bluuebunny/crossref_metadata_2025_split huggingface dataset:
Investigating the role of blood metabolites as biomarkers of cognitive function and dementia in the MRC 1946 British Birth Cohort
Authors: Green Rebecca 
Published: December 2020

AbstractBackgroundDecline in cognitive function in late midlife is indicative of dementia. Identifying early markers in this key phase is critical for identifying those at risk and guiding effective interventional strategies. As blood metabolites closely reflect the physiological status of an individual, they show promise as minimally invasive markers for population‐level implementation. Using the MRC 1946 British Birth Cohort, we aim to: 1) Identify blood metabolites associated with early cognitive changes relevant to dementia 2) Tease apart mediating life course factors to address key aetiological questions 3) Employ a network approach, weighted coexpression network analysis (WGCNA), to glean insights into underlying biology.MethodAt age 60‐64, levels of 1019 metabolites were quantified using liquid chromatography‐mass spectrometry (N=1740). At the same age and 5‐9 years later, four aspects of cognitive function were assessed: short‐term and delayed verbal memory, processing speed, and the Addenbrook’s Cognitive Examination‐IIII (ACE‐III). Using WGCNA, 14 modules of highly connected metabolites were identified. Linear regression models evaluated associations between metabolites/metabolite modules and cognitive outcomes, sequentially adjusting for sex, childhood cognitive ability, education, socioeconomic status (SES), lipid medication and lifestyle factors. To infer biological function, pathway analyses were conducted to identify enriched pathways within metabolite modulesResultAfter correcting for multiple testing, 154 metabolites were associated with cognitive outcomes. Many associations attenuated after adjusting for early life cognition and SES, with seven metabolites belonging to lipid, amino acid and xenobiotic families remaining. WGCNA revealed two modules, enriched in vitamin A and sphingolipid metabolism, to be positively associated with cognitive outcomes. However, after adjusting for education and childhood cognition, these associations were largely reduced. Two modules, enriched in amino acid and fatty acid metabolism, were negatively associated with cognitive outcomes, with associations of the fatty acid metabolism module remaining after adjusting for life course factors.ConclusionThese results provide support for a role for metabolites in reflecting early changes relevant to dementia, revealing insights into underlying biological mechanisms and lifelong interplay. It is hoped that these insights can be utilised to inform early diagnosis and the development of disease‐modifying treatments and interventions.
CSF NFL levels and neuroimaging‐derived neurite density index improve prediction of MCI and dementia clinical diagnosis within the Alzheimer’s pathologic framework
Authors: Gallagher Rigina L. , Jonaitis Erin M. , Van Hulle Carol A. , Johnson Sterling C. , Carlsson Cynthia M. , Asthana Sanjay , Blennow Kaj , Zetterberg Henrik , Bendlin Barbara B. 
Published: December 2020

AbstractBackgroundCognitive decline in Alzheimer’s disease (AD) may be explained by degeneration of long myelinated axons in brain white matter. Neurofilament Light Chain (NFL) protein is a cytoplasmic protein in large caliber myelinated axons, released when neurodegenerative processes occur, and elevated in AD [(Preische et al., 2019), (Skillbäck et al., 2013), (Gaetani L et al., 2019)]. A similar biomarker may be derived from neuroimaging—specifically the Neurite Density Index (NDI)—a metric generated via neurite orientation dispersion and density imaging (NODDI) [(Fukutomi et al., 2017)]. In this study, we examined the extent to which these axonal markers add value to predicting clinical diagnosis within the AT(N) framework [(Jack et al., 2018)].Method216 individuals from the Wisconsin Registry for Alzheimer’s Prevention study and Alzheimer’s Disease Research Center underwent lumbar puncture for CSF collection and were diagnosed as either cognitively unimpaired (CU), mild cognitive impairment due to AD (MCI‐AD), or dementia due to AD (Dementia‐ AD). Participants also underwent diffusion‐weighted MRI to derive NDI. We computed Spearman rank‐order correlations between CSF NFL levels, white matter (WM) NDI, clinical diagnosis (CU, MCI‐AD, Dementia‐AD), and CSF Biomarker Status (A‐/T‐, A+/T‐, A+/T+). The added value of CSF NFL and WM NDI in discriminating diagnosis was assessed, controlling for A+/T+ status, age and sex, and accounting for interactions with NFL and NDI, with logistic regression models and ROC analysis.ResultDemographics are in Table 1. NFL correlated positively with biomarker status and negatively with NDI (Figure 1). NFL levels increased and NDI values decreased with diagnostic severity (Figure 2a). Modelsincorporating both A+/T+ status and NFL levels estimated diagnosis better than models relying only on A+/T+ status (Figure 2b). Models that discriminated between AD vs. CU, and incorporated WM NDI, had lower AIC compared to those relying only on A+/T+ (Figure 4a, 4b).ConclusionIn line with prior studies, NFL—a marker of large caliber myelinated axons—provides additional information that helps discriminate clinical severity in dementia, particularly as diagnosis progresses from MCI to AD. Decreasing AIC scores for models discriminating AD from CU suggest that, like NFL, neurite density in white matter regions improves identification of clinical status, especially as diagnosis severity increases.
CSF AD‐like profile in SCD, pre‐MCI and MCI subjects: Application of the A/T/N classification model in a retrospective cohort from a memory clinic
Authors: Chipi Elena , Salvadori Nicola , Bellomo Giovanni , Biscetti Leonardo , Farotti Lucia , Lisetti Viviana , Montanucci Chiara , Cataldi Samuela , Parnetti Lucilla 
Published: December 2020

