bluuebunny/crossref_metadata_2025_split huggingface dataset:
Integrating Noninvasive Radiofrequency Monitoring Technology Into Clinical Decision‐Making
Authors: Bolstad Britni A , Johnson Brian B 
Published: January 2025

AbstractBackgroundAcross a broad range of physical and cognitive outcomes in gerontology and geriatric practice, it is fundamentally important to establish systems for clinical decision‐making. Current technological solutions include sensors for monitoring movement activity, however many of these methods are complex, invasive, and dependent on factors such as compliance, context, and idyllic conditions. An alternative approach to monitoring is through continuously active, noninvasive radiofrequency (NRF) sensors. Our hypothesis is that NRF sensors can be used to detect clinically relevant changes and support clinical decision‐making because they provide a more accurate and reliable method for monitoring movement activity than camera‐based systems and wearable sensors, thus the identification and progression of physical and cognitive decline could be measured through movement data and enhance the care management of older adults.MethodNRF sensors are highly suited to optimizing supportive actions by clinicians and healthcare organizations. However, despite the interest in NRF systems, it is mostly absent from clinical context aside from research initiatives. Commercial technologies and technologies developed in research initiatives are often developed without a proper understanding of the specific needs of older adults or what clinicians consider useful. Our approach is to use NRF sensors and an algorithm library to identify changes in movement activity and inform clinical decision‐making thereby facilitating care management based on individual needs.ResultInitial lab tests at the University of Minnesota showed the NRF sensor to be effective for monitoring falls in real‐time, movement activity, and resting states. These results suggest that NRF sensors could be used to measure clinically relevant changes among the older adult population.ConclusionNRF sensors may provide clinicians with pertinent data that would otherwise be inaccessible and help them to develop an individualized plan of care. With movement data, clinicians could further assess risk factors, monitor for adverse events, and detect physical or cognitive decline to improve care management. NRF monitoring technology is conceptualized as a means of better fulfilling the needs of older adults. Further research questions seek to evaluate the impact of using NRF sensors for quantitative monitoring, particularly within healthcare organizations.
A Decade‐Long Analysis of Dementia Incidence in Argentina: Insights from a Public Registry Study
Authors: Calandri Ismael Luis , Suemoto Claudia Kimie , Allegri Ricardo ,   Equipo de Estadística e Investigación social, Agencia Nacional de Discapacidad, Argentina
Published: January 2025

AbstractBackgroundArgentina is the second‐largest country in Latin America, home to 9% of the world’s Latino population. Like the rest of Latin America, it is grappling with the aging of its population and an increase in risk factors. Despite this, there are currently no studies on the incidence of dementia in the national territory. This study aims to present the current incidence rates and changes over the past decade for dementia in Argentina. Additionally, we explore sex‐specific and early‐onset dementia trends.MethodsUtilizing 10‐year data from the “National Disability Registry,” a comprehensive national database offering specialized health coverage, we identified cases certified by an interdisciplinary board. Dementia subjects were defined as those with mental disability onset post‐18 years, with etiological diagnosis as per the ICD‐10 code for dementia causes. We calculated age‐standardized incidence rates per 100,000 inhabitants using population projections based on the 2010 and 2022 national censuses. To explore incidence time trends, we fitted linear models to predict incidence, considering time and sex as covariates.ResultsIn 2023, Argentina exhibited an age‐standardized incidence of 25.3 new dementia cases per 100,000 inhabitants (19.1 for men and 31.1 for women). (Table 1, Figure 1) A significant increase in incidence (p = 0.029) was observed over the 10 years (yearly effect: beta = 0.14, 95% CI [0.02, 0.26]) (Figure 2), with no significant sex‐specific impact on incidence evolution (p = 0.8). Early‐onset dementia reported an incidence of 5 per 100,000, contrasting with late‐onset dementia at 26.1. Late‐onset dementia displayed significant growth compared to early‐onset cases (p = 0.07).ConclusionThe increasing dementia incidence in Argentina emphasizes the urgent need for targeted interventions. Although there is no differential growth in incidence between men and women, women consistently exhibit a higher occurrence. Early‐onset dementia, traditionally associated with genetic predisposition, has remained stable. In contrast, late‐onset dementia has surged recently, likely due to the aging of the population and the rise in modifiable risk factors reported by other regional studies. These findings serve as a call to action, emphasizing significant regional prevention potential.
Plasma and MRI biomarkers predict post‐mortem tau pathology
Authors: Hu Mingzhao , Moloney Christina M. , Przybelski Scott A. , Algeciras‐Schimnich Alicia , Fought Angela J. , Rothberg Darren M. , Nguyen Aivi T. , Reichard Ross R. , Dickson Dennis W. , Knopman David S. , Jack Clifford R. , Petersen Ronald C. , Mielke Michelle M. , Graff‐Radford Jonathan , Murray Melissa E. , Vemuri Prashanthi 
Published: January 2025

