bluuebunny/crossref_metadata_2025_split huggingface dataset:
Combining plasma p‐tau231 and glial fibrillary acidic protein produces higher discriminative accuracy for amyloid positivity than other blood‐based biomarker combinations
Authors: Bolton Corey J , Khan Omair A. , Liu Dandan , Houston Michelle L , Pechman Kimberly R. , Gifford Katherine A. , Hohman Timothy J. , Blennow Kaj , Zetterberg Henrik , Jefferson Angela L. 
Published: December 2023

AbstractBackgroundPlasma phosphorylated tau (p‐tau)231 is an emerging blood‐based biomarker that has shown great promise for early identification of individuals at risk of Alzheimer’s disease (AD). However, it is unclear if this biomarker is sufficient as a standalone test or if diagnostic classification improves when simultaneously considering other plasma biomarkers. We investigated various plasma biomarkers, individually and in combination, in relation to cerebrospinal fluid (CSF) amyloid positivity.MethodVanderbilt Memory and Aging Project participants free of clinical dementia and stroke (n = 155, 72±6 years, 33% female) underwent fasting lumbar puncture and blood draw. CSF β‐amyloid42 (Aβ42) and β‐amyloid40 (Ab40) and plasma p‐tau231, glial fibrillary acidic protein (GFAP), Aβ42, Aβ40, and neurofilament light chain (NfL) were analyzed in batch. The Aβ42/40 ratio was used for all analyses and CSF amyloid positivity was defined as Aβ42/40 ratio <0.072. Logistic regression models related each biomarker to amyloid positivity, adjusting for age, sex, race/ethnicity, apolipoprotein E‐ε4 status, and cognitive status. Subsequent models included p‐tau231 as the predictor in a base model with other biomarkers being added individually and then in combination with other biomarkers to the base model. Models were compared using likelihood ratio test (LRTest) and Akaike Information Criterion (AIC) values.ResultIn single predictor models, p‐tau231 (C‐index = 0.87, p = 0.005) and GFAP (C‐index = 0.87, p = 0.01) were associated with amyloid positivity, while other biomarkers were not (p‐values>0.35). In models including p‐tau231 and various biomarker combinations, p‐tau231 remained significant in each model (p‐values<0.02). In comparison to the base model with p‐tau231 only, models adding GFAP (AIC = 143.8, LRTest p = 0.008), GFAP and NfL (AIC = 145.5, LRTest p = 0.02), and GFAP and Aβ42/40 (AIC = 145.8, LRTest p = 0.03) improved prediction of amyloid positivity. The model including plasma p‐tau231 and GFAP was the best fitting model (C‐index = 0.89).ConclusionIn this sample of community‐dwelling older adults free of clinical dementia, we found plasma p‐tau231 and GFAP were associated with amyloid positivity. Models including both p‐tau231 and GFAP performed best, and adding additional plasma biomarkers to this combination did not produce a better fitting model. Taken together, these findings highlight the utility of plasma p‐tau231 for screening for amyloid status and suggest that including plasma GFAP will improve discriminative accuracy.
Elevated cerebrospinal fluid chitinase‐3 protein like‐1 (YKL‐40) is associated with decreased glucose metabolism in amyloid‐positive participants.
