bluuebunny/crossref_metadata_2025_split huggingface dataset:
The effect of global ancestry and diabetes on the 3MS score in older Puerto Ricans
Authors: Tejada Sergio J. , Rajabli Farid , Mena Pedro Ramon , Martin Eden R. , Rodriguez Vanessa C. , Celis Katrina , Adams Larry D. , Bussies Parker , Prough Michael , Hamilton‐Nelson Kara L. , Silva‐Vergara Concepcion , Acosta Heriberto , Vance Jeffery M. , Feliciano‐Astacio Briseida E. , Beecham Gary W. , Pericak‐Vance Margaret A. , Cuccaro Michael L. 
Published: December 2020

AbstractBackgroundThere is limited study of the effect of genetically determined ancestral background and diabetic risk on cognitive status in admixed populations. Puerto Ricans are an admixed population with European (EU), African (AF), and Amerindian (AI) backgrounds. We analyzed the impact of ancestry and diabetes on cognitive status in older Puerto Ricans.MethodThe dataset consisted of Puerto Ricans > 60 years of age enrolled in the Puerto Rican Alzheimer Diseases Initiative (PRADI). At study entry, all participants were administered the 3MS to screen for cognitive problems. Medical history, including the presence of hypertension, diabetes, and cholesterolemia was also collected at that time. Model building analyses indicated that diabetes was the sole vascular predictor. Admixture proportions were estimated using the ADMIXTURE software. Using a Generalized Estimating Equations (GEE) approach, we modelled the contribution of sex, age at 3MS testing, global ancestry proportion, and diabetes to the participants 3MS score.ResultThe dataset consisted of 689 Puerto Ricans (70% female) with a mean age at 3MS of 74.9 years (SD=8 years). Mean global ancestry proportions were highest for EU (71%) followed by AF (18%) and AI (11%). As expected, we found significant associations between age, sex, and an age‐sex interaction and the 3MS score. However, we also found significant interactions between ancestry proportions and diabetes as predictors of 3MS score. Specifically, when compared to the effects of combined EU and AI ancestry on the 3MS score, the effect of diabetes on 3MS score is larger in individuals with higher proportions of AF ancestry (p < 0.01).ConclusionCognitive decline can be secondary to many different deleterious factors. Along with Alzheimer disease, small vessel vascular disease is a major contributor to the development of dementia. Indeed, diabetes affects small blood vessels, and these data support the importance of controlling diabetes in the Puerto Rican population, particularly those with a strong AF ancestry. This highlights the importance of looking for a history of diabetes when evaluating cognition in dementia‐related disorders in Hispanic populations with AF admixture.
Principal components from untargeted CSF metabolomics associated with tau
Authors: Dong Ruocheng , Deming Yuetiva , Lu Qiongshi , Ma Yue , Carlsson Cynthia M. , Johnson Sterling C. , Asthana Sanjay , Blennow Kaj , Zetterberg Henrik , Engelman Corinne D. 
Published: December 2020

AbstractBackgroundThere has been increasing evidence for metabolic contribution in Alzheimer’s disease (AD) pathology aided by recent advancements in metabolomics which allow measurement of a large number of metabolites reflecting changes downstream of genomic, transcriptomic and proteomic fluctuations (Trushina, & Mielke, 2014). Studying the correlation between cerebrospinal fluid (CSF) metabolites and AD endophenotypes may offer a window to the alterations of the brain metabolome and unveil potential biological mechanism underlying AD.MethodCSF samples from 139 unrelated cognitively unimpaired individuals from the Wisconsin Registry for Alzheimer’s Prevention (WRAP) and 313 cognitively unimpaired and MCI individuals from the Wisconsin Alzheimer’s Disease Research Center (W‐ADRC) were analyzed to determine the relative abundance of untargeted metabolites via Metabolon’s UHPLC/MS platform. This resulted in 308 CSF metabolites that were included in a principal component analysis (PCA). Longitudinal values were averaged and data from WRAP and W‐ADRC were combined. After PCA, the principal components (PCs) explaining most of the variance in all CSF metabolites, were tested for association with CSF total tau (T‐tau), phosphorylated tau (P‐tau), and amyloid β 42/40 (Aβ 42/40) using linear regression models adjusting for age, sex, race, years of education, and cohort. Pathway analysis was employed for metabolites with top loadings in each significant PC.Result32 PCs explained up to 90% of the variance in the 308 CSF metabolites. Among them, four PCs, PC20, 21, 22 and 23, were significantly associated with CSF T‐tau and P‐tau after Bonferroni adjustment (Table 1). No PCs were associated with Aβ 42/40. Pathway analysis of the four significant PCs suggested that caffeine metabolism, nicotinate and nicotinamide metabolism, glycerophospholipid metabolism, and vitamin B6 metabolism, which had significant FDR p values (<0.05) or reached the significant impact score threshold (≥0.15), might be of interest for future research (Table 2).ConclusionThis study provides evidence that CSF metabolites are related to AD pathology through T‐tau and P‐tau and, thus, may provide insight into the mechanisms involved in tau tangles.