AbstractBackgroundImaging and plasma markers are used as key indicators of disease for Alzheimer’s disease (AD) but their usefulness in predicting regional tau pathology is relatively understudied. Our objective was to construct predictive models for regional tau pathology measured on postmortem brain tissue using multiple ante‐mortem AD biomarkers. We focused on hippocampal and parietal regions that were immunostained with AT8 and 2E9 that reflect early and advanced aspects of tangle maturity, respectively.MethodsUsing autopsy data from 63 participants enrolled in the Mayo Clinic Study of Aging (MCSA) where the last clinical visit is within three years of death, we analyzed three ante‐mortem plasma markers ‐ phosphorylated tau protein at the threonine 181 residue, glial fibrillary acidic protein, total β‐amyloid Aβ42 relative to Aβ40 ratio (Quanterix Simoa HD‐X Analyzer), and three regional MRI markers computed using SPM12 ‐ parietal thickness, parietal volume relative to total intracranial volume ratio, and hippocampal volume adjusted for total intracranial volume. Based on eight separate cross‐sectional linear regression models weighted by the inverse of time from last visit to death, we estimated the multiple and partial R2 predicting post‐mortem pathology measures, including AT8 and 2E9 in both the hippocampal and parietal regions. All models were adjusted for age, sex, and cardiovascular and metabolic conditions score.ResultsThe MRI models (R2 of 0.26‐0.53) had greater predictive power compared to the plasma models (R2 of 0.17‐0.26). The best multiple R2 was 0.53 for prediction of hippocampal AT8 using antemortem hippocampal volume adjusted for total intracranial volume followed by R2 of 0.43 for prediction of hippocampal 2E9 using hippocampal volume adjusted for total intracranial volume. Though the model prediction of parietal AT8 and 2E9 measurements was low (R2 of 0.21‐0.26), parietal thickness exhibited better predictive performance than parietal volume relative to total intracranial volume ratio.ConclusionsThe data we presented here may be utilized to validate biomarkers using regional pathology. Our results support that regional MRI is sensitive to tau pathology. Newer MRI methods should be tested to examine the specificity of newer quantitative measurements to tau pathology.
Identification of cross‐diagnostic protein signatures and subtypes of neurodegenerative diseases using multi‐task deep learning on the UK Biobank proteomics dataset
Authors: Gargas Tomasz , Hou Liping , Wrzesinski Tomasz , Wojtowicz Konrad , Kowalska Marta , Kamuda Malgorzata , Jetka Tomasz , Whelan Christopher D , Smets Bart 
Published: January 2025