AbstractBackgroundSubtle deficits not fulfilling Mild Cognitive Impairment (MCI) can be associated with pathophysiological AD biomarkers positivity and higher risk of clinical progression (Parnetti et al., 2019). Several attempts have been carried out in order to characterize neuropsychological features and Alzheimer’s disease (AD) biomarkers in pre‐MCI populations (Chipi et al., 2019). We aimed to explore, by applying the A/T/(N) classification scheme (Jack et al., 2018), the prevalence of cerebrospinal fluid (CSF) AD‐like profile (A+/T+) in a cohort of subjective cognitive decline (SCD), pre‐MCI and MCI subjects.MethodSeventy‐six consecutive patients (34 M; 42F; mean age: 68.7 years; mean education: 10.7 years), referred to our Memory Clinic from 2016 to 2019, were defined as SCD (n.11) (Jessen et al., 2014), pre‐MCI (n. 28) (Duara et al., 2011) and MCI (n.37) (Albert et al., 2011) by means of an advance neuropsychological battery and underwent lumbar puncture for the analysis of CSF core AD biomarkers (Aβ42/Aβ40 ratio, total tau, phospho‐tau). According to the cut‐off used in our lab, we considered Aβ42/40 ratio <0.069 as A+, phospho‐tau >70 pg/mL as T+, total tau > 400 pg/mL as N+.ResultNo differences were found in age, gender and education among groups. AD‐like CSF profile (A+T+) was found in 10.7% of pre‐MCI and in 54.1% of MCI (p<0.001). None of SCD subjects showed AD‐like profile. Prevalence of A+ was found in 9.1% of SCD and 35.7% of pre‐MCI, compared to 73% of MCI (SCD vs MCI: p<0.001; pre‐MCI vs MCI: p=0.0049).ConclusionConsistent proportion (10.7%) of subjects with subtle cognitive deficits not fulfilling MCI (pre‐MCI) in our cohort showed evidence of AD‐like profile, while none of SCD resulted A+/T+. Amyloidosis was detectable in both SCD and pre‐MCI subjects. Accordingly, pre‐MCI with evidence of AD‐like biomarkers may be considered as the earliest clinical manifestation of AD, and may represent an important target for treatment with disease‐modifying drugs.
A novel longitudinal approach in individual subjects to investigate the movement of tau over time using graph theory in clinical and preclinical stages of Alzheimer’s disease
Authors: Protas Hillary D. , Ghisays Valentina , Chen Yinghua , Goradia Dhruman D. , Luo Ji , Malek‐Ahmadi Michael H. , Lee Wendy , Devadas Vivek , Bauer Robert , Reiman Eric M. , Chen Kewei , Su Yi 
Published: December 2020

