bluuebunny/crossref_metadata_2025_split huggingface dataset:
Social Vulnerability Index and Cognitive Decline in a Population‐Based Cohort Study
Authors: Desai Pankaja , Ng Ted K.S. , Krueger Kristin R , Evans Denis A , Rajan Kumar B 
Published: January 2025

AbstractBackgroundSocial determinants of health (SDOH) are critically important to reduce disparities in Alzheimer’s disease (AD). Yet, we often focus on individual‐level characteristics, excluding socio‐environmental factors, when evaluating predictors of AD or developing interventions that target AD prevention. To address these gaps, we examine the association between the social vulnerability index (SVI) and cognitive decline in a bi‐racial cohort of older adults.MethodIn this investigation, we use data from participants (N = 6,763) in the Chicago Health and Aging Project (CHAP), a longitudinal, population‐based cohort study in four south‐side Chicago communities. The study sample consists of participants who completed at least two neuropsychological assessments approximately three years apart. SVI is a score which utilizes US Census data to measure certain SDOH and calculates percentile rankings by census tract. Linear mixed effects regression models examined the association between SVI and cognitive decline, adjusting for age, sex, race, and education.ResultThe study sample comprised of more AA (63%) and female (61%) participants. The greatest percentage of AA participants were represented in quartile 3, and the greatest percentage of White participants were represented in quartile 1. We found that participants living in communities with greater SVI had more rapid global cognitive decline (quartile 2 (β = ‐0.010, SE = 0.005, p = 0.05), quartile 3 (β = ‐0.010, SE = 0.004 p = 0.024), and quartile 4 (β = ‐0.014, SE = 0.005, p = 0.005) compared to participants living in the lowest SVI communities.ConclusionSocial vulnerability plays a crucial role in faster cognitive decline, thereby increasing AD risk in more vulnerable areas. Reducing social vulnerability in communities might help reduce the future risk of AD.
Nutritional influences on Alzheimer’s disease: Insights from transformer models and explainable AI
Authors: Liu Ziming , Liu Longjian , Heidel Robert , Zhao Xiaopeng 
Published: January 2025

AbstractBackgroundThis study utilizes transformer‐based machine learning models and explainable AI (XAI) techniques to investigate the complex relationship between various nutritional factors and AD mortality. Drawing data from the Third National Health and Nutrition Examination Survey (NHANES III 1988 to 1994) and the NHANES III Mortality‐Linked File (2019), it aims to dissect the multifaceted interactions between nutrition and AD.MethodThe study employs advanced transformer models alongside traditional machine learning methods like random forests and support vector machines. These models are applied to the NHANES III dataset to predict mortality outcomes in AD, HD, and CA, with a particular focus on nutritional factors. XAI techniques, including the Shapley Additive Explanations (SHAP) and integrated gradients, are used to enhance the interpretability of these models. The aim is to identify and understand the most influential nutritional elements contributing to AD mortality and how these compare across different disease contexts.ResultTransformer models demonstrated a high degree of accuracy and reliability in identifying AD cases, outperforming traditional models in recall scores. XAI analysis highlighted several key nutritional factors significantly associated with AD mortality, such as serum apolipoprotein AI, vitamin B12, and selenium levels. These findings underscore the potential of transformer models and XAI in extracting deeper insights from complex medical data. Additionally, the study revealed distinct nutritional patterns associated with AD compared to HD and CA, suggesting specific nutritional pathways influencing the progression and mortality of these diseases.ConclusionThis research represents a significant step forward in applying advanced AI methodologies to medical research, particularly in the context of AD. By leveraging transformer models and XAI, the study provides a nuanced understanding of the impact of nutrition on AD mortality. It highlights the potential of these techniques in unraveling the complex interplay between diet and disease progression, offering a foundation for future investigations into targeted nutritional interventions for AD management. The findings also suggest broader implications for the use of AI in healthcare, paving the way for more sophisticated and personalized approaches to disease prevention and management.
Towards improved dementia prediction in the population: accounting for peripheral factors associated with levels of AD biomarkers
Authors: Hu Yi‐Han , Meirelles Osorio , Satizabal Claudia L , Gudnason Vilmundur , Tracy Russell P. , Seshadri Sudha , Launer Lenore J. 
Published: January 2025