AbstractBackgroundNeurodegenerative diseases are a heterogeneous group of illnesses. Differences across patients exist in the underlying biological drivers of disease. Furthermore, cross‐diagnostic disease mechanisms exist, and different pathologies often co‐occur in the brain. Clinical symptoms fail to capture this heterogeneity. Molecular biomarker‐driven approaches are needed to improve patient identification & stratification with the aim of moving towards more targeted patient treatment strategies.MethodThe UK Biobank Pharma Proteomics Project has generated a proteomics dataset of unprecedented size. It consists of plasma proteomic profiles measured using the Olink 3k protein panel from over 54,000 individuals, complemented with broad phenotypic & genetic information. It is a unique resource to explore the biological correlation between neurodegenerative diseases as well as with other indications. We applied a multi‐task deep learning (MTL) approach to generate models that predict disease status based on plasma proteomics profiles for a broad range of indications, including neurodegenerative diseases such as Alzheimer’s and Parkinson’s disease. The MTL approach first learns fundamental biological patterns by knowledge sharing between diseases in the initial segment of the network. Subsequently, understanding of a specific indication is refined with dedicated training. This improves model generalizability and statistical power. The neural network also produces low‐dimensional embeddings of proteomic profiles that can be used for sample clustering and to derive insights about disease‐associated processes.ResultThe average precision‐recall area‐under‐the‐curve (PR‐AUC) of the MTL models across all diseases is 0.72 vs. 0.67 for the baseline single task logistic regression models. For Alzheimer’s disease, the MTL classifier has a PR‐AUC of 0.76. Feature importance scores were calculated using the SHAP method. Top features for Alzheimer’s disease included several known biomarkers (e.g., GFAP, NPTXR). A UMAP projection of all diseases using the feature importance scores clusters diseases by disease category. Sample clustering revealed biologically interpretable patient subgroups, such as a Parkinson’s cluster linked to lysosomal biology.ConclusionHigh performance of the MTL approach signifies good characterization of cross‐disease biology. This is corroborated by the model’s capability to produce meaningful low‐dimensional representations of plasma proteomics profiles that can be used for identification of cross‐diagnostic protein signatures and subtypes of neurodegenerative diseases.UKB application number 65851.
Prediction of amyloid and tau status in nondemented older adults using tree‐based ensemble models
Authors: Seo Younghoon , Oh Hwamee 
Published: January 2025

AbstractBackgroundPredicting amyloid and tau status in nondemented older adults with AD pathologies using more affordable and accessible measures can facilitate clinical trials by reducing the screen failure rate. The goal of the present study was to develop tree‐based ensemble models to predict PET‐based amyloid and tau burden using non‐invasive measures.MethodTwo datasets, amyloid (Aβ; n = 1062) and tau (n = 410), from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were used to predict the biomarker load in the subjects with normal cognition and mild cognitive impairment. Amyloid PET with the [18F]Florbetapir tracer was used as the gold‐standard measure for binary amyloid status classification), while tau PET with the [18F]Flortaucipir tracer was used for the three‐stage (low, intermediate, and high) determination. We trained random forest (RF), extreme gradient boosting machine (XGBoost), and light gradient boosting machine (lightGBM) models using different combinations of demographic, neuropsychological, APOE genotype, and volumetric MRI data, and measured the model performance using area under the receiver operating curve (AUROC).ResultThe performance of baseline model with demographics showed modest performance for Aβ (RF = 0.665, XGB = 0.650, LGBM = 0.659). Subsequent additions of features improved the predictive performance, with the model using demographic data, cognitive data, and volumetric MRI measures demonstrating the highest performance (RF = 0.762, XGB = 0.763, LGBM = 0.761). Meanwhile, the baseline model achieved modest performance for the three‐stage tau classification (RF = 0.643, XGB = 0.654, LGBM = 0.643), and the further addition of features improved the performance, with the feature combination of demographic data, cognitive, volumetric MRI measures, and continuous Aβ PET SUVRs achieving very good performance (RF = 0.799, XGB = 0.801, LGBM = 0.800). SHAP summary plots showed that age, entorhinal cortex volume, and neuropsychological and functional measures were important for Aβ classification, while Aβ load, high global cognition scores, hippocampal and middle temporal gyrus volume were shown to predict tau status.ConclusionWithout using amyloid and tau PET, tree‐based ensemble machine learning models predict amyloid and tau status among nondemented older adults with modest to very good performance and could be incorporated for future clinical trials.
Analysis of Lifestyle‐Related Risk Factors in Stroke Patients
Authors: Nam Sanghun , Lim Young Myoung , Kim Ah‐Ram , Lim Seungju , Park Ji‐Hyuk 
Published: January 2025

