Over time SYT1, NPTX‐1, NPTX‐2 and NPTX‐receptor decreased in AD dementia patients (st.B between ‐0.10 and ‐0.15; figure 2A), while 14‐3‐3 zeta/delta increased in MCI subjects (st.B[SE] 0.08[0.03]). Similar changes were seen in non‐demented AD+ subjects (Figure 2B). In SCD subjects lower baseline levels of NPTX‐2 were associated with lower baseline MMSE. In SCD and MCI subjects higher levels of SNAP25, Ng, synucleins and 14‐3‐3 proteins were associated with faster cognitive decline.ConclusionCSF levels of SYT1 and NPTX decreased over time in subjects with early symptomatic AD, while higher baseline levels of other synaptic proteins were associated with future disease progression. This dissociation may reflect complex synaptic changes in early disease stages and may have added value in a prognostic model for AD.
Self‐reported sleep quality interacts with Alzheimer’s disease biomarkers on brain structure and metabolism in cognitively unimpaired adults
Authors: Stankeviciute Laura , Falcon Carles , Operto Grégory , Garcia Marina , Shekari Mahnaz , Niñerola‐Baizán Aida , Perissinotti Andrés , Minguillón Carolina , Fauria Karine , Molinuevo Jose Luis , Zetterberg Henrik , Blennow Kaj , Suárez‐Calvet Marc , Cacciaglia Raffaele , Gispert Juan Domingo , Grau‐Rivera Oriol 
Published: December 2022

AbstractBackgroundPoor sleep quality has been associated with a greater burden of AD pathology and altered brain structure in cognitively unimpaired (CU) individuals. However, there is a lack of studies analysing associations between sleep quality and brain structure and function, while taking into account AD pathology. We aimed to test associations of self‐reported sleep quality with grey matter volume (GMv) and brain glucose metabolism in middle‐aged CU and sought interactions with biomarkers of AD pathology.MethodThis study included 340 CU adults from the ALFA+ cohort (Table 1). Sleep quality was assessed using the Pittsburgh sleep quality index (PSQI) questionnaire. We used Statistical Parametric Mapping software to process T1‐weighted MRI and [18F]fluorodeoxyglucose (FDG) PET scans, and analyse voxel‐wise associations with sleep quality. We created separate general linear models with GMv and FDG metabolism as dependent variables, and PSQI total score, age, sex, APOE‐ε4 status, education level and sleep medication as predictors. GMv analyses were additionally corrected for total intracranial volume. In additional models, CSF Aβ42/40 and phosphorylated tau (p‐tau181) ‐ were entered as covariates. We also tested the interaction between PSQI and AD biomarker status, (A+: Aβ42/40<0.071, T+: p‐tau181>24 pg/mL). Statistical significance was set at p<0.005 uncorrected for multiple comparisons, with a cluster‐level threshold of 50 voxels.ResultHigher PSQI scores, indicating poorer sleep quality, were significantly associated with lower GMv in the left occipital fusiform gyrus and right middle orbitofrontal gyrus, and with lower brain glucose metabolism in the right temporal pole and parahippocampal gyrus (Figure 1.). Interaction analyses showed poorer sleep quality is associated with higher GMv in bilateral supramarginal gyrus in A+T‐ vs A‐T‐, and with higher glucose metabolism in bilateral thalamus in A+T+ vs A‐T‐ individuals (Figure 2).ConclusionPoorer sleep quality is associated with lower GMv and glucose metabolism in CU individuals, involving AD‐vulnerable regions. Sleep quality interacted with altered AD biomarkers to determine GMv and glucose metabolism in brain areas in which they are commonly found to be increased in preclinical AD stages. Altogether, our findings support sleep quality as a relevant factor early in the AD continuum.