Identifying key features for dementia diagnosis using machine learning
Authors: Guest Felicity , Kuzma Elzbieta , Everson Richard , Llewellyn David J. 
Published: December 2020

AbstractBackgroundDiagnosing dementia is clinically challenging due to substantial heterogeneity in presenting symptoms and underlying pathologies. However, machine learning provides a powerful approach to uncover hidden patterns in complex data that may in turn inform clinical practice.MethodWe used patient data from the National Alzheimer’s Coordinating Center Uniform Data Set (versions 1.2 and 2) collected at 35 Alzheimer’s Disease Centers across the United States between September 2005 and February 2015. All 32,573 patients (median age=73 years, 57% female) were assessed according to standardized protocols involving extensive clinical and neuropsychological assessments which resulted in a pool of 260 continuous, binary, ordinal or categorical features. Patients were randomly split 70:30 into training (n=22,801) and test (n=9,772) samples. All‐cause dementia and dementia subtypes (Alzheimer’s disease, vascular, Lewy body, frontotemporal and ‘other’) were diagnosed by either a single clinician or consensus panel using established international criteria. Random forests (Extra‐Trees algorithm) were used to differentiate between all‐cause dementia and no dementia, and between different subtypes, through the construction of an ensemble of decision trees which makes classifications via voting. We used similarity‐based imputation and the missingness incorporated in attributes approach to deal with missing data whilst preserving relationships between variables.ResultThe all‐cause dementia classifier incorporating all 260 features was able to differentiate between dementia and no dementia with an accuracy of 94.2% in the test sample (sensitivity 92.8% and specificity of 95.1%). The most important features related to impaired cognitive domains, particularly judgement, planning or problem solving and orientation, and everyday activities such as home and hobbies, community affairs and difficulty with bills. It was found the performance of the original classifier could be matched with just the 42 most important features. Subtype classifiers ranged in accuracy from 80.8% for frontotemporal dementia versus ‘other’ dementia to 99.4% for Alzheimer’s disease versus vascular dementia. The most important features for differential diagnosis included stroke history, visual hallucinations and Parkinson’s disease.ConclusionMachine learning can be used to uncover hidden patterns in high‐dimensional clinical data and identify the most important characteristics for accurately diagnosing dementia.