Authors: Page Frederick A , Crook Harry , Edison Paul 
Published: December 2023

AbstractBackgroundThe diverse role played by astrocytes in neurodegeneration remains elusive. Astrocytes elicit a substantial impact on the fluorodeoxyglucose (FDG)‐positron emission tomography (PET) signal, an imaging modality that is used as a surrogate marker for neuronal loss. The association between astrocyte activation and FDG‐PET uptake has been evaluated with astrocyte‐targeting PET. Measuring reactive astrocytosis with fluid biomarkers may be a simpler and affordable way of studying astrocytes and their association with FDG‐PET. The cerebrospinal fluid (CSF) biomarker chitinase‐3 protein like‐1 (YKL‐40), a marker of reactive astrocytosis is elevated in the CSF of AD patients, although its role is unclear. Here, we examined the relationship between CSF YKL‐40 and PET imaging markers across the AD continuum.Method421 participants, including 89 patients with AD, 234 patients with mild cognitive impairment (MCI), and 98 cognitively normal control (CN) participants were selected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database based on the availability of coinciding amyloid (Aβ)‐PET, FDG‐PET and structural magnetic resonance imaging (MRI), and CSF YKL‐40. The relationships between CSF YKL‐40 and PET imaging markers were explored using both a region of interest (ROI) and a voxel‐wise approach.ResultAD patients demonstrated elevated CSF YKL‐40 compared to CN (p<0.001) and MCI (p<0.001) participants. CSF YKL‐40 was also significantly elevated in Aβ positive participants (p<0.001). ROI analysis revealed negative associations between CSF YKL‐40 and FDG‐PET uptake throughout the cortex in Aβ positive participants but limited to the cingulate cortex in Aβ negative participants. At a voxel‐level, Aβ‐PET uptake was positively associated with CSF YKL‐40 in Aβ positive participants. Meanwhile, a negative association was observed between CSF YKL‐40 and FDG‐PET across the brain in Aβ positive participants, a relationship lost in Aβ negative participants.ConclusionCSF YKL‐40 levels likely become elevated as AD progresses due to gradual amyloid deposition. Contrary to previous findings, reactive astrocytosis, as measured here with CSF YKL‐40, was associated with a reduction in glucose metabolism in amyloid positive participants, possibly due to the damaged caused to neurons by the AD pathology.
Passive non‐wearable technology for real‐time detection of COVID‐19 infection in people living with dementia
Authors: Kolanko Magdalena A. , Soreq Eyal , Lai Helen , Purnell Matthew , Walsh Chloe , Whitethread Nicole , Sharp David J 
Published: December 2023

AbstractBackgroundPeople with dementia (PLWD) are disproportionately affected by COVID‐19, as they are at a greater risk of becoming infected (1), and have an increased risk of disease‐related morbidity and mortality (2,3). The clinical presentation of COVID‐19 in PLWD is often asymptomatic or atypical, reducing early recognition of symptoms and timely treatment (4). Continuous monitoring of physiological parameters such as heart rate and respiratory rate using digital health technologies holds promise for early disease detection. Recent studies demonstrated that consumer wearable devices such as smartwatches can identify COVID‐19 by capturing subtle intra‐individual changes in physiological parameters (5‐11). However, the use of wearable devices in PLWD is significantly limited due to the lack of acceptance and poor compliance (11‐14). Here we present the first non‐contact monitoring technology using an under‐mattress pressure‐sensor mat to detect and predict COVID‐19 infection in PLWD.MethodWe used contactless Withings Sleep Analyser (WSA) to continuously monitor the night‐time physiology of 120 PLWD enrolled in the CR&T MINDER cohort study. A total of 38 positive COVID‐19 cases were reported since late 2021. Based on the minute‐to‐minute time series extracted from the WSA, average nocturnal heart and respiratory rates were calculated. We examined whether physiological deviations from a person’s baseline were detected in retrospect around the time of the reported illness.ResultOut of the 38 individuals who tested positive for COVID‐19 during the study (Fig.1), 25 had sleep data available around the time of their positive test. Of these 25 cases, 24 (96%) had abnormally high respiratory rate (Fig.2a), and 22 also had abnormally high heart rates (Fig.2b) within the 2‐week period preceding the COVID‐19 diagnosis. Our anomaly detection algorithm, which flags deviations in heart and respiratory rates above a certain threshold, correctly identified all 24 cases of abnormal physiology associated with COVID‐19 infection.ConclusionPassive monitoring of night‐time physiology using non‐invasive and non‐wearable technology allows early identification of changes in an individual’s baseline physiology, which can be used for real‐time detection of respiratory infections, including COVID‐19 in vulnerable groups of patients. Such technology could help healthcare providers to proactively identify and treat infections early, potentially improving patient outcomes.
