bluuebunny/crossref_metadata_2025_split huggingface dataset:
Using a Community Based Participatory Research Approach to recruit American Samoans for Alzheimer’s Disease and Related Dementia Research
Authors: Tuatoo Grace 
Published: January 2025

AbstractBackgroundThe Puipui Malu Manatu (PMM) study (RF1AG075904) is determining Alzheimer’s Disease and Related Dementias (ADRD) prevalence by administering cognitive assessment tools and determining prevalence of known genetic and plasma biomarkers. The sampling method uses a cluster and selection process to obtain a randomized sample of 1089 adults that is generalizable to the population. Research hesitancy exists due to historical abuse of non‐Indigenous researchers conducting studies that are not reflective of the community health needs. Community based participatory research (CBPR) approaches utilize diverse stakeholders in various levels of a study to ensure trust, respect, and reciprocity between the researchers and a community. Our previous studies have shown using CBPR approach with a cultural lens that shows fa’aaloalo (respect) have been effective in recruiting participants.MethodsWe incorporated CBPR approaches that follow the Fa’aSamoa (Samoan way/culture) to promote awareness of the PMM objectives and recruit eligible participants. The adherence of cultural protocol was accomplished by presenting at the Office of Samoan Affairs (OSA). The OSA is comprised of the Pulenu’u (village mayor), led by an Ali’i (Paramount Chief), and is the cultural governance body for villages. Essentially making them gatekeepers to eligible participants. The Secretary of Samoan Affairs provided a letter of support, granting the access to the villages via Pulenu’u. We implemented the “PMM on Wheels” in the remote village of Aoa in collaboration with the Pulenu’u.ResultsThe support from the OSA and Pulenu’u allowed for the first “PMM on Wheels” to be conducted. The staff recruited 10 eligible participants who would not be able to access the two study site locations. Furthermore, we were able to maintain randomization in our sampling strategy. Participants completed cognitive assessments and provided a blood sample. Future events are scheduled in the beginning of 2024.ConclusionNegative reactions towards research studies in Indigenous populations have been supported by historical abuses by non‐Indigenous researchers. Utilizing CBPR with a cultural lens in the American Samoa population has shown rebuild trust and fa’aaloalo to better the health outcomes of the community.
Foodomics: Links to cognition and AD markers of disease
Authors: Labus Jennifer S , Nho Kwangsik , Zuffa Simone , Batra Richa , Meehan Michael , West Kiana , Zemlin Jasmine , Caraballo‐Rodriguez Andres Mauricio , Dorrestein Pieter , Kaddurah‐Daouk Rima ,   Alzheimer's Gut Microbiome Project Consortium (AGMP)
Published: January 2025

AbstractBackgroundDiet has been associated with memory, emotion/stress regulation, structure and function of the hippocampus and amygdala and attenuation of cognitive aging. There is a well‐recognized lack of reliability in self‐reported dietary intake and great interest in objective metabolic readout of dietary patterns. In this study we constructed dietary profiles from untargeted metabolomics data using a novel metadata‐based source annotation method developed at the Dorrestein Lab, also referred to as “foodomics”. The aim of was to link these objective metabolic readouts of dietary intake to cognition and AD markers of disease.MethodUntargeted metabolomic profiling (LC‐MS/MS) was applied to blood samples from 872 individuals from the ADNI cohort (182 cognitive normal individuals, 449 MCI, 103 significant memory concern, and 139 AD); for whom brain imaging data was also collected. Principal component analysis (PCA) was to derive a dietary profile that explained the most variance in the dataset. Whole‐brain voxel‐wise analysis was applied to determine association between dietary intake profiles and brain amyloid‐β deposition (amyloid PET), gray matter density (brain atrophy; MRI), and glucose metabolism (FDG PET) using a multivariate regression analysis with SPM12. The general linear model was used to examining the associations of the dietary profile with cognition and AD markers. Covariates included age, education, sex, and BMI.ResultCorrelation between the first principal component (PC1) and the relative abundances of foods at level 3 of the food ontology showed that PC1 explained variance derived from food intake within poultry, dairy, meat, egg, vegetable, legume and seafood categories (Figure 1). Greater intake of poultry, dairy, and meat was significantly associated with 1) reduced glucose metabolism and brain gray matter atrophy in the frontal, parietal, and temporal (including the hippocampus; Figure 2,3) and 2) difficulties with executive functioning (p=0.03) and memory at baseline (p=0.002), longitudinal decreases in memory functioning over time (p=.02), and increases in baseline plasma levels of neurofilament light chain at baseline (p=.02).ConclusionDietary intake selectively influences glucose metabolism and gray matter atrophy of brain regions preferentially targeted in AD, cognition and neurofilament light chain. This proof‐of‐concept study supports the hypotheses that diet influences the progression of the disease. NIA U19 AG063744
Sex differences in the association of cognitive impairment and sleep‐disordered breathing measures among diverse adults
Authors: Milton Sasha , Peltz Carrie B. , Redline Susan , O'Bryant Sid E. , Yaffe Kristine , Leng Yue 
Published: January 2025