Synaptic proteins relate to memory scores in preclinical Alzheimer’s disease and cognitively healthy controls depending on amyloid
Authors: Wesenhagen Kirsten E.J. , Visser Pieter Jelle , Gobom Johan , Bos Isabelle , Vos Stephanie J.B. , Martinez‐Lage Pablo , Tainta Mikel , Popp Julius , Peyratout Gwendoline , Tsolaki Magda , Vandenberghe Rik , Freund‐Levi Yvonne , Verhey Frans R.J. , Lovestone Simon , Streffer Johannes , Blennow Kaj , Scheltens Philip , Teunissen Charlotte E. , Zetterberg Henrik , Tijms Betty M. ,   Alzheimer’s Disease Neuroimaging Initiative
Published: December 2020

AbstractBackgroundMemory loss is central to Alzheimer’s disease (AD), but the precise underlying mechanisms remain unclear. Post‐mortem research suggests that synaptic loss best explains symptoms. Here, we investigated in cerebrospinal fluid (CSF) whether levels of synaptic proteins are related with memory scores in older individuals with normal cognition and in preclinical AD.MethodParticipants were selected from ADNI and EMIF‐AD MBD if they had CSF proteomics and memory scores available. In total, we selected 85 controls (i.e., normal cognition (CN) and normal CSF amyloid‐1‐42 and tau levels; mean±sd age 69±9 years, 58% female) and 46 preclinical AD (i.e. normal cognition and abnormal CSF amyloid‐1‐42 level, normal or abnormal CSF t‐tau and p‐tau; mean±sd age 74±7 years, 50% female). Age‐, sex‐ and education‐specific norms were used to normalize memory tests (ADNI: 30‐minute delayed Rey auditory verbal learning test (ADNI), EMIF: centre‐specific delayed memory tests). We selected 37 proteins available in both cohorts that were specific for synapses according to synGO (https://www.syngoportal.org/), Z‐transformed protein levels relative to controls, and pooled these across cohorts. Protein‐memory associations were assessed with linear models; model1: including covariates education, sex and age; model2: model1 + covariate amyloid status; and model3: model2 + protein level‐amyloid status interaction.ResultMemory scores were similar in the preclinical AD and control groups (P=0.52, Figure 1). Three proteins (NCSTN, FGA and NRGN) had increased levels in preclinical AD vs. controls. Across the total group, no proteins were associated with memory functioning. Repeating models including an interaction term with amyloid status showed that 9 synaptic proteins (24.3%) were associated with memory depending on amyloid status. In controls, lower protein levels related with better memory, while in preclinical AD, lower protein levels tended to be related with worse memory (Figure 2).ConclusionOf the synaptic proteins tested, associations with memory were mediated by amyloid status. The finding that lower synaptic protein levels related to better memory in controls, but to worse memory in preclinical AD suggests that synaptic protein levels may reflect different biological processes in these groups.
Psycholinguistic properties of words on fluency testing relate to different aspects of subjective cognitive decline
Authors: Barker Megan S. , Chapman Silvia , Mejia Arlene , Joyce Jillian L. , Sunderaraman Preeti , Idnay Betina R.S. , Kreisl William Charles , Cosentino Stephanie 
Published: December 2020

AbstractBackgroundSubjective Cognitive Decline (SCD) is posited as a marker of preclinical Alzheimer’s disease. However, the nature of SCD and its relationship with objective cognition remain the subject of debate. More sensitive markers of objective cognition are likely needed to capture variability in SCD; one candidate is the psycholinguistic information embedded in language tasks, such as lexical frequency and concreteness, which is rich in cognitive information. In addition, attention to the nature of SCD is important for advancing our understanding of its relationship with objective cognition. Here we investigate how psycholinguistic information (lexical frequency and concreteness) relate to different sets of items on an SCD scale.MethodParticipants included 74 cognitively healthy older adults (mean age = 73.76 (7.20), education =16.91 (2.20), 66% female, 85% Caucasian, 10% African American, 4% Asian and 1% other). SCD was measured with a 20‐item questionnaire, using a 7‐point Likert scale to rate difficulty on each item compared with others their age. Principal Component Analysis (PCA) was conducted to derive underlying SCD factors. Participants also completed semantic fluency (animals) and phonemic fluency (CFL) tasks. Lexical frequency values were computed for each word generated on fluency tasks. Concreteness was computed for CFL only; this value would not vary within a semantic task. Nonparametric partial correlations between psycholinguistic measures and SCD factors were conducted, controlling for age, sex and education.ResultsPCA identified three SCD factors with Eigen values >1 that together explained 63% of the total variance. Factor 1 loaded primarily on memory, Factor 2 was a combination of memory and language, and Factor 3 related to attention/executive function. Lexical frequency (animals) was correlated with Factor 1 only; higher SCD related to higher frequency (more common) animals (r=.27, p=.025). Concreteness (CFL) was correlated with Factor 2 only; higher SCD was associated with more concrete, less abstract, words (r=.25, p=.035).ConclusionTwo measures of psycholinguistic information were uniquely related to different SCD items. This double dissociation reinforces the idea that mapping SCD to objective cognition requires a carefully refined measurement approach with regard to subjective experiences and the information collected from objective measures.