AbstractBackgroundBlood‐based biomarkers, glial fibrillary acidic protein (GFAP) and neurofilament light chain (NfL), show potential for dementia risk stratification. Yet, their predictive performance for incident dementia in heterogeneous older population with multi‐morbidities, is not well tested. This study evaluates their predictive ability for incident dementia accounting for differences in factors affecting health.MethodData from 910 baseline non‐demented participants of the population‐based Age, Gene/Environment Susceptibility‐Reykjavik Study (AGES‐RS) from 2002 to 2015 (average age 76.6 years, 54.2% female, 6.7% incident dementia) were analyzed. Factors (n = 360) (sociodemographic, clinical, sensory, cardiometabolic, musculoskeletal, medical history, and lifestyle) clustered into 34 groups. Incident dementia cases were identified following the baseline exam until December 2015. Logistic regression models, with 5000 bootstraps, evaluated the predictive power of GFAP and NfL for incident dementia. Predictor sets included: biomarker only; plus age and sex; plus basic factors (education, APOE e4, eGFR); and a full model with associated peripheral clusters. An additional model with age and sex served as the reference for assessing performance without biomarker. Model effectiveness was assessed using area under the precision‐recall curve (AUPRC), precision, and sensitivity (recall), suitable for low dementia prevalence contexts.ResultBiomarker levels between cases and non‐cases showed significant overlap (Figure 1a). In predictive performance for incident dementia, NfL outperformed GFAP in the biomarker‐only model (AUPRCs, GFAP: 0.17, NfL: 0.20). Integrating peripheral clusters associated with these biomarkers improved the models’ performance, surpassing both biomarker‐only and basic models, especially for NfL (Figure 1b). Precision rates for the final GFAP and NfL models were 0.304 and 0.61, respectively, reflecting approximately 30% and 61% prediction accuracy for incident dementia. The GFAP’s AUPRC improved to 0.339, accurately identifying 5 out of 100 incident dementia cases, while the NfL’s AUPRC was 0.367, predicted 17% of cases.ConclusionOur findings indicate that due to the low prevalence of dementia in the general population, using plasma GFAP and NfL as dementia screening tools is not highly effective. However, the addition of peripheral factor clusters does enhance their predictive performance, with NfL having a slightly better performance over GFAP.
Development of Xanamem, an isoform 1 specific inhibitor of 11‐βHSD, as a procognitive and disease modifying treatment for mild/moderate Alzheimer’s disease. A pharmacodynamic and biomarker driven approach
Authors: Rolan Paul , Taylor Jack , Seckl Jonathan , Harrison John E , Chen Christopher Li‐Hsian , Farrell Colm , Maruff Paul , Woodward Michael , Hilt Dana 
Published: January 2025

AbstractBackgroundSelecting the optimal dose for clinical development is especially problematic for drugs directed at CNS‐specific targets. For drugs with a novel mechanism of action, these problems are often greater. We describe Xanamem’s clinical pharmacology, including the approach to dose selection and proof‐of‐concept studies. Xanamem is an isoform 1 specific inhibitor of 11‐βHSD which acts to reduce the tissue production of cortisol. Xanamem is under clinical development as a pro‐cognitive and disease modifying drug for Alzheimer’s disease as well as other neurological and psychiatric diseases involving CNS dysregulation of cortisol. Elevation of CNS cortisol has been associated with impaired cognition, neuroinflammation, and neuronal death.MethodClinical pharmacology analyses included pharmacokinetic and pharmacodynamic data from SAD and MAD dose trials in healthy volunteers; population pharmacokinetics; cognition from patients in a phase 2a trial in Alzheimer’s disease; multiple dose PET target occupancy study to determine target inhibition in the brain in healthy elderly participants and Alzheimer’s patients; two phase 1b trials in healthy older participants that assessed safety; and pharmacodynamic endocrinology and quantitative cognitive parameters.ResultXanamem demonstrated high CNS target occupancy at all doses studied by PET (5‐30 mg/day). At doses of 5 to 70 mg/day ACTH levels approximately doubled at steady‐state. After first doses, plasma cortisol was slightly reduced but was not decreased at steady‐state in doses of ≤ 10mg/day but was within normal levels. In three trials Xanamem demonstrated pro‐cognitive effects, primarily on attention and working memory (5‐20 mg/day). Xanamem also demonstrated potentially disease modifying treatment effects in mild AD in patients with biomarker positive AD (elevation of plasma pTau181) as shown by decrease in the rate of CDR‐SB worsening over 12 weeks (10 mg/day) versus placebo.ConclusionThis series of trials demonstrates that the optimal dose of Xanamem can be selected based on CNS PET occupancy data coupled with pharmacodynamic endocrinology and quantitative cognition testing. Pro‐cognitive and potentially disease modifying effects were observed at doses consistent with high CNS target occupancy. Xanamem is now in larger Phase 2 clinical outcome trials in MDD and mild/mod AD.
Comprehensive lipidomic analysis for identification of lipid biomarkers associated with Alzheimer´s disease
Authors: Wretlind Asger , Xu Jin , Proitsi Petroula , Legido‐Quigley Cristina 
Published: January 2025