Cognitive Reserve and Age‐Related Decline in Cognitive and Gait Domains: Results from the LonGenity Study
Authors: Blumen Helena M , Jayakody Shanika Oshadi , Emmeline Ayers , Barzilai Nir , Milman Sofiya , Weiss Erica , Verghese Joe 
Published: December 2023

AbstractBackgroundIndividuals with high cognitive reserve (CR) can adapt to dementia‐related brain changes and delay the onset of cognitive impairment and dementia. High CR is accumulated from frequent participation in cognitive, physical, and social activities. Whether high CR protects against age‐related cognitive and gait decline, however, is not well‐understood. We have shown that high CR is associated with a slower rate of age‐related cognitive decline, but not gait speed decline. Here, we examined associations between cognitive, physical, and social CR proxies and age‐related decline in different cognitive (processing speed, memory, and executive function) and gait (pace, rhythm, and variability) domains.MethodUp to 12 years of cognitive and gait data from 1,079 older (75.4±6.7 years; 56.0% women) LonGenity study participants were examined. Three cognitive domains – processing speed (trails A, digit‐symbol substitution), memory (free recall, logical memory) executive function (trails B‐A, digit span) – and three gait domains – pace (gait speed, step length), rhythm (step time, swing time) and variability (step‐time variability, step‐length variability) – were derived with principal components analyses. Cognitive (years of education), physical (blocks walked daily; weekly physical activity), and social (working/volunteering, living with someone) CR proxies were obtained from self‐report at baseline. Linear mixed‐effects models were adjusted for sex, parental longevity, baseline cognitive impairment, and comorbidities.ResultHigher CR when assessed with any CR proxy (except weekly physical activity) slowed the rate of decline in processing speed. Higher weekly physical activity and more years of education slowed the rate of decline in executive function. Higher number of blocks walked daily slowed the rate of decline in memory. No CR proxies were associated with pace and variability gait domains. Higher social CR when assessed with working/volunteering, however, slowed the rate of increases (worsening) in rhythm.ConclusionThese findings suggest that CR is associated with age‐related decline in several cognitive domains (processing speed, memory, and executive function) and select gait domains (rhythm) – yet the protective effects of cognitive, physical, and social CR on cognitive and gait domain decline are not uniform. Future research is needed to confirm these initial observations and develop interventions for domain‐specific decline in cognition and gait.
Differentiation of frontotemporal dementia subtypes using neuroimaging‐based multi‐type parallel feature embedding
Authors: Ma Da , Stocks Jane K , Lockhart Samuel N. , Craft Suzanne , Rosen Howard J. , Gurcan Metin Nafi , Popuri Karteek , Beg Mirza Faisal , Wang Lei 
Published: December 2023

AbstractBackgroundFrontotemporal dementia (FTD) represents a collection of neurocognitive syndromes with frontotemporal lobar degeneration (FTLD) neuropathology, and is associated with significant clinical, pathological, and genetic heterogeneity. We trained a deep neural network (DNN) classifier to differentiate behavioral‐variant FTD (bvFTD), semantic variant primary progressive aphasia (svPPA), and non‐fluent variant (nfvPPA) patients using MRI scans drawn from two multi‐site neuroimaging consortiums.MethodsBvFTD (N = 173), nfvPPA (N = 63), and svPPA (N = 41) patients with T1‐MRI were extracted from the FTLD Neuroimaging Initiative (FTLDNI) and the ARTFL‐LEFFTDS Longitudinal Frontotemporal Lobar Degeneration (ALLFTD) databases. MRI data were preprocessed with FreeSurfer, with cortical thickness, cortical volume, and subcortical volumes extracted. Cortical measures were parcellated into 360 patches (i.e., ROIs) according to the HCP‐MMP1 atlas. Both the patch‐based cortical thickness and volume features were harmonized to control confounding effects of sex, age, total intracranial volume (TIV), cohort, and scanner. Multi‐type features were parallelly fed into a multi‐layer‐perceptron (MLP)‐based classifier. Weighted cross‐entropy loss function was used to account for unbalanced sample sizes across FTD subtypes. 10‐fold nested cross‐validation was used to evaluate the robustness of the classification model, with data split into 80/10/10 of training/validation/test data sets.ResultsVisual evaluation z‐scores of cortical features among FTLDNI and ALLFTD groups revealed site‐specific differences significantly reduced by feature harmonization, especially for volume (Figure 1). The balanced accuracy of the ensembled DNN classifier of each FTD subtype on test sets across all ten folds reached 0.76 ± 0.09 for bvFTD, 0.79 ± 0.07 for nfvPPA, and 0.88 ± 0.07 for svPPA (Figure 2). Figure 3 shows confusion matrices for classification performance in the test set across ten cross‐validation folds.ConclusionWe developed a deep‐learning‐based framework to classify three FTD subtypes: bvFTD, nfvPPA, and svPPA. The combination of feature harmonization and parallel multi‐type feature embedding framework showed promising differentiation power. This work can be used to recognize at‐risk populations for early and precise diagnosis, to aid intervention planning. Future studies will compare classification based on different input features and use visualization methods to identify the most discriminative regions and explore their clinical relevance.