Evaluation of the neurocognitive and neuroprotective potentials of selected Nigerian medicinal plants in preclinical models of learning and memory
Authors: Atunwa Soliu Abiola 
Published: December 2020

AbstractBackgroundDementia is one of the most common forms of neurocognitive disorders with increasing global prevalence and attendant huge socio‐economic burden on the existing healthcare systems. The pathophysiology of amnesia which is unarguably common to all stages in dementia is poorly understood. Meanwhile, the inhabitants of Ibarapa Area of Oyo State, South‐West, Nigeria have long been using certain medicinal plants as memory‐enhancers and neuroprotective remedies. These ethnomedicinal claims however have not been scientifically evaluated, hence, this study.MethodEthno‐pharmacological survey of medicinal plants used in the management of neurocognitive disorders in Ibarapa Area of Oyo State, South‐West, Nigeria was carried out among the relevant study groups of the inhabitants through semi‐structured questionnaires and interviews in local language. Informed consent was obtained orally from all respondents before the administration of research tools. The Use Mention Index (UMI), frequency and percentage of occurrence as well as literature search were used as selection criteria for choosing medicinal plants to be screened for possible neurocognitive activities. Data will be expressed as mean ± standard error of mean (SEM) and evaluated using one‐way analysis of variance (ANOVA) followed by post‐hoc test (student‐newman‐keuls test) to determine the source of significant effect or interaction. Graphpad prism version 6 (Graphpad Prism Inc., CA, U. S. A.) will be used as a statistical tool.Resultdata obtained from the ethnomedicinal survey revealed that about 105 medicinal plants are being used as memory enhancers and anti‐aging remedies in Ibarapa Area of Oyo State. They are called different local names as: “marugbo takebe”, ”oogun isoye,”, ”ajidewe”, ”sagba dewe”, “sarugbo domidan” among others. Basic information about geographical distribution; scientific, common and local names; identification, collection/harvesting, part(s) used; method(s) of preparation/processing of herbal remedies; formulations; mode of administration and posology; duration and frequency of use; potency/efficacy, toxicity, conservation and economic issues; poly‐pharmacy; peculiarities of each remedies; and other ethno‐medicinal uses were recorded.Conclusionpreliminary results obtained so far showed that some of the medicinal plants have central activities thus, scientifically suggesting the rationale for their potential use in the management of memory disorders.
Evaluation of 18F‐MK‐6240 and 18F‐AV‐1451 tau PET tracers in Alzheimer disease
Authors: Mansor Syahir , Zhou Yun , Flores Shaney E. , Hornbeck Russ C. , Dincer Aylin , Gordon Brian A. , Cash Lisa , Swisher Laura , Ances Beau M , Tu Zhude , LaForest Richard , Morris John C. , McDade Eric , Bateman Randall J , Benzinger Tammie L.S. ,   Dominantly Inherited Alzheimer Network
Published: December 2020

AbstractBackgroundTwo tau tracers, 18F‐MK‐6240 and 18F‐AV‐1451, have recently been introduced into many studies of Alzheimer disease (AD) but it remains unclear how each tracer evaluates heterogenous tau pathology. In this study we compared the binding of two tau‐specific PET tracers in asymptomatic control (AC) and (AD) participants enrolled in DIAN.MethodNine AC and three AD participants were recruited from DIAN underwent PET/CT imaging twice, one with 18F‐MK‐6240 and the other one with 18F‐AV‐1451 on consecutive days. AC participants were scanned with full dynamic scan (0‐110 min for 18F‐MK‐6240 and 0‐105 min for 18F‐AV‐1451) while 20 min (90‐110 for 18F‐MK‐6240 and 80‐100 min for 18F‐AV‐1451) scan for AD participants. Data were analysed using standardized uptake ratios (SUVr) with whole cerebellum used as a reference tissue from an early frame duration (SUVr0.75‐2.5 min) for all nine AC participants and late SUVr (SUVr20min) for all