AbstractBackgroundAlzheimer’s disease (AD) is a devastating disease at an individual level and for the wider society. Despite huge research efforts the underlaying causes of AD is still not well understood. We know that lipid metabolism is fundamental for maintaining a heathy brain and that some of the strongest risk factors for AD, such as APOE4, affect lipids. In this study we set out to measure the lipid changes associated with AD.MethodWe used a total of 841 participants from the AddNeuroMed and Dementia Case Register cohorts: 306 people with AD, 165 people with mild cognitive impairment (MCI) (n = 165), and 370 healthy controls.Mass spectrometry based lipidomics was carried out on plasma samples using a Waters ACQUITY UPLC and XEVO QTOF‐MS.Lipid molecules were clustered into modules based on correlation networks using Weighted Gene Correlation Network Analysis (WGCNA). Module eigengenes were used to investigate the association of each module with MCI and AD. Key lipid drivers (‘Hub’) associated with AD were selected and investigated further. Linear regression analyses were used to investigate the association of modules and key lipids with AD and MCI status after adjustment for sampling site, age, sex and presence of APOE4 allele. The False Discovery Rate (FDR) was applied to control for multiple testing.ResultLipidomics analysis resulted in 261 identified lipid molecules, which were analyzed with WGCNA resulting in 10 robust modules. Four modules were significantly associated with AD vs. healthy control after adjusting for covariates and following correction for multiple testing. Investigating the lipid drivers of these modules highlighted 55 lipid hubs, 26 of which were significantly associated with AD vs. control, after adjustment for confounders and multiple testing. These lipids included triglycerides, phosphatidylcholines, phosphatidylethanolamines and lysophosphatidylcholines.ConclusionHere, we present a workflow for robust identification of lipid molecules associated with Alzheimer’s disease. Further work will establish how these lipids relate to AD risk factors, clinical measures, and genetic risk for AD.
“My Wife and I Invariably Go Together”: A Mixed Methods Analysis of Physical Activity and its Influences on Older Adult Dyads
Authors: Wales Jenny L , Hamilton Calum Alexander , McCarthy Louis , Bayat Sayeh , Tangen Gro Gujord , Ireson Neil , Lanfranchi Vita , Farina Nicolas , Hicks Ben , Ardle Ríona Mc 
Published: January 2025

AbstractBackgroundPhysical activity (PA) is an imperative factor to healthy cognitive and functional ageing and may act as a protective factor against cognitive decline. Evidence suggests that as we age, PA declines, leaving a large proportion of older adults (OAs) ‘underactive’ and ‘unprotected’. Socialisation/social support is considered a beneficial influence on PA in OAs. It is unclear whether dyadic (e.g. spousal) influences impact PA in healthy ageing, and how this might impact digital activity monitoring or intervention development to support prevention of cognitive decline. This research aims to quantify volume and types of PA and explore influential factors for PA participation in OAs.MethodParticipants were recruited via the ActivDyad study, where they wore a lumbar‐based‐accelerometer (AX6, Axivity) for 7 days, and completed a 7‐day activity diary. PA (i.e. daily steps) was derived from accelerometers using validated algorithms. Qualitative content analysis of the diaries was adopted for contextual understanding of the types and influences of PA. The Theoretical Domains Framework was utilised to identify common facilitators/barriers of PA in OAs.Result21 dyads (42 OAs) participated in the study; 10 dyads were included in preliminary analysis (mean age (mean±SD): 68.6±4.8; 100% heterosexual & married; mean relationship time: 45±10 years; 80% retired). Participants carried out an average of 15,680 daily steps (range: 7,243‐22,668), and indicated their most frequent PA as: shopping, gardening, housekeeping, and recreational walks (Table 1). Participants identified potential facilitators (Table 2) and barriers (Table 3) for partaking in PA. Social influence (e.g. partner) was identified as most influential; 75% of participants mentioned this as a facilitator for PA. Environmental context, particularly weather conditions, were frequently discussed as both a facilitator (50%) and barrier (45%) to PA.ConclusionResults suggest that dyadic interactions are a key facilitator of PA in OAs. Weather appears to have a bi‐conditional influence, promoting and/or discouraging PA; this may impact activity monitoring during adverse weather conditions (e.g. lower steps). Adherence may be higher in group PA interventions, and outdoor PA should be promoted in suitable weather. Analysis of the full sample will inform studies focusing activity monitoring and intervention development for dementia‐carer dyads.
Diurnal variation in blood‐based biomarkers for neurodegeneration and the circadian markers melatonin and cortisol in Alzheimer's
Authors: Monica Ciro della , Revell Victoria Louise , Atzori Giuseppe , Laban Rhiannon , Skene Simon S. , Heslegrave Amanda J , Hassanin Hana , Nilforooshan Ramin , Zetterberg Henrik , Dijk Derk‐Jan 
Published: January 2025