A modified Mediterranean‐ketogenic diet favorably modulates the metabolic profiles of mild cognitively impaired individuals
Authors: Batra Richa , Huynh Kevin , Schweickart Annalise , Neth Bryan J. , Arnold Matthias , Kastenmuller Gabi , Meikle Peter J , Craft Suzanne , Krumsiek Jan , Kaddurah‐Daouk Rima F. 
Published: December 2023

AbstractBackgroundImpaired glucose utilization is central to the pathogenesis of Alzheimer’s disease. In a hypometabolic state, the brain depends on alternative sources of energy, including amino acids, fatty acids, and ketone bodies (KBs). As KBs can cross the blood‐brain barrier, it is considered that a ketogenic diet could augment the high energetic demand of the central nervous system (CNS). A ketogenic diet is already a part of clinical care in epilepsy a CNS disorder. However, the holistic benefits of this diet for Alzheimer’s remain to be determined.MethodsA randomized crossover pilot trial was conducted where participants consumed either the Modified Mediterranean‐Ketogenic Diet (MMKD) or the American Heart Association Diet (AHAD) for 6 weeks, followed by a 6‐week washout, after which the respective other diet was consumed for 6 weeks. The cohort included both mild cognitively impaired (MCI) and cognitively normal (CN) individuals. Serum and cerebrospinal fluid (CSF) samples of these individuals were profiled using two metabolic platforms: MS‐based and NMR‐based metabolomics. Diet‐related changes in levels of metabolites were determined using linear‐mixed effect models with age and sex as confounders.ResultsWe identified post‐diet changes in the two diet groups (AHAD and MMKD), two cognitive groups (MCI and CN), and two fluids (serum and CSF). AHAD was associated with a decrease in serum levels of only a single metabolite, glutamine. MMKD led to widespread lipidomic and metabolic changes in the serum of mild cognitively impaired individuals. Remarkably, MMKD was associated with the reversal of AD‐related metabolic alterations identified in large cohorts like ADNI, AIBL, ROS/MAP, and signature for incident dementia in large epidemiological studies. The post‐MMKD changes include correcting metabolic dysregulation in branched‐chain amino acids, triglycerides, and plasmalogens a class of lipids implicated in membrane structure and function. Peripheral‐central metabolic correlations will be shared.ConclusionOur findings highlight the potential of dietary interventions, specifically the MMKD, in improving the cognitive and metabolic state of individuals with MCI. Further investigation is needed to capture the entirety of MMKD‐related modulation in metabolic profiles and the resulting benefit for AD.
A novel role for APP in neural circuit function
Authors: Harris Samuel S , Rajani Rikesh , Zuenkler Jana , Ellingford Robert , Yang Mengke , Lee Byung Il , Hyman Bradley T. , Müller Ulrike , Busche Marc Aurel 
Published: December 2023

AbstractBackgroundDespite the importance of the amyloid‐precursor protein (APP) to Alzheimer’s Disease pathophysiology and therapy, and notable pan‐cellular and synaptic abundance, the physiological function of APP and its homologues remains to be fully elucidated in‐vivo. Here, we interrogated how the APP family regulates neuronal cell function and long‐range neural circuit dynamics in the intact brain.MethodAPPflox/flox/APLP2flox /flox/APLP1−/− mice were crossed with NexCre mice expressing Cre prenatally from ∼E12.5 in postmitotic neuronal precursor cells of the cortex and hippocampus, producing a conditional triple knockout model (cTKO) of the APP family in excitatory forebrain neurons that is associated with pronounced behavioral deficits (Steubler et al., 2021, EMBO Journal). Neuronal function at the single cell/unit and circuit‐level was characterised using in‐vivo two‐photon and mesoscopic calcium imaging, and bilateral Neuropixels recordings in awake and anesthetised mice.ResultcTKO mice exhibited pronounced suppression in spike timing variability and dynamic excitability in visual cortex and hippocampus CA1 relative to controls, with a marked inter‐hemispheric decoherence of oscillatory activity (including low‐frequency and sleep‐associated slow waves) and dysfunctional intrahemispheric propagation of neuronal activity. Further, cTKO mice also displayed behavioural abnormalities in open field tests relative to controls which was linked to functional deficits. High‐resolution synaptic imaging revealed a significant reduction in the number of synapses containing the NMDA receptor subunit GluN1 in cTKO mice versus controls, with partial NMDA receptor agonism (d‐cycloserine, 30mg/kg, i.p.) ameliorating cellular and circuit‐level impairments as well as behavioral impairments.ConclusionThese findings provide novel in‐vivo evidence for an essential role of APP in regulating synaptic function via an effect on NMDA receptors, that elucidate the physiological role of this preeminent protein family and have important implications for disease and therapy.