AbstractBackgroundGrowing evidence reports an association between sleep‐disordered breathing (SDB) and cognitive impairment, including mild cognitive impairment (MCI) and dementia. However, there is limited research on the link between cognitive impairment and in‐home measures of SDB and how this association may differ by race, ethnicity, and sex.MethodWe studied 822 individuals who were enrolled in the community‐based Health and Aging Brain Study‐Health Disparities (HABS‐HD)‐Dormir study. In‐home measures of SDB were collected using WatchPAT (WatchPAT‐200). Data were processed using the zzzPAT software (5.2.79.7p) and manually edited by trained polysomnologists. Measures included the respiratory event index (REI), oxygen desaturation index (ODI), snoring volume (percentage of the night over 40 dB), and oxygen saturation (SpO2; three measures, including percentage of night at SpO2 <90%, mean SpO2, and minimum SpO2). A comprehensive clinical evaluation was used to determine normal cognition (NC), MCI, or dementia.ResultOur sample (mean [SD] age = 66.6 [8.5] years) was 66.2% women; 34.7% Mexican American and 21.2% Black; and 17.3% MCI and 4.1% dementia. After adjustment for age, race, sex, body mass index (BMI), and comorbidities, participants with MCI/dementia had greater odds of a non‐rapid eye movement (NREM)‐ODI of ≥15 [OR (95% CI) = 1.60 (1.02,2.5)] compared to those with NC. Additionally, men with MCI/dementia were more likely to snore at >40 dB for >25% of the night [OR (95% CI) = 2.08 (1.13,3.82)] and to have a rapid eye movement (REM)‐REI ≥15 [OR (95% CI) = 1.87 (1.01,3.61)] than men with NC, while these associations were not present for women [OR (95% CI), snoring = 0.74 (0.42,1.27), p for interaction = 0.02; REM‐REI: 0.88 (0.49,1.62), 0.09]. Other measures of SDB were not associated with cognitive diagnoses. The associations between cognitive diagnoses and SDB measures were similar across race groups.ConclusionAmong middle‐aged to older men and women, measures of SDB varied in their association with MCI or dementia by sleep stage and by sex. Future investigations should consider sleep‐state specific sleep disturbances as markers or risk factors for cognitive impairment and examine potential sex differences in those associations.
Potential probiotic (L. plantarum, L. pentosus, P. pentosaceus, W. cibaria and L. rhamnosus) alleviates memory impairment in lipopolysaccharide‐induced cognitive dysfunction and neurotoxicity in mice brain via protection against neuroinflammation and neurodegeneration
Authors: Obisesan Abiola Oluwatosin , Dodo Samuel O , Adeniyi Funmilayo Racheal , Olayinka Juliet Nnenda , Adeoluwa Olusegun Adebayo , Fafure Adedamola O 
Published: January 2025