AbstractBackgroundWe previously introduced an index characterizing inter‐regional tau deposition variability based on graph theory. Extending our method to additionally characterize the longitudinal tau spread for individual subjects based on directed graph theory, we examined the difference between longitudinal network measures and the regional tau deposit measures over time with respect to 1) differentiation of patients with AD, MCI, and cognitively unimpaired(CU) subjects, 2) correlation with memory measures in Aβ+ CU, and 3) sample size needed for a prevention trial for Aβ+/‐ CU.MethodThe longitudinal tau PET data is from 13 AD, 42 MCI, and 72 CU in ADNI . Using cerebellar grey reference region, we calculated the difference over two time points (1.1 years ± 0.2 (0.6‐1.6)) for the entorhinal, inferior temporal and metaROI SUVR, and the difference for indices, in‐strength and out‐strength each based on the directed tau longitudinal network using predefined nodes(inferior temporal and limbic). We examined regional/network based indices in term of the group differences, their correlation with AVLT‐LTM change. In addition, we also calculated sample size for a prevention clinical trial for Aβ+/‐ subjects separately with 80% power and 25% treatment effect.ResultThe longitudinal tau network measures(instrength and outstrength) are significantly different between the three groups (p<0.004) but not the SUVR change at entorhinal(p=0.62), inferior temporal(p=0.36) or tau meta ROI rates(p=0.21). In CU Aβ+ subjects, limbic outstrength significantly correlated to AVLT‐LTM rate(rs=‐0.52, p=0.013). The most significant regional value(tau metaROI) rate is not significantly correlated with AVLT LTM rate(rs=‐0.25,p=0.26). For a clinical trial, the Inferior temporal in‐out strength(net influx) requires a sample size of 584 subjects in CU Aβ+ group and a sample size of 26448 subjects in CU Aβ‐ Entorhinal tau SUVR, inferior temporal and tau metaROI SUVR needs a sample size for CU Aβ+ is 1623, 2450,2629 respectively. For CU Aβ‐ subjects, the sample size is 53537 for entorhinal SUVR, 50605 for inferior temporal SUVR and 916184 for tau metaROI SUVRConclusionThe network based longitudinal indices provide better power for differentiating patients at different stages, for examining tau’s effects on memory and for detecting a treatment effects in prevention trial.
Differences in everyday cognitive difficulties in demographically diverse community‐dwelling older adults using daily diaries: A comparison among older adults with and without SCD
Authors: Mogle Jacqueline , Hill Nikki L , Rabin Laura , Katz Mindy J. 
Published: December 2020

AbstractBackgroundNeuropsychologically intact older adults who report experiencing subjective cognitive decline (SCD) likely draw on daily experiences of cognitive difficulties as an indicator of worsening cognitive functioning. Specifically, the frequency of problems (e.g., memory lapses, difficulty planning) and/or the degree that these problems interfere with their daily activities may play a role in their perception of cognitive changes. The current study examined differences in daily cognitive difficulties in older adults without SCD compared to those with SCD or MCI.MethodParticipants (n=212, Mage=76.97 years, range: 70‐90, 65% female, 50% White) completed a battery of objective cognitive performance tasks, psychosocial questionnaires, and they self‐reported experience of and interference from cognitive difficulties each day for 14 days (via smartphones). Fifty‐seven participants were classified as having mild cognitive impairment (MCI) using the Jak, Bondi (2009) criteria. One hundred and three participants not classified as MCI were classified as SCD as they reported their memory functioning worsening in the past 5 years. The remaining 52 participants with no SCD were normal controls. Generalized linear modeling was used to predict total number of and average interference from memory lapses and general thinking problems from daily reports.ResultAfter accounting for age, sex, race/ethnicity, education, and depressive symptoms, cognitively normal participants with SCD and those with MCI reported significantly more everyday cognitive difficulties compared to cognitively normal participants without SCD (Figures 1‐3). The MCI and SCD groups did not differ from each other (p’s>.07). For retrospective memory lapses and general thinking problems, normal controls reported significantly lower interference than participants with SCD and those with MCI (p’s>.02; Figures 5 and 6). For prospective memory lapses, only participants with MCI reported significantly higher interference than normal controls (p=.01; Figure 4).ConclusionCompared to participants without SCD, participants with SCD and those with MCI reported more frequent everyday cognitive problems and that problems were more likely to interfere in daily activities. Everyday cognitive difficulties may differentiate those individuals more likely to develop functional impairments related to declining cognition. Difficulties meeting daily cognitive demands has the potential to interfere with the ability of older adults to safely live independently.
Quantitative susceptibility mapping MRI reveals a relationship between iron accumulation, CDR score and cognition across the spectrum from healthy aging to Alzheimer’s disease
Authors: Killiany Ronald J , DeVivo Renee , Ellison Andrew , Zhao Yansong , Steinberg Eric , Alosco Michael L , Stern Robert A , Mez Jesse 
Published: December 2020