Socioeconomic variation in modifiable risk factors for dementia
Authors: Bothongo Phazha LK , Waters Sheena , Chandra Avinash , Uchegbu Ijeoma , Jeraldo Rosario Isabel Espinoza , Anjum Rifah , Byford Michelle , Livingston Gill , Mukadam Naheed , Marshall Charles R 
Published: December 2023

AbstractBackgroundMost studies on modifiable risk factors for dementia are in relatively affluent populations, but dementia risk is higher among those who are more materially deprived. We aimed to consider whether this is accounted for by potentially modifiable risk factors, and compare the overall population attributable fraction (PAF) in the least and most socioeconomically deprived participants within the UK Biobank (UKB).MethodThe UKB is a cohort study with 502,656 volunteer participants registered between 2006‐2010, aged 40‐60 years at baseline, and followed for up to 16.8 years. Material deprivation was quantified using the Townsend Deprivation Index grouped into quintiles, with higher scores meaning higher deprivation. We assessed interactions between Townsend quintiles (using least deprived as reference), and each modifiable risk factor. To estimate the PAFs of 12 modifiable risk factors (diabetes, hypertension, depression, hearing loss, obesity, traumatic brain injury, less education, social isolation, physical inactivity, air pollution, and smoking), we first estimated hazard ratios (HRs) (adjusted for age, gender, and ethnicity) stratified by Townsend. HRs were then converted to relative risk for PAF calculation.Results6,624 incident cases of dementia developed in 1.6%(95%CI = 1.5‐1.7)(n = 11679/10369) of participants in the most deprived group, compared to 1.2%(95%CI = 1.1‐1.3)(n = 1174/98074) in the least deprived group. The mean age at dementia diagnosis were 72.7 years(SD,6.3){in most deprived}, and 73.8 years(SD,5.1){in least deprived}. The combined PAF(95%CI) for dementia was 40.3%(40.0‐40.6){most deprived}, versus 27.2%(27.0‐27.5){least deprived}. The most deprived, compared to the least deprived, had higher PAFs for: (a)Midlife hypertension: 7.7%(7.6‐7.9) vs 4.1%(3.9‐4.2); (b)Less education: 5.8%(5.6‐5.9) vs 3.4%(3.2‐3.6); (c)Diabetes: 4.4%(4.3‐4.6) vs 3.1%(3.0‐3.2); (d)Physical inactivity: 3.5%(3.4‐3.6) vs 0.7%(0.6‐0.7);) (e)Social isolation: 3.4%(3.3‐3.5) vs 1.0%(1.0‐1.1); (f)Midlife obesity: 2.1%(2.0‐2.2) vs 1.2%(1.2‐1.3); and (g)Smoking 1.8%(1.7‐1.9) vs 0.5%(0.5‐0.6). Air pollution had a PAF of 1.8%(1.7‐1.9) in the least deprived, and 0.0% in the most deprived. The remaining risk factor PAFs did not differ between the most and least deprived.ConclusionsIn this volunteer cohort, which is relatively wealthy, a higher proportion of dementia cases were attributable to major modifiable risk factors in more socioeconomically disadvantaged participants. Targeted interventions particularly addressing midlife hypertension, education, physical activity and social isolation might mitigate known inequalities in dementia risk according to socioeconomic status.