bluuebunny/crossref_metadata_2025_split huggingface dataset:
QyPredict prognostic model enriches selection for faster decliners in mild cognitive impairment
Authors: Gordon Elizabeth , Samper‐González Jorge , Villa Luca , Jubault Thomas , Guizard Nicolas 
Published: December 2023

AbstractBackgroundThe suboptimal selection of patients is a key challenge for disease‐modifying trials in mild cognitive impairment (MCI) and Alzheimer’s disease. Improved selection strategies are urgently needed to better power trials. Recent advances in AI predictive modeling, such as the QyPredict® algorithm, are promising tools to improve the selection of patients likely to clinically progress during the clinical trial timeframe.MethodQyPredict® was applied to 519 MCI patients from ADNI: age: (71.8(±7.1), 303 Male, MMSE range 24‐30, CDR = 0.5, with available amyloid and APOE‐ε4 + status. QyPredict®, a tunable machine learning model, incorporated baseline QyScore® volumetric MRI results, demographic (age and sex) and clinical (MMSE and CDR) inputs. A QyPredict® score (0‐1) was produced for each individual, representing the probability they will demonstrate a modelled outcome (here an increase in CDR‐SOB of 0.5+ over 24 months). A score close to zero indicating very low probability of change.Predictive performance was evaluated using positive predictive value (PPV), sensitivity and specificity. Mean and standard deviation for change in CDR‐SOB over 24 months were calculated and compared using Mann‐Whitney test at increasing QyPredict® cut‐offs (0.1, 0.2, 0.3, 0.4 and 0.5). Actual change versus predicted change in CDR‐SB scores was further investigates for ‘Stable’ (QyPredict®<0.5) versus ‘Decliners’ (QyPredict®>0.5) (Figure 1). Sample sizes to detect a 30% treatment effect (reduction in change in CDR‐SOB) were calculated for the full sample and a cohort enriched with only ‘Decliners’.ResultFor the full sample without QyPredict® enrichment, change in CDR‐SOB at 24 months was 2.3 (±2.11), which significantly increase (p<0.001) from a QyPredict®>0.2, reaching a change of 3.4 (±2.30) points for MCI patients with QyPredict®>0.5. PPV, sensitivity and specificity were: 0.76, 0.70 and 0.75 (the full cohort); 0.83, 0.77 and 0.70 (Amyloid positive patients) and 0.82, 0.75 and 0.74 (APOE‐ε4 + patients). Sample sizes for a putative trial where 304 (full sample) compared with 110 (enriched cohort) per treatment arm.ConclusionUsing baseline QyScore® metrics, basic demographic, and typical clinical data available at screening visits, QyPredict® accurately modelled the likelihood of future clinical decline, resulting in substantially reduced patient cohorts to detect treatment effects when enriched with ‘decliners’.
Use of a diagnostic decision support tool in memory clinic daily practice – The DAILY feasibility study
Authors: van Gils Aniek M. , Meester Hanneke F.M. Rhodius‐ , Handgraaf Dédé , Hendriksen Heleen M.A. , van Strien Astrid M , Schoonenboom Niki S.M. , Schipper Annemieke C , Kleijer Mariska , Griffioen Annemiek , Muller Majon , Lötjönen Jyrki , Tolonen Antti , van der Flier Wiesje M. , Visser Leonie N.C. 
Published: December 2023