AbstractBackgroundPrevious research has predominantly examined life satisfaction in stroke patients as a singular variable, overlooking its multidimensional nature encompassing health, income, spouse, and child satisfaction. To address this research gap, our longitudinal study utilizes survival analysis to investigate how various aspects of life satisfaction are influenced by diverse lifestyle factors.MethodWe employed data from the Korean Longitudinal Study of Aging (2010‐2020), specifically focusing on stroke patients who survived from 2010 to 2020. Dependent variables included health, income, spouse, and child satisfaction. Lifestyle factors encompassed health behavior, health status, and social interactions, while Cox regression analysis incorporated control variables such as age, sex, education, and residential area.ResultExercise demonstrated the highest hazard ratio (HR) in child satisfaction (HR = 2.111, CI = 1.04‐4.31), while health screening exhibited the highest HR in health satisfaction (HR = 1.489). Depression had the highest HR in child satisfaction (HR = 6.343, CI = 2.67‐14.98). The lowest HR in child satisfaction was observed for BMI (HR = 0.500, CI = 0.27‐0.94), whereas diabetes showed the highest HR (HR = 1.800, CI = 1.05‐3.08). Employment status had the highest HR in health satisfaction (HR = 1.678, CI = 1.68‐1.01), with no significant differences in other variables.ConclusionOur study delves into the complex dynamics of the relationship between lifestyle factors and the various dimensions of life satisfaction among stroke patients. Through meticulous examination, we unravel the intricate connections that exist within this association.
Association between brain tau deposition and default mode network connectivity in cognitively normal older adults
Authors: Cha Woo‐Jin , Yi Dahyun , Chumin Evgeny J. , Byun Min Soo , Jung Joon Hyung , Ahn Hyejin , Kim Yu Kyeong , Lee Yun‐Sang , Kang Koung Mi , Sohn Chul‐Ho , Risacher Shannon L. , Sporns Olaf , Nho Kwangsik , Saykin Andrew J. , Lee Dong Young ,   KBASE Research Group
Published: January 2025

AbstractBackgroundAlzheimer’s disease (AD) pathology occurs in the brain before manifestation of significant cognitive decline. Growing evidence suggests that brain networks such as default mode network (DMN) or salience network, identified through resting‐state functional magnetic resonance imaging (MRI), are affected by AD pathology. In this study, we investigated the relationship between network segregation and the key in vivo AD pathologies including beta‐amyloid (Aβ) and tau deposition in old adults with no cognitive impairment.MethodA total 283 older adults with normal cognition aging from 55 to 87 were recruited from the Korean Brain Aging Study for the Early Diagnosis and Prediction of Alzheimer’s Disease (KBASE) cohort. The participants underwent comprehensive clinical and neuropsychological assessment, [11C] Pittsburgh Compound B PET for measuring Aβ deposition, [18F] AV‐1451 PET for measuring tau deposition, structural MRI, and resting‐state functional MRI for measuring functional connectivity (FC). For PET scans, standard uptake value ratio (SUVR) was used for the analyses; combined regions of inferior cerebellum and pons were used as the reference region when obtaining SUVRs. For FC, segregation values (ratios between median z‐transformed Pearson correlation of within‐ and between‐network connectivity) for overall and the seven individual resting state networks were computed (Table). The relationships between Aβ or tau deposition and network connectivity segregation were examined through cross‐sectional approach using multiple regression analyses. In the analyses, Aβ or tau deposition was used as an independent variable and segregation values of the networks were used as dependent variables.ResultTau deposition had a significant negative association with the DMN segregation (β = ‐0.249, p = 0.007); but, tau had no relationships with any other networks (Table). Aβ deposition was not associated with any segregation values for the seven brain networks (Table).ConclusionOur finding suggests that impaired functional connectivity of DMN is closely linked to tau deposition even in cognitively unimpaired older individuals.
Assessment of AI‐generated digital twin (DT) methodology on reduction of treatment effect variance and potential clinical trial sample size saving using a Phase 2 trial dataset from patients with Alzheimer’s disease (AD)
Authors: Wang Deli , Florian Hana , Yu Lynch Shau , Robieson Weining Z , Wang Li , Zhong Sheng , Xing Yunzhao , Zhuang Run , Kusiak Coco , Millar Stefanie , Turner Michelle , Ross Jess , Walsh Jonathan R. , Begany Katelyn , Graff Ole 
Published: January 2025