AbstractBackgroundDisruption in diurnal rest‐activity rhythms is a hallmark of Alzheimer’s disease. Currently, we know little about how physiology, symptoms, and biomarkers change over the 24‐hour day in people living with Alzheimer’s disease. In particular, we don’t know whether plasma biomarkers of neurodegeneration, which offer promise as diagnostic or stratification tools, vary with time of day, and whether these associate with the circadian markers melatonin and cortisol.MethodsWe studied 38 participants (70.8 ± 7.6 years; mean ± SD) in a 27‐hour laboratory protocol including a sleep episode with either two blood samples taken 12 hours apart (n = 17) or 3‐hourly blood sampling for 24 hours (n = 21). The participants were people living with mild Alzheimer’s disease (PLWA, n = 8), their partners/caregivers (n = 6), and cognitively intact older adults (n = 24). The neurodegeneration biomarkers phosphorylated tau (p‐tau217), amyloid‐beta 40 (Aβ40), amyloid‐beta 42 (Aβ42), glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) levels were measured with single molecule array technology. A mixed linear model (PROC MIXED, SAS) was run to assess the impact of time‐of‐day, group, and any interaction. The gold‐standard markers of the circadian clock, plasma melatonin and cortisol, were measured using liquid chromatography mass spectrometry (LC‐MS) for the 24‐hour timeseries (n = 21). Correlations were run (R) to explore relationships between 24‐hour levels of dementia biomarkers, melatonin and cortisol.ResultsDiurnal rhythms in melatonin and cortisol were observed in all participant groups. Significant time of day variation was observed for p‐tau217, Aβ40, Aβ42, NfL, and a significant effect of group for p‐tau217 with highest levels in PLWA. There were no significant correlations between 24‐hour levels of melatonin or cortisol and any of the neurodegeneration biomarkers.ConclusionsTime of day variation was observed in central circadian hormone markers and commonly used biomarkers of dementia in all participants. Outputs of the central circadian clock were not markedly altered in PLWA, and there was no relationship between 24‐hour levels of these outputs and blood‐based biomarkers of dementia. Time of day variation in biomarkers may be relevant to their implementation and interpretation in research and clinical practice.
The Emory‐Sage‐SGC‐JAX TREAT‐AD Center: Target Enabling Packages for Alzheimer’s Disease Emerging Targets
Authors: Leal Karina , Axtman Alison D. , Betarbet Ranjita , Brennan Paul E , Carter Gregory W , Fu Haian , Greenwood Anna K , Longo Frank M. , Edwards Aled M , Levey Allan I. , Center Emory‐Sage‐SGC‐Jax TREAT‐AD 
Published: January 2025