AbstractBackgroundTo optimally serve increasing numbers of memory clinic patients through a timely and adequate diagnosis, digital health technologies offer a unique potential. Diagnostic decision support systems (DDSS) can aid clinicians in diagnostics and patient communication, but such tools are not widely used in daily practice. We assessed the feasibility of a DDSS prototype consisting of four modules (Figure 1): (1) web‐based cognitive test tool (cCOG); (2) automated MRI‐quantification (cMRI); (3) AI‐based classifier (cDSI); (4) diagnostic report page, providing an easy‐to‐understand overview of all information for clinicians and patients.MethodWe conducted the DAILY feasibility study in five Dutch memory clinics, including 11 clinicians (internal medicine/geriatricians(n = 5), neurologists(n = 2), residents(n = 3), nurse practitioner(n = 1), mean age 38±7y, 11±6y experience), and 43 patients(7 subjective complaints, 19 mild cognitive impairment, 14 dementia, 3 postponed diagnosis, mean age 73±11y), and their care partners(n = 28, 65±11y). Clinicians received instructions to integrate the tool into routine care. Feasibility, usability, and satisfaction were assessed using questionnaires (clinicians, patients, care partners) and interviews (clinicians).ResultClinicians reported using the tool in 34/43(80%) patients. Their overall satisfaction with the tool was acceptable (median 70(IQR 50‐80), scale 0‐100). Out of the four modules, the diagnostic report was most often used (in 70% of patients) and rated as useful by 89% of clinicians, with 70% indicating it contributed to more efficient patient communication. Patients and care partners evaluated the diagnostic report as understandable and helpful. In the interviews, clinicians were predominantly positive regarding the diagnostic report, indicating that it helped create an overview for themselves and their patients. Widely varying opinions indicated that (time‐efficient) use of the complete tool demands more education and practice to obtain the proficiency level needed to adequately integrate the tool into routine practice.ConclusionThe lasting adoption of digital technologies in healthcare is challenging, and efficient and user‐friendly tools are needed. In this study, clinicians, patients, and care partners valued an automatically‐generated, comprehensible report summarizing individual test results. Implementing such a digital report page could contribute to improved patient communication and, ultimately, better care management. Furthermore, our results identified an educational need regarding the use of digital tools.
Separating Alzheimer’s disease from Dementia with Lewy bodies using a volumetric MRI classifier
Authors: van Gils Aniek M. , Tolonen Antti , Meester Hanneke F.M. Rhodius‐ , Mecocci Patrizia , Vanninen Ritva , Frederiksen Kristian Steen , Barkhof Frederik , van der Flier Wiesje M. , Lemstra Afina 
Published: December 2023

AbstractBackgroundSeparating Alzheimer’s dementia (AD) from dementia with Lewy bodies (DLB) can be challenging, especially in DLB patients with concomitant AD pathology. It is imperative to distinguish the diseases for optimal patient management. To date, no single MRI feature can accurately separate the two diseases. This study examines whether a linear combination of regional atrophy measures can differentiate cognitively normal (CN), AD, and DLB patients.MethodWe computed volumes of 70 brain regions using an automatic segmentation tool. For each region, we derived age, sex, and head size‐dependent z‐scores. A linear classifier with all brain regions was used to predict if a patient belonged to CN, AD, or DLB. Diagnoses were based on international consensus criteria and CSF‐biomarkers. The classifier was tested in 327 CN (mean age 67±10y, 48%F), 501 AD (70±9y, 49%F), and 99 DLB (69±7y, 15%F) patients from three cohorts (ADNI, PredictND, ADC). The ADC cohort was assessed separately, as almost all DLB patients were from the ADC. In addition, we assessed classification performance in a subgroup of DLB patients with AD‐like CSF (DLB+, n = 57). Performance was evaluated using 10‐fold cross‐validation.ResultPerformance measures for the classifier are summarized in Table 1. The classifier accuracy was assessed in the three‐class classification task and in two‐class classifications where one of the groups was excluded from the analysis. The balanced accuracy in the three‐class classification was 67% for the DLB group and the DLB+ subgroup. For AD vs. DLB, the AUC was 0.77(95% CI:0.70‐0.82), for AD vs. DLB+ AUC was 0.72(0.64‐0.80). The classifier correctly classified CN and AD patients more often than DLB patients (Table 2). Most DLB+ patients were misclassified as AD. The hippocampus was the best single region for distinguishing AD vs. DLB, with an AUC of 0.70(0.64‐0.76).ConclusionThe joint classifier achieved high accuracy in separating AD vs. DLB patients. Although the accuracy was lower, the classifier could distinguish DLB patients with concomitant AD pathology from AD. Next, we will combine different brain regions to discern disease‐specific patterns and add clinical data to the classifier to improve differential diagnostic accuracy for DLB and DLB with concomitant AD pathology.
Translating AD Clinical Trial Results to Meaningful Benefits: Expectations, Definitions, Challenges and Opportunities.
Authors: Atri Alireza 
Published: December 2023