AbstractBackgroundIn Alzheimer’s Disease (AD) trials, clinical scales are used to assess treatment effect in patients. Minimizing statistical uncertainty of trial outcomes is an important consideration to increase statistical power. Machine learning models can leverage baseline data to create AI‐generated digital twins – individualized predictions (or prognostic scores) of how each patient’s clinical outcomes may change during a trial assuming they received placebo. Incorporating prognostic scores into trial design and analysis as a covariate increases statistical power, or reduces sample size, in Phase 2 and 3 trials (Figures 1/2). We assessed these properties using data from a Phase 2 clinical trial of tilavonemab in patients diagnosed with early AD (NCT02880956) and digital twin (DT) methodology (PROCOVATM).MethodIn a double‐blind, Phase 2 trial (AWARE), 453 patients aged 55‐85 years with early AD (met NIA‐AA clinical criteria for mild cognitive impairment or probable AD), were randomized to receive placebo or 1 of 3 doses of tilavonemab (1:1:1:1 ratio) over a 96‐week treatment period. Prognostic scores were produced for the change from baseline (Δ) in Clinical Dementia Rating Scale Sum of Boxes (CDR‐SB) and the Δ in AD Assessment Scale‐Cognitive Subscale 14 (ADAS‐Cog 14). Sample size savings were calculated from partial Pearson correlations (controlled for treatment) between prognostic scores and trial outcomes. Variability reductions were assessed using a covariance modelling approach that adjusts for the prognostic score (PROCOVATM).ResultFor Δ CDR‐SB and Δ ADAS‐Cog 14 at Week 96, standard deviations of the prognostic scores were lower than the trial’s outcomes. Partial correlation coefficients were moderate for both Δ CDR‐SB (p = 0.360) and Δ ADAS‐Cog 14 (p = 0.305) at Week 96. Total residual variance for both outcomes was reduced by ∼11% with DT methodology compared to an unadjusted model.ConclusionDepending on correlations between prognostic scores and actual trial outcomes, a potential overall sample size reduction of 5‐10% could be achieved using DT methodology (PROCOVATM) while maintaining statistical power, based on Δ CDR‐SB and Δ ADAS‐Cog 14 in the AWARE study. Sample size savings could enable shortening of the recruitment period and reduce the number of patients on placebo, encouraging greater patient participation.
Cognitive symptoms and medical history prior to mild cognitive impairment and dementia diagnosis: a retrospective case‐control study using Dutch primary care data
Authors: Baak Brenda N. , de Boer Casper , Nooralishahi Mahsa , van Harten Argonde C. , Vijverberg Everard G.B. , van der Flier Wiesje M. , Herings Ron M.C. 
Published: January 2025

AbstractBackgroundGeneral practitioners (GPs) play a crucial role in recognizing cognitive deficits and diagnosing dementia. Currently, dementia diagnosis in primary care is prone to be missed or delayed. Electronic health records from GPs can offer insights into the trajectory leading up to a dementia diagnosis. Our aim was to characterize prior medical history of health conditions known to be associated with dementia and cognitive concerns in dementia cases and controls using Dutch primary care data.MethodIn this retrospective case‐control study using data from the ABOARD‐GP Cohort (2011‐2022), we identified dementia cases and an equal number of controls (matched on birth year, sex, general practice and registration year). A prior medical history of cardiovascular disease, diabetes, hypercholesterolemia, cerebrovascular disease, psychiatric conditions, sleep disturbances, osteoporosis/arthritis, and fracture was assessed. Cognitive concerns mentioned during a GP consultation was operationalized as mentioning of (1) memory/concentration/orientation complaints/disorders, (2) overall decline, and/or (3) Mini‐Mental State Examination. Logistic regression compared each condition in medical history between dementia cases and controls. Time from first recorded cognitive concern to dementia diagnosis was estimated.ResultWe identified 70,628 dementia cases and 70,628 controls (mean age 80 years, 60% female) (Table 1). We found significant associations between all conditions in medical history and a dementia diagnosis. Cerebrovascular disease and psychiatric conditions, the two strongest associations, corresponded to a 58% and 59% higher odds of having a dementia diagnosis. Prior cognitive concerns mentioned during GP consultations were recorded in 47% of dementia cases (Table 2), indicating that over half had no documented history of cognitive concerns. For those with mentioning of a cognitive concern, the mean time from first cognitive concern to diagnosis was 2.14 years.ConclusionThis study using GP data shows that cases with dementia were more likely to have pre‐existing health conditions than controls. One hypothesis for this could be that these conditions are known risk factors for dementia. Alternatively, individuals with these other conditions have previously sought medical attention from a GP, facilitating the detection of cognitive concerns. These results underscore the importance of timely recognition of cognitive symptoms and addressing risk factors to reduce the impact on cognitive health.