Development and validation of a blood‐based protein predictor of 20‐year dementia risk from middle age
Authors: Paterson Clare , Zhang Amy , Ostroff Rachel , Hagar Yolanda , Loupy Kelsey , Williams Stephen 
Published: December 2022

AbstractBackgroundIn the US the number of individuals affected by dementia is expected to double by 2040. Thus, tools enabling identification of at‐risk individuals earlier in disease progression, or before disease onset, are vital. Proteomics‐driven risk stratification for dementia may enable the initiation of life‐planning and/or elicit changes in modifiable lifestyle‐related risk factors. Moreover, test results may facilitate enrichment of clinical trial enrollment, and ultimately as an aid in precision prescribing of preventative therapeutics. Therefore, we sought to develop and validate a blood‐based protein‐only predictor of 20‐year dementia risk from middle age.MethodUsing modified‐aptamer proteomics technology, SomaScan® assay v4.0, we scanned ∼5,000 proteins in 11,277 EDTA plasma samples from individuals aged 49‐73 without known dementia at visit 3 blood draw of the Atherosclerosis Risk in Communities (ARIC) study, totaling ∼56million protein measurements. A total 1,305 incident dementia diagnoses occurred within 20 years of blood‐draw. Time to dementia diagnosis events were modeled with protein measurements using machine learning methods in 70% of ARIC V3 as a training dataset. A model was selected based on performance in a 15% holdout sample subset and validated in the remaining 15% samples. In a subset of 3,852 individuals with known ApoE status, the performance of the proteomic model was compared to that of the predictive performance of risk genotype.ResultA 25‐feature protein‐only accelerated‐failure‐time Weibull model was successfully developed to predict the probability of a dementia diagnosis within 20 years of blood draw. Model performance was measured using AUC, which was equal to 0.73, 0.73, and 0.70, in the training, model selection, and validation datasets, respectively. Based on predicted probabilities from the model, individuals were stratified into low, medium‐low, medium‐high, and high risk bins, with a 20‐year event rate of 3.79% vs 35.58% in low vs high risk bins. Model performance was significantly superior to that of ApoE genotype.ConclusionWe successfully developed a well calibrated protein‐only model with a 10‐fold dynamic range that predicts 20‐y risk of a dementia diagnosis. Performance of the protein model out‐performs leading genetic risk factors for dementia and given the exclusion of immutable factors it has the potential to monitor changing risk.
How Naturalistic Driving Compares to Aβ‐related CSF and Plasma Biomarkers in the Detection of Amyloid PET Positivity: A Machine Learning Approach
Authors: Bayat Sayeh , Schindler Suzanne E. , Roe Catherine M , Babulal Ganesh M 
Published: December 2022

AbstractBackgroundIdentifying individuals with Alzheimer disease (AD) pathology a decade or more before the onset of dementia symptoms can be achieved with amyloid positron emission tomography (PET), cerebrospinal fluid (CSF) biomarkers, or blood biomarkers. This study compares driving behavior with CSF and blood biomarkers to identify amyloid deposition in AD, as defined by the “gold standard” PET‐amyloid.MethodWe used commercial‐in‐vehicle‐GPS dataloggers to study naturalistic driving behaviors among 101 cognitively normal older drivers (aged 65+). All participants had blood, CSF, and PET biomarker data. The cohort included 37 individuals with and 64 without PET‐amyloid positivity.Driving variables included total number of trips, number of trips with a distance less than 1‐mile, trips in evening rush hour, trips with hard acceleration, speeding events, and average speed. Three Multi‐layer Perceptron (MLP) classifiers were trained with three sets of inputs: (1) driving variables, (2) driving variables and age, and (3) driving variables, age, and APOE ε4 status. Additionally, plasma Aβ42/Aβ40 < 0.1013, CSF Aβ42/Aβ40 < 0.0673, or Amyloid Probability Score (APS) (modeled score incorporating plasma Aβ42/40, age, and APOE ε4 status) > 15 were used to detect PET‐amyloid positivity. The driving models were trained on 80% of the data and all biomarkers were evaluated on the remaining 20% of the data. Specificity (Spe), sensitivity (Sen), F1‐score (F1), and area under the receiver operating curve (AUC) were compared across the three driving models and three fluid‐based biomarkers.ResultFor predicting PET‐amyloid positivity, CSF Aβ42/Aβ40 achieved the highest performance (Spe=0.96, Sen=0.98, F1=0.97) followed by the MLP model with driving variables, age, and APOE ε4 status (Spe=0.93, Sen=0.91, and F1=0.92), MLP model with driving variables and age (Spe=0.88, Sen=0.85, and F1=0.86), APS (Spe=0.82, Sen=0.84, and F1=0.81), plasma Aβ42/Aβ40 (Spe=0.79, Sen=0.80, and F1=0.76), and MLP model with driving variables (Spe=0.71, Sen=0.72, and F1=0.71). The AUC from driving variables was 0.800, and improved by adding age to 0.933, and age and APOE ε4 status to 0.977. Furthermore, AUC scores were 0.986, 0.911, and 0.873 for CSF Aβ42/Aβ40, APS, and plasma Aβ42/Aβ40, respectively.ConclusionDriving is a useful neurobehavioral marker in predicting PET‐amyloid positivity among cognitively normal older drivers.