AbstractBackgroundClinical trials in Alzheimer’s disease (AD) are designed and powered to detect treatment impact on validated research outcome measures necessary for regulatory approval. However, what constitutes a clinically meaningful benefit, how to define, measure and translate it from the results of clinical trial results to multiple stakeholders, including clinicians and patients requires better clarification.MethodsReview and synthesis of recent publications (Assuncao, Sperling, et al. Alz Res Therapy 2022; Petersen, Aisen Andrews, Atri, et al. Alzheimers Dementia. 2023 in press) and AD therapeutics developments to provide overview and frameworks for translating AD clinical trial results to meaningful benefits with respect to expectations, definitions, challenges and opportunities that may resonate with multiple stakeholders, including patients, patient care partners and clinicians.ResultsRational for realistic expectations of putative AD disease‐modifying treatments (DMTs) are reviewed including expectations of not improving symptoms but of slowing disease progression and modestly moderating between‐group (treatment versus placebo) clinical decline. Definitions postulated to represent clinically meaningful benefits are reviewed. Confusion, misunderstanding or misinterpretations in regards to representation of thresholds for meaningful within‐patient progression are discussed, including recent misinterpretation of necessary thresholds for determining meaningful group‐level differences having created misguided expectations for clinical meaningfulness in DMT clinical trial results. Discuss confounding minimal detectable change (MCD) and utilizing between‐group differences (to assess outcome efficacy) with and to define minimal clinically important differences (MCID) (an individual, within‐patient change in outcome assessment). Within‐patient change thresholds are not intended to assess meaningfulness of differences between group‐level changes over time, but may represent meaningful within‐patient trajectories via complementary (e.g. responder/progressor) analyses. Multiple frameworks show the challenges and opportunities to multidimensionally better represent and translate results to face‐valid and patient‐centered representations to resonate with AD stakeholders. These include expanded outcomes (e.g. neuropsychiatric symptoms, socioeconomic burden); patient‐ and caregiver‐reported outcomes; complementary analyses (standardized effect‐sizes, RR/OR, NNT, Time‐to‐Event, Time‐Equivalent‐Gained); and concepts such as predictive benefit (via a disease biomarker/core domain) and cumulative benefits (accrual and potentially increasing over time).ConclusionExpanded data, analyses and frameworks for defining and translating clinically meaningful patient‐centered benefit to multiple stakeholders, particularly patients, care‐partners and clinicians, are proposed.
Establishing glial responses to anti‐Aβ immunotherapy through single‐cell RNAseq to gain insights into mechanisms of ARIA
Authors: Foley Kate E 
Published: December 2023

AbstractBackgroundWhile anti‐Aβ immunotherapies effectively clear Aβ plaques, mouse and human data show consistent and significant cerebrovascular adverse events of this treatment approach. Anti‐Aβ immunotherapies have been plagued in clinical trials by the occurrence of amyloid‐related imaging abnormalities (ARIA), signifying cerebrovascular disruptions of the edema (ARIA‐E) or hemorrhagic (ARIA‐H) type in the brain. Despite these cerebrovascular adverse effects, the FDA granted accelerated approval to aducanumab and lecanemab. However, our mechanistic understanding underlying ARIA remains minimal. Elucidating the mechanism(s) by which anti‐Aβ antibodies cause ARIA is critical towards improving safety of these therapies for AD.
MethodIn the current study, we aimed to determine the glial responses to anti‐Ab antibodies with the hypothesis that astrocytes and microglia have temporal cellular shifts that contribute to cerebrovascular dysfunction after acute anti‐Aβ antibody administration. To identify changes in glial gene expression in response to acute anti‐Aβ antibody we performed a single cell sequencing and digital spatial profiling (DSP) on 14mo Tg2576 APP mice (Fig. 1). We observed differences in size of clusters between anti‐Aβ (6E10) and IgG control (IgG1) intracranial injections after 24hr and 3days, suggesting some clusters of cells respond to anti‐Aβ in a time‐dependent or antibody manner (Fig. 2).ResultPreliminary data identified some of the clusters as microglia (Cx3cr1) and astrocytes (Aldoc), as well as oligodendrocytes (Plp). Apoe was increased in multiple cell types. DSP on these mice is ongoing, however we aim to further characterize the transcriptional profile of glia at the vasculature, and in the vicinity of Aβ plaques and cerebral amyloid angiopathy (CAA). Contralateral hemispheres are utilized for downstream validation studies.ConclusionOur work will elucidate the mechanisms of anti‐Aβ antibody‐induced glial response and establish the role of astrocytes and microglia in cerebrovascular dysfunction and ARIA. This work is critical to improve safety and increase generalizability of these therapies.
Testing the Feasibility of Tracking Behavior and Physiology Patterns using Wearable Technology to Detect Potential Cognitive Risk among Rural, Multicultural Older Adults
Authors: Cook Diane J , Wiese Lisa Ann Kirk , Luna Catherine , Schmitter‐Edgecombe Maureen 
Published: December 2023