AbstractBackgroundThe impact of probiotics as gut and immunological modulator in restoring gut microbial balance and immune cells expression have generated much attention in the health sector. Its inhibitory effect on bacterial translocation and associated neural inflammatory processes has been reported. However, there is scarcity of data on its neuroprotective impact against neuroinflammation‐associated neurodegeneration and memory impairment. Therefore, we tested the neuroprotective capabilities of the probiotic strains against lipopolysaccharide (LPS)‐induced neurotoxicity and memory impairment in mice.MethodForty eight mice (n = 12) weighing 25 ‐ 30 g were randomly classified into four groups consisting of probiotic strains (107 CFU/ml, orally), donepezil (3 mg/kg, intraperitoneally (i.p.)), normal saline (10 ml/kg, orally), and LPS (0.25 mg/kg i.p) group. LPS was injected 60 min after treatments for seven days. Novel object recognition (NORT) and Y‐maze tests were performed 24 h following the last treatment to assess cognitive functions. Dendritic spine of the dentate gyrus was stained with Golgi cox. Immunohistochemistry detected brain‐derived neurotrophic factor (BDNF) expression as well as Iba 1 and amyloid beta activation in the hippocampus and prefrontal cortex, while enzyme‐linked immunosorbent assay was used to measure neuroinflammation (tumor necrosis factor‐α (TNF‐α), interleukin‐1 beta (IL‐1β), interleukin‐12 (IL‐12), and interferon gamma (IFN‐γ)) and oxidative stress biomarkers (glutathione (GSH) and malondialdehyde (MDA)).ResultProbiotics reduced elevation of pro‐inflammatory cytokines, MDA levels and enhanced GSH levels in the brain. Probiotic strains enhanced BDNF expressions, reduced microglia and amyloid beta activation in the hippocampus and prefrontal cortex of LPS‐induced mice.ConclusionThese finding show that probiotic strains may possess neuroprotective effect against LPS‐induced cognitive impairment and neurodegeneration via antioxidant and anti‐inflammatory mechanisms.
Assessing treatment value under uncertainty: Measuring treatment value for a new treatment for cognitive impairment
Authors: Shafrin Jason , Than Kyi‐Sin , Kim Jaehong , Fajnor Jacob , Mearns Elizabeth S , Kowal Stacey L , Majda Thomas , Hlavka Jakub 
Published: January 2025

AbstractBackgroundThe value payers and policymakers traditionally place on novel treatments for Alzheimer’s and other neurological diseases that cause cognitive impairment (CI) and dementia often ignores disease baseline severity. More patient‐centered economic evaluations—such as those estimating “insurance value”—incorporate the fact that individuals are typically risk‐averse and place high value on generous coverage of treatments in the event that they develop CI, such as severe dementia, later in life. This study aims to estimate this insurance value, accounting for risk aversion, relative to the value of the same treatment based on traditional payer cost effectiveness approaches.MethodA survey was administered to US residents aged ≥ 21 years. First, a multiple random staircase design was used to elicit respondents’ willingness to pay for insurance coverage of a hypothetical, novel treatment that delays progression from mild CI to dementia relative to best supportive care. The treatment’s “insurance value” was calculated as respondent’s stated willingness to pay less traditional value assessment estimates [i.e., the product of annual incidence (0.4%) and monetized, discounted value of quality‐adjusted life years (QALY) gained from treatment (0.652 QALYs x $100,000 per QALY)]. Risk aversion over quality‐of‐life outcomes was also estimated.ResultAmong n = 295 respondents, 64.9% were female and average age was 51.1 (SD = 16.0). Willingness to pay for generous insurance coverage of a novel treatment delaying the onset of dementia was $646.88 per year compared to the value of the same treatment of $260.80 calculated under traditional cost‐effectiveness approaches. In other words, treatment value of novel treatments for CI were 2.5 times higher using the patient‐centered insurance value approach than using the traditional cost‐effectiveness methodology. Respondents were found to be risk averse (relative risk aversion = 1.49, 95% CI: 1.29, 1.68).ConclusionIndividuals place significant value on treatments that delay onset of cognitive impairments such as severe dementia. Traditional cost‐effectiveness may understate treatment value relative to more patient‐centered approaches that incorporate “insurance value”.
Structural and metabolic topological alterations underlying butylphthalide treatment in mild cognitive impairment due to Alzheimer’s disease and topology‐based machine learning for drug efficacy
Authors: Han Xiaodong , Gong Shuting , Wang Pin , Li Ruina , Chen Runqi , Xu Chang , Sun Wenxian , Gong Jin , Li Shaoqi , Wei Cuibai 
Published: January 2025