twelve participants. In addition, centrum semiovale was tested as reference region for SUVr20min. Relative delivery (R1) and distribution volume ratio (DVR = BPND+1) were generated using SRTM2 methods from nine AC dynamic data with cerebellum as reference. Bland–Altman analyses were used to evaluate an agreement between two tracers both for AC and AD participants.ResultBoth tracers show an agreement for relative flow and binding in AC participants as shown in Figure 1. In AD participants, 18F‐MK‐6240 shows a proportionally higher tracer specific binding compare to 18F‐AV‐1451 for regions with an average SUVr value higher than 1.5 as seen in Figure 2. 18F‐MK‐6240 shows relatively lower uptake compared to 18F‐AV‐1451 in AC and significantly higher in AD as in Figure 3. The SUVr attain stable after 80 min post injection for both tracers, but the centrum semiovale reference tissue‐based SUVr shows higher contrast between both tracers than the ones using cerebellum reference tissue (Fig 4).ConclusionBoth tracers show an agreement in AC participants. 18F‐MK‐6240 demonstrates higher SUVr compared to 18F‐AV‐1451 for participants with significant tau deposition in AD. Centrum semiovale is suggested for reference region for both tracers and will be evaluated carefully by the ongoing evaluation study.
Early‐mid adulthood measures of HDL, triglycerides and fasting glucose are associated with late‐onset Alzheimer disease
Authors: Zhang Xiaoling , Tong Tong , Chang Andrew , Ang Ting Fang Alvin , Tao Qiushan , Auerbach Sandford H , Devine Sherral , Qiu Wendy , Mez Jesse , Massaro Joseph , Lunetta Kathryn L , Au Rhoda , Farrer Lindsay A 
Published: December 2020

AbstractBackgroundMeasures of cardiovascular and metabolic disease are well‐established risk factors for late‐onset Alzheimer disease (AD). However, it is poorly understood whether exposure to these risk factors in early adulthood is associated with AD incidence in late‐life.MethodAssociation of incident AD occurring after age 60 with measures of lipid fractions, hypertension, diabetes, blood glucose, BMI and smoking obtained prospectively across multiple exams starting in early adulthood was evaluated in the Framingham Heart Study (FHS) Offspring cohort (n = 5,100) using Cox proportional hazard models. Three models were tested for each risk factor measured at each exam with progressive adjustment for the following covariates: Model 1 ‐ age and sex; Model 2 – age, sex, and risk factor‐specific treatment; and Model 3 – age, sex, and other vascular risk factors. Hazard ratio (HR) was calculated for a 10 mg/dL unit increase of risk factor.ResultDuring a mean follow‐up period of 31.7 ± 6.7 years, a consensus diagnosis of incident AD was established for 261 participants. Higher high‐density lipoprotein (HDL) measured at early (mean age =36) and midlife (mean age = 59) were associated with a decreased risk of AD (HR=0.90 [95%CI 0.83, 0.98], p=0.020 and HR=0.90 [0.79, 0.98], p=0.020, respectively). These associations remained significant after adjusting for lipid treatment. Higher triglyceride (TG) measured at these exams were associated with an increased risk of AD (HR=1.015 [1.003, 1.03], p=0.0013 and HR=1.02 [1.004, 1.04], p=0.0035, respectively). Of note, higher fasting blood glucose (FBG) measured at early, midlife and late life were associated with an increased risk of AD (HR=1.06 [1.01, 1.11], p=0.03; HR=1.08 [1.03, 1.13], p=0.0006; and HR=1.24 [1.02, 1.52], p=0.03, respectively), with attenuated associations after adjusting for diabetes treatment. In the model adjusting for other vascular risk factors, FBG at early, middle and late life remained significant.ConclusionHDL, TG and Blood Glucose levels measured at approximately age 36 years are significantly associated with incident AD several decades later. To our knowledge this is the first evidence of the association of AD with decreased HDL level and increased TG and fasting glucose levels measured in cognitively normal individuals during early to middle adulthood.