AbstractBackgroundRural residents often experience multiple factors that increase their risk of cognitive impairment, e.g. less access to formal education and specialized healthcare (Livingston et al., 2020). Early, accurate diagnosis in the mild cognitive impairment stage could save up to 7.9 trillion dollars in health and long‐term care costs by 2050 (Alzheimer’s Association, 2021). First year results from this federally‐funded new investigator mentoring program between computer science researchers and a nurse investigator are reported. The purpose was to test the feasibility of a novel approach to detecting early cognitive decline using smartwatch technology among a 92% racially and ethnically diverse rural community challenged by low digital access (51%) and digital literacy (48%).MethodWe tested smartwatches programmed with the novel “n‐back” shape test for cognitive assessment, ecological momentary assessment, and movement tracking. Paper‐and pencil measures to assess cognitive risk were the PROMIS ® Applied Cognition, Mini‐Cog (Borson, 2003), and Mini‐MoCA (Nasreddine, 2019). Another novel aspect of the study was hiring the local high school students to mentor older adults in use of smartwatches during home visits. Student engagement with their mentees was monitored by their high school advisor, with oversight by well‐known faith‐based research assistants. Six additional pre‐post intervention surveys included “Technology Activities of Daily Living (T‐ADL) (Muñoz‐Neira, 2012) and “Relating to Older People” (Cherry and Palmore, 2008).ResultMean years lived rural of the n = 28 enrolled was 60.3 (SD = 18.2), with an average age of 71.6 (SD = 8.1). Results regarding cognitive risk using the n‐back Shape test included correlation with Ecological Momentary Assessment Question 7 (“Right now, my environment is distracting”; r = ‐.35, p = .046) and approached significance with Question 1 (“In the past 2 hours, do you think your mind has been as sharp as usual?”; r = .33, p = .058). Pre‐post correlations in T‐ADL (r = .87) and Applied Cognition (r = .38) were significant (p < .001, p < .04).ConclusionTesting this novel two‐pronged approach of detecting cognitive risk through smartwatch technology, and hiring high school students to mentor older adults in the home setting yielded implications for ADRD‐related research among larger samples of underrepresented groups.
Ameliorating amyloid beta oligomer toxicity with NAC
Authors: Keske Ezgi , TUNALI Nagehan ERSOY 
Published: December 2023

AbstractBackgroundAlzheimer’s Disease (AD) has a complex neuropathology, involving diverse cellular pathways. Molecular mechanisms induced by beta‐amyloid oligomer (Aβo) toxicity are considered to be the major initiators of neuropathology. Therefore, Aβos are commonly used to mimic toxicity in AD. The primary effect of Abeta toxicity is increase in cellular oxidative stress. Likewise, recent AD drug research and preclinical trials has been focused on reducing ROS and/or increasing antioxidant capacity. N‐acetyl cysteine (NAC) is a well‐known mucolytic agent and a powerful antioxidant via increasing GSH levels. In this respect, we aimed to investigate the effect of NAC in reducing oxidative stress in Aβo‐induced cellular toxicity.MethodSH‐SY5Y cells were cultured in DMEM medium containing 10% FBS and 1% pen/strep. In order to establish Aβo toxicity, Aβ(1‐42) peptide was dissolved in NH4OH at various concentrations (5‐100 μM) and incubated at 4°C for 24h. Oligomer formation was verified with Bis‐ANS test and Aβo toxicity was analyzed using WST‐1. Changes in the oxidative stress levels of the cells due to Aβo toxicity and NAC was quantified using Total Oxidant/Antioxidant Capacity kits based on spectrophotometric absorbance.ResultBis‐ANS test showed a linear correlation between oligomer formation and Aβo concentration. According to the cell viability assay results, 10 μM Aβ42o for 24 hrs was used to establish Aβo toxicity in vitro. At 1 and 5mM concentrations, NAC was shown to increase the viability, GSH levels and antioxidant capacity of the SH‐SY5Y cells significantly. Likewise, 1,5, and 10mM NAC was shown to reduce Aβo‐induced toxicity significantly. Furthermore, although 20 and 40mM NAC decreases viability of healthy cells, they increase viability in Aβo‐treated cells.ConclusionAccording to our results, NAC can be regarded as a potent molecule in ameliorating Aβo‐induced toxicity.
Examining a telecognitive assessment‐derived Preclinical Alzheimer’s Cognitive Composite, the tPACC, for cognitive migration over one year in community‐dwelling older adults
Authors: Duran Tugce , Bateman James R. , Gaussoin Sarah A. , Rundle Melissa M. , Okonmah‐Obazee Stephanie E. , Espeland Mark A. , Williams Benjamin J. , Hughes Tim M. , Craft Suzanne , Lockhart Samuel N. , Sachs Bonnie C. 
Published: December 2023