AbstractBackgroundEffective early intervention of mild cognitive impairment (MCI) is the key for preventing dementia. However, there is currently no drug for MCI. As a multi‐targeted neuroprotective agent, butylphthalide has been demonstrated to repair cognition in patients with vascular cognitive impairment, and has the potential to treat MCI due to Alzheimer’s disease (AD). However, the pharmacological mechanism of butylphthalide from brain network perspective is not clear.MethodIn this clinical trial, 270 patients with MCI due to AD were assigned at a ratio of 1:1 to receive either butylphthalide or placebo for one year. Alzheimer’s disease assessment scale‐cognitive section (ADAS‐cog) scores was set up as the primary endpoint, and effective treatment was defined as the decrease of ADAS‐cog>2.5. T1‐magnetic resonance imaging and fluorodexoyglucose positron emission tomography data were collected in baseline and follow‐up, and the Kullback‐Leibler divergence similarity estimation method was applied to construct the individual structural and metabolic networks. We used repeated‐measures analysis of variance to investigate group (drug group vs. placebo group; effective group vs. ineffective group)‐time interactions on ADAS‐cog and network metrics, and support vector machine was applied to develop the predictive models.ResultButylphthalide significantly improved the cognition of patients with MCI due to AD (multivariable‐adjusted P<0.05). There were some overlapped structural network metrics on which both the treatment group‐time interactions and efficacy group‐time interactions were potentially significant (uncorrected multivariable‐adjusted P<0.05), and the metrics were: global efficiency, degree centrality and nodal efficiency of left paracentral lobule, degree centrality of right inferior temporal gyrus, and nodal efficiency of left superior frontal gyrus, medial. Of these an increased degree centrality of left paracentral lobule was significantly related to poorer cognitive improvement. Further simple effects analyses revealed an significantly increased global efficiency in structural network under both treatment and effective treatment of butylphthalide (multivariable‐adjusted P<0.05). Predictive model based on baseline multimodal network metrics exhibited the highest accuracy (88.93%) for predicting butylphthalide’s efficacy.ConclusionButylphthalide may improve cognitive function in patients with MCI due to AD by repairing abnormal organization in structural networks. Baseline multimodal network metrics could be potential predictive markers of therapeutic efficacy of anti‐AD drugs.
APPLICATION OF CLASSICAL AND MACHINE LEARNING MODELS ON OASIS ‐ 2 DATA
Authors: Arslan Rümeysa Rana 
Published: January 2025

AbstractBackgroundAlzheimer’s disease (AD) is a significant global health issue that affects both individuals and society for older adults. Early detection is essential in Alzheimer’s disease, as there is no definite treatment, and prevention is possible through early detection. In the studies of Alzheimer's disease, different approaches are used to understand the characteristics of the disease, including imaging methods such as MRI scans, biomarkers, and quantitative data from MRI scans. With these methods, the cognitive status of individuals could be determined. It is essential to detect the features affecting Alzheimer’s disease and detect the disease early and automate the detection and prediction by using modeling tools and by examining the datasets.MethodThe symptoms of the disease can be observed over time, making the structure of the study longitudinal. Classical statistical models and machine learning algorithms can be used to analyze these datasets. The OASIS – 2 dataset is employed to find the features affecting dementia status and compare models' performances. The classical mixed models, their extended versions, and hybrid models, Boruta, GEE, GLMM, HGLM, GLMMLasso, GPBoost, GLMMTree, and HRF, are used to analyze the longitudinal dataset with a small sample size and binary outcome. Model performances are measured using accuracy, F1 score, sensitivity, and specificity, and the results are interpreted using the odds ratio.ResultGPBoost learns and classifies the dementia status well but overfits due to the small sample size in the dataset, and tree‐based algorithms are efficient in predicting the dementia status when a new subject enters the study.ConclusionThe common significant variables to address the dementia status of individuals are MMSE, nWBV, education, gender, and age. The results also indicate that the increase in MMSE score means the patient is less likely to be demented; the increase in normalized whole brain volume indicates a less probability to be demented; increase in socioeconomic status results in the patient having less probability to be demented; males are more likely to be demented; age has a negative impact on being demented; the increase in education years results in the patient having less probability to be demented.
Sleep and Cognition in the Long Life Family Study
Authors: Gordon Emma , Gu Yian , Cosentino Stephanie , Andersen Stacy L. , Ukraintseva Svetlana 
Published: January 2025