AbstractBackgroundThere is an urgent need for new therapeutic and diagnostic targets for Alzheimer’s disease (AD). Dementia afflicts roughly 55 million individuals worldwide, and the prevalence is increasing with longer lifespans and the absence of preventive therapies. Given the demonstrated heterogeneity of Alzheimer’s disease in biological and genetic components, it is critical to identify new therapeutic approaches. The Emory‐Sage‐SGC‐JAX TREAT‐AD Center is generating and openly distributing validated experimental tools necessary to test target predictions generated through sequence‐based characterization of human disease state. We believe that these tools and reagents, including chemical and biological probes that target the multifaceted dysregulation in the brains of AD patients, will advance the discovery of potential drug targets for AD.MethodOur Center has focused on developing a robust target prioritization and target enabling package (TEP) development pipeline, where we not only produced, characterized, and validated key enabling reagents for dozens of potential AD drug targets for which there were few reagents, but also developed new approaches to expedite target prioritization and organization, antibody characterization, and chemical probe discovery. The current target portfolio was assembled by evaluating prioritized understudied proteins by the AMP‐AD consortium and additional NIA‐supported AD consortia. Nominated targets were evaluated by calculating an unbiased AD risk score and then mapped to 19 biological domains (BDs) that describe and codify the different processes that are dysregulated in AD and prioritized based on multiple lines of evidence for overall AD‐risk. Targets that meet criteria for development are evaluated to identify a set of experimental reagents necessary for hypothesis testing.ResultWe have prioritized more than 50 understudied targets for TEP development. For each understudied target, a TEP includes expression constructs, purified protein and methods, validated knockout cell lines, and antibody validation. For a subset of tractable targets additional TEP components include assay development, crystal structures, screening, and probe development. Advanced targets include SYK, DDX1, SDC4, ARHGEF2, and MDK.ConclusionAll data, protocols, reagent sets, and chemical tools will be made widely available on the AD Knowledge Portal with no intellectual property claims. For more information see www.treatad.org.
Fosgonimeton attenuates amyloid‐β‐induced autophagic impairments in primary cortical neurons
Authors: Reda Sherif M , Wu Wei , Berthiaume Andrée‐Anne , Setti Sharay E. , Taylor Robert W , Johnston Jewel L , Church Kevin J. 
Published: January 2025

AbstractBackgroundAccumulating evidence highlights impairment of autophagy as a key pathological feature of neurodegenerative diseases including Alzheimer’s disease (AD). Autophagy is a highly dynamic, lysosome‐based degradation process that promotes the clearance of degenerative factors to maintain cellular functions, preserve metabolic integrity, and ensure survival. Impaired autophagic function leads to the abnormal accumulation of autophagic vesicles (i.e., autophagosomes, lysosomes), build‐up of toxic protein aggregates, dysregulated homeostasis, synaptic dysfunction, and neurotoxicity. We have previously demonstrated that fosgonimeton, an investigational small‐molecule positive modulator of the neurotrophic hepatocyte growth factor (HGF) system, is neuroprotective and reduces pTau levels in amyloid‐beta (Aβ)‐driven preclinical models of AD. Herein, we investigate the effect of fosgonimeton on autophagic function in cortical neurons following Aβ injury to further characterize the neuroprotective effects of fosgonimeton treatment.MethodTo evaluate potential effects on autophagic function, primary rat cortical neurons were treated with the active metabolite of fosgonimeton (fosgo‐AM), challenged with Aβ1‐42 (15 µM) for 24 hours, and immunostained for microtubule‐associated protein‐2 (neuronal marker), phospho‐tau, LC3 (autophagosome marker), and LAMP2 (lysosomal marker). Immunofluorescence analyses were used to determine neuronal survival, neurite network integrity, pTau levels, and expression/co‐localization of LC3 and LAMP2. Beclin‐1 (autophagy initiator) expression was also measured via western blot.ResultFosgo‐AM treatment promoted neuronal survival, preserved network connectivity, and reduced levels of pTau following Aβ1‐42 challenge. Application of Aβ1‐42 significantly decreased the expression of Beclin‐1, led to the abnormal accumulation of autophagic vesicles (LC3, LAMP2, and LC3‐LAMP2 co‐localization), and increased the co‐localization of pTau within LC3‐autophagic vesicles, all of which are indicative of impaired autophagic function. Such effects were significantly attenuated in the presence of fosgo‐AM, suggesting that the neuroprotective effects of fosgonimeton are associated with improved autophagic function.ConclusionThe data presented demonstrate the ability of fosgonimeton to potentially address autophagic impairment in AD, which may have important implications regarding the attenuation of pTau pathology. These effects of fosgonimeton, in addition to the neurotrophic and neuroprotective effects previously demonstrated in preclinical models, highlight the therapeutic potential of the HGF system for neurodegenerative disorders, wherein autophagic impairment is a key pathological hallmark.