AbstractBackgroundThe objective of this study was to determine if there is a relationship between iron accumulation in the various white and gray matter regions of the brain, as assessed by susceptibility changes, measured with Quantitative Susceptibility MRI (QSM‐MRI), Clinical Dementia Rating (CDR) Score and a Cognitive Measure of Delayed Recall. Iron is an essential element for normal neuronal functioning. The accumulation of brain iron may signal a reduction in neuronal metabolism and/or trigger a neuroinflammatory response with microglia transporting iron to regions where pathology is taking place. This has led to the hypothesis that disruption of brain iron homeostasis contributes to Alzheimer’s disease symptoms.MethodThis study used a cross‐sectional design. MRI scans (T1 MPRAGE, QSM‐MRI with multi‐echo acquisition) were acquired on 57 participants from the Boston University Alzheimer’s Disease Center. Freesurfer 6.0 was used to generate regional brain volumes. The T1 maps from Freesurfer were co‐registered with the QSM scans to get regional brain QSM intensity values for each of the Desikan‐Killiany gray matter regions and Salat white matter regions. Values from the two hemispheres were combined. Associations between QSM values and clinical outcomes were assessed using stepwise ordinal logistic regression (CDR) or multiple regression (Neuropsychological Assessment Battery list learning long delay) in JMP 15 with age, sex and whole brain volume forced into the analyses as control variables.ResultThe ordinal logistic analysis produced a significant model (R Squared = 0.76). Following FDR correction, the volume of the hippocampus combined with susceptibility regions from subcortical gray matter and parietal white matter regions were important contributors to the logistic model. The multiple regression also produced a significant model (R Squared = 0.73). Following FDR correction, susceptibility values from medial temporal gray matter and parietal white matter regions were selected as important contributors to the regression model. Little overlap was found amongst the predictors chosen in these models.ConclusionThese findings support to the notion that a disruption of brain iron homeostasis is related to clinical status and memory function. As such, it may help us to better understand the clinical syndrome of Alzheimer’s disease.
People living alone with dementia: Ethical guidelines for social research
Authors: de Medeiros Kate , Girling Laura 
Published: December 2020

AbstractBackgroundAlthough it is believed that around 1/3 of people with Alzheimer’s and related dementias (ADRD) live alone in the community, the actual percentage is likely higher given dementias are often underdiagnosed and underreported. In addition, significant barriers (e.g., compromised capacity, accessibility issues, stigmatization) to the research inclusion of those residing alone with ADRD has led this population to be overlooked and/or deliberately excluded from social inquiry. Research systems that pose insurmountable barriers to the inclusion of vulnerable populations with ADRD prevent evidenced‐based knowledge that could constructively benefit future patients and caregivers. The present study was designed to create an important evidence base for ethical considerations regarding inclusion and exclusion of people living alone with ADRD into social research.MethodFunded by the National Institute on Aging, the present study conducted a comprehensive narrative synthesis of published ADRD and gray literature and 10 in‐depth interviews with key gatekeepers (e.g., people who can provide or deny access to people living alone with ADRD) to develop data‐driven recommendations to support the research inclusion of people living alone with ADRD.ResultThe combined narrative synthesis and primary data collection underscored three areas of inclusionary/exclusionary concern: 1) fluctuating capacity to give informed consent, 2) absence of study partners or surrogate decisions‐makers and 3) social and environmental barriers exacerbating disparities by discouraging inclusion of a severely marginalized population. Detailed guidelines will be discussed in each of these three areas.ConclusionThe developed guidelines provide significant insights into how the dementia research community can overcome barriers to research‐driven learning in order to ethically incorporate live‐alone people with dementia into social science explorations, providing direction for current and future research.
Content validity analysis of the computer proficiency and mobile proficiency questionnaires: Study on proposed Brazilian Portuguese version translated and culturally adapted
Authors: Andrade Alan Cronemberger , Sampaio Gustavo Guedes Vaz , Bertolucci Paulo Henrique Ferreira 
Published: December 2020