Financial Mismanagement in Alzheimer’s Disease and Frontotemporal Dementia
Authors: Jackson Ashley J. , Ngo Sang , Manivannan Madhumitha , Young J. Clayton , Leggins Brandon , Cryns Noah G. , Tran Sheila T. , Grant Harli E. , Knudtson Marguerite V. , Chiong Winston 
Published: December 2022

AbstractBackgroundClinical experience in dementia indicates high risk for financial mismanagement, with grave consequences for patients and families. However, to date there has been little scientific study of real world financial errors in dementia or differential vulnerability in different syndromes.MethodCaregivers for people with Alzheimer’s disease (n = 50), behavioral variant frontotemporal dementia (bvFTD, n = 58), and primary progressive aphasia (PPA, n = 39) reported whether care recipients had made financial mistakes within the last year; and if so, categorized these as resulting from: (a) being too trusting or gullible, (b) being wasteful or careless with money, or (c) trouble with memory. In a pre‐registered analysis (https://archive.org/details/osf-registrations-vupj7-v1), we examined two hypotheses regarding vulnerability in different syndromes. Hypothesis 1: Financial mistakes due to being too trusting or gullible and being wasteful or careless with money are more prevalent in bvFTD than in Alzheimer’s disease. Hypothesis 2: Financial mistakes due to trouble with memory are more prevalent in Alzheimer’s disease than in bvFTD.Result32% of Alzheimer’s disease caregivers, 51.7% of bvFTD caregivers, and 30.7% of PPA caregivers reported financial errors in the last year. Concordant with our Hypothesis 1, people with bvFTD were more likely than those with Alzheimer’s disease to make mistakes referable to being too trusting or gullible (OR 4.90, 95% CI 1.55‐19.2, p = 0.012) and to being wasteful or careless (OR 4.41, 95% CI 1.55‐14.7, p = 0.009) in analyses corrected for age, gender, and education. Contrary to Hypothesis 2, people with bvFTD and with Alzheimer’s disease were nearly equally likely to make financial mistakes due problems with memory (bvFTD OR 1.05, 95% CI 0.43‐2.59, p>0.9). In a planned exploratory analysis, no significant differences were found between Alzheimer’s disease and PPA.ConclusionOur positive findings for Hypothesis 1 are concordant with other studies demonstrating socioemotional impairment and insensitivity to adverse outcomes in bvFTD. Meanwhile, our negative finding for Hypothesis 2 may cohere with other work indicating comparable memory impairment in bvFTD and Alzheimer’s disease. More broadly, our findings indicate that caregiver reports can distinguish mechanisms underlying financial mismanagement across different syndromes.