AbstractBackgroundADRD clinical trials often target preclinical disease, where hope of averting the pathologic cascade and eventual cognitive decline remains highest. The preclinical Alzheimer’s cognitive composite (PACC5) was developed for in‐person administration to capture subtle cognitive decline. Early cognitive fluctuations may inform multidirectional disease progression, defined as cognitive migration. It is desirable to have a harmonized composite measurement derived from in‐person and remote assessments for detecting cognitive migration.MethodWe examined in‐person data from 417 adults (70.6±8.0y) from the Wake Forest ADRC Clinical Core, who received MRI, clinical evaluation and cognitive testing. The primary outcome was cognitive migration status determined by global CDR (0 and 0.5) increase/decrease between baseline (BL) and follow‐up (mean difference = 13.9 months): CDR‐0 Stable (51% maintained CDR‐0), CDR‐0.5 Stable (27% maintained CDR‐0.5), Reverter+ (13% migrated from CDR‐0.5 to CDR‐0) and Migrant− (9% migrated from CDR‐0 to CDR‐0.5). In‐person PACC5 was calculated (RAVLT Delayed Recall, Digit Symbol Coding [DSC], semantic fluency, Craft Story Delayed Verbatim, total MMSE) using BL participants with global CDR = 0 as reference. tPACC used measurements available at both in‐person and remote visits with minor modifications from PACC5: Montreal Cognitive Assessment (MoCA) replaced MMSE and DSC not included. We performed intraclass correlation coefficient (ICC) analysis and generated Bland‐Altman plots for BL tPACC and PACC5. We also evaluated associations between tPACC and neuroimaging.ResultParticipant characteristics and group differences in MRI markers are in Table 1. Bland‐Altman plots show differences and mean of tPACC and PACC5 (dashed line: ±1.96 SD, solid line: mean difference/bias). 92% of values lie within limit of agreement, indicating good agreement (overall ICC = .960, 95%CI = [.952, .967]), between tPACC and PACC5 in each group (Figure 1). At BL, there was a significant absolute agreement between in‐person tPACC and PACC5 in all groups (Figure 1). tPACC showed significant associations (Table 2) with Temporal Meta ROI thickness, hippocampal volume as % head size (ICV) and white matter hyperintensities (logWMH controlled for ICV) overall and in some cognitive migration groups.ConclusionThere is generally good agreement between BL tPACC and PACC5 across cognitive migration groups. BL tPACC may predict BL neuroimaging based on cognitive migration status. Longitudinal tPACC examination is warranted.
Feasibility of a digital cognitive screening test to detect cognitive impairment
Authors: Sullivan Sydney C. , Mankodiya Kunal , Constant Nicholas , Denby Charles , Ott Brian R. , Korthauer Laura E. , De Vito Alyssa N. , Maynard Taylor R. , Zammitti Melissa G. , Tremont Geoffrey 
Published: December 2023

AbstractBackgroundAssessment of cognitive functioning is important for early detection of Alzheimer’s disease and related dementias. Current cognitive screening tests are not often administered due to time limitations and the availability of trained staff. We developed the Rhode Island Cognitive Assessment Tool (RIMCAT), a self administered tablet‐based test with special attention given to creating an age‐friendly interface. The objective of this project was to collect feasibility data on the instrument.Method50 participants (Healthy Controls [HC] n = 33; Cognitively Impaired [CI]; diagnosed with mild cognitive impairment or dementia n = 17) were administered the RIMCAT with a proctor who documented any difficulty with a test component and provided necessary verbal prompts. Participants also completed a survey about their experience. The RIMCAT includes measures of orientation, attention, language, verbal and visual memory, computation, and executive functioning. Data were analyzed using frequencies, descriptive statistics, and independent sample t‐tests.ResultParticipants were 98% white, 76% female, had a mean age of 71.28 (SD = 8.63), and were well‐educated (M = 15.88; SD = 2.34). They were regular users of smartphones (83%), computers (85%), and tablets (61%). The HC group (M = 2.06; SD = 1.90) required significantly fewer prompts during the RIMCAT administration compared to the CI group (M = 4.82, SD = 3.57), p < .001. Two CI individuals were unable to complete the measure despite verbal prompting. The verbal memory task was the most technologically challenging, with comparable rates of prompting in both groups. Common problems included difficulty with input methods (keyboard and microphone entry), button use, and comprehension of instructions. Survey responses however, revealed that 80% or more participants rated instruction and image clarity, video and voice quality, and button usage as very good or excellent.ConclusionResults demonstrated initial feasibility of the RIMCAT for individuals with and without cognitive impairment. Design features were rated highly and most individuals completed the measure with minimal prompting, although CI individuals needed significantly more prompts. Therefore, future research will continue to refine the RIMCAT using participatory design and feedback to increase the likelihood of successful self‐administration for older adults with varying levels of cognition.