AbstractBackgroundDigital technologies can enhance benefits in healthcare. Its use is increasingly present in elderly people daily life. Being a proficient user maybe important for social communication and independent living, and additionally can bring benefits to researchers in testing patients outside the office. The Computer Proficiency (CPQ) and the Mobile Proficiency Questionnaires (MDPQ) proved to be useful in English (United States) and Spanish (Spain) speakers to quantify the degree of proficiency in electronic devices in adults. We lack instruments to measure this in Portuguese.MethodFollowing established practices, an instrument translation from English to Portuguese was prepared by a Brazilian bilingual neurologist. Then, a back‐translation was performed by 2 other specialists for additional adjustments. Afterwards, a panel of 7 experts (4 Cognition and Behavioral neurologists, 1 geriatric psychiatrist, and 1 information technology PhD) categorized the questionnaires items to calculate the Face Validity Index (FVI) for language clarity, and the Content Validity Index (CVI) for practical and theoretical relevance for both individual items (i‐CVI) and whole scales (S‐CVI).ResultThe results of the translation are shown in the tables (final version in Portuguese included). The comparative back translations obtained good equivalences. The experts group proposed small changes in the use of idiom in a few questionnaires’ items. The expert consensus for the translation assertiveness was considered very good (S‐FVI/Average CPQ=0.96 and MDPQ=0.99), with high levels of unanimous agreement (S‐FVI/UA of 81% and 95%, respectively). The S‐CVI/Ave were considered good for practical (CPQ=0.86 and MDPQ=0.93) and theoretical relevance (CPQ=0.81 and MDPQ=0.85), although the experts’ unanimous agreement was considerably lower.ConclusionThe CPQ and MDPQ questionnaires in their Brazilian Portuguese versions achieved satisfactory translated content quality and language clarity. They will need consequent validation and reliability studies in our population. Small changes to better improve the questionnaires in our language are discussed. We aim they can provide a useful tool for Brazilian research on digital devices. Correlation with demographic, cognitive, and functionality data may provide more information on how digital proficiency can be useful to track our patients.
Validation of EEG‐based brain age index as a biomarker for dementia
Authors: Ye Elissa M , Sun Haoqi M , Lam Alice D , Westover M Brandon 
Published: December 2020

AbstractBackgroundDementia is a growing cause of disability and loss of independence in the elderly, yet remains largely under‐diagnosed. A biomarker for dementia that can identify individuals with or at risk for developing dementia may help close this diagnostic gap. Deviations from the normal aging trajectory, known as Brain Age Index (BAI), have shown potential to serve as biomarkers for cognitive impairment. We aimed to investigate the association between BAI and dementia and determine whether an overnight sleep EEG‐based BAI could serve as a useful biomarker for dementia. We hypothesized that patients with dementia‐related diseases have significantly higher brain age indices than patients without dementia.MethodUsing a dataset of polysomnograms from 11,039 patients, we computed BAI using an EEG‐based brain age algorithm. Patients were categorized into four groups, including dementia, mild cognitive impairment (MCI), symptomatic, and non‐dementia, based on clinical diagnoses, Montreal Cognitive Assessment (MoCA), and/or Mini‐Mental State Exam (MMSE) scores.ResultWe found an overall significant trend across dementia groups using Cuzick's test (p=0.0007). The BAI of patients with dementia (4.11 ± 10.02 yrs) were significantly higher than those of patients with no dementia (0.516 ±10.40 yrs), with p‐value of 0.002, and those of healthy subjects (‐0.67 ± 9.52 yrs), with p‐value of 0.0002. BAI was negatively correlated with MoCA (R = ‐0.1359, p = 0.006) and MMSE (R = ‐0.1201, p = 0.005). Features related to delta activity in non‐REM sleep stages 2 (N2) and 3 (N3) tend to correlate with non‐dementia, and features related to theta and delta waves in the Wake and N1 stages tend to correlate with dementia.ConclusionOur results suggests a potential for an EEG‐based Brain Age Index to serve as a biomarker of dementia. This opens new possibilities for using BAI as an assessment tool for the presence of underlying neurodegenerative disease and monitoring of disease progression.