AbstractBackgroundMany studies have demonstrated an association between sleep and cognition in older adults. The Long Life Family Study (LLFS) has followed families enriched with longevity since 2006 and has shown that this cohort has slower rates of cognitive decline than their spouse controls and the general public. Understanding how sleep and cognition are related in this population can improve our understanding of the key to long life.MethodOur study includes 2344 participants from the LLFS, who were ≥60 years at baseline, and completed sleep and cognitive measures. A neuropsychological battery was used to measure episodic (Logical Memory IA and IIA from the Wechsler Memory Scale ‐Revised (WMS‐R)), semantic (Categorical Fluency Test), and working memory (Number Span Forward and Backward Tests) at baseline (2006‐2009), and in a subset of 1633 participants at the follow‐up visits 6.9 (SD = 2.7) years later. Sleep habits were assessed once using the Health Habits (HH) Questionnaire between the two cognitive measures. The association between sleep duration and memory was examined using generalized estimating equations models that accounted for relationship with the long‐life families.ResultCompared to those with 6.5‐8.5 hours of sleep, those with > 8.5 hours of sleep had lower episodic memory scores at baseline (b = ‐0.705, p = 0.021), but not at follow‐up (b = ‐0.627, p = 0.149), after controlling for age, sex, field center, APOE, and generation. Sleep duration was not associated with semantic or working memory. Those who had > 8.5 hours of sleep had faster decline in all memory scores, although not significantly. There was no difference in memory scores comparing < 6.5 hours of sleep to 6.5‐8.5 hours of sleep.ConclusionLower memory scores at baseline predicted longer sleep time subsequently, while sleep time did not predict memory performance on follow‐up. Overall, the current study did not support sleep duration as a significant contributor to memory decline. This contradicts some earlier findings and prompts further investigation into the connection between sleep and cognition, especially within this population and specifically into whether increased sleep time and memory decline share an underlying mechanism, or memory decline causes longer sleep time.
Design of a Phase 1, First‐in‐human, Randomized, Double‐blind, Placebo‐controlled, Single and Multiple Ascending Dose Study of a Novel Orally Administered TREM2 Agonist (VG‐3927) in Healthy Volunteers
Authors: Rajagovindan Raj , Gaudreault Francois , O’Mara Ryan , Thackaberry Evan A. , Stromme Jessica , Gray David L. , Meier Andreas 
Published: January 2025

AbstractBackgroundVG‐3927 is a highly potent, selective, brain penetrant, oral small molecule TREM2 agonist that is currently under development for the treatment of Alzheimer’s disease (AD). TREM2, a receptor expressed on microglia in the brain is critical to microglial function in health and in disease. Among microglia‐associated AD risk genes, partial loss‐of‐function variants of TREM2 confer 2‐3 fold increase in risk for developing AD, motivating efforts to identify pharmacological agonists targeting TREM2 as a therapeutic option. VG‐3927 is designed to act as a molecular glue that potentiates the TREM2 signaling response to natural damage ligands and is hypothesized to promote microglial function and health, and tissue repair mechanisms regulated by microglia thereby slowing disease progression in AD.MethodsThe Phase 1 randomized, double‐blind, placebo‐controlled study will evaluate the safety, tolerability, pharmacokinetics, and pharmacodynamics after single‐ (SAD) and multiple‐ascending dose (MAD) of orally administered VG‐3927 in healthy volunteers. The study will enroll healthy adults aged 18 to 55 years and include SAD and MAD cohorts that are planned to be tested sequentially in a dose‐escalating manner. Eight participants will be randomized in a 6:2 allocation ratio to VG‐3927 or placebo into each of the planned cohorts. The treatment period for the SAD and the MAD portions will be 1 day and 14 days respectively with safety follow‐up through 28 days after the last dose for both portions. Study assessments to investigate safety and tolerability will include physical exams, neurological exams, vitals, ECG, clinical laboratory, C‐SSRS and AE reporting in all cohorts. Blood samples will be collected in all cohorts to characterize the pharmacokinetics and CSF samples will be collected in selected cohorts to investigate biomarkers of target engagement and downstream pharmacodynamic activity.ResultsThe study dosed its first participant in October 2023 and is currently ongoing. The design of the Phase 1 study will be presented.ConclusionsThe ongoing Phase 1 study will assess the safety, tolerability, pharmacokinetics, and pharmacodynamics of VG‐3927 in healthy volunteers and is expected to inform subsequent development in AD.