Uncovering Diverse Mechanistic Spreading Pathways in Disease Progression of Alzheimer's Disease
Authors: Yu Zhentao , Dere Mustaf , Li Quefeng , Wu Guorong 
Published: December 2022

AbstractBackgroundAlthough the root cause of Alzheimer's disease (AD) is largely elusive, the amyloid hypothesis has been widely used in the AD research framework, where amyloidosis induces (A biomarker) or facilitates the spread of pathologic tau (T biomarker) followed by immediate neurodegeneration ([N] biomarker) and progressive cognitive decline. As the massive heterogeneities manifested in the clinical symptoms, it is critical to understand the pathophysiological mechanism of how whole‐brain AT[N] biomarkers exert a synergistic effect on cognitive decline in the long period of disease progression.MethodTo answer this important scientific question, we employ a novel multivariate statistical inference approach to uncover the causal‐like effect of AT[N] biomarkers on cognitive decline as well as the underlying non‐modifiable risk factors through high‐dimensional neuroimaging phenotypes. Specifically, we first present a multivariate variable selection method to identify a set of brain regions where the AT[N] biomarkers exhibit strong correlations to the clinical outcomes, such as the composite scores regarding memory or executive function domains. Next, we apply mediation analysis to uncover all possible mechanistic spreading pathways of AT[N] biomarkers that lead to cognitive decline. On top of this, we further investigate the survival time for each identified (either direct or mediated) pathological pathway toward the onset of clinical symptoms, which allows us to manage the priority of risk factors more effectively in routine clinical practice.Result(1) There are several brain regions on which the amyloid burdens exert an indirect effect on cognitive decline, being mediated by the reduction of metabolism level at other topologically connected brain regions. (2) There is no overlap between memory‐specific and EF (executive function)‐specific pathways in terms of involved brain region. (3) Having two APOE ε4 alleles significantly increases the risk of developing AD in our survival model, where the odds ratio is 0.474 (p‐value: 0.01) regarding memory problem and 0.60 (p‐value: 0.002) for decline of executive functions.ConclusionWe have identified a set of mechanistic pathways in the brain where AT[N] biomarkers from multiple regions exert synergistic effect to cognitive decline. The survival rates estimated on these pathways set the stage for the precision medicine in AD treatment.
Neurodegeneration blood markers in largely Hispanic and non‐Hispanic Black COVID‐19 patients: from acute hospitalization to long‐term PASC with/without brain fog
Authors: Nordvig Anna S , Purpura Lawrence , Xi Ke , Kevin Strobino , Noble James M , de Leon Mony J. 
Published: December 2022

AbstractBackgroundUnderstanding the effect of COVID‐19 and Post‐Acute Sequelae of SARS CoV‐2 infection (PASC) on neurodegeneration using accessible blood biomarkers of neurofilament light (NfL) and phosphorylated tau 181 (p‐tau‐181) is of critical importance. Few studies to date have explored levels of neurodegenerative markers in COVID‐19 and PASC among race and ethnic minority populations.MethodNfL and p‐tau181 blood levels were measured using highly sensitive Simoa technology across three cohorts: (1) 102 Hispanic and non‐Hispanic Black non‐intubated COVID‐19 inpatients (75 +/‐9yo) treated in NYC March to June 2020, 95 of whom had serial samples with a second sample average 17d later; (2) 96 contemporaneously sampled outpatient controls, pre‐vaccination era, without history of COVID‐19, matched for age, sex and race/ethnicity (72+/‐8yo) and (3) 119 PASC diverse outpatients examined at least three months after acute infection, including 55 with brain fog (47+/‐14yo) and 64 without (41+/‐15yo). Associations with clinically‐obtained inflammatory markers (C‐reactive protein [CRP], ferritin, interleukin‐6 [IL‐6], ESR, LDH) were explored. An external quality control allowed direct comparison of NfL and p‐tau‐181 across the groups.ResultWhen controlling for age, NfL was significantly elevated in hospitalized COVID‐19 patients vs matched non‐COVID patients, but neither NfL nor p‐tau‐181 levels were significantly elevated in long‐term PASC outpatients with and without cognitive symptoms. NfL, but not p‐tau‐181, increased significantly between time points 1 and 2. At the second time‐point in the hospitalization, but not initially, NfL was significantly associated with CRP, ferritin, IL‐6; p‐tau‐181 was significantly associated with ferritin and IL‐6. There were no associations between NfL or p‐tau181 with either LDH or ESR.ConclusionPlasma NfL, but not p‐tau‐181, was elevated in hospitalized non‐intubated COVID‐19 patients in an aged cohort; after controlling for age, neither neurodegenerative marker was identifiable in PASC in middle aged adults. NfL increased significantly after prolonged hospitalization, and persistently elevated inflammatory markers correlated with NfL and p‐tau‐181 after a prolonged hospitalization period. The relationships between NfL, p‐tau‐181, and markers of systemic inflammation during acute COVID‐19 support other pathobiological models for neurodegenerative sequelae in older adults following COVID‐19 infection, but provide some reassurance for PASC brain fog sufferers.
The Proteasome Inhibitor Bortezomib Induces Neurogenesis and Toxicity in Rat Neural Stem Cells
Authors: Sohn Seung Yeon , Kim Hyun‐Jung 
Published: December 2022

AbstractBackgroundDysregulation of protein degradation is related with various diseases including Alzheimer’s disease. Bortezomib is the first drug to treat multiple myeloma, however, the major side effect is induction of peripheral neuropathy. This study was performed to explore the effects and mechanisms of proteasome inhibition in neural stem cells (NSCs).MethodCortical neural stem cells were obtained and cultured from embryonic day 14 rats. By immunocytochemistry (ICC), western blot analysis and real‐time PCR followed by reverse transcription PCR, the cell fate of neural stem cells were identified. Toxicity was determined by MTT assay and neurosphere size measurement. BCL2‐BAX protein ratio was detected by western blot to explore the cytotoxic mechanisms.ResultNeurogenesis was significantly increased by bortezomib in proliferating NSCs when determine by ICC, western blot and PCR. However, such effect was not observed during NSC differentiation. In addition, Bortezomib induced toxicity in both proliferating NSCs and differentiated cells.ConclusionBortezomib is toxic to NSCs and differentiated cells, however, it induced neurogenesis in NSCs.
Brain Structure and Allelic Associations in Alzheimer’s Disease
Authors: Moon Seok Woo , Kim Taeho 
Published: December 2022

AbstractBackgroundThis paper examines late‐onset dementia‐related cognitive impairment utilizing neuroimaging‐genetics biomarker associations.MethodThe participants, ages 65 to 85, included 266 normal controls (CN’s), 572 mild cognitive impairment (MCI’s), and 188 Alzheimer’s disease (AD) patients. Genotype dosage data for AD‐associated single nucleotide polymorphisms (SNPs) were extracted from the imputed ADNI genetics dosage data using sample‐major additive coding. 29 such SNPs were selected, representing a subset of independent SNPs reported to be highly associated with AD in a recent AD meta‐GWAS study by Jansen et al.ResultThe expected significant correlations between the SNPs and the neuroimaging phenotypes were confirmed using the top 29 genomic markers (GMs) and 200 neuroimaging markers (NIMs). Relative to CN, odds ratios and relative risks for AD and MCI were employed for prediction using multinominal linear modeling of diagnosis. In the CN and MCI cohorts, mainly cortical thickness measures were associated with GMs among thickness and the other measures, even though it was not in the AD cohort. Network patterns within the CN and AD groups were shown as separated 3 group metrics including thickness, volume, and pct (the proportion of White to Gray Matter), but not in the MCI cohort. Multinominal linear model of clinical diagnosis showed precisely the specific NIMs and GMs that were most impactful in discriminating between AD and CN and between MCI and CN.ConclusionAdvanced analytics provide mechanisms for exploring the interrelations between morphometric biomarkers and genomic indicators. This facilitates clinical investigation of phenotypic associations and facilitates deep systematic understanding of AD